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Nadal-Sala D, Grote R, Birami B, Knüver T, Rehschuh R, Schwarz S, Ruehr NK. Leaf Shedding and Non-Stomatal Limitations of Photosynthesis Mitigate Hydraulic Conductance Losses in Scots Pine Saplings During Severe Drought Stress. FRONTIERS IN PLANT SCIENCE 2021; 12:715127. [PMID: 34539705 PMCID: PMC8448192 DOI: 10.3389/fpls.2021.715127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/08/2021] [Indexed: 06/01/2023]
Abstract
During drought, trees reduce water loss and hydraulic failure by closing their stomata, which also limits photosynthesis. Under severe drought stress, other acclimation mechanisms are trigged to further reduce transpiration to prevent irreversible conductance loss. Here, we investigate two of them: the reversible impacts on the photosynthetic apparatus, lumped as non-stomatal limitations (NSL) of photosynthesis, and the irreversible effect of premature leaf shedding. We integrate NSL and leaf shedding with a state-of-the-art tree hydraulic simulation model (SOX+) and parameterize them with example field measurements to demonstrate the stress-mitigating impact of these processes. We measured xylem vulnerability, transpiration, and leaf litter fall dynamics in Pinus sylvestris (L.) saplings grown for 54 days under severe dry-down. The observations showed that, once transpiration stopped, the rate of leaf shedding strongly increased until about 30% of leaf area was lost on average. We trained the SOX+ model with the observations and simulated changes in root-to-canopy conductance with and without including NSL and leaf shedding. Accounting for NSL improved model representation of transpiration, while model projections about root-to-canopy conductance loss were reduced by an overall 6%. Together, NSL and observed leaf shedding reduced projected losses in conductance by about 13%. In summary, the results highlight the importance of other than purely stomatal conductance-driven adjustments of drought resistance in Scots pine. Accounting for acclimation responses to drought, such as morphological (leaf shedding) and physiological (NSL) adjustments, has the potential to improve tree hydraulic simulation models, particularly when applied in predicting drought-induced tree mortality.
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Affiliation(s)
- Daniel Nadal-Sala
- Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
| | - Rüdiger Grote
- Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
| | - Benjamin Birami
- Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
- University of Bayreuth, Chair of Plant Ecology, Bayreuth, Germany
| | - Timo Knüver
- Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
- Department of Botany, University of Innsbruck, Innsbruck, Austria
| | - Romy Rehschuh
- Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
| | - Selina Schwarz
- Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
| | - Nadine K. Ruehr
- Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
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103
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Jones CM, Jones S, Petrasova A, Petras V, Gaydos D, Skrip MM, Takeuchi Y, Bigsby K, Meentemeyer RK. Iteratively forecasting biological invasions with PoPS and a little help from our friends. FRONTIERS IN ECOLOGY AND THE ENVIRONMENT 2021; 19:411-418. [PMID: 34588928 PMCID: PMC8453564 DOI: 10.1002/fee.2357] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Ecological forecasting has vast potential to support environmental decision making with repeated, testable predictions across management-relevant timescales and locations. Yet resource managers rarely use co-designed forecasting systems or embed them in decision making. Although prediction of planned management outcomes is particularly important for biological invasions to optimize when and where resources should be allocated, spatial-temporal models of spread typically have not been openly shared, iteratively updated, or interactive to facilitate exploration of management actions. We describe a species-agnostic, open-source framework - called the Pest or Pathogen Spread (PoPS) Forecasting Platform - for co-designing near-term iterative forecasts of biological invasions. Two case studies are presented to demonstrate that iterative calibration yields higher forecast skill than using only the earliest-available data to predict future spread. The PoPS framework is a primary example of an ecological forecasting system that has been both scientifically improved and optimized for real-world decision making through sustained participation and use by management stakeholders.
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Affiliation(s)
- Chris M Jones
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNC
| | - Shannon Jones
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNC
| | - Anna Petrasova
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNC
| | - Vaclav Petras
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNC
| | - Devon Gaydos
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNC
- Animal and Plant Health Inspection Service (APHIS)US Department of Agriculture (USDA)RiverdaleMD
| | - Megan M Skrip
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNC
| | - Yu Takeuchi
- Center for Integrated Pest ManagementNorth Carolina State UniversityRaleighNC
| | | | - Ross K Meentemeyer
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNC
- Department of Forestry and Environmental ResourcesNorth Carolina State UniversityRaleighNC
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104
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Scavia D, Bertani I, Testa JM, Bever AJ, Blomquist JD, Friedrichs MAM, Linker LC, Michael BD, Murphy RR, Shenk GW. Advancing estuarine ecological forecasts: seasonal hypoxia in Chesapeake Bay. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02384. [PMID: 34128283 PMCID: PMC8459276 DOI: 10.1002/eap.2384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 04/28/2021] [Accepted: 05/26/2021] [Indexed: 06/12/2023]
Abstract
Ecological forecasts are quantitative tools that can guide ecosystem management. The coemergence of extensive environmental monitoring and quantitative frameworks allows for widespread development and continued improvement of ecological forecasting systems. We use a relatively simple estuarine hypoxia model to demonstrate advances in addressing some of the most critical challenges and opportunities of contemporary ecological forecasting, including predictive accuracy, uncertainty characterization, and management relevance. We explore the impacts of different combinations of forecast metrics, drivers, and driver time windows on predictive performance. We also incorporate multiple sets of state-variable observations from different sources and separately quantify model prediction error and measurement uncertainty through a flexible Bayesian hierarchical framework. Results illustrate the benefits of (1) adopting forecast metrics and drivers that strike an optimal balance between predictability and relevance to management, (2) incorporating multiple data sources in the calibration data set to separate and propagate different sources of uncertainty, and (3) using the model in scenario mode to probabilistically evaluate the effects of alternative management decisions on future ecosystem state. In the Chesapeake Bay, the subject of this case study, we find that average summer or total annual hypoxia metrics are more predictable than monthly metrics and that measurement error represents an important source of uncertainty. Application of the model in scenario mode suggests that absent watershed management actions over the past decades, long-term average hypoxia would have increased by 7% compared to 1985. Conversely, the model projects that if management goals currently in place to restore the Bay are met, long-term average hypoxia would eventually decrease by 32% with respect to the mid-1980s.
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Affiliation(s)
- Donald Scavia
- School for Environment and SustainabilityUniversity of MichiganAnn ArborMichigan48103USA
| | - Isabella Bertani
- Chesapeake Bay Program OfficeUniversity of Maryland Center for Environmental ScienceAnnapolisMaryland21403USA
| | - Jeremy M. Testa
- Chesapeake Biological LaboratoryUniversity of Maryland Center for Environmental ScienceSolomonsMaryland20688USA
| | | | - Joel D. Blomquist
- U.S. Geological Survey, Water Observing Systems ProgramBaltimoreMaryland21228USA
| | | | - Lewis C. Linker
- U.S. EPA Chesapeake Bay Program OfficeAnnapolisMaryland21403USA
| | | | - Rebecca R. Murphy
- Chesapeake Bay Program OfficeUniversity of Maryland Center for Environmental ScienceAnnapolisMaryland21403USA
| | - Gary W. Shenk
- U.S. Geological Survey Chesapeake Bay Program OfficeAnnapolisMaryland21403USA
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Affiliation(s)
- Timothy H Keitt
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA.
| | - Eric S Abelson
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA
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Bastidas Navarro M, Schenone L, Martyniuk N, Vega E, Modenutti B, Balseiro E. Predicting Dissolved Organic Matter Lability and Carbon Accumulation in Temperate Freshwater Ecosystems. Ecosystems 2021. [DOI: 10.1007/s10021-021-00682-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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107
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Schlaepfer DR, Bradford JB, Lauenroth WK, Shriver RK. Understanding the future of big sagebrush regeneration: challenges of projecting complex ecological processes. Ecosphere 2021. [DOI: 10.1002/ecs2.3695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
- Daniel R. Schlaepfer
- Southwest Biological Science Center U.S. Geological Survey Flagstaff Arizona 86001 USA
- Center for Adaptable Western Landscapes Northern Arizona University Flagstaff Arizona 86011 USA
- Yale School of the Environment Yale University New Haven Connecticut 06511 USA
| | - John B. Bradford
- Southwest Biological Science Center U.S. Geological Survey Flagstaff Arizona 86001 USA
| | - William K. Lauenroth
- Yale School of the Environment Yale University New Haven Connecticut 06511 USA
- Department of Botany University of Wyoming Laramie Wyoming 82071 USA
| | - Robert K. Shriver
- Department of Natural Resources and Environmental Science University of Nevada‐Reno Reno Nevada 89557 USA
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108
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Covert MW, Gillies TE, Kudo T, Agmon E. A forecast for large-scale, predictive biology: Lessons from meteorology. Cell Syst 2021; 12:488-496. [PMID: 34139161 PMCID: PMC8217727 DOI: 10.1016/j.cels.2021.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 04/01/2021] [Accepted: 05/18/2021] [Indexed: 11/19/2022]
Abstract
Quantitative systems biology, in which predictive mathematical models are constructed to guide the design of experiments and predict experimental outcomes, is at an exciting transition point, where the foundational scientific principles are becoming established, but the impact is not yet global. The next steps necessary for mathematical modeling to transform biological research and applications, in the same way it has already transformed other fields, is not completely clear. The purpose of this perspective is to forecast possible answers to this question-what needs to happen next-by drawing on the experience gained in another field, specifically meteorology. We review here a number of lessons learned in weather prediction that are directly relevant to biological systems modeling, and that we believe can enable the same kinds of global impact in our field as atmospheric modeling makes today.
