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Kotamäki N, Arhonditsis G, Hjerppe T, Hyytiäinen K, Malve O, Ovaskainen O, Paloniitty T, Similä J, Soininen N, Weigel B, Heiskanen AS. Strategies for integrating scientific evidence in water policy and law in the face of uncertainty. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 931:172855. [PMID: 38692324 DOI: 10.1016/j.scitotenv.2024.172855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 05/03/2024]
Abstract
Understanding how human actions and environmental change affect water resources is crucial for addressing complex water management issues. The scientific tools that can produce the necessary information are ecological indicators, referring to measurable properties of the ecosystem state; environmental monitoring, the data collection process that is required to evaluate the progress towards reaching water management goals; mathematical models, linking human disturbances with the ecosystem state to predict environmental impacts; and scenarios, assisting in long-term management and policy implementation. Paradoxically, despite the rapid generation of data, evolving scientific understanding, and recent advancements in systems modeling, there is a striking imbalance between knowledge production and knowledge utilization in decision-making. In this paper, we examine the role and potential capacity of scientific tools in guiding governmental decision-making processes and identify the most critical disparities between water management, policy, law, and science. We demonstrate how the complex, uncertain, and gradually evolving nature of scientific knowledge might not always fit aptly to the legislative and policy processes and structures. We contend that the solution towards increased understanding of socio-ecological systems and reduced uncertainty lies in strengthening the connections between water management theory and practice, among the scientific tools themselves, among different stakeholders, and among the social, economic, and ecological facets of water quality management, law, and policy. We conclude by tying in three knowledge-exchange strategies, namely - adaptive management, Driver-Pressure-Status-Impact-Response (DPSIR) framework, and participatory modeling - that offer complementary perspectives to bridge the gap between science and policy.
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Affiliation(s)
- Niina Kotamäki
- Finnish Environment Institute, Survontie 9A, FI-40500 Jyväskylä, Finland.
| | - George Arhonditsis
- Department of Physical & Environmental Sciences, University of Toronto, Ontario M1C1A4, Canada
| | - Turo Hjerppe
- Ministry of the Environment, P.O. Box 35, 00023 Government, Finland
| | - Kari Hyytiäinen
- Faculty of Agriculture and Forestry, P.O. Box 27, FI-00014, University of Helsinki, Finland
| | - Olli Malve
- Finnish Environment Institute, Latokartanonkaari 11, FI-00790 Helsinki, Finland
| | - Otso Ovaskainen
- Department of Biological and Environmental Science, University of Jyväskylä, P.O. Box 35, FI-40014 Jyväskylä, Finland; Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, P.O. Box 65, Helsinki 00014, Finland; Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, N-7491 Trondheim, Norway
| | - Tiina Paloniitty
- University of Helsinki, Faculty of Law, P.O. Box 4, FI-00014, Finland
| | - Jukka Similä
- University of Lapland, Faculty of Law, Yliopistonkatu 8, FI-96300 Rovaniemi, Finland
| | - Niko Soininen
- Law School, Center for Climate Change, Energy, and Environmental Law, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland
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2
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Caldwell JM, Liu G, Geiger E, Heron SF, Eakin CM, De La Cour J, Greene A, Raymundo L, Dryden J, Schlaff A, Stella JS, Kindinger TL, Couch CS, Fenner D, Hoot W, Manzello D, Donahue MJ. Multi-Factor Coral Disease Risk: A new product for early warning and management. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2024; 34:e2961. [PMID: 38522943 DOI: 10.1002/eap.2961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/04/2023] [Accepted: 01/17/2024] [Indexed: 03/26/2024]
Abstract
Ecological forecasts are becoming increasingly valuable tools for conservation and management. However, there are few examples of near-real-time forecasting systems that account for the wide range of ecological complexities. We developed a new coral disease ecological forecasting system that explores a suite of ecological relationships and their uncertainty and investigates how forecast skill changes with shorter lead times. The Multi-Factor Coral Disease Risk product introduced here uses a combination of ecological and marine environmental conditions to predict the risk of white syndromes and growth anomalies across reefs in the central and western Pacific and along the east coast of Australia and is available through the US National Oceanic and Atmospheric Administration Coral Reef Watch program. This product produces weekly forecasts for a moving window of 6 months at a resolution of ~5 km based on quantile regression forests. The forecasts show superior skill at predicting disease risk on withheld survey data from 2012 to 2020 compared with predecessor forecast systems, with the biggest improvements shown for predicting disease risk at mid- to high-disease levels. Most of the prediction uncertainty arises from model uncertainty, so prediction accuracy and precision do not improve substantially with shorter lead times. This result arises because many predictor variables cannot be accurately forecasted, which is a common challenge across ecosystems. Weekly forecasts and scenarios can be explored through an online decision support tool and data explorer, co-developed with end-user groups to improve use and understanding of ecological forecasts. The models provide near-real-time disease risk assessments and allow users to refine predictions and assess intervention scenarios. This work advances the field of ecological forecasting with real-world complexities and, in doing so, better supports near-term decision making for coral reef ecosystem managers and stakeholders. Secondarily, we identify clear needs and provide recommendations to further enhance our ability to forecast coral disease risk.
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Affiliation(s)
- Jamie M Caldwell
- Hawai'i Institute of Marine Biology, Kaneohe, Hawaii, USA
- High Meadows Environmental Institute, Princeton University, Princeton, New Jersey, USA
| | - Gang Liu
- NOAA/NESDIS/STAR Coral Reef Watch, College Park, Maryland, USA
| | - Erick Geiger
- NOAA/NESDIS/STAR Coral Reef Watch, College Park, Maryland, USA
- Global Science & Technology, Inc., Greenbelt, Maryland, USA
| | - Scott F Heron
- Physical Sciences and Marine Geophysics Laboratory, College of Science and Engineering, James Cook University, Townsville, Queensland, Australia
| | - C Mark Eakin
- Corals and Climate, Silver Spring, Maryland, USA
| | - Jacqueline De La Cour
- NOAA/NESDIS/STAR Coral Reef Watch, College Park, Maryland, USA
- Global Science & Technology, Inc., Greenbelt, Maryland, USA
| | - Austin Greene
- Hawai'i Institute of Marine Biology, Kaneohe, Hawaii, USA
- Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
| | | | - Jen Dryden
- Great Barrier Reef Marine Park Authority, Townsville, Queensland, Australia
| | - Audrey Schlaff
- Great Barrier Reef Marine Park Authority, Townsville, Queensland, Australia
| | - Jessica S Stella
- Great Barrier Reef Marine Park Authority, Townsville, Queensland, Australia
| | - Tye L Kindinger
- Pacific Islands Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Honolulu, Hawaii, USA
| | - Courtney S Couch
- Pacific Islands Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Honolulu, Hawaii, USA
- Cooperative Institute for Marine and Atmospheric Research, University of Hawai'i at Mānoa, Honolulu, Hawaii, USA
| | - Douglas Fenner
- Lynker Technologies, LLC, Contractor, NOAA Fisheries Service, Pacific Islands Regional Office, Honolulu, Hawaii, USA
| | - Whitney Hoot
- Guam Coral Reef Initiative, Government of Guam, Hagatña, Guam, USA
| | - Derek Manzello
- NOAA/NESDIS/STAR Coral Reef Watch, College Park, Maryland, USA
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3
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Brimacombe C, Bodner K, Fortin MJ. Applying a method before its proof of concept: A cautionary tale using inferred food webs. GLOBAL CHANGE BIOLOGY 2024; 30:e17360. [PMID: 38822572 DOI: 10.1111/gcb.17360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 05/15/2024] [Indexed: 06/03/2024]
Abstract
There are significant pitfalls associated with developing food webs using inferred approaches, including violations of ecological assumptions, which considerably undermine their potentiality to resemble ecological communities and hence be practically useful. As data‐driven scientists, we must, at the very least, test against some empirical data to establish confidence that indeed the inferred food webs reflect their supposed in situ communities. Otherwise, using inferred networks to make bold claims—like the titular statement by Botella et al. (2024) that food webs are influenced by land‐use intensity—is highly unlikely to be useful, especially when the aforementioned claim is unsubstantiated by the Authors' own statistical analyses.
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Affiliation(s)
- Chris Brimacombe
- Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Korryn Bodner
- MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Marie-Josée Fortin
- Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
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4
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Enquist BJ, Kempes CP, West GB. Developing a predictive science of the biosphere requires the integration of scientific cultures. Proc Natl Acad Sci U S A 2024; 121:e2209196121. [PMID: 38640256 PMCID: PMC11087787 DOI: 10.1073/pnas.2209196121] [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] [Indexed: 04/21/2024] Open
Abstract
Increasing the speed of scientific progress is urgently needed to address the many challenges associated with the biosphere in the Anthropocene. Consequently, the critical question becomes: How can science most rapidly progress to address large, complex global problems? We suggest that the lag in the development of a more predictive science of the biosphere is not only because the biosphere is so much more complex, or because we do not have enough data, or are not doing enough experiments, but, in large part, because of unresolved tension between the three dominant scientific cultures that pervade the research community. We introduce and explain the concept of the three scientific cultures and present a novel analysis of their characteristics, supported by examples and a formal mathematical definition/representation of what this means and implies. The three cultures operate, to varying degrees, across all of science. However, within the biosciences, and in contrast to some of the other sciences, they remain relatively more separated, and their lack of integration has hindered their potential power and insight. Our solution to accelerating a broader, predictive science of the biosphere is to enhance integration of scientific cultures. The process of integration-Scientific Transculturalism-recognizes that the push for interdisciplinary research, in general, is just not enough. Unless these cultures of science are formally appreciated and their thinking iteratively integrated into scientific discovery and advancement, there will continue to be numerous significant challenges that will increasingly limit forecasting and prediction efforts.