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Affiliation(s)
- Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Taryn E Gillies
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Takamasa Kudo
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA 94305, USA
| | - Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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109
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Simonis JL, White EP, Ernest SKM. Evaluating probabilistic ecological forecasts. Ecology 2021; 102:e03431. [PMID: 34105774 DOI: 10.1002/ecy.3431] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 03/10/2021] [Accepted: 03/21/2021] [Indexed: 11/12/2022]
Abstract
Probabilistic near-term forecasting facilitates evaluation of model predictions against observations and is of pressing need in ecology to inform environmental decision-making and effect societal change. Despite this imperative, many ecologists are unfamiliar with the widely used tools for evaluating probabilistic forecasts developed in other fields. We address this gap by reviewing the literature on probabilistic forecast evaluation from diverse fields including climatology, economics, and epidemiology. We present established practices for selecting evaluation data (end-sample hold out), graphical forecast evaluation (times-series plots with uncertainty, probability integral transform plots), quantitative evaluation using scoring rules (log, quadratic, spherical, and ranked probability scores), and comparing scores across models (skill score, Diebold-Mariano test). We cover common approaches, highlight mathematical concepts to follow, and note decision points to allow application of general principles to specific forecasting endeavors. We illustrate these approaches with an application to a long-term rodent population time series currently used for ecological forecasting and discuss how ecology can continue to learn from and drive the cross-disciplinary field of forecasting science.
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Affiliation(s)
- Juniper L Simonis
- Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, 32611, USA.,DAPPER Stats, 3519 NE 15th Avenue, Suite 467, Portland, Oregon, 97212, USA
| | - Ethan P White
- Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, 32611, USA
| | - S K Morgan Ernest
- Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, 32611, USA
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110
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Nadal-Sala D, Grote R, Birami B, Lintunen A, Mammarella I, Preisler Y, Rotenberg E, Salmon Y, Tatarinov F, Yakir D, Ruehr NK. Assessing model performance via the most limiting environmental driver in two differently stressed pine stands. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02312. [PMID: 33630380 DOI: 10.1002/eap.2312] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 11/06/2020] [Accepted: 12/06/2020] [Indexed: 06/12/2023]
Abstract
Climate change will impact forest productivity worldwide. Forecasting the magnitude of such impact, with multiple environmental stressors changing simultaneously, is only possible with the help of process-based models. In order to assess their performance, such models require careful evaluation against measurements. However, direct comparison of model outputs against observational data is often not reliable, as models may provide the right answers due to the wrong reasons. This would severely hinder forecasting abilities under unprecedented climate conditions. Here, we present a methodology for model assessment, which supplements the traditional output-to-observation model validation. It evaluates model performance through its ability to reproduce observed seasonal changes of the most limiting environmental driver (MLED) for a given process, here daily gross primary productivity (GPP). We analyzed seasonal changes of the MLED for GPP in two contrasting pine forests, the Mediterranean Pinus halepensis Mill. Yatir (Israel) and the boreal Pinus sylvestris L. Hyytiälä (Finland) from three years of eddy-covariance flux data. Then, we simulated the same period with a state-of-the-art process-based simulation model (LandscapeDNDC). Finally, we assessed if the model was able to reproduce both GPP observations and MLED seasonality. We found that the model reproduced the seasonality of GPP in both stands, but it was slightly overestimated without site-specific fine-tuning. Interestingly, although LandscapeDNDC properly captured the main MLED in Hyytiälä (temperature) and in Yatir (soil water availability), it failed to reproduce high-temperature and high-vapor pressure limitations of GPP in Yatir during spring and summer. We deduced that the most likely reason for this divergence is an incomplete description of stomatal behavior. In summary, this study validates the MLED approach as a model evaluation tool, and opens up new possibilities for model improvement.
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Affiliation(s)
- Daniel Nadal-Sala
- Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, 82467, Germany
| | - Rüdiger Grote
- Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, 82467, Germany
| | - Benjamin Birami
- Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, 82467, Germany
| | - Anna Lintunen
- Faculty of Agriculture and Forestry, Institute for Atmospheric and Earth System Research/Forest Sciences, University of Helsinki, Latokartanonkaari 7, P.O. Box 27, Helsinki,, 00014, Finland
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, P.O. Box 68, Gustaf Hällströmin katu 2b, Helsinki,, 00014, Finland
| | - Ivan Mammarella
- Faculty of Agriculture and Forestry, Institute for Atmospheric and Earth System Research/Forest Sciences, University of Helsinki, Latokartanonkaari 7, P.O. Box 27, Helsinki,, 00014, Finland
| | - Yakir Preisler
- Department of Organismic and Evolutionary Biology, Harvard University, 16 Divinity Avenue, Cambridge, Massachusetts, 02138, USA
| | - Eyal Rotenberg
- Deptartment of Environmental Sciences and Energy Research, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Yann Salmon
- Faculty of Agriculture and Forestry, Institute for Atmospheric and Earth System Research/Forest Sciences, University of Helsinki, Latokartanonkaari 7, P.O. Box 27, Helsinki,, 00014, Finland
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, P.O. Box 68, Gustaf Hällströmin katu 2b, Helsinki,, 00014, Finland
| | - Fedor Tatarinov
- Deptartment of Environmental Sciences and Energy Research, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Dan Yakir
- Deptartment of Environmental Sciences and Energy Research, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Nadine K Ruehr
- Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, 82467, Germany
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111
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McCord SE, Webb NP, Van Zee JW, Burnett SH, Christensen EM, Courtright EM, Laney CM, Lunch C, Maxwell C, Karl JW, Slaughter A, Stauffer NG, Tweedie C. Provoking a Cultural Shift in Data Quality. Bioscience 2021; 71:647-657. [PMID: 34084097 PMCID: PMC8169311 DOI: 10.1093/biosci/biab020] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Ecological studies require quality data to describe the nature of ecological processes and to advance understanding of ecosystem change. Increasing access to big data has magnified both the burden and the complexity of ensuring quality data. The costs of errors in ecology include low use of data, increased time spent cleaning data, and poor reproducibility that can result in a misunderstanding of ecosystem processes and dynamics, all of which can erode the efficacy of and trust in ecological research. Although conceptual and technological advances have improved ecological data access and management, a cultural shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this cultural shift. The data quality framework flexibly supports different collaboration models, supports all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies.
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Affiliation(s)
- Sarah E McCord
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
- University of Texas—El Paso, El Paso, Texas, United States
| | - Nicholas P Webb
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
- New Mexico State University, in Las Cruces, New Mexico,United States
| | - Justin W Van Zee
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
| | - Sarah H Burnett
- Bureau of Land Management, National Operations Center, Denver, Colorado, United States
| | - Erica M Christensen
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
- New Mexico State University, in Las Cruces, New Mexico,United States
| | - Ericha M Courtright
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
| | - Christine M Laney
- Battelle-National Ecological Observatory Network, Boulder, Colorado, United States
| | - Claire Lunch
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
| | - Connie Maxwell
- New Mexico State University, in Las Cruces, New Mexico,United States
| | - Jason W Karl
- Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, Idaho, United States
| | - Amalia Slaughter
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
| | - Nelson G Stauffer
- US Department of Agriculture ARS Jornada Experimental Range, Las Cruces, New Mexico, United States
| | - Craig Tweedie
- University of Texas—El Paso, El Paso, Texas, United States
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112
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Affiliation(s)
- Lars A. Brudvig
- Department of Plant Biology Michigan State University East Lansing MI 48824 U.S.A
- Program in Ecology, Evolution, and Behavior Michigan State University East Lansing MI 48824 U.S.A
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113
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Seaborn T, Andrews KR, Applestein CV, Breech TM, Garrett MJ, Zaiats A, Caughlin TT. Integrating genomics in population models to forecast translocation success. Restor Ecol 2021. [DOI: 10.1111/rec.13395] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Travis Seaborn
- Department of Fish and Wildlife Sciences University of Idaho Moscow ID U.S.A
| | - Kimberly R. Andrews
- Institute for Bioinformatics and Evolutionary Studies (IBEST) University of Idaho Moscow ID U.S.A
| | | | - Tyler M. Breech
- Department of Biological Sciences Idaho State University Pocatello ID U.S.A
| | - Molly J. Garrett
- Department of Fish and Wildlife Sciences University of Idaho Moscow ID U.S.A
| | - Andrii Zaiats
- Biological Sciences Boise State University Boise ID U.S.A
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114
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Scale gaps in landscape phenology: challenges and opportunities. Trends Ecol Evol 2021; 36:709-721. [PMID: 33972119 DOI: 10.1016/j.tree.2021.04.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 04/02/2021] [Accepted: 04/14/2021] [Indexed: 11/22/2022]
Abstract
Phenology, or the timing of life history events, can be heterogeneous across biological communities and landscapes and can vary across a wide variety of spatiotemporal scales. Here, we synthesize information from landscape phenology studies across different scales of measurement around a set of core concepts. We highlight why phenology is scale dependent and identify gaps in the spatiotemporal scales of phenological observations and inferences. We discuss the consequences of these gaps and describe opportunities to address the inherent sensitivities of phenological metrics to measurement scale. Although most studies we review and discuss are focused on plants, our work provides a broadly relevant overview of the role of observation scale in landscape phenology and a general approach for measuring and reporting scale dependence.
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115
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Tredennick AT, Hooker G, Ellner SP, Adler PB. A practical guide to selecting models for exploration, inference, and prediction in ecology. Ecology 2021; 102:e03336. [PMID: 33710619 PMCID: PMC8187274 DOI: 10.1002/ecy.3336] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 10/08/2020] [Accepted: 12/06/2020] [Indexed: 11/12/2022]
Abstract
Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that much confusion surrounding statistical model selection results from failing to first clearly specify the purpose of the analysis. We argue that there are three distinct goals for statistical modeling in ecology: data exploration, inference, and prediction. Once the modeling goal is clearly articulated, an appropriate model selection procedure is easier to identify. We review model selection approaches and highlight their strengths and weaknesses relative to each of the three modeling goals. We then present examples of modeling for exploration, inference, and prediction using a time series of butterfly population counts. These show how a model selection approach flows naturally from the modeling goal, leading to different models selected for different purposes, even with exactly the same data set. This review illustrates best practices for ecologists and should serve as a reminder that statistical recipes cannot substitute for critical thinking or for the use of independent data to test hypotheses and validate predictions.
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Affiliation(s)
- Andrew T Tredennick
- Western EcoSystems Technology, Inc., 1610 East Reynolds Street, Laramie, Wyoming, 82072, USA
| | - Giles Hooker
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, 14853, USA
| | - Stephen P Ellner
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, 14853, USA
| | - Peter B Adler
- Department of Wildland Resources and the Ecology Center, Utah State University, 5230 Old Main Hill, Logan, Utah, 84322, USA
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116
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Boult VL, Evans LC. Mechanisms matter: Predicting the ecological impacts of global change. GLOBAL CHANGE BIOLOGY 2021; 27:1689-1691. [PMID: 33501769 DOI: 10.1111/gcb.15527] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 01/20/2021] [Indexed: 06/12/2023]
Affiliation(s)
- Victoria L Boult
- National Centre for Atmospheric Sciences and Department of Meteorology, University of Reading, Reading, UK
| | - Luke C Evans
- School of Biological Sciences, University of Reading, Reading, UK
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117
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Tucker AM, Runge MC. Optimal Strategies for Managing Wildlife Harvest Under System Change. J Wildl Manage 2021. [DOI: 10.1002/jwmg.22047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Anna M. Tucker
- U.S. Geological Survey, Eastern Ecological Science Center at the Patuxent Research Refuge 12100 Beech Forest Road Laurel MD 20708 USA
| | - Michael C. Runge
- U.S. Geological Survey, Eastern Ecological Science Center at the Patuxent Research Refuge 12100 Beech Forest Road Laurel MD 20708 USA
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118
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Applestein C, Caughlin TT, Germino MJ. Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites. Ecosphere 2021. [DOI: 10.1002/ecs2.3446] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Cara Applestein
- Forest and Rangeland Ecosystem Science Center U.S. Geological Survey 970 South Lusk Street Boise Idaho83706USA
- Department of Biological Sciences Boise State University Boise Idaho USA
| | - T. Trevor Caughlin
- Department of Biological Sciences Boise State University Boise Idaho USA
| | - Matthew J. Germino
- Forest and Rangeland Ecosystem Science Center U.S. Geological Survey 970 South Lusk Street Boise Idaho83706USA
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119
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Marolla F, Henden JA, Fuglei E, Pedersen ÅØ, Itkin M, Ims RA. Iterative model predictions for wildlife populations impacted by rapid climate change. GLOBAL CHANGE BIOLOGY 2021; 27:1547-1559. [PMID: 33448074 DOI: 10.1111/gcb.15518] [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: 06/23/2020] [Accepted: 12/15/2020] [Indexed: 06/12/2023]
Abstract
To improve understanding and management of the consequences of current rapid environmental change, ecologists advocate using long-term monitoring data series to generate iterative near-term predictions of ecosystem responses. This approach allows scientific evidence to increase rapidly and management strategies to be tailored simultaneously. Iterative near-term forecasting may therefore be particularly useful for adaptive monitoring of ecosystems subjected to rapid climate change. Here, we show how to implement near-term forecasting in the case of a harvested population of rock ptarmigan in high-arctic Svalbard, a region subjected to the largest and most rapid climate change on Earth. We fitted state-space models to ptarmigan counts from point transect distance sampling during 2005-2019 and developed two types of predictions: (1) explanatory predictions to quantify the effect of potential drivers of ptarmigan population dynamics, and (2) anticipatory predictions to assess the ability of candidate models of increasing complexity to forecast next-year population density. Based on the explanatory predictions, we found that a recent increasing trend in the Svalbard rock ptarmigan population can be attributed to major changes in winter climate. Currently, a strong positive effect of increasing average winter temperature on ptarmigan population growth outweighs the negative impacts of other manifestations of climate change such as rain-on-snow events. Moreover, the ptarmigan population may compensate for current harvest levels. Based on the anticipatory predictions, the near-term forecasting ability of the models improved nonlinearly with the length of the time series, but yielded good forecasts even based on a short time series. The inclusion of ecological predictors improved forecasts of sharp changes in next-year population density, demonstrating the value of ecosystem-based monitoring. Overall, our study illustrates the power of integrating near-term forecasting in monitoring systems to aid understanding and management of wildlife populations exposed to rapid climate change. We provide recommendations for how to improve this approach.
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Affiliation(s)
- Filippo Marolla
- Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Tromsø, Norway
| | - John-André Henden
- Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Eva Fuglei
- Norwegian Polar Institute, Fram Centre, Tromsø, Norway
| | | | - Mikhail Itkin
- Norwegian Polar Institute, Fram Centre, Tromsø, Norway
| | - Rolf A Ims
- Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Tromsø, Norway
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120
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Oberpriller J, Cameron DR, Dietze MC, Hartig F. Towards robust statistical inference for complex computer models. Ecol Lett 2021; 24:1251-1261. [PMID: 33783944 DOI: 10.1111/ele.13728] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/16/2020] [Accepted: 02/11/2021] [Indexed: 01/08/2023]
Abstract
Ecologists increasingly rely on complex computer simulations to forecast ecological systems. To make such forecasts precise, uncertainties in model parameters and structure must be reduced and correctly propagated to model outputs. Naively using standard statistical techniques for this task, however, can lead to bias and underestimation of uncertainties in parameters and predictions. Here, we explain why these problems occur and propose a framework for robust inference with complex computer simulations. After having identified that model error is more consequential in complex computer simulations, due to their more pronounced nonlinearity and interconnectedness, we discuss as possible solutions data rebalancing and adding bias corrections on model outputs or processes during or after the calibration procedure. We illustrate the methods in a case study, using a dynamic vegetation model. We conclude that developing better methods for robust inference of complex computer simulations is vital for generating reliable predictions of ecosystem responses.
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Affiliation(s)
- Johannes Oberpriller
- Theoretical Ecology, University of Regensburg, Universitätsstraße 31, Regensburg, 93053, Germany
| | - David R Cameron
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, EH260QB, UK
| | - Michael C Dietze
- Department of Earth & Environment, Boston University, Boston, MA, USA
| | - Florian Hartig
- Theoretical Ecology, University of Regensburg, Universitätsstraße 31, Regensburg, 93053, Germany
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121
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Comparing sample bias correction methods for species distribution modeling using virtual species. Ecosphere 2021. [DOI: 10.1002/ecs2.3422] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
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122
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Whytock RC, Świeżewski J, Zwerts JA, Bara‐Słupski T, Koumba Pambo AF, Rogala M, Bahaa‐el‐din L, Boekee K, Brittain S, Cardoso AW, Henschel P, Lehmann D, Momboua B, Kiebou Opepa C, Orbell C, Pitman RT, Robinson HS, Abernethy KA. Robust ecological analysis of camera trap data labelled by a machine learning model. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13576] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Robin C. Whytock
- Faculty of Natural Sciences University of Stirling Stirling UK
- Agence Nationale des Parcs Nationaux Libreville Gabon
| | | | | | | | | | | | - Laila Bahaa‐el‐din
- School of Life Sciences University of KwaZulu‐Natal Pietermaritzburg South Africa
| | - Kelly Boekee
- Program for the Sustainable Management of Natural Resources South West Region Buea Cameroon
- Center for Tropical Forest Science Smithsonian Tropical Research Institute Balboa, Ancon Republic of Panama
| | - Stephanie Brittain
- Department of Zoology The Interdisciplinary Centre for Conservation Science University of Oxford Oxford UK
- The Institute of ZoologyZoological Society of London London UK
| | - Anabelle W. Cardoso
- Department of Ecology and Evolutionary Biology Yale University New Haven CT USA
| | - Philipp Henschel
- Panthera New York NY USA
- Institut de Recherche en Ecologie Tropicale CENAREST Libreville Gabon
| | - David Lehmann
- Faculty of Natural Sciences University of Stirling Stirling UK
- Agence Nationale des Parcs Nationaux Libreville Gabon
| | - Brice Momboua
- Agence Nationale des Parcs Nationaux Libreville Gabon
| | | | - Christopher Orbell
- Faculty of Natural Sciences University of Stirling Stirling UK
- Panthera New York NY USA
| | | | - Hugh S. Robinson
- Panthera New York NY USA
- Wildlife Biology Program W.A. Franke College of Forestry and Conservation University of Montana Missoula MT USA
| | - Katharine A. Abernethy
- Faculty of Natural Sciences University of Stirling Stirling UK
- Institut de Recherche en Ecologie Tropicale CENAREST Libreville Gabon
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123
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Graham EB, Averill C, Bond-Lamberty B, Knelman JE, Krause S, Peralta AL, Shade A, Smith AP, Cheng SJ, Fanin N, Freund C, Garcia PE, Gibbons SM, Van Goethem MW, Guebila MB, Kemppinen J, Nowicki RJ, Pausas JG, Reed SP, Rocca J, Sengupta A, Sihi D, Simonin M, Słowiński M, Spawn SA, Sutherland I, Tonkin JD, Wisnoski NI, Zipper SC. Toward a Generalizable Framework of Disturbance Ecology Through Crowdsourced Science. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.588940] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Disturbances fundamentally alter ecosystem functions, yet predicting their impacts remains a key scientific challenge. While the study of disturbances is ubiquitous across many ecological disciplines, there is no agreed-upon, cross-disciplinary foundation for discussing or quantifying the complexity of disturbances, and no consistent terminology or methodologies exist. This inconsistency presents an increasingly urgent challenge due to accelerating global change and the threat of interacting disturbances that can destabilize ecosystem responses. By harvesting the expertise of an interdisciplinary cohort of contributors spanning 42 institutions across 15 countries, we identified an essential limitation in disturbance ecology: the word ‘disturbance’ is used interchangeably to refer to both the events that cause, and the consequences of, ecological change, despite fundamental distinctions between the two meanings. In response, we developed a generalizable framework of ecosystem disturbances, providing a well-defined lexicon for understanding disturbances across perspectives and scales. The framework results from ideas that resonate across multiple scientific disciplines and provides a baseline standard to compare disturbances across fields. This framework can be supplemented by discipline-specific variables to provide maximum benefit to both inter- and intra-disciplinary research. To support future syntheses and meta-analyses of disturbance research, we also encourage researchers to be explicit in how they define disturbance drivers and impacts, and we recommend minimum reporting standards that are applicable regardless of scale. Finally, we discuss the primary factors we considered when developing a baseline framework and propose four future directions to advance our interdisciplinary understanding of disturbances and their social-ecological impacts: integrating across ecological scales, understanding disturbance interactions, establishing baselines and trajectories, and developing process-based models and ecological forecasting initiatives. Our experience through this process motivates us to encourage the wider scientific community to continue to explore new approaches for leveraging Open Science principles in generating creative and multidisciplinary ideas.