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Affiliation(s)
- Brian J. Enquist
- Department of Ecology & Evolutionary Biology, University of Arizona, Tucson, AZ85721
- The Santa Fe Institute, Santa Fe, NM87501
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5
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Thomas SM, Verhoeven MR, Walsh JR, Larkin DJ, Hansen GJA. Improving species distribution forecasts by measuring and communicating uncertainty: An invasive species case study. Ecology 2024; 105:e4297. [PMID: 38613235 DOI: 10.1002/ecy.4297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 11/22/2023] [Accepted: 01/18/2024] [Indexed: 04/14/2024]
Abstract
Forecasting invasion risk under future climate conditions is critical for the effective management of invasive species, and species distribution models (SDMs) are key tools for doing so. However, SDM-based forecasts are uncertain, especially when correlative statistical models extrapolate to nonanalog environmental domains, such as future climate conditions. Different assumptions about the functional form of the temperature-suitability relationship can impact predicted habitat suitability under novel conditions. Hence, methods to understand the sources of uncertainty are critical when applying SDMs. Here, we use high-resolution predictions of lake water temperatures to project changes in habitat suitability under future climate conditions for an invasive macrophyte (Myriophyllym spicatum). Future suitability was predicted using five global circulation models and three statistical models that assumed different species-temperature functional responses. The suitability of lakes for M. spicatum was overall predicted to increase under future climate conditions, but the magnitude and direction of change in suitability varied greatly among lakes. Variability was most pronounced for lakes under nonanalog temperature conditions, indicating that predictions for these lakes remained highly uncertain. Integrating predictions from SDMs that differ in their species-environment response function, while explicitly quantifying uncertainty across analog and nonanalog domains, can provide a more robust and useful approach to forecasting invasive species distribution under climate change.
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Affiliation(s)
- Shyam M Thomas
- Department of Fisheries, Wildlife and Conservation Biology and Minnesota Aquatic Invasive Species Research Center, University of Minnesota, Saint Paul, Minnesota, USA
| | - Michael R Verhoeven
- Department of Fisheries, Wildlife and Conservation Biology and Minnesota Aquatic Invasive Species Research Center, University of Minnesota, Saint Paul, Minnesota, USA
| | - Jake R Walsh
- Department of Fisheries, Wildlife and Conservation Biology and Minnesota Aquatic Invasive Species Research Center, University of Minnesota, Saint Paul, Minnesota, USA
| | - Daniel J Larkin
- Department of Fisheries, Wildlife and Conservation Biology and Minnesota Aquatic Invasive Species Research Center, University of Minnesota, Saint Paul, Minnesota, USA
| | - Gretchen J A Hansen
- Department of Fisheries, Wildlife and Conservation Biology and Minnesota Aquatic Invasive Species Research Center, University of Minnesota, Saint Paul, Minnesota, USA
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6
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Arroyo-Esquivel J, Klausmeier CA, Litchman E. Using neural ordinary differential equations to predict complex ecological dynamics from population density data. J R Soc Interface 2024; 21:20230604. [PMID: 38745459 DOI: 10.1098/rsif.2023.0604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/25/2024] [Indexed: 05/16/2024] Open
Abstract
Simple models have been used to describe ecological processes for over a century. However, the complexity of ecological systems makes simple models subject to modelling bias due to simplifying assumptions or unaccounted factors, limiting their predictive power. Neural ordinary differential equations (NODEs) have surged as a machine-learning algorithm that preserves the dynamic nature of the data (Chen et al. 2018 Adv. Neural Inf. Process. Syst.). Although preserving the dynamics in the data is an advantage, the question of how NODEs perform as a forecasting tool of ecological communities is unanswered. Here, we explore this question using simulated time series of competing species in a time-varying environment. We find that NODEs provide more precise forecasts than autoregressive integrated moving average (ARIMA) models. We also find that untuned NODEs have a similar forecasting accuracy to untuned long-short term memory neural networks and both are outperformed in accuracy and precision by empirical dynamical modelling . However, we also find NODEs generally outperform all other methods when evaluating with the interval score, which evaluates precision and accuracy in terms of prediction intervals rather than pointwise accuracy. We also discuss ways to improve the forecasting performance of NODEs. The power of a forecasting tool such as NODEs is that it can provide insights into population dynamics and should thus broaden the approaches to studying time series of ecological communities.
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Affiliation(s)
| | - Christopher A Klausmeier
- Department of Global Ecology, Carnegie Institution for Science , Stanford, CA, USA
- W. K. Kellogg Biological Station, Michigan State University , Hickory Corners, MI, USA
- Program in Ecology and Evolutionary Biology, Michigan State University , East Lansing, MI, USA
- Department of Integrative Biology, Michigan State University , East Lansing, MI, USA
- Department of Plant Biology, Michigan State University , East Lansing, MI, USA
| | - Elena Litchman
- Department of Global Ecology, Carnegie Institution for Science , Stanford, CA, USA
- W. K. Kellogg Biological Station, Michigan State University , Hickory Corners, MI, USA
- Program in Ecology and Evolutionary Biology, Michigan State University , East Lansing, MI, USA
- Department of Integrative Biology, Michigan State University , East Lansing, MI, USA
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7
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Haubrock PJ, Soto I, Ahmed DA, Ansari AR, Tarkan AS, Kurtul I, Macêdo RL, Lázaro-Lobo A, Toutain M, Parker B, Błońska D, Guareschi S, Cano-Barbacil C, Dominguez Almela V, Andreou D, Moyano J, Akalın S, Kaya C, Bayçelebi E, Yoğurtçuoğlu B, Briski E, Aksu S, Emiroğlu Ö, Mammola S, De Santis V, Kourantidou M, Pincheira-Donoso D, Britton JR, Kouba A, Dolan EJ, Kirichenko NI, García-Berthou E, Renault D, Fernandez RD, Yapıcı S, Giannetto D, Nuñez MA, Hudgins EJ, Pergl J, Milardi M, Musolin DL, Cuthbert RN. Biological invasions are a population-level rather than a species-level phenomenon. GLOBAL CHANGE BIOLOGY 2024; 30:e17312. [PMID: 38736133 DOI: 10.1111/gcb.17312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/08/2024] [Accepted: 04/21/2024] [Indexed: 05/14/2024]
Abstract
Biological invasions pose a rapidly expanding threat to the persistence, functioning and service provisioning of ecosystems globally, and to socio-economic interests. The stages of successful invasions are driven by the same mechanism that underlies adaptive changes across species in general-via natural selection on intraspecific variation in traits that influence survival and reproductive performance (i.e., fitness). Surprisingly, however, the rapid progress in the field of invasion science has resulted in a predominance of species-level approaches (such as deny lists), often irrespective of natural selection theory, local adaptation and other population-level processes that govern successful invasions. To address these issues, we analyse non-native species dynamics at the population level by employing a database of European freshwater macroinvertebrate time series, to investigate spreading speed, abundance dynamics and impact assessments among populations. Our findings reveal substantial variability in spreading speed and abundance trends within and between macroinvertebrate species across biogeographic regions, indicating that levels of invasiveness and impact differ markedly. Discrepancies and inconsistencies among species-level risk screenings and real population-level data were also identified, highlighting the inherent challenges in accurately assessing population-level effects through species-level assessments. In recognition of the importance of population-level assessments, we urge a shift in invasive species management frameworks, which should account for the dynamics of different populations and their environmental context. Adopting an adaptive, region-specific and population-focused approach is imperative, considering the diverse ecological contexts and varying degrees of susceptibility. Such an approach could improve and refine risk assessments while promoting mechanistic understandings of risks and impacts, thereby enabling the development of more effective conservation and management strategies.
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Affiliation(s)
- Phillip J Haubrock
- Department of River Ecology and Conservation, Senckenberg Research Institute and Natural History Museum Frankfurt, Gelnhausen, Germany
- Faculty of Fisheries and Protection of Waters, South Bohemian Research Centre of Aquaculture and Biodiversity of Hydrocenoses, University of South Bohemia in České Budějovice, Vodňany, Czech Republic
- CAMB, Center for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, Hawally, Kuwait
| | - Ismael Soto
- Faculty of Fisheries and Protection of Waters, South Bohemian Research Centre of Aquaculture and Biodiversity of Hydrocenoses, University of South Bohemia in České Budějovice, Vodňany, Czech Republic
| | - Danish A Ahmed
- CAMB, Center for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, Hawally, Kuwait
| | - Ali R Ansari
- CAMB, Center for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, Hawally, Kuwait
| | - Ali Serhan Tarkan
- Department of Ecology and Vertebrate Zoology, Faculty of Biology and Environmental Protection, University of Lodz, Lodz, Poland
- Department of Basic Sciences, Faculty of Fisheries, Muğla Sıtkı Koçman University, Muğla, Turkey
- Department of Life and Environmental Sciences, Bournemouth University, Poole, UK
| | - Irmak Kurtul
- Department of Life and Environmental Sciences, Bournemouth University, Poole, UK
- Faculty of Fisheries, Marine and Inland Waters Sciences and Technology Department, Ege University, İzmir, Turkey
| | - Rafael L Macêdo
- Institute of Biology, Freie Universität Berlin, Berlin, Germany
- Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany
- Graduate Program in Ecology and Natural Resources, Department of Ecology and Evolutionary Biology, Federal University of São Carlos, UFSCar, São Carlos, Brazil
| | - Adrián Lázaro-Lobo
- Biodiversity Research Institute IMIB (Univ. Oviedo-CSIC-Princ. Asturias), Mieres, Spain
| | - Mathieu Toutain
- Université de Rennes, CNRS, ECOBIO [(Ecosystèmes, biodiversité, évolution)], UMR 11 6553, Rennes, France
| | - Ben Parker
- Department of Life and Environmental Sciences, Bournemouth University, Poole, UK
| | - Dagmara Błońska
- Department of Ecology and Vertebrate Zoology, Faculty of Biology and Environmental Protection, University of Lodz, Lodz, Poland
- Department of Life and Environmental Sciences, Bournemouth University, Poole, UK
| | - Simone Guareschi
- Department of Life Sciences and Systems Biology, University of Turin, Torino, Italy
| | - Carlos Cano-Barbacil
- Department of River Ecology and Conservation, Senckenberg Research Institute and Natural History Museum Frankfurt, Gelnhausen, Germany
| | | | - Demetra Andreou
- Department of Life and Environmental Sciences, Bournemouth University, Poole, UK
| | - Jaime Moyano
- Grupo de Ecología de Invasiones, INIBIOMA, CONICET, Universidad Nacional del Comahue, San Carlos de Bariloche, Argentina
| | - Sencer Akalın
- Faculty of Fisheries, Marine and Inland Waters Sciences and Technology Department, Ege University, İzmir, Turkey
| | - Cüneyt Kaya
- Faculty of Fisheries, Recep Tayyip Erdogan University, Rize, Turkey
| | - Esra Bayçelebi
- Faculty of Fisheries, Recep Tayyip Erdogan University, Rize, Turkey
| | - Baran Yoğurtçuoğlu
- Department of Biology, Faculty of Science, Hacettepe University, Ankara, Turkey
| | | | - Sadi Aksu
- Vocational School of Health Services, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Özgür Emiroğlu
- Department of Biology, Faculty of Arts and Sciences, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Stefano Mammola
- Water Research Institute, National Research Council (CNR-IRSA), Verbania Pallanza, Italy
- NBFC, National Biodiversity Future Center, Palermo, Italy
- Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland
| | - Vanessa De Santis
- Water Research Institute, National Research Council (CNR-IRSA), Verbania Pallanza, Italy
| | | | | | - J Robert Britton
- Department of Life and Environmental Sciences, Bournemouth University, Poole, UK
| | - Antonín Kouba
- Faculty of Fisheries and Protection of Waters, South Bohemian Research Centre of Aquaculture and Biodiversity of Hydrocenoses, University of South Bohemia in České Budějovice, Vodňany, Czech Republic
| | - Ellen J Dolan
- School of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, Belfast, UK
| | - Natalia I Kirichenko
- Sukachev Institute of Forest, Siberian Branch of Russian Academy of Sciences, Federal Research Center «Krasnoyarsk Science Center SB RAS», Krasnoyarsk, Russia
- Siberian Federal University, Krasnoyarsk, Russia
- All-Russian Plant Quarantine Center, Krasnoyarsk Branch, Krasnoyarsk, Russia
| | | | - David Renault
- Université de Rennes, CNRS, ECOBIO [(Ecosystèmes, biodiversité, évolution)], UMR 11 6553, Rennes, France
| | - Romina D Fernandez
- Instituto de Ecología Regional, Universidad Nacional de Tucumán-CONICET, Yerba Buena, Argentina
| | - Sercan Yapıcı
- Department of Basic Sciences, Faculty of Fisheries, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Daniela Giannetto
- Department of Biology, Faculty of Sciences, Muğla Sıtkı Koçman University, Mugla, Turkey
| | - Martin A Nuñez
- Department of Biology and Biochemistry, University of Houston, Houston, Texas, USA
| | - Emma J Hudgins
- School of Agriculture, Food and Ecosystem Sciences, The University of Melbourne, Parkville, Victoria, Australia
| | - Jan Pergl
- Institute of Botany; Department of Invasion Ecology, Academy of Sciences of the Czech Republic, Průhonice, Czech Republic
| | - Marco Milardi
- Southern Indian Ocean Fisheries Agreement (SIOFA), Le Port, La Reunion, France
| | - Dmitrii L Musolin
- European and Mediterranean Plant Protection Organization (EPPO), Paris, France
| | - Ross N Cuthbert
- School of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, Belfast, UK
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Wang L, Shan K, Yi Y, Yang H, Zhang Y, Xie M, Zhou Q, Shang M. Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 922:171009. [PMID: 38402991 DOI: 10.1016/j.scitotenv.2024.171009] [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: 10/30/2023] [Revised: 01/05/2024] [Accepted: 02/14/2024] [Indexed: 02/27/2024]
Abstract
Harmful cyanobacterial blooms (CyanoHABs) are increasingly impacting the ecosystem of lakes, reservoirs and estuaries globally. The integration of real-time monitoring and deep learning technology has opened up new horizons for early warnings of CyanoHABs. However, unlike traditional methods such as pigment quantification or microscopy counting, the high-frequency data from in-situ fluorometric sensors display unpredictable fluctuations and variability, posing a challenge for predictive models to discern underlying trends within the time-series sequence. This study introduces a hybrid framework for near-real-time CyanoHABs predictions in a cyanobacterium Microcystis-dominated lake - Lake Dianchi, China. The proposed model was validated using hourly Chlorophyll-a (Chl a) concentrations and algal cell densities. Our results demonstrate that applying decomposition-based singular spectrum analysis (SSA) significantly enhances the prediction accuracy of subsequent CyanoHABs models, particularly in the case of temporal convolutional network (TCN). Comparative experiments revealed that the SSA-TCN model outperforms other SSA-based deep learning models for predicting Chl a (R2 = 0.45-0.93, RMSE = 2.29-5.89 μg/L) and algal cell density (R2 = 0.63-0.89, RMSE = 9489.39-16,015.37 cells/mL) at one to four steps ahead predictions. The forecast of bloom intensities achieved a remarkable accuracy of 98.56 % and an average precision rate of 94.04 % ± 0.05 %. In addition, scenarios involving various input combinations of environmental factors demonstrated that water temperature emerged as the most effective driver for CyanoHABs predictions, with a mean RMSE of 2.94 ± 0.12 μg/L, MAE of 1.55 ± 0.09 μg/L, and R2 of 0.83 ± 0.01. Overall, the newly developed approach underscores the potential of a well-designed hybrid deep-learning framework for accurately predicting sensor-based algal parameters. It offers novel perspectives for managing CyanoHABs through online monitoring and artificial intelligence in aquatic ecosystems.
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Affiliation(s)
- Lan Wang
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; School of Artificial Intelligence, Chongqing University of Education, Chongqing 400065, China
| | - Kun Shan
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
| | - Yang Yi
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
| | - Hong Yang
- Department of Geography and Environmental Science, University of Reading, Reading RG6 6AB, UK
| | - Yanyan Zhang
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Mingjiang Xie
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
| | - Qichao Zhou
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Sciences, Yunnan University, Kunming 650500, China
| | - Mingsheng Shang
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
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9
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Neupane N, Larsen EA, Ries L. Ecological forecasts of insect range dynamics: a broad range of taxa includes winners and losers under future climate. CURRENT OPINION IN INSECT SCIENCE 2024; 62:101159. [PMID: 38199562 DOI: 10.1016/j.cois.2024.101159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 12/12/2023] [Accepted: 01/04/2024] [Indexed: 01/12/2024]
Abstract
Species distribution models are the primary tools to project future species' distributions, but this complex task is influenced by data limitations and evolving best practices. The majority of the 53 studies we examined utilized correlative models and did not follow current best practices for validating retrospective or future environmental data layers. Despite this, a summary of results is largely unsurprising: shifts toward cooler regions, but otherwise mixed dynamics emphasizing winners and losers. Harmful insects were more likely to show positive outcomes compared with beneficial species. Our restricted ability to consider mechanisms complicates interpretation of any single study. To improve this area of modeling, more classic field and lab studies to uncover basic ecology and physiology are crucial.
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Affiliation(s)
- Naresh Neupane
- Georgetown University, Department of Biology, Washington, DC 20057, USA.
| | - Elise A Larsen
- Georgetown University, Department of Biology, Washington, DC 20057, USA
| | - Leslie Ries
- Georgetown University, Department of Biology, Washington, DC 20057, USA
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10
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Bull JW. Life Is Uncertain: Inherent Variability Exhibited by Organisms, and at Higher Levels of Biological Organization. ASTROBIOLOGY 2024; 24:318-327. [PMID: 38350125 DOI: 10.1089/ast.2023.0094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Organisms act stochastically. A not uncommon view in the ecological literature is that this is mainly due to the observer having insufficient information or a stochastic environment-and not partly because organisms themselves respond with inherent unpredictability. In this study, I compile the evidence that contradicts that view. Organisms generate uncertainty internally, which results in irreducible stochastic responses. I consider why: for instance, stochastic responses are associated with greater adaptability to changing environments and resource availability. Over longer timescales, biologically generated uncertainty influences behavior, evolution, and macroecological processes. Indeed, it could be stated that organisms are systems defined by the internal generation, magnification, and record-keeping of uncertainty as inputs to responses. Important practical implications arise if organisms can indeed be defined by an association with specific classes of inherent uncertainty: not least that isolating those signatures then provides a potential means for detecting life, for considering the forms that life could theoretically take, and for exploring the wider limits to how life might become distributed. These are all fundamental goals in astrobiology.