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124
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Stewart JD, Kremer P, Shakya KM, Conway M, Saad A. Outdoor Atmospheric Microbial Diversity Is Associated With Urban Landscape Structure and Differs From Indoor-Transit Systems as Revealed by Mobile Monitoring and Three-Dimensional Spatial Analysis. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.620461] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Microbes are abundant inhabitants of the near-surface atmosphere in urban areas. The distribution of microbial communities may benefit or hinder human wellbeing and ecosystem function. Surveys of airborne microbial diversity are uncommon in both natural and built environments and those that investigate diversity are stationary in the city, thus missing continuous exposure to microbes that covary with three-dimensional urban structure. Individuals in cities are generally mobile and would be exposed to diverse urban structures outdoors and within indoor-transit systems in a day. We used mobile monitoring of microbial diversity and geographic information system spatial analysis, across Philadelphia, Pennsylvania, USA in outdoor and indoor-transit (subways and train cars) environments. This study identifies to the role of the three-dimensional urban landscape in structuring atmospheric microbiomes and employs mobile monitoring over ~1,920 kilometers to measure continuous biodiversity. We found more diverse communities outdoors that significantly differ from indoor-transit air in microbial community structure, function, likely source environment, and potentially pathogenic fraction of the community. Variation in the structure of the urban landscape was associated with diversity and function of the near-surface atmospheric microbiome in outdoor samples.
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125
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McLeish MJ, Fraile A, García-Arenal F. Population Genomics of Plant Viruses: The Ecology and Evolution of Virus Emergence. PHYTOPATHOLOGY 2021; 111:32-39. [PMID: 33210987 DOI: 10.1094/phyto-08-20-0355-fi] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
The genomics era has revolutionized studies of adaptive evolution by monitoring large numbers of loci throughout the genomes of many individuals. Ideally, the investigation of emergence in plant viruses requires examining the population dynamics of both virus and host, their interactions with each other, with other organisms and the abiotic environment. Genetic mechanisms that affect demographic processes are now being studied with high-throughput technologies, traditional genetics methods, and new computational tools for big-data. In this review, we discuss the utility of these approaches to monitor and detect changes in virus populations within cells and individuals, and over wider areas across species and communities of ecosystems. The advent of genomics in virology has fostered a multidisciplinary approach to tackling disease risk. The ability to make sense of the information now generated in this integrated setting is by far the most substantial obstacle to the ultimate goal of plant virology to minimize the threats to food security posed by disease. To achieve this goal, it is imperative to understand and forecast how populations respond to future changes in complex natural systems.
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Affiliation(s)
- Michael J McLeish
- Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) and Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Aurora Fraile
- Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) and Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Fernando García-Arenal
- Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) and Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
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126
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Johnson‐Bice SM, Ferguson JM, Erb JD, Gable TD, Windels SK. Ecological forecasts reveal limitations of common model selection methods: predicting changes in beaver colony densities. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02198. [PMID: 32583507 PMCID: PMC7816246 DOI: 10.1002/eap.2198] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Revised: 03/13/2020] [Accepted: 03/30/2020] [Indexed: 05/20/2023]
Abstract
Over the past two decades, there have been numerous calls to make ecology a more predictive science through direct empirical assessments of ecological models and predictions. While the widespread use of model selection using information criteria has pushed ecology toward placing a higher emphasis on prediction, few attempts have been made to validate the ability of information criteria to correctly identify the most parsimonious model with the greatest predictive accuracy. Here, we used an ecological forecasting framework to test the ability of information criteria to accurately predict the relative contribution of density dependence and density-independent factors (forage availability, harvest, weather, wolf [Canis lupus] density) on inter-annual fluctuations in beaver (Castor canadensis) colony densities. We modeled changes in colony densities using a discrete-time Gompertz model, and assessed the performance of four models using information criteria values: density-independent models with (1) and without (2) environmental covariates; and density-dependent models with (3) and without (4) environmental covariates. We then evaluated the forecasting accuracy of each model by withholding the final one-third of observations from each population and compared observed vs. predicted densities. Information criteria and our forecasting accuracy metrics both provided strong evidence of compensatory density dependence in the annual dynamics of beaver colony densities. However, despite strong within-sample performance by the most complex model (density-dependent with covariates) as determined using information criteria, hindcasts of colony densities revealed that the much simpler density-dependent model without covariates performed nearly as well predicting out-of-sample colony densities. The hindcast results indicated that the complex model over-fit our data, suggesting that parameters identified by information criteria as important predictor variables are only marginally valuable for predicting landscape-scale beaver colony dynamics. Our study demonstrates the importance of evaluating ecological models and predictions with long-term data and revealed how a known limitation of information criteria (over-fitting of complex models) can affect our interpretation of ecological dynamics. While incorporating knowledge of the factors that influence animal population dynamics can improve population forecasts, we suggest that comparing forecast performance metrics can likewise improve our knowledge of the factors driving population dynamics.
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Affiliation(s)
- Sean M. Johnson‐Bice
- Department of Biological SciencesUniversity of Manitoba50 Sifton RoadWinnipegManitobaR3T 2N2Canada
- Natural Resources Research InstituteUniversity of Minnesota Duluth5013 Miller Trunk HighwayDuluthMinnesota55812USA
| | - Jake M. Ferguson
- Department of BiologyUniversity of Hawai`i at Mānoa2538 McCarthy MallHonoluluHawaii96822USA
| | - John D. Erb
- Forest Wildlife Populations and Research GroupMinnesota Department of Natural Resources1201 E. highway 2Grand RapidsMinnesota55744USA
| | - Thomas D. Gable
- Department of Fisheries, Wildlife and Conservation BiologyUniversity of Minnesota Twin Cities2003 Upper Buford CircleSt. PaulMinnesota55108USA
| | - Steve K. Windels
- Natural Resources Research InstituteUniversity of Minnesota Duluth5013 Miller Trunk HighwayDuluthMinnesota55812USA
- Department of Fisheries, Wildlife and Conservation BiologyUniversity of Minnesota Twin Cities2003 Upper Buford CircleSt. PaulMinnesota55108USA
- Voyageurs National Park360 Highway 11 E.International FallsMinnesota56649USA
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127
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Fer I, Gardella AK, Shiklomanov AN, Campbell EE, Cowdery EM, De Kauwe MG, Desai A, Duveneck MJ, Fisher JB, Haynes KD, Hoffman FM, Johnston MR, Kooper R, LeBauer DS, Mantooth J, Parton WJ, Poulter B, Quaife T, Raiho A, Schaefer K, Serbin SP, Simkins J, Wilcox KR, Viskari T, Dietze MC. Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data-model integration. GLOBAL CHANGE BIOLOGY 2021; 27:13-26. [PMID: 33075199 PMCID: PMC7756391 DOI: 10.1111/gcb.15409] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 09/16/2020] [Indexed: 05/10/2023]
Abstract
In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is a key to meeting the pressing needs of science and society in the 21st century.