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Affiliation(s)
- Joseph W Bull
- Department of Biology, University of Oxford, Oxford, United Kingdom
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11
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Gebreyohannes DT, Houlahan JE. Weak evidence of density dependent population regulation when using the ability of two simple density dependent models to predict population size. Sci Rep 2024; 14:5051. [PMID: 38424456 PMCID: PMC10904816 DOI: 10.1038/s41598-024-55533-4] [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: 02/07/2024] [Accepted: 02/24/2024] [Indexed: 03/02/2024] Open
Abstract
The relative importance of density dependence regulation in natural population fluctuations has long been debated. The concept of density dependence implies that current abundance is determined by historical abundance. We have developed four models-two density dependent and two density independent-to predict population size one year beyond the training set and used predictive performance on more than 16,000 populations from 14 datasets to compare the understanding captured by those models. For 4 of 14 datasets the density dependent models make better predictions (i.e., density dependent regulated) than either of the density independent models. However, neither of the density dependent models is statistically significantly superior to density independent models for any of the 14 datasets. We conclude that the evidence for widespread density dependent population regulation in the forms represented by these two simple density-dependent models is weak. However, the density dependent models used here-the Logistic and Gompertz models-are simple representations of how population density might regulate natural populations and only examine density-dependent effects on population size. A comprehensive assessment of the relative importance of density-dependent population regulation will require testing the predictive ability of a wider range of density-dependent models including models examining effects on population characteristics other than population size.
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Affiliation(s)
| | - Jeff E Houlahan
- Department of Biological Sciences, University of New Brunswick, Saint John, E2L 4L5, Canada
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12
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Clare JDJ, de Valpine P, Moanga DA, Tingley MW, Beissinger SR. A cloudy forecast for species distribution models: Predictive uncertainties abound for California birds after a century of climate and land-use change. GLOBAL CHANGE BIOLOGY 2024; 30:e17019. [PMID: 37987241 DOI: 10.1111/gcb.17019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 10/04/2023] [Accepted: 10/07/2023] [Indexed: 11/22/2023]
Abstract
Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi- or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within-time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site-specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long-term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states.
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Affiliation(s)
- John D J Clare
- Museum of Vertebrate Zoology, University of California-Berkeley, Berkeley, California, USA
- Department of Environmental Science, Policy, and Management, University of California-Berkeley, Berkeley, California, USA
| | - Perry de Valpine
- Department of Environmental Science, Policy, and Management, University of California-Berkeley, Berkeley, California, USA
| | - Diana A Moanga
- Department of Earth System Science, Stanford University, Palo Alto, California, USA
| | - Morgan W Tingley
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California, USA
| | - Steven R Beissinger
- Museum of Vertebrate Zoology, University of California-Berkeley, Berkeley, California, USA
- Department of Environmental Science, Policy, and Management, University of California-Berkeley, Berkeley, California, USA
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13
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Zipkin EF, Doser JW. Context matters in ecological forecasting: Lessons in predicting species distributions. GLOBAL CHANGE BIOLOGY 2024; 30:e17123. [PMID: 38273489 DOI: 10.1111/gcb.17123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/14/2023] [Indexed: 01/27/2024]
Abstract
Forecasting the future state of a species is a tricky process, as there are numerous hidden factors that influence species trajectories in addition to the obvious unknowns about the future state of the planet. We echo the guidance of Clare et al. (2024) to use near‐term and long‐term forecasting in complementary ways. Near‐term forecasts can be used to guide specific management and conservation actions, which can be updated as new data and evidence are collected. Long‐term forecasts can be used to characterize uncertainty further into the future, which can help guide longstanding conservation planning and legislative actions that are based on such uncertainty in possible future outcomes.
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Affiliation(s)
- Elise F Zipkin
- Ecology, Evolution, and Behavior Program, Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
| | - Jeffrey W Doser
- Ecology, Evolution, and Behavior Program, Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
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14
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Schaeffer BA, Reynolds N, Ferriby H, Salls W, Smith D, Johnston JM, Myer M. Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 349:119518. [PMID: 37944321 PMCID: PMC10842250 DOI: 10.1016/j.jenvman.2023.119518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/19/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
This forecasting approach may be useful for water managers and associated public health managers to predict near-term future high-risk cyanobacterial harmful algal blooms (cyanoHAB) occurrence. Freshwater cyanoHABs may grow to excessive concentrations and cause human, animal, and environmental health concerns in lakes and reservoirs. Knowledge of the timing and location of cyanoHAB events is important for water quality management of recreational and drinking water systems. No quantitative tool exists to forecast cyanoHABs across broad geographic scales and at regular intervals. Publicly available satellite monitoring has proven effective in detecting cyanobacteria biomass near-real time within the United States. Weekly cyanobacteria abundance was quantified from the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite as the response variable. An Integrated Nested Laplace Approximation (INLA) hierarchical Bayesian spatiotemporal model was applied to forecast World Health Organization (WHO) recreation Alert Level 1 exceedance >12 μg L-1 chlorophyll-a with cyanobacteria dominance for 2192 satellite resolved lakes in the United States across nine climate zones. The INLA model was compared against support vector classifier and random forest machine learning models; and Dense Neural Network, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gneural Network (GNU) neural network models. Predictors were limited to data sources relevant to cyanobacterial growth, readily available on a weekly basis, and at the national scale for operational forecasting. Relevant predictors included water surface temperature, precipitation, and lake geomorphology. Overall, the INLA model outperformed the machine learning and neural network models with prediction accuracy of 90% with 88% sensitivity, 91% specificity, and 49% precision as demonstrated by training the model with data from 2017 through 2020 and independently assessing predictions with data from the 2021 calendar year. The probability of true positive responses was greater than false positive responses and the probability of true negative responses was less than false negative responses. This indicated the model correctly assigned lower probabilities of events when they didn't exceed the WHO Alert Level 1 threshold and assigned higher probabilities when events did exceed the threshold. The INLA model was robust to missing data and unbalanced sampling between waterbodies.
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Affiliation(s)
| | | | | | - Wilson Salls
- US EPA, Office of Research and Development, Durham, NC, USA
| | - Deron Smith
- US EPA, Office of Research and Development, Athens, GA, USA
| | | | - Mark Myer
- US EPA, Office of Chemical Safety and Pollution Prevention, Durham, NC, USA
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15
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Brodie S, Pozo Buil M, Welch H, Bograd SJ, Hazen EL, Santora JA, Seary R, Schroeder ID, Jacox MG. Ecological forecasts for marine resource management during climate extremes. Nat Commun 2023; 14:7701. [PMID: 38052808 DOI: 10.1038/s41467-023-43188-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 11/02/2023] [Indexed: 12/07/2023] Open
Abstract
Forecasting weather has become commonplace, but as society faces novel and uncertain environmental conditions there is a critical need to forecast ecology. Forewarning of ecosystem conditions during climate extremes can support proactive decision-making, yet applications of ecological forecasts are still limited. We showcase the capacity for existing marine management tools to transition to a forecasting configuration and provide skilful ecological forecasts up to 12 months in advance. The management tools use ocean temperature anomalies to help mitigate whale entanglements and sea turtle bycatch, and we show that forecasts can forewarn of human-wildlife interactions caused by unprecedented climate extremes. We further show that regionally downscaled forecasts are not a necessity for ecological forecasting and can be less skilful than global forecasts if they have fewer ensemble members. Our results highlight capacity for ecological forecasts to be explored for regions without the infrastructure or capacity to regionally downscale, ultimately helping to improve marine resource management and climate adaptation globally.
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Affiliation(s)
- Stephanie Brodie
- Institute of Marine Sciences, University of California Santa Cruz, Monterey, CA, USA.
- Environmental Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Monterey, CA, USA.
- Environment, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, Queensland, Australia.
| | - Mercedes Pozo Buil
- Institute of Marine Sciences, University of California Santa Cruz, Monterey, CA, USA
- Environmental Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Monterey, CA, USA
| | - Heather Welch
- Institute of Marine Sciences, University of California Santa Cruz, Monterey, CA, USA
- Environmental Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Monterey, CA, USA
| | - Steven J Bograd
- Institute of Marine Sciences, University of California Santa Cruz, Monterey, CA, USA
- Environmental Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Monterey, CA, USA
| | - Elliott L Hazen
- Institute of Marine Sciences, University of California Santa Cruz, Monterey, CA, USA
- Environmental Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Monterey, CA, USA
| | - Jarrod A Santora
- Fisheries Ecology Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, CA, USA
- Department of Applied Math, University of California, 1156, Santa Cruz, CA, USA
| | - Rachel Seary
- Institute of Marine Sciences, University of California Santa Cruz, Monterey, CA, USA
- Environmental Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Monterey, CA, USA
- Fisheries Ecology Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, CA, USA
| | - Isaac D Schroeder
- Institute of Marine Sciences, University of California Santa Cruz, Monterey, CA, USA
- Environmental Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Monterey, CA, USA
| | - Michael G Jacox
- Institute of Marine Sciences, University of California Santa Cruz, Monterey, CA, USA
- Environmental Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Monterey, CA, USA
- Physical Sciences Laboratory, Earth System Research Laboratories, National Oceanic and Atmospheric Administration, Boulder, CO, USA
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16
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de Koning K, Broekhuijsen J, Kühn I, Ovaskainen O, Taubert F, Endresen D, Schigel D, Grimm V. Digital twins: dynamic model-data fusion for ecology. Trends Ecol Evol 2023; 38:916-926. [PMID: 37208222 DOI: 10.1016/j.tree.2023.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/21/2023]
Abstract
Digital twins (DTs) are an emerging phenomenon in the public and private sectors as a new tool to monitor and understand systems and processes. DTs have the potential to change the status quo in ecology as part of its digital transformation. However, it is important to avoid misguided developments by managing expectations about DTs. We stress that DTs are not just big models of everything, containing big data and machine learning. Rather, the strength of DTs is in combining data, models, and domain knowledge, and their continuous alignment with the real world. We suggest that researchers and stakeholders exercise caution in DT development, keeping in mind that many of the strengths and challenges of computational modelling in ecology also apply to DTs.
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Affiliation(s)
- Koen de Koning
- Wageningen University and Research, Environmental Systems Analysis Group, P.O. Box 47, 6700, AA, Wageningen, The Netherlands
| | - Jeroen Broekhuijsen
- Nederlandse organisatie voor toegepast natuurwetenschappenlijk onderzoek - TNO, Department of Monitoring & Control Services, Eemsgolaan 3, 9727 DW Groningen, The Netherlands
| | - Ingolf Kühn
- Helmholtz Centre for Environmental Research - UFZ, Department of Community Ecology, Theodor-Lieser-Strasse, 4, 06120 Halle, Germany; Martin Luther University Halle-Wittenberg, Institute for Biology/Geobotany & Botanical Garden, Große Steinstraße 79/80, 06108 Halle, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, 04103 Leipzig, Germany
| | - Otso Ovaskainen
- Department of Biological and Environmental Science, University of Jyväskylä, P.O. Box 35 (Survontie 9C), FI-40014 Jyväskylä, Finland; Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, P.O. Box 65, Helsinki 00014, Finland; Department of Biology, Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim N-7491, Norway
| | - Franziska Taubert
- Helmholtz Centre for Environmental Research - UFZ, Department of Ecological Modelling, Permoserstr. 15, 04318 Leipzig, Germany
| | - Dag Endresen
- University of Oslo, Natural History Museum, Sars gate 1, NO-0562 Oslo, Norway.
| | - Dmitry Schigel
- Global Biodiversity Information Facility - GBIF Secreteriat, Universitetsparken 15, DK-2100 Copenhagen Ø, Denmark
| | - Volker Grimm
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, 04103 Leipzig, Germany; Helmholtz Centre for Environmental Research - UFZ, Department of Ecological Modelling, Permoserstr. 15, 04318 Leipzig, Germany; University of Potsdam, Plant Ecology and Nature Conservation, Am Mühlenberg 3, 14476 Potsdam, Germany
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17
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Welch H, Savoca MS, Brodie S, Jacox MG, Muhling BA, Clay TA, Cimino MA, Benson SR, Block BA, Conners MG, Costa DP, Jordan FD, Leising AW, Mikles CS, Palacios DM, Shaffer SA, Thorne LH, Watson JT, Holser RR, Dewitt L, Bograd SJ, Hazen EL. Impacts of marine heatwaves on top predator distributions are variable but predictable. Nat Commun 2023; 14:5188. [PMID: 37669922 PMCID: PMC10480173 DOI: 10.1038/s41467-023-40849-y] [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: 02/01/2023] [Accepted: 08/11/2023] [Indexed: 09/07/2023] Open
Abstract
Marine heatwaves cause widespread environmental, biological, and socio-economic impacts, placing them at the forefront of 21st-century management challenges. However, heatwaves vary in intensity and evolution, and a paucity of information on how this variability impacts marine species limits our ability to proactively manage for these extreme events. Here, we model the effects of four recent heatwaves (2014, 2015, 2019, 2020) in the Northeastern Pacific on the distributions of 14 top predator species of ecological, cultural, and commercial importance. Predicted responses were highly variable across species and heatwaves, ranging from near total loss of habitat to a two-fold increase. Heatwaves rapidly altered political bio-geographies, with up to 10% of predicted habitat across all species shifting jurisdictions during individual heatwaves. The variability in predicted responses across species and heatwaves portends the need for novel management solutions that can rapidly respond to extreme climate events. As proof-of-concept, we developed an operational dynamic ocean management tool that predicts predator distributions and responses to extreme conditions in near real-time.
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Affiliation(s)
- Heather Welch
- NOAA, Southwest Fisheries Science Center, Environmental Research Division, Monterey, CA, USA.
- Institute of Marine Science, UC Santa Cruz, Santa Cruz, CA, USA.
| | - Matthew S Savoca
- Hopkins Marine Station, Stanford University, Pacific Grove, CA, USA
| | - Stephanie Brodie
- NOAA, Southwest Fisheries Science Center, Environmental Research Division, Monterey, CA, USA
- Institute of Marine Science, UC Santa Cruz, Santa Cruz, CA, USA
| | - Michael G Jacox
- NOAA, Southwest Fisheries Science Center, Environmental Research Division, Monterey, CA, USA
- Institute of Marine Science, UC Santa Cruz, Santa Cruz, CA, USA
- NOAA, Physical Sciences Laboratory, Boulder, CO, USA
| | - Barbara A Muhling
- Institute of Marine Science, UC Santa Cruz, Santa Cruz, CA, USA
- NOAA Southwest Fisheries Science Center, Fisheries Resources Division, San Diego, CA, USA
| | - Thomas A Clay
- NOAA, Southwest Fisheries Science Center, Environmental Research Division, Monterey, CA, USA
- Institute of Marine Science, UC Santa Cruz, Santa Cruz, CA, USA
- People and Nature, Environmental Defense Fund, Monterey, CA, USA
| | - Megan A Cimino
- NOAA, Southwest Fisheries Science Center, Environmental Research Division, Monterey, CA, USA
- Institute of Marine Science, UC Santa Cruz, Santa Cruz, CA, USA
| | - Scott R Benson
- NOAA, Southwest Fisheries Science Center, Marine Mammal and Turtle Division, Moss Landing, CA, USA
- Moss Landing Marine Laboratories, San Jose State University, Moss Landing, CA, USA
| | - Barbara A Block
- Hopkins Marine Station, Stanford University, Pacific Grove, CA, USA
| | - Melinda G Conners
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Daniel P Costa
- Institute of Marine Science, UC Santa Cruz, Santa Cruz, CA, USA
- Department of Ecology and Evolutionary Biology, UC Santa Cruz, Santa Cruz, CA, USA
| | - Fredrick D Jordan
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Andrew W Leising
- NOAA, Southwest Fisheries Science Center, Environmental Research Division, Monterey, CA, USA
| | - Chloe S Mikles
- Hopkins Marine Station, Stanford University, Pacific Grove, CA, USA
| | - Daniel M Palacios
- Marine Mammal Institute, Oregon State University, Newport, OR, USA
- Department of Fisheries, Wildlife, and Conservation Sciences, Oregon State University, Newport, OR, USA
| | - Scott A Shaffer
- Department of Biological Sciences, San Jose State University, San Jose, CA, USA
| | - Lesley H Thorne
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Jordan T Watson
- NOAA, Alaska Fisheries Science Center, Auke Bay Laboratory, Juneau, AK, USA
- Pacific Islands Ocean Observing System, University of Hawai'i Mānoa, Honolulu, HI, USA
| | - Rachel R Holser
- Institute of Marine Science, UC Santa Cruz, Santa Cruz, CA, USA
| | - Lynn Dewitt
- NOAA, Southwest Fisheries Science Center, Environmental Research Division, Monterey, CA, USA
| | - Steven J Bograd
- NOAA, Southwest Fisheries Science Center, Environmental Research Division, Monterey, CA, USA
- Institute of Marine Science, UC Santa Cruz, Santa Cruz, CA, USA
| | - Elliott L Hazen
- NOAA, Southwest Fisheries Science Center, Environmental Research Division, Monterey, CA, USA
- Institute of Marine Science, UC Santa Cruz, Santa Cruz, CA, USA
- Hopkins Marine Station, Stanford University, Pacific Grove, CA, USA
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18
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Kays R, Wikelski M. The Internet of Animals: what it is, what it could be. Trends Ecol Evol 2023; 38:859-869. [PMID: 37263824 DOI: 10.1016/j.tree.2023.04.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 04/07/2023] [Accepted: 04/14/2023] [Indexed: 06/03/2023]
Abstract
One of the biggest trends in ecology over the past decade has been the creation of standardized databases. Recently, this has included live data, formal linkages between disparate databases, and automated analytics, a synergy that we recognize as the Internet of Animals (IoA). Early IoA systems relate animal locations to remote-sensing data to predict species distributions and detect disease outbreaks, and use live data to inform management of endangered species. However, meeting the future potential of the IoA concept will require solving challenges of taxonomy, data security, and data sharing. By linking data sets, integrating live data, and automating workflows, the IoA has the potential to enable discoveries and predictions relevant to human societies and the conservation of animals.
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Affiliation(s)
- Roland Kays
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA; North Carolina Museum of Natural Sciences, Raleigh, NC, USA; Smithsonian Tropical Research Institute, Balboa, Republic of Panama.
| | - Martin Wikelski
- Smithsonian Tropical Research Institute, Balboa, Republic of Panama; Department of Animal Migration, Max Planck Institute of Animal Behaviour, Radolfzell, Germany; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
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19
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Keetz LT, Lieungh E, Karimi-Asli K, Geange SR, Gelati E, Tang H, Yilmaz YA, Aas KS, Althuizen IHJ, Bryn A, Falk S, Fisher R, Fouilloux A, Horvath P, Indrehus S, Lee H, Lombardozzi D, Parmentier FJW, Pirk N, Vandvik V, Vollsnes AV, Skarpaas O, Stordal F, Tallaksen LM. Climate-ecosystem modelling made easy: The Land Sites Platform. GLOBAL CHANGE BIOLOGY 2023; 29:4440-4452. [PMID: 37303068 DOI: 10.1111/gcb.16808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 05/03/2023] [Indexed: 06/13/2023]
Abstract
Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.