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Affiliation(s)
- Istem Fer
- Finnish Meteorological InstituteHelsinkiFinland
| | - Anthony K. Gardella
- Department of Earth and EnvironmentBoston UniversityBostonMAUSA
- School for Environment and SustainabilityUniversity of MichiganAnn ArborMIUSA
| | | | | | | | - Martin G. De Kauwe
- ARC Centre of Excellence for Climate ExtremesSydneyNSWAustralia
- Climate Change Research CentreUniversity of New South WalesSydneyNSWAustralia
- Evolution & Ecology Research CentreUniversity of New South WalesSydneyNSWAustralia
| | - Ankur Desai
- Department of Atmospheric and Oceanic SciencesUniversity of Wisconsin‐MadisonMadisonWIUSA
| | | | - Joshua B. Fisher
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | | | - Forrest M. Hoffman
- Computational Earth Sciences Group and Climate Change Science InstituteOak Ridge National LaboratoryOak RidgeTNUSA
- Department of Civil and Environmental EngineeringUniversity of TennesseeKnoxvilleTNUSA
| | - Miriam R. Johnston
- Department of Organismic and Evolutionary BiologyHarvard UniversityCambridgeMAUSA
| | - Rob Kooper
- NCSA (National Center for Supercomputing Applications)University of Illinois at Urbana ChampaignUrbanaILUSA
| | - David S. LeBauer
- College of Agriculture and Life SciencesUniversity of ArizonaTucsonAZUSA
| | | | - William J. Parton
- Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsCOUSA
| | - Benjamin Poulter
- Biospheric Sciences Laboratory (618)NASA Goddard Space Flight CenterGreenbeltMDUSA
| | - Tristan Quaife
- UK National Centre for Earth Observation and Department of MeteorologyUniversity of ReadingReadingUK
| | - Ann Raiho
- Fish, Wildlife, and Conservation Biology DepartmentColorado State UniversityFort CollinsCOUSA
| | - Kevin Schaefer
- National Snow and Ice Data CenterCooperative Institute for Research in Environmental SciencesUniversity of ColoradoBoulderCOUSA
| | - Shawn P. Serbin
- Brookhaven National LaboratoryEnvironmental and Climate Sciences DepartmentUptonNYUSA
| | | | - Kevin R. Wilcox
- Ecosystem Science and ManagementUniversity of WyomingLaramieWYUSA
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128
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Caughlin TT, Barber C, Asner GP, Glenn NF, Bohlman SA, Wilson CH. Monitoring tropical forest succession at landscape scales despite uncertainty in Landsat time series. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02208. [PMID: 32627902 DOI: 10.1002/eap.2208] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 05/04/2020] [Indexed: 06/11/2023]
Abstract
Forecasting rates of forest succession at landscape scales will aid global efforts to restore tree cover to millions of hectares of degraded land. While optical satellite remote sensing can detect regional land cover change, quantifying forest structural change is challenging. We developed a state-space modeling framework that applies Landsat satellite data to estimate variability in rates of natural regeneration between sites in a tropical landscape. Our models work by disentangling measurement error in Landsat-derived spectral reflectance from process error related to successional variability. We applied our modeling framework to rank rates of forest succession between 10 naturally regenerating sites in Southwestern Panama from about 2001 to 2015 and tested how different models for measurement error impacted forecast accuracy, ecological inference, and rankings of successional rates between sites. We achieved the greatest increase in forecasting accuracy by adding intra-annual phenological variation to a model based on Landsat-derived normalized difference vegetation index (NDVI). The best-performing model accounted for inter- and intra-annual noise in spectral reflectance and translated NDVI to canopy height via Landsat-lidar fusion. Modeling forest succession as a function of canopy height rather than NDVI also resulted in more realistic estimates of forest state during early succession, including greater confidence in rank order of successional rates between sites. These results establish the viability of state-space models to quantify ecological dynamics from time series of space-borne imagery. State-space models also provide a statistical approach well-suited to fusing high-resolution data, such as airborne lidar, with lower-resolution data that provides better temporal and spatial coverage, such as the Landsat satellite record. Monitoring forest succession using satellite imagery could play a key role in achieving global restoration targets, including identifying sites that will regain tree cover with minimal intervention.
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Affiliation(s)
- T Trevor Caughlin
- Biological Sciences, Boise State University, Boise, Idaho, 83725, USA
| | - Cristina Barber
- Biological Sciences, Boise State University, Boise, Idaho, 83725, USA
| | - Gregory P Asner
- Center for Global Discovery and Conservation Science, Arizona State University, Hilo, Hawaii, 96720, USA
- Center for Global Discovery and Conservation Science, Arizona State University, Tempe, Arizona, 85287, USA
| | - Nancy F Glenn
- Department of Geosciences, Boise State University, Boise, Idaho, 83725, USA
- School of Civil and Environmental Engineering, University of New South Wales, Sydney, 2052, Australia
| | - Stephanie A Bohlman
- School of Forest Resources and Conservation, University of Florida, Gainesville, Florida, 32611, USA
| | - Chris H Wilson
- Agronomy Department, University of Florida, Gainesville, Florida, 32611, USA
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129
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Rollinson CR, Dawson A, Raiho AM, Williams JW, Dietze MC, Hickler T, Jackson ST, McLachlan J, Jp Moore D, Poulter B, Quaife T, Steinkamp J, Trachsel M. Forest responses to last-millennium hydroclimate variability are governed by spatial variations in ecosystem sensitivity. Ecol Lett 2020; 24:498-508. [PMID: 33377307 DOI: 10.1111/ele.13667] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 11/18/2020] [Accepted: 11/23/2020] [Indexed: 01/13/2023]
Abstract
Forecasts of future forest change are governed by ecosystem sensitivity to climate change, but ecosystem model projections are under-constrained by data at multidecadal and longer timescales. Here, we quantify ecosystem sensitivity to centennial-scale hydroclimate variability, by comparing dendroclimatic and pollen-inferred reconstructions of drought, forest composition and biomass for the last millennium with five ecosystem model simulations. In both observations and models, spatial patterns in ecosystem responses to hydroclimate variability are strongly governed by ecosystem sensitivity rather than climate exposure. Ecosystem sensitivity was higher in models than observations and highest in simpler models. Model-data comparisons suggest that interactions among biodiversity, demography and ecophysiology processes dampen the sensitivity of forest composition and biomass to climate variability and change. Integrating ecosystem models with observations from timescales extending beyond the instrumental record can better understand and forecast the mechanisms regulating forest sensitivity to climate variability in a complex and changing world.
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Affiliation(s)
- Christine R Rollinson
- Center for Tree Science, The Morton Arboretum, 4100 Illinois Route 53, Lisle, IL, 60532, USA
| | - Andria Dawson
- Department of General Education, Mount Royal University, Calgary, Alberta, T3E 6K6, Canada
| | - Ann M Raiho
- Department of Biological Sciences, University of Notre Dame, 100 Galvin Life Science Center, Notre Dame, IN, 46556, USA
| | - John W Williams
- Department of Geography and Center for Climatic Research, University of Wisconsin-Madison, Madison, WI, 53704, USA
| | - Michael C Dietze
- Department of Earth and Environment, Boston University, 685 Commonwealth Ave, Boston, MA, 02215, USA
| | - Thomas Hickler
- Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberganlage 25, Frankfurt/Main, 60325, Germany.,Department of Physical Geography, Goethe University, Frankfurt/Main, Germany
| | - Stephen T Jackson
- US Geological Survey, Southwest and South Central Climate Adaptation Centers, Denver, DE, USA.,Department of Geosciences, University of Arizona, Tucson, AZ, 85721, USA
| | - Jason McLachlan
- Department of Biological Sciences, University of Notre Dame, 100 Galvin Life Science Center, Notre Dame, IN, 46556, USA
| | - David Jp Moore
- School of Natural Resources, University of Arizona, Tucson, AZ, 85721, USA
| | | | - Tristan Quaife
- Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
| | - Jörg Steinkamp
- Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt/Main, Germany.,Johannes Gutenberg University, Mainz, Germany
| | - Mathias Trachsel
- Department of Geography, University of Wisconsin-Madison, Madison, WI, 53704, USA
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130
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Lasky JR, Hooten MB, Adler PB. What processes must we understand to forecast regional-scale population dynamics? Proc Biol Sci 2020; 287:20202219. [PMID: 33290672 PMCID: PMC7739927 DOI: 10.1098/rspb.2020.2219] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 11/12/2020] [Indexed: 12/14/2022] Open
Abstract
An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.
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Affiliation(s)
- Jesse R. Lasky
- Department of Biology, Pennsylvania State University, University Park, PA, USA
| | - Mevin B. Hooten
- U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, Fort Collins, CO, USA
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, USA
- Department of Statistics, Colorado State University, Fort Collins, CO, USA
| | - Peter B. Adler
- Department of Wildland Resources and the Ecology Center, Utah State University, Logan, UT, USA
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131
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A unifying framework for studying and managing climate-driven rates of ecological change. Nat Ecol Evol 2020; 5:17-26. [PMID: 33288870 DOI: 10.1038/s41559-020-01344-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 10/12/2020] [Indexed: 01/22/2023]
Abstract
During the Anthropocene and other eras of rapidly changing climates, rates of change of ecological systems can be described as fast, slow or abrupt. Fast ecological responses closely track climate change, slow responses substantively lag climate forcing, causing disequilibria and reduced fitness, and abrupt responses are characterized by nonlinear, threshold-type responses at rates that are large relative to background variability and forcing. All three kinds of climate-driven ecological dynamics are well documented in contemporary studies, palaeoecology and invasion biology. This fast-slow-abrupt conceptual framework helps unify a bifurcated climate-change literature, which tends to separately consider the ecological risks posed by slow or abrupt ecological dynamics. Given the prospect of ongoing climate change for the next several decades to centuries of the Anthropocene and wide variations in ecological rates of change, the theory and practice of managing ecological systems should shift attention from target states to target rates. A rates-focused framework broadens the strategic menu for managers to include options to both slow and accelerate ecological rates of change, seeks to reduce mismatch among climate and ecological rates of change, and provides a unified conceptual framework for tackling the distinct risks associated with fast, slow and abrupt ecological rates of change.
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132
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McClintock BT, Langrock R, Gimenez O, Cam E, Borchers DL, Glennie R, Patterson TA. Uncovering ecological state dynamics with hidden Markov models. Ecol Lett 2020; 23:1878-1903. [PMID: 33073921 PMCID: PMC7702077 DOI: 10.1111/ele.13610] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 08/13/2020] [Accepted: 08/25/2020] [Indexed: 01/03/2023]
Abstract
Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or 'hidden'. Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes. By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that might otherwise be intractable. However, HMMs have only recently begun to gain traction within the broader ecological community. We provide a gentle introduction to HMMs, establish some common terminology, review the immense scope of HMMs for applied ecological research and provide a tutorial on implementation and interpretation. By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.