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Affiliation(s)
- Lasse T Keetz
- Department of Geosciences, University of Oslo, Oslo, Norway
| | - Eva Lieungh
- Natural History Museum, University of Oslo, Oslo, Norway
| | | | - Sonya R Geange
- Department of Biological Sciences, University of Bergen, Bergen, Norway
| | | | - Hui Tang
- Department of Geosciences, University of Oslo, Oslo, Norway
- Natural History Museum, University of Oslo, Oslo, Norway
- Finnish Meteorological Institute, Climate System Research, Helsinki, Finland
| | - Yeliz A Yilmaz
- Department of Geosciences, University of Oslo, Oslo, Norway
- Centre for Biogeochemistry in the Anthropocene, University of Oslo, Oslo, Norway
| | - Kjetil S Aas
- Department of Geosciences, University of Oslo, Oslo, Norway
- CICERO Center for International Climate Research, Oslo, Norway
| | - Inge H J Althuizen
- Division of Climate and Environment, NORCE Norwegian Research Centre, Bergen, Norway
| | - Anders Bryn
- Natural History Museum, University of Oslo, Oslo, Norway
- Centre for Biogeochemistry in the Anthropocene, University of Oslo, Oslo, Norway
| | - Stefanie Falk
- Department of Geography, Ludwig Maximilian University of Munich, Munich, Germany
| | - Rosie Fisher
- CICERO Center for International Climate Research, Oslo, Norway
- Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
| | | | - Peter Horvath
- Natural History Museum, University of Oslo, Oslo, Norway
| | | | - Hanna Lee
- Division of Climate and Environment, NORCE Norwegian Research Centre, Bergen, Norway
- Department of Biology, Norwegian University of Science and Technology NTNU, Trondheim, Norway
| | - Danica Lombardozzi
- Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
| | - Frans-Jan W Parmentier
- Department of Geosciences, University of Oslo, Oslo, Norway
- Centre for Biogeochemistry in the Anthropocene, University of Oslo, Oslo, Norway
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
| | - Norbert Pirk
- Department of Geosciences, University of Oslo, Oslo, Norway
| | - Vigdis Vandvik
- Department of Biological Sciences, University of Bergen, Bergen, Norway
- Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
| | - Ane V Vollsnes
- Centre for Biogeochemistry in the Anthropocene, University of Oslo, Oslo, Norway
- Department of Biosciences, University of Oslo, Oslo, Norway
| | - Olav Skarpaas
- Natural History Museum, University of Oslo, Oslo, Norway
| | - Frode Stordal
- Department of Geosciences, University of Oslo, Oslo, Norway
- Centre for Biogeochemistry in the Anthropocene, University of Oslo, Oslo, Norway
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20
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Brilli L, Martin R, Argenti G, Bassignana M, Bindi M, Bonet R, Choler P, Cremonese E, Della Vedova M, Dibari C, Filippa G, Galvagno M, Leolini L, Moriondo M, Piccot A, Stendardi L, Targetti S, Bellocchi G. Uncertainties in the adaptation of alpine pastures to climate change based on remote sensing products and modelling. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 336:117575. [PMID: 36893538 DOI: 10.1016/j.jenvman.2023.117575] [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: 12/06/2022] [Revised: 02/02/2023] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Over the last century, the management of pastoral systems has undergone major changes to meet the livelihood needs of alpine communities. Faced with the changes induced by recent global warming, the ecological status of many pastoral systems has seriously deteriorated in the western alpine region. We assessed changes in pasture dynamics by integrating information from remote-sensing products and two process-based models, i.e. the grassland-specific, biogeochemical growth model PaSim and the generic crop-growth model DayCent. Meteorological observations and satellite-derived Normalised Difference Vegetation Index (NDVI) trajectories of three pasture macro-types (high, medium and low productivity classes) in two study areas - Parc National des Écrins (PNE) in France and Parco Nazionale Gran Paradiso (PNGP) in Italy - were used as a basis for the model calibration work. The performance of the models was satisfactory in reproducing pasture production dynamics (R2 = 0.52 to 0.83). Projected changes in alpine pastures due to climate-change impacts and adaptation strategies indicate that: i) the length of the growing season is expected to increase between 15 and 40 days, resulting in changes in the timing and amount of biomass production, ii) summer water stress could limit pasture productivity; iii) earlier onset of grazing could enhance pasture productivity; iv) higher livestock densities could increase the rate of biomass regrowth, but major uncertainties in modelling processes need to be considered; and v) the carbon sequestration potential of pastures could decrease under limited water availability and warming.
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Affiliation(s)
- L Brilli
- National Research Council - Institute of BioEconomy (IBE-CNR), 50145, Sesto Fiorentino, Italy; University of Florence, DAGRI, 50144, Florence, Italy.
| | - R Martin
- Université Clermont Auvergne, INRAE, VetAgro Sup, UREP, 63000, Clermont-Ferrand, France
| | - G Argenti
- University of Florence, DAGRI, 50144, Florence, Italy
| | | | - M Bindi
- University of Florence, DAGRI, 50144, Florence, Italy
| | - R Bonet
- Parc National des Ecrins, Domaine de Charance, 05000, Gap, France
| | - P Choler
- Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LECA, 38000, Grenoble, France
| | - E Cremonese
- Climate Change Unit, Environmental Protection Agency of Aosta Valley, Saint-Christophe, Italy
| | - M Della Vedova
- Parc National des Ecrins, Domaine de Charance, 05000, Gap, France
| | - C Dibari
- University of Florence, DAGRI, 50144, Florence, Italy
| | - G Filippa
- Climate Change Unit, Environmental Protection Agency of Aosta Valley, Saint-Christophe, Italy
| | - M Galvagno
- Climate Change Unit, Environmental Protection Agency of Aosta Valley, Saint-Christophe, Italy
| | - L Leolini
- University of Florence, DAGRI, 50144, Florence, Italy
| | - M Moriondo
- National Research Council - Institute of BioEconomy (IBE-CNR), 50145, Sesto Fiorentino, Italy; University of Florence, DAGRI, 50144, Florence, Italy
| | - A Piccot
- Institut Agricole Régional, 11100, Aosta, Italy
| | - L Stendardi
- University of Florence, DAGRI, 50144, Florence, Italy
| | - S Targetti
- University of Bologna, Department of Agricultural and Food Sciences, Viale Fanin, 50, 40127, Bologna, Italy
| | - G Bellocchi
- Université Clermont Auvergne, INRAE, VetAgro Sup, UREP, 63000, Clermont-Ferrand, France
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21
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Boult VL. Forecast-based action for conservation. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2023; 37:e14054. [PMID: 36661067 DOI: 10.1111/cobi.14054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/23/2022] [Accepted: 12/21/2022] [Indexed: 05/30/2023]
Abstract
Extreme weather events pose an immediate threat to biodiversity, but existing conservation strategies have limitations. Advances in meteorological forecasting and innovation in the humanitarian sector provide a possible solution-forecast-based action (FbA). The growth of ecological forecasting demonstrates the huge potential to anticipate conservation outcomes, but a lack of operational examples suggests a new approach is needed to translate forecasts into action. FbA provides such a framework, formalizing the use of meteorological forecasts to anticipate and mitigate the impacts of extreme weather. Based on experience from the humanitarian sector, I suggest how FbA could work in conservation, demonstrating key concepts using the theoretical example of heatwave impacts on sea turtle embryo mortality, and address likely challenges in realizing FbA for conservation, including establishing a financing mechanism, allocating funds to actions, and decision-making under uncertainty. FbA will demand changes in conservation research, practice, and governance. Researchers must increase efforts to understand the impacts of extreme weather at more immediate and actionable timescales and should coproduce forecasts of such impacts with practitioners. International conservation funders should establish systems to fund anticipatory actions based on uncertain forecasts.
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Affiliation(s)
- Victoria L Boult
- Department of Meteorology, University of Reading, Reading, UK
- National Centre for Atmospheric Science, Reading, UK
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22
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Zweifel R, Pappas C, Peters RL, Babst F, Balanzategui D, Basler D, Bastos A, Beloiu M, Buchmann N, Bose AK, Braun S, Damm A, D'Odorico P, Eitel JUH, Etzold S, Fonti P, Rouholahnejad Freund E, Gessler A, Haeni M, Hoch G, Kahmen A, Körner C, Krejza J, Krumm F, Leuchner M, Leuschner C, Lukovic M, Martínez-Vilalta J, Matula R, Meesenburg H, Meir P, Plichta R, Poyatos R, Rohner B, Ruehr N, Salomón RL, Scharnweber T, Schaub M, Steger DN, Steppe K, Still C, Stojanović M, Trotsiuk V, Vitasse Y, von Arx G, Wilmking M, Zahnd C, Sterck F. Networking the forest infrastructure towards near real-time monitoring - A white paper. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 872:162167. [PMID: 36775147 DOI: 10.1016/j.scitotenv.2023.162167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
Forests account for nearly 90 % of the world's terrestrial biomass in the form of carbon and they support 80 % of the global biodiversity. To understand the underlying forest dynamics, we need a long-term but also relatively high-frequency, networked monitoring system, as traditionally used in meteorology or hydrology. While there are numerous existing forest monitoring sites, particularly in temperate regions, the resulting data streams are rarely connected and do not provide information promptly, which hampers real-time assessments of forest responses to extreme climate events. The technology to build a better global forest monitoring network now exists. This white paper addresses the key structural components needed to achieve a novel meta-network. We propose to complement - rather than replace or unify - the existing heterogeneous infrastructure with standardized, quality-assured linking methods and interacting data processing centers to create an integrated forest monitoring network. These automated (research topic-dependent) linking methods in atmosphere, biosphere, and pedosphere play a key role in scaling site-specific results and processing them in a timely manner. To ensure broad participation from existing monitoring sites and to establish new sites, these linking methods must be as informative, reliable, affordable, and maintainable as possible, and should be supplemented by near real-time remote sensing data. The proposed novel meta-network will enable the detection of emergent patterns that would not be visible from isolated analyses of individual sites. In addition, the near real-time availability of data will facilitate predictions of current forest conditions (nowcasts), which are urgently needed for research and decision making in the face of rapid climate change. We call for international and interdisciplinary efforts in this direction.
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Affiliation(s)
- Roman Zweifel
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Christoforos Pappas
- Department of Civil Engineering, University of Patras, Rio, Patras 26504, Greece.
| | - Richard L Peters
- Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Flurin Babst
- School of Natural Resources and the Environment, University of Arizona, 1064 E Lowell St, Tucson, AZ 85721, USA; Laboratory of Tree-Ring Research, University of Arizona, 1215 E Lowell St, Tucson, AZ 85721, USA.
| | - Daniel Balanzategui
- GFZ German Research Centre for Geosciences, Wissenschaftpark "Albert Einstein", Telegrafenberg, Potsdam, Germany; Geography Department, Humboldt University of Berlin, Rudower Ch 16, 12489 Berlin, DE, USA.
| | - David Basler
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland; Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Ana Bastos
- Max Planck Institute for Biogeochemistry, Dept. of Biogeochemical Integration, Hans Knöll Str. 10, 07745 Jena, Germany.
| | - Mirela Beloiu
- Institute of Terrestrial Ecosystems, ETH Zurich, Zurich, Switzerland.
| | - Nina Buchmann
- Department of Environmental Systems Science, ETH Zurich, Universitätstr. 2, LFW C56, 8092 Zurich, Switzerland.
| | - Arun K Bose
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland; Forestry and Wood Technology Discipline, Khulna University, Khulna 9208, Bangladesh.
| | - Sabine Braun
- Institute for Applied Plant Biology, Benkenstrasse 254A, 4108 Witterswil, Switzerland.
| | - Alexander Damm
- Department of Geography, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland; Eawag, Swiss Federal Institute of Aquatic Science & Technology, Surface Waters - Research and Management, Ueberlandstrasse 133, 8600 Duebendorf, Switzerland.
| | - Petra D'Odorico
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Jan U H Eitel
- Department of Natural Resource and Society, University of Idaho, 1800 University Lane, 83638 McCall, ID, USA.
| | - Sophia Etzold
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Patrick Fonti
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | | | - Arthur Gessler
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Matthias Haeni
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Günter Hoch
- Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Ansgar Kahmen
- Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Christian Körner
- Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Jan Krejza
- Global Change Research Institute of the Czech Academy of Sciences, Bělidla 4a, 603 00 Brno, Czech Republic.
| | - Frank Krumm
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Michael Leuchner
- Department of Physical Geography and Climatology, Institute of Geography, RWTH Aachen University, 52056 Aachen, Germany.
| | - Christoph Leuschner
- Plant Ecology, University of Göttingen, Untere Karspüle 2, 37073 Göttingen, Germany.
| | - Mirko Lukovic
- Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf 8600, Switzerland.
| | - Jordi Martínez-Vilalta
- CREAF, Bellaterra (Cerdanyola del Valles), Catalonia E08193, Spain; Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Valles), Catalonia E08193, Spain.
| | - Radim Matula
- Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Praha 6, Suchdol 16521, Czech Republic.
| | - Henning Meesenburg
- Northwest German Forest Research Institute, Grätzelstr. 2, D-37079 Göttingen, Germany.
| | - Patrick Meir
- School of Geosciences, University of Edinburgh, Alexander Crum Brown Road, Edinburgh EH93FF, UK.
| | - Roman Plichta
- Department of Forest Botany, Dendrology and Geobiocoenology, Mendel University in Brno, Zemedelska 1, 61300 Brno, Czech Republic.
| | - Rafael Poyatos
- CREAF, Bellaterra (Cerdanyola del Valles), Catalonia E08193, Spain; Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Valles), Catalonia E08193, Spain.
| | - Brigitte Rohner
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Nadine Ruehr
- Institute of Meteorology and Climate Research - Atmospheric Environmental Research, Karlsruhe Institute of Technology KIT, Garmisch-Partenkirchen 82467, Germany.
| | - Roberto L Salomón
- Departamento de Sistemas y Recursos Naturales, Universidad Politécnica de Madrid, 28040 Madrid, Spain.
| | - Tobias Scharnweber
- DendroGreif, University Greifswald, Soldmannstrasse 15, D-17487 Greifswald, Germany.
| | - Marcus Schaub
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - David N Steger
- Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Kathy Steppe
- Laboratory of Plant Ecology, Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, 9000 Gent, Belgium.
| | - Christopher Still
- Forest Ecosystems and Society Department, Oregon State University, Corvallis, OR 97331, USA.
| | - Marko Stojanović
- Global Change Research Institute of the Czech Academy of Sciences, Bělidla 4a, 603 00 Brno, Czech Republic.
| | - Volodymyr Trotsiuk
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Yann Vitasse
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland.
| | - Georg von Arx
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland; Oeschger Centre for Climate Change Research, University of Bern, 3012 Bern, Switzerland.
| | - Martin Wilmking
- DendroGreif, University Greifswald, Soldmannstrasse 15, D-17487 Greifswald, Germany.
| | - Cedric Zahnd
- Department of Environmental Sciences, Institute of Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland.
| | - Frank Sterck
- Forest Ecology and Forest Management Group, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands.
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23
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Tang B, Kamakura RP, Barnett DT, Clark JS. Learning from monitoring networks: Few-large vs. many-small plots and multi-scale analysis. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2023.1114569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023] Open
Abstract
In order to learn about broad scale ecological patterns, data from large-scale surveys must allow us to either estimate the correlations between the environment and an outcome and/or accurately predict ecological patterns. An important part of data collection is the sampling effort used to collect observations, which we decompose into two quantities: the number of observations or plots (n) and the per-observation/plot effort (E; e.g., area per plot). If we want to understand the relationships between predictors and a response variable, then lower model parameter uncertainty is desirable. If the goal is to predict a response variable, then lower prediction error is preferable. We aim to learn if and when aggregating data can help attain these goals. We find that a small sample size coupled with large observation effort coupled (few large) can yield better predictions when compared to a large number of observations with low observation effort (many small). We also show that the combination of the two values (n and E), rather than one alone, has an impact on parameter uncertainty. In an application to Forest Inventory and Analysis (FIA) data, we model the tree density of selected species at various amounts of aggregation using linear regression in order to compare the findings from simulated data to real data. The application supports the theoretical findings that increasing observational effort through aggregation can lead to improved predictions, conditional on the thoughtful aggregation of the observational plots. In particular, aggregations over extremely large and variable covariate space may lead to poor prediction and high parameter uncertainty. Analyses of large-range data can improve with aggregation, with implications for both model evaluation and sampling design: testing model prediction accuracy without an underlying knowledge of the datasets and the scale at which predictor variables operate can obscure meaningful results.
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24
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Gilbert NA, McGinn KA, Nunes LA, Shipley AA, Bernath-Plaisted J, Clare JDJ, Murphy PW, Keyser SR, Thompson KL, Maresh Nelson SB, Cohen JM, Widick IV, Bartel SL, Orrock JL, Zuckerberg B. Daily activity timing in the Anthropocene. Trends Ecol Evol 2023; 38:324-336. [PMID: 36402653 DOI: 10.1016/j.tree.2022.10.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 10/12/2022] [Accepted: 10/27/2022] [Indexed: 11/17/2022]
Abstract
Animals are facing novel 'timescapes' in which the stimuli entraining their daily activity patterns no longer match historical conditions due to anthropogenic disturbance. However, the ecological effects (e.g., altered physiology, species interactions) of novel activity timing are virtually unknown. We reviewed 1328 studies and found relatively few focusing on anthropogenic effects on activity timing. We suggest three hypotheses to stimulate future research: (i) activity-timing mismatches determine ecological effects, (ii) duration and timing of timescape modification influence effects, and (iii) consequences of altered activity timing vary biogeographically due to broad-scale variation in factors compressing timescapes. The continued growth of sampling technologies promises to facilitate the study of the consequences of altered activity timing, with emerging applications for biodiversity conservation.
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Affiliation(s)
- Neil A Gilbert
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Kate A McGinn
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Laura A Nunes
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Amy A Shipley
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA; School of Natural Resources, University of Missouri, Columbia, MO 65211, USA
| | - Jacy Bernath-Plaisted
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - John D J Clare
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA; Museum of Vertebrate Zoology, University of California, Berkeley, CA 94720, USA
| | - Penelope W Murphy
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Spencer R Keyser
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Kimberly L Thompson
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA; German Centre for Integrative Biodiversity Research (iDiv), 04103 Halle-Jena-Leipzig, Germany
| | - Scott B Maresh Nelson
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jeremy M Cohen
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Ivy V Widick
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Savannah L Bartel
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - John L Orrock
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Benjamin Zuckerberg
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA.
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25
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The effects of light pollution on migratory animal behavior. Trends Ecol Evol 2023; 38:355-368. [PMID: 36610920 DOI: 10.1016/j.tree.2022.12.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 12/05/2022] [Accepted: 12/09/2022] [Indexed: 01/07/2023]
Abstract
Light pollution is a global threat to biodiversity, especially migratory organisms, some of which traverse hemispheric scales. Research on light pollution has grown significantly over the past decades, but our review of migratory organisms demonstrates gaps in our understanding, particularly beyond migratory birds. Research across spatial scales reveals the multifaceted effects of artificial light on migratory species, ranging from local and regional to macroscale impacts. These threats extend beyond species that are active at night - broadening the scope of this threat. Emerging tools for measuring light pollution and its impacts, as well as ecological forecasting techniques, present new pathways for conservation, including transdisciplinary approaches.