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Affiliation(s)
| | - Roland Langrock
- Department of Business Administration and EconomicsBielefeld UniversityBielefeldGermany
| | - Olivier Gimenez
- CNRS Centre d'Ecologie Fonctionnelle et EvolutiveMontpellierFrance
| | - Emmanuelle Cam
- Laboratoire des Sciences de l'Environnement MarinInstitut Universitaire Européen de la MerUniv. BrestCNRS, IRDIfremerFrance
| | - David L. Borchers
- School of Mathematics and StatisticsUniversity of St AndrewsSt AndrewsUK
| | - Richard Glennie
- School of Mathematics and StatisticsUniversity of St AndrewsSt AndrewsUK
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133
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Albrich K, Rammer W, Turner MG, Ratajczak Z, Braziunas KH, Hansen WD, Seidl R. Simulating forest resilience: A review. GLOBAL ECOLOGY AND BIOGEOGRAPHY : A JOURNAL OF MACROECOLOGY 2020; 29:2082-2096. [PMID: 33380902 PMCID: PMC7756463 DOI: 10.1111/geb.13197] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 08/18/2020] [Accepted: 09/10/2020] [Indexed: 06/02/2023]
Abstract
AIM Simulation models are important tools for quantifying the resilience (i.e., persistence under changed environmental conditions) of forest ecosystems to global change. We synthesized the modelling literature on forest resilience, summarizing common models and applications in resilience research, and scrutinizing the implementation of important resilience mechanisms in these models. Models applied to assess resilience are highly diverse, and our goal was to assess how well they account for important resilience mechanisms identified in experimental and empirical research. LOCATION Global. TIME PERIOD 1994 to 2019. MAJOR TAXA STUDIED Trees. METHODS We reviewed the forest resilience literature using online databases, selecting 119 simulation modelling studies for further analysis. We identified a set of resilience mechanisms from the general resilience literature and analysed models for their representation of these mechanisms. Analyses were grouped by investigated drivers (resilience to what) and responses (resilience of what), as well as by the type of model being used. RESULTS Models used to study forest resilience varied widely, from analytical approaches to complex landscape simulators. The most commonly addressed questions were associated with resilience of forest cover to fire. Important resilience mechanisms pertaining to regeneration, soil processes, and disturbance legacies were explicitly simulated in only 34 to 46% of the model applications. MAIN CONCLUSIONS We found a large gap between processes identified as underpinning forest resilience in the theoretical and empirical literature, and those represented in models used to assess forest resilience. Contemporary forest models developed for other goals may be poorly suited for studying forest resilience during an era of accelerating change. Our results highlight the need for a new wave of model development to enhance understanding of and management for resilient forests.
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Affiliation(s)
- Katharina Albrich
- Institute of SilvicultureUniversity of Natural Resources and Life Sciences (BOKU) ViennaWienAustria
- Ecosystem Dynamics and Forest Management GroupTechnical University of MunichFreisingGermany
| | - Werner Rammer
- Institute of SilvicultureUniversity of Natural Resources and Life Sciences (BOKU) ViennaWienAustria
- Ecosystem Dynamics and Forest Management GroupTechnical University of MunichFreisingGermany
| | - Monica G. Turner
- Department of Integrative BiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Zak Ratajczak
- Department of Integrative BiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Kristin H. Braziunas
- Department of Integrative BiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | | | - Rupert Seidl
- Institute of SilvicultureUniversity of Natural Resources and Life Sciences (BOKU) ViennaWienAustria
- Ecosystem Dynamics and Forest Management GroupTechnical University of MunichFreisingGermany
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134
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White ER, Hastings A. Seasonality in ecology: Progress and prospects in theory. ECOLOGICAL COMPLEXITY 2020. [DOI: 10.1016/j.ecocom.2020.100867] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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135
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Gillson L, Seymour CL, Slingsby JA, Inouye DW. What Are the Grand Challenges for Plant Conservation in the 21st Century? FRONTIERS IN CONSERVATION SCIENCE 2020. [DOI: 10.3389/fcosc.2020.600943] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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136
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Abstract
The virus causing COVID-19 has spread rapidly worldwide and threatens millions of lives. It remains unknown, as of April 2020, whether summer weather will reduce its spread, thereby alleviating strains on hospitals and providing time for vaccine development. Early insights from laboratory studies and research on related viruses predicted that COVID-19 would decline with higher temperatures, humidity, and ultraviolet (UV) light. Using current, fine-scaled weather data and global reports of infections, we develop a model that explains 36% of the variation in maximum COVID-19 growth rates based on weather and demography (17%) and country-specific effects (19%). UV light is most strongly associated with lower COVID-19 growth. Projections suggest that, without intervention, COVID-19 will decrease temporarily during summer, rebound by autumn, and peak next winter. Validation based on data from May and June 2020 confirms the generality of the climate signal detected. However, uncertainty remains high, and the probability of weekly doubling rates remains >20% throughout summer in the absence of social interventions. Consequently, aggressive interventions will likely be needed despite seasonal trends.
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Affiliation(s)
- Cory Merow
- Eversource Energy Center, University of Connecticut, Storrs, CT 06268;
- Center of Biological Risk, University of Connecticut, Storrs, CT 06268
- Department of Ecology & Evolutionary Biology, University of Connecticut, Storrs, CT 06268
| | - Mark C Urban
- Center of Biological Risk, University of Connecticut, Storrs, CT 06268
- Department of Ecology & Evolutionary Biology, University of Connecticut, Storrs, CT 06268
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137
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Xia J, Wang J, Niu S. Research challenges and opportunities for using big data in global change biology. GLOBAL CHANGE BIOLOGY 2020; 26:6040-6061. [PMID: 32799353 DOI: 10.1111/gcb.15317] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 07/13/2020] [Indexed: 06/11/2023]
Abstract
Global change biology has been entering a big data era due to the vast increase in availability of both environmental and biological data. Big data refers to large data volume, complex data sets, and multiple data sources. The recent use of such big data is improving our understanding of interactions between biological systems and global environmental changes. In this review, we first explore how big data has been analyzed to identify the general patterns of biological responses to global changes at scales from gene to ecosystem. After that, we investigate how observational networks and space-based big data have facilitated the discovery of emergent mechanisms and phenomena on the regional and global scales. Then, we evaluate the predictions of terrestrial biosphere under global changes by big modeling data. Finally, we introduce some methods to extract knowledge from big data, such as meta-analysis, machine learning, traceability analysis, and data assimilation. The big data has opened new research opportunities, especially for developing new data-driven theories for improving biological predictions in Earth system models, tracing global change impacts across different organismic levels, and constructing cyberinfrastructure tools to accelerate the pace of model-data integrations. These efforts will uncork the bottleneck of using big data to understand biological responses and adaptations to future global changes.
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Affiliation(s)
- Jianyang Xia
- Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, Research Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China
| | - Jing Wang
- Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, Research Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai, China
| | - Shuli Niu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
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138
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Johnson B, Gomez M, Munch SB. Leveraging spatial information to forecast nonlinear ecological dynamics. Methods Ecol Evol 2020. [DOI: 10.1111/2041-210x.13511] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Bethany Johnson
- Department of Applied Mathematics University of California Santa Cruz CA USA
| | - Marcella Gomez
- Department of Applied Mathematics University of California Santa Cruz CA USA
| | - Stephan B. Munch
- Southwest Fisheries Science Center Fisheries Ecology Division National Oceanic and Atmospheric Administration Santa Cruz CA USA
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139
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Integrating across knowledge systems to drive action on chronic biological invasions. Biol Invasions 2020. [DOI: 10.1007/s10530-020-02388-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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140
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Tulloch AIT, Hagger V, Greenville AC. Ecological forecasts to inform near-term management of threats to biodiversity. GLOBAL CHANGE BIOLOGY 2020; 26:5816-5828. [PMID: 32652624 PMCID: PMC7540556 DOI: 10.1111/gcb.15272] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 06/01/2020] [Indexed: 05/19/2023]
Abstract
Ecosystems are being altered by rapid and interacting changes in natural processes and anthropogenic threats to biodiversity. Uncertainty in historical, current and future effectiveness of actions hampers decisions about how to mitigate changes to prevent biodiversity loss and species extinctions. Research in resource management, agriculture and health indicates that forecasts predicting the effects of near-term or seasonal environmental conditions on management greatly improve outcomes. Such forecasts help resolve uncertainties about when and how to operationalize management. We reviewed the scientific literature on environmental management to investigate whether near-term forecasts are developed to inform biodiversity decisions in Australia, a nation with one of the highest recent extinction rates across the globe. We found that forecasts focused on economic objectives (e.g. fisheries management) predict on significantly shorter timelines and answer a broader range of management questions than forecasts focused on biodiversity conservation. We then evaluated scientific literature on the effectiveness of 484 actions to manage seven major terrestrial threats in Australia, to identify opportunities for near-term forecasts to inform operational conservation decisions. Depending on the action, between 30% and 80% threat management operations experienced near-term weather impacts on outcomes before, during or after management. Disease control, species translocation/reintroduction and habitat restoration actions were most frequently impacted, and negative impacts such as increased species mortality and reduced recruitment were more likely than positive impacts. Drought or dry conditions, and rainfall, were the most frequently reported weather impacts, indicating that near-term forecasts predicting the effects of low or excessive rainfall on management outcomes are likely to have the greatest benefits. Across the world, many regions are, like Australia, becoming warmer and drier, or experiencing more extreme rainfall events. Informing conservation decisions with near-term and seasonal ecological forecasting will be critical to harness uncertainties and lower the risk of threat management failure under global change.
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Affiliation(s)
| | - Valerie Hagger
- School of Biological SciencesThe University of QueenslandSt. LuciaQldAustralia
| | - Aaron C. Greenville
- School of Life and Environmental SciencesUniversity of SydneySydneyNSWAustralia
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141
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Nareddy VR, Machta J, Abbott KC, Esmaeili S, Hastings A. Dynamical Ising model of spatially coupled ecological oscillators. J R Soc Interface 2020; 17:20200571. [PMID: 33109024 PMCID: PMC7653388 DOI: 10.1098/rsif.2020.0571] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 10/07/2020] [Indexed: 12/22/2022] Open
Abstract
Long-range synchrony from short-range interactions is a familiar pattern in biological and physical systems, many of which share a common set of 'universal' properties at the point of synchronization. Common biological systems of coupled oscillators have been shown to be members of the Ising universality class, meaning that the very simple Ising model replicates certain spatial statistics of these systems at stationarity. This observation is useful because it reveals which aspects of spatial pattern arise independently of the details governing local dynamics, resulting in both deeper understanding of and a simpler baseline model for biological synchrony. However, in many situations a system's dynamics are of greater interest than their static spatial properties. Here, we ask whether a dynamical Ising model can replicate universal and non-universal features of ecological systems, using noisy coupled metapopulation models with two-cycle dynamics as a case study. The standard Ising model makes unrealistic dynamical predictions, but the Ising model with memory corrects this by using an additional parameter to reflect the tendency for local dynamics to maintain their phase of oscillation. By fitting the two parameters of the Ising model with memory to simulated ecological dynamics, we assess the correspondence between the Ising and ecological models in several of their features (location of the critical boundary in parameter space between synchronous and asynchronous dynamics, probability of local phase changes and ability to predict future dynamics). We find that the Ising model with memory is reasonably good at representing these properties of ecological metapopulations. The correspondence between these models creates the potential for the simple and well-known Ising class of models to become a valuable tool for understanding complex biological systems.
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Affiliation(s)
| | - Jonathan Machta
- Department of Physics, University of Massachusetts, Amherst, MA 01003, USA
- Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
| | - Karen C. Abbott
- Department of Biology, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Shadisadat Esmaeili
- Department of Environmental Science and Policy, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Alan Hastings
- Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
- Department of Environmental Science and Policy, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
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142
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Baker CM, Bode M. Recent advances of quantitative modeling to support invasive species eradication on islands. CONSERVATION SCIENCE AND PRACTICE 2020. [DOI: 10.1111/csp2.246] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Christopher M. Baker
- School of Mathematics and Statistics, The University of Melbourne Melbourne Victoria Australia
- Melbourne Centre for Data Science, The University of Melbourne Melbourne Victoria Australia
- Centre of Excellence for Biosecurity Risk Analysis The University of Melbourne Melbourne Victoria Australia
| | - Michael Bode
- School of Mathematical Sciences, Queensland University of Technology Brisbane Queensland Australia
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143
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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.
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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
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144
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Adamson MW, Dawes JHP, Hastings A, Hilker FM. Forecasting resilience profiles of the run-up to regime shifts in nearly-one-dimensional systems. J R Soc Interface 2020; 17:20200566. [PMID: 32933374 DOI: 10.1098/rsif.2020.0566] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The forecasting of sudden, irreversible shifts in natural systems is a challenge of great importance, whose realization could allow pre-emptive action to be taken to avoid or mitigate catastrophic transitions, or to help systems adapt to them. In recent years, there have been many advances in the development of such early warning signals. However, much of the current toolbox is based around the tracking of statistical trends and therefore does not aim to estimate the future time scale of transitions or resilience loss. Metric-based indicators are also difficult to implement when systems have inherent oscillations which can dominate the indicator statistics. To resolve these gaps in the toolbox, we use additional system properties to fit parsimonious models to dynamics in order to predict transitions. Here, we consider nearly-one-dimensional systems-higher dimensional systems whose dynamics can be accurately captured by one-dimensional discrete time maps. We show how the nearly one-dimensional dynamics can be used to produce model-based indicators for critical transitions which produce forecasts of the resilience and the time of transitions in the system. A particularly promising feature of this approach is that it allows us to construct early warning signals even for critical transitions of chaotic systems. We demonstrate this approach on two model systems: of phosphorous recycling in a shallow lake, and of an overcompensatory fish population.
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Affiliation(s)
- Matthew W Adamson
- Institute for Environmental Systems Research and Institute of Mathematics, University of Osnabrück, Barbarastraße 12, 49076 Osnabrück, Germany
| | - Jonathan H P Dawes
- Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK
| | - Alan Hastings
- Department of Environmental Science and Policy, University of California, Davis, CA 95616, USA.,Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
| | - Frank M Hilker
- Institute for Environmental Systems Research and Institute of Mathematics, University of Osnabrück, Barbarastraße 12, 49076 Osnabrück, Germany
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145
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A guide to ecosystem models and their environmental applications. Nat Ecol Evol 2020; 4:1459-1471. [PMID: 32929239 DOI: 10.1038/s41559-020-01298-8] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 08/04/2020] [Indexed: 12/12/2022]
Abstract
Applied ecology has traditionally approached management problems through a simplified, single-species lens. Repeated failures of single-species management have led us to a new paradigm - managing at the ecosystem level. Ecosystem management involves a complex array of interacting organisms, processes and scientific disciplines. Accounting for interactions, feedback loops and dependencies between ecosystem components is therefore fundamental to understanding and managing ecosystems. We provide an overview of the main types of ecosystem models and their uses, and discuss challenges related to modelling complex ecological systems. Existing modelling approaches typically attempt to do one or more of the following: describe and disentangle ecosystem components and interactions; make predictions about future ecosystem states; and inform decision making by comparing alternative strategies and identifying important uncertainties. Modelling ecosystems is challenging, particularly when balancing the desire to represent many components of an ecosystem with the limitations of available data and the modelling objective. Explicitly considering different forms of uncertainty is therefore a primary concern. We provide some recommended strategies (such as ensemble ecosystem models and multi-model approaches) to aid the explicit consideration of uncertainty while also meeting the challenges of modelling ecosystems.
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146
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Soranno PA, Cheruvelil KS, Liu B, Wang Q, Tan PN, Zhou J, King KBS, McCullough IM, Stachelek J, Bartley M, Filstrup CT, Hanks EM, Lapierre JF, Lottig NR, Schliep EM, Wagner T, Webster KE. Ecological prediction at macroscales using big data: Does sampling design matter? ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2020; 30:e02123. [PMID: 32160362 DOI: 10.1002/eap.2123] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 12/13/2019] [Accepted: 01/06/2020] [Indexed: 06/10/2023]
Abstract
Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially extensive data set to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8-million-km2 area. We estimated model predictive performance for different subsets of the data set to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in data sets from some forms of targeted sampling may limit predictive performance, compiling existing spatially extensive data sets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.
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Affiliation(s)
- Patricia A Soranno
- Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, Michigan, 48824, USA
| | - Kendra Spence Cheruvelil
- Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, Michigan, 48824, USA
- Lyman Briggs College, Michigan State University, 919 East Shaw Lane, East Lansing, Michigan, 48825, USA
| | - Boyang Liu
- Department of Computer Science and Engineering, Michigan State University, 428 South Shaw Lane, East Lansing, Michigan, 48824, USA
| | - Qi Wang
- Department of Computer Science and Engineering, Michigan State University, 428 South Shaw Lane, East Lansing, Michigan, 48824, USA
| | - Pang-Ning Tan
- Department of Computer Science and Engineering, Michigan State University, 428 South Shaw Lane, East Lansing, Michigan, 48824, USA
| | - Jiayu Zhou
- Department of Computer Science and Engineering, Michigan State University, 428 South Shaw Lane, East Lansing, Michigan, 48824, USA
| | - Katelyn B S King
- Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, Michigan, 48824, USA
| | - Ian M McCullough
- Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, Michigan, 48824, USA
| | - Joseph Stachelek
- Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, Michigan, 48824, USA
| | - Meridith Bartley
- Department of Statistics, The Pennsylvania State University, 324 Thomas Building, University Park, Pennsylvania, 16802, USA
| | - Christopher T Filstrup
- Natural Resources Research Institute, University of Minnesota Duluth, 5013 Miller Trunk Highway, Duluth, Minnesota, 55811, USA
| | - Ephraim M Hanks
- Department of Statistics, The Pennsylvania State University, 324 Thomas Building, University Park, Pennsylvania, 16802, USA
| | - Jean-François Lapierre
- Sciences Biologiques, Universite de Montreal, Pavillon Marie-Victorin, CP 6128, succursale Centre-Ville, Montreal, Quebec, H3C 3J7, Canada
| | - Noah R Lottig
- Center for Limnology Trout Lake Station, University of Wisconsin Madison, Boulder Junction, Wisconsin, 54512, USA
| | - Erin M Schliep
- Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, Missouri, 65211, USA
| | - Tyler Wagner
- U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, Pennsylvania State University, Forest Resources Building, University Park, Pennsylvania, 16802, USA
| | - Katherine E Webster
- Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, Michigan, 48824, USA
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147
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Klesse S, DeRose RJ, Babst F, Black BA, Anderegg LDL, Axelson J, Ettinger A, Griesbauer H, Guiterman CH, Harley G, Harvey JE, Lo YH, Lynch AM, O'Connor C, Restaino C, Sauchyn D, Shaw JD, Smith DJ, Wood L, Villanueva-Díaz J, Evans MEK. Continental-scale tree-ring-based projection of Douglas-fir growth: Testing the limits of space-for-time substitution. GLOBAL CHANGE BIOLOGY 2020; 26:5146-5163. [PMID: 32433807 DOI: 10.1111/gcb.15170] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Revised: 04/02/2020] [Accepted: 04/25/2020] [Indexed: 06/11/2023]
Abstract
A central challenge in global change research is the projection of the future behavior of a system based upon past observations. Tree-ring data have been used increasingly over the last decade to project tree growth and forest ecosystem vulnerability under future climate conditions. But how can the response of tree growth to past climate variation predict the future, when the future does not look like the past? Space-for-time substitution (SFTS) is one way to overcome the problem of extrapolation: the response at a given location in a warmer future is assumed to follow the response at a warmer location today. Here we evaluated an SFTS approach to projecting future growth of Douglas-fir (Pseudotsuga menziesii), a species that occupies an exceptionally large environmental space in North America. We fit a hierarchical mixed-effects model to capture ring-width variability in response to spatial and temporal variation in climate. We found opposing gradients for productivity and climate sensitivity with highest growth rates and weakest response to interannual climate variation in the mesic coastal part of Douglas-fir's range; narrower rings and stronger climate sensitivity occurred across the semi-arid interior. Ring-width response to spatial versus temporal temperature variation was opposite in sign, suggesting that spatial variation in productivity, caused by local adaptation and other slow processes, cannot be used to anticipate changes in productivity caused by rapid climate change. We thus substituted only climate sensitivities when projecting future tree growth. Growth declines were projected across much of Douglas-fir's distribution, with largest relative decreases in the semiarid U.S. Interior West and smallest in the mesic Pacific Northwest. We further highlight the strengths of mixed-effects modeling for reviving a conceptual cornerstone of dendroecology, Cook's 1987 aggregate growth model, and the great potential to use tree-ring networks and results as a calibration target for next-generation vegetation models.