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26
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Stewart FEC, Micheletti T, Cumming SG, Barros C, Chubaty AM, Dookie AL, Duclos I, Eddy I, Haché S, Hodson J, Hughes J, Johnson CA, Leblond M, Schmiegelow FKA, Tremblay JA, McIntire EJB. Climate-informed forecasts reveal dramatic local habitat shifts and population uncertainty for northern boreal caribou. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2023; 33:e2816. [PMID: 36752658 DOI: 10.1002/eap.2816] [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: 06/14/2022] [Revised: 12/02/2022] [Accepted: 12/14/2022] [Indexed: 06/18/2023]
Abstract
Most research on boreal populations of woodland caribou (Rangifer tarandus caribou) has been conducted in areas of high anthropogenic disturbance. However, a large portion of the species' range overlaps relatively pristine areas primarily affected by natural disturbances, such as wildfire. Climate-driven habitat change is a key concern for the conservation of boreal-dependent species, where management decisions have yet to consider knowledge from multiple ecological domains integrated into a cohesive and spatially explicit forecast of species-specific habitat and demography. We used a novel ecological forecasting framework to provide climate-sensitive projections of habitat and demography for five boreal caribou monitoring areas within the Northwest Territories (NWT), Canada, over 90 years. Importantly, we quantify uncertainty around forecasted mean values. Our results suggest habitat suitability may increase in central and southwest regions of the NWT's Taiga Plains ecozone but decrease in southern and northwestern regions driven by conversion of coniferous to deciduous forests. We do not project that boreal caribou population growth rates will change despite forecasted changes to habitat suitability. Our results emphasize the importance of efforts to protect and restore northern boreal caribou habitat despite climate uncertainty while highlighting expected spatial variations that are important considerations for local people who rely on them. An ability to reproduce previous work, and critical thought when incorporating sources of uncertainty, will be important to refine forecasts, derive management decisions, and improve conservation efficacy for northern species at risk.
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Affiliation(s)
- Frances E C Stewart
- Wilfrid Laurier University, Waterloo, ON, Canada
- Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada, Victoria, BC, Canada
| | - Tatiane Micheletti
- Department of Forest Resources Management, University of British Columbia, Vancouver, BC, Canada
| | - Steven G Cumming
- Department of Wood and Forest Science, Laval University, Québec, QC, Canada
| | - Ceres Barros
- Department of Forest Resources Management, University of British Columbia, Vancouver, BC, Canada
| | | | | | - Isabelle Duclos
- Environment and Climate Change Canada, Yellowknife, NT, Canada
| | - Ian Eddy
- Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada, Victoria, BC, Canada
| | - Samuel Haché
- Canadian Wildlife Service, Environment and Climate Change Canada, Yellowknife, NT, Canada
| | - James Hodson
- Department of Environment and Natural Resources, Government of the Northwest Territories, Yellowknife, NT, Canada
| | - Josie Hughes
- Landscape Science and Technology Division, Environment and Climate Change Canada, Ottawa, ON, Canada
| | - Cheryl A Johnson
- Landscape Science and Technology Division, Environment and Climate Change Canada, Ottawa, ON, Canada
| | - Mathieu Leblond
- Landscape Science and Technology Division, Environment and Climate Change Canada, Ottawa, ON, Canada
| | - Fiona K A Schmiegelow
- Department of Renewable Resources, University of Alberta, Edmonton, AB, Canada
- Yukon University, Yukon Research Centre, Whitehorse, YT, Canada
| | - Junior A Tremblay
- Department of Wood and Forest Science, Laval University, Québec, QC, Canada
- Wildlife Research Division, Environment and Climate Change Canada, Québec, QC, Canada
| | - Eliot J B McIntire
- Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada, Victoria, BC, Canada
- Department of Forest Resources Management, University of British Columbia, Vancouver, BC, Canada
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27
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Lofton ME, Howard DW, Thomas RQ, Carey CC. Progress and opportunities in advancing near-term forecasting of freshwater quality. GLOBAL CHANGE BIOLOGY 2023; 29:1691-1714. [PMID: 36622168 DOI: 10.1111/gcb.16590] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/23/2022] [Indexed: 05/28/2023]
Abstract
Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water-related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in the early stages of development (i.e., non-operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end-user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.
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Affiliation(s)
- Mary E Lofton
- Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA
| | - Dexter W Howard
- Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA
| | - R Quinn Thomas
- Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA
- Department of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, Virginia, USA
| | - Cayelan C Carey
- Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA
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Kim J, Jung W, An J, Oh HJ, Park J. Self-optimization of training dataset improves forecasting of cyanobacterial bloom by machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 866:161398. [PMID: 36621510 DOI: 10.1016/j.scitotenv.2023.161398] [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: 09/30/2022] [Revised: 11/30/2022] [Accepted: 01/01/2023] [Indexed: 06/17/2023]
Abstract
Data-driven model (DDM) prediction of aquatic ecological responses, such as cyanobacterial harmful algal blooms (CyanoHABs), is critically influenced by the choice of training dataset. However, a systematic method to choose the optimal training dataset considering data history has not yet been developed. Providing a comprehensive procedure with self-based optimal training dataset-selecting algorithm would self-improve the DDM performance. In this study, a novel algorithm was developed to self-generate possible training dataset candidates from the available input and output variable data and self-choose the optimal training dataset that maximizes CyanoHAB forecasting performance. Nine years of meteorological and water quality data (input) and CyanoHAB data (output) from a site on the Nakdong River, South Korea, were acquired and pretreated via an automated process. An artificial neural network (ANN) was chosen from among the DDM candidates by first-cut training and validation using the entire collected dataset. Optimal training datasets for the ANN were self-selected from among the possible self-generated training datasets by systematically simulating the performance in response to 46 periods and 40 sizes (number of data elements) of the generated training datasets. The best-performing models were screened to identify the candidate models. The best performance corresponded to 6-7 years of training data (∼18 % lower error) for forecasting 1-28 d ahead (1-28 d of forecasting lead time (FLT)). After the hyperparameters of the screened model candidates were fine-tuned, the best-performing model (7 years of data with 14 d FLT) was self-determined by comparing the forecasts with unseen CyanoHAB events. The self-determined model could reasonably predict CyanoHABs occurring in Korean waters (cyanobacteria cells/mL ≥ 1000). Thus, our proposed method of self-optimizing the training dataset effectively improved the predictive accuracy and operational efficiency of the DDM prediction of CyanoHAB.
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Affiliation(s)
- Jayun Kim
- Department of Civil and Environmental Engineering, Yonsei University, Seoul, Republic of Korea
| | - Woosik Jung
- Department of Civil and Environmental Engineering, Yonsei University, Seoul, Republic of Korea
| | - Jusuk An
- Department of Civil and Environmental Engineering, Yonsei University, Seoul, Republic of Korea; Department of Environmental Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Republic of Korea
| | - Hyun Je Oh
- Department of Environmental Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Republic of Korea
| | - Joonhong Park
- Department of Civil and Environmental Engineering, Yonsei University, Seoul, Republic of Korea.
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29
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Munch SB, Rogers TL, Symons CC, Anderson D, Pennekamp F. Constraining nonlinear time series modeling with the metabolic theory of ecology. Proc Natl Acad Sci U S A 2023; 120:e2211758120. [PMID: 36930600 PMCID: PMC10041132 DOI: 10.1073/pnas.2211758120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 02/08/2023] [Indexed: 03/18/2023] Open
Abstract
Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM), an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a "metabolic time step," our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19% on average), with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate, rather than the form, of population dynamics, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable, at least approximately, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends.
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Affiliation(s)
- Stephan B. Munch
- Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, CA95060
- Department of Applied Mathematics, University of California, Santa Cruz, CA95060
| | - Tanya L. Rogers
- Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, CA95060
| | - Celia C. Symons
- Department of Ecology and Evolutionary Biology, University of California, Irvine, CA92697
| | - David Anderson
- Department of Zoology, University of British Columbia, Vancouver, BCV6T 1Z4, Canada
| | - Frank Pennekamp
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich8057, Switzerland
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30
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Smith JW, Johnson LR, Thomas RQ. Assessing Ecosystem State Space Models: Identifiability and Estimation. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2023. [DOI: 10.1007/s13253-023-00531-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
AbstractHierarchical probability models are being used more often than non-hierarchical deterministic process models in environmental prediction and forecasting, and Bayesian approaches to fitting such models are becoming increasingly popular. In particular, models describing ecosystem dynamics with multiple states that are autoregressive at each step in time can be treated as statistical state space models (SSMs). In this paper, we examine this subset of ecosystem models, embed a process-based ecosystem model into an SSM, and give closed form Gibbs sampling updates for latent states and process precision parameters when process and observation errors are normally distributed. Here, we use simulated data from an example model (DALECev) and study the effects changing the temporal resolution of observations on the states (observation data gaps), the temporal resolution of the state process (model time step), and the level of aggregation of observations on fluxes (measurements of transfer rates on the state process). We show that parameter estimates become unreliable as temporal gaps between observed state data increase. To improve parameter estimates, we introduce a method of tuning the time resolution of the latent states while still using higher-frequency driver information and show that this helps to improve estimates. Further, we show that data cloning is a suitable method for assessing parameter identifiability in this class of models. Overall, our study helps inform the application of state space models to ecological forecasting applications where (1) data are not available for all states and transfers at the operational time step for the ecosystem model and (2) process uncertainty estimation is desired.
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31
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Integrated Population Models: Achieving Their Potential. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2023. [DOI: 10.1007/s42519-022-00302-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
AbstractPrecise and accurate estimates of abundance and demographic rates are primary quantities of interest within wildlife conservation and management. Such quantities provide insight into population trends over time and the associated underlying ecological drivers of the systems. This information is fundamental in managing ecosystems, assessing species conservation status and developing and implementing effective conservation policy. Observational monitoring data are typically collected on wildlife populations using an array of different survey protocols, dependent on the primary questions of interest. For each of these survey designs, a range of advanced statistical techniques have been developed which are typically well understood. However, often multiple types of data may exist for the same population under study. Analyzing each data set separately implicitly discards the common information contained in the other data sets. An alternative approach that aims to optimize the shared information contained within multiple data sets is to use a “model-based data integration” approach, or