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Affiliation(s)
- Stefan Klesse
- Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USA
- Swiss Federal Research Institute WSL, Swiss Forest Protection, Birmensdorf, Switzerland
| | - Robert Justin DeRose
- U.S. Forest Service, Rocky Mountain Research Station, Forest Inventory and Analysis, Ogden, UT, USA
- Department Wildland Resources and Ecology Center, Utah State University, Logan, UT, USA
| | - Flurin Babst
- Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USA
- Swiss Federal Research Institute WSL, Swiss Forest Protection, Birmensdorf, Switzerland
- Department of Ecology, W. Szafer Institute of Botany, Polish Academy of Sciences, Krakow, Poland
| | - Bryan A Black
- Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USA
| | - Leander D L Anderegg
- Department of Integrative Biology, University of California, Berkeley, CA, USA
- Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA
| | - Jodi Axelson
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA
| | | | - Hardy Griesbauer
- Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Prince George, BC, Canada
| | | | - Grant Harley
- Department of Geography, University of Idaho, Moscow, ID, USA
| | - Jill E Harvey
- Natural Resources Canada, Canadian Forest Service, Edmonton, AB, Canada
| | - Yueh-Hsin Lo
- Department of Science, Universidad Publica de Navarra, Pamplona, Spain
| | - Ann M Lynch
- Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USA
- U.S. Forest Service, Rocky Mountain Research Station, Tucson, AZ, USA
| | | | | | - Dave Sauchyn
- Prairie Adaptation Research Collaborative, University of Regina, Regina, SK, Canada
| | - John D Shaw
- U.S. Forest Service, Rocky Mountain Research Station, Forest Inventory and Analysis, Ogden, UT, USA
| | - Dan J Smith
- Department of Geography, University of Victoria, Victoria, BC, Canada
| | - Lisa Wood
- Ecosystem Science and Management, University of Northern British Columbia, Prince George, BC, Canada
| | - Jose Villanueva-Díaz
- Instituto Nacional de Investigaciones Forestales y Agropecuarias, CENID-RASPA, Gomez Palacio, Mexico
| | - Margaret E K Evans
- Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USA
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148
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Henden JA, Ims RA, Yoccoz NG, Asbjørnsen EJ, Stien A, Mellard JP, Tveraa T, Marolla F, Jepsen JU. End-user involvement to improve predictions and management of populations with complex dynamics and multiple drivers. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2020; 30:e02120. [PMID: 32159900 DOI: 10.1002/eap.2120] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 12/21/2019] [Accepted: 02/10/2020] [Indexed: 06/10/2023]
Abstract
Sustainable management of wildlife populations can be aided by building models that both identify current drivers of natural dynamics and provide near-term predictions of future states. We employed a Strategic Foresight Protocol (SFP) involving stakeholders to decide the purpose and structure of a dynamic state-space model for the population dynamics of the Willow Ptarmigan, a popular game species in Norway. Based on local knowledge of stakeholders, it was decided that the model should include food web interactions and climatic drivers to provide explanatory predictions. Modeling confirmed observations from stakeholders that climate change impacts Ptarmigan populations negatively through intensified outbreaks of insect defoliators and later onset of winter. Stakeholders also decided that the model should provide anticipatory predictions. The ability to forecast population density ahead of the harvest season was valued by the stakeholders as it provides the management extra time to consider appropriate harvest regulations and communicate with hunters prior to the hunting season. Overall, exploring potential drivers and predicting short-term future states, facilitate collaborative learning and refined data collection, monitoring designs, and management priorities. Our experience from adapting a SFP to a management target with inherently complex dynamics and drivers of environmental change, is that an open, flexible, and iterative process, rather than a rigid step-wise protocol, facilitates rapid learning, trust, and legitimacy.
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Affiliation(s)
- John-André Henden
- University of Tromsø, The Arctic University, Hansine Hansens veg 18, Tromsø, 9019, Norway
| | - Rolf A Ims
- University of Tromsø, The Arctic University, Hansine Hansens veg 18, Tromsø, 9019, Norway
- Norwegian Institute for Nature Research (NINA), Fram Centre, Postboks 6606 Langnes, Tromsø, 9296, Norway
| | - Nigel G Yoccoz
- University of Tromsø, The Arctic University, Hansine Hansens veg 18, Tromsø, 9019, Norway
- Norwegian Institute for Nature Research (NINA), Fram Centre, Postboks 6606 Langnes, Tromsø, 9296, Norway
| | | | - Audun Stien
- Norwegian Institute for Nature Research (NINA), Fram Centre, Postboks 6606 Langnes, Tromsø, 9296, Norway
| | - Jarad Pope Mellard
- University of Tromsø, The Arctic University, Hansine Hansens veg 18, Tromsø, 9019, Norway
| | - Torkild Tveraa
- Norwegian Institute for Nature Research (NINA), Fram Centre, Postboks 6606 Langnes, Tromsø, 9296, Norway
| | - Filippo Marolla
- University of Tromsø, The Arctic University, Hansine Hansens veg 18, Tromsø, 9019, Norway
| | - Jane Uhd Jepsen
- Norwegian Institute for Nature Research (NINA), Fram Centre, Postboks 6606 Langnes, Tromsø, 9296, Norway
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149
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Schenone L, Balseiro EG, Bastidas Navarro M, Modenutti BE. Modelling the consequence of glacier retreat on mixotrophic nanoflagellate bacterivory: a Bayesian approach. OIKOS 2020. [DOI: 10.1111/oik.07170] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Luca Schenone
- Laboratorio de Limnología, INIBIOMA (CONICET‐UNCo), Quintral 1250 San Carlos de Bariloche (8400) Río Negro Argentina
| | - Esteban G. Balseiro
- Laboratorio de Limnología, INIBIOMA (CONICET‐UNCo), Quintral 1250 San Carlos de Bariloche (8400) Río Negro Argentina
| | - Marcela Bastidas Navarro
- Laboratorio de Limnología, INIBIOMA (CONICET‐UNCo), Quintral 1250 San Carlos de Bariloche (8400) Río Negro Argentina
| | - Beatriz E. Modenutti
- Laboratorio de Limnología, INIBIOMA (CONICET‐UNCo), Quintral 1250 San Carlos de Bariloche (8400) Río Negro Argentina
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150
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Smith ME, Griswold E, Singh BK, Miri E, Eigege A, Adelamo S, Umaru J, Nwodu K, Sambo Y, Kadimbo J, Danyobi J, Richards FO, Michael E. Predicting lymphatic filariasis elimination in data-limited settings: A reconstructive computational framework for combining data generation and model discovery. PLoS Comput Biol 2020; 16:e1007506. [PMID: 32692741 PMCID: PMC7394457 DOI: 10.1371/journal.pcbi.1007506] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 07/31/2020] [Accepted: 05/12/2020] [Indexed: 11/25/2022] Open
Abstract
Although there is increasing importance placed on the use of mathematical models for the effective design and management of long-term parasite elimination, it is becoming clear that transmission models are most useful when they reflect the processes pertaining to local infection dynamics as opposed to generalized dynamics. Such localized models must also be developed even when the data required for characterizing local transmission processes are limited or incomplete, as is often the case for neglected tropical diseases, including the disease system studied in this work, viz. lymphatic filariasis (LF). Here, we draw on progress made in the field of computational knowledge discovery to present a reconstructive simulation framework that addresses these challenges by facilitating the discovery of both data and models concurrently in areas where we have insufficient observational data. Using available data from eight sites from Nigeria and elsewhere, we demonstrate that our data-model discovery system is able to estimate local transmission models and missing pre-control infection information using generalized knowledge of filarial transmission dynamics, monitoring survey data, and details of historical interventions. Forecasts of the impacts of interventions carried out in each site made by the models estimated using the reconstructed baseline data matched temporal infection observations and provided useful information regarding when transmission interruption is likely to have occurred. Assessments of elimination and resurgence probabilities based on the models also suggest a protective effect of vector control against the reemergence of LF transmission after stopping drug treatments. The reconstructive computational framework for model and data discovery developed here highlights how coupling models with available data can generate new knowledge about complex, data-limited systems, and support the effective management of disease programs in the face of critical data gaps. As modelling becomes commonly used in the design and evaluation of parasite elimination programs, the need for well-defined models and datasets describing the nature of transmission processes in local settings is becoming pronounced. For many neglected tropical diseases, however, data for site-specific model identification are typically sparse or incomplete. In this study, we present a new data-model computational discovery system that couples data-assimilation methods based on existing monitoring survey data with model-generated data about baseline conditions to discover the local transmission models required for simulating the impacts of interventions in typical endemic locations for the macroparasitic disease, lymphatic filariasis (LF). Using data from eight study sites in Nigeria and elsewhere, we show that our reconstructive computational framework is able to combine information contained within partially-available site-specific monitoring data with knowledge of parasite transmission dynamics embedded in process-based models to generate the missing data required for inducing reliable locally applicable LF models. We also show that the models so discovered are able to generate the intervention forecasts required for supporting management-relevant decisions in parasite elimination.
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Affiliation(s)
- Morgan E. Smith
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Emily Griswold
- The Carter Center, One Copenhill, Atlanta, Georgia, United States of America
| | - Brajendra K. Singh
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | | | | | | | | | | | | | | | - Jacob Danyobi
- Nasarawa State Ministry of Health, Lafia, Nasarawa, Nigeria
| | - Frank O. Richards
- The Carter Center, One Copenhill, Atlanta, Georgia, United States of America
| | - Edwin Michael
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
- * E-mail:
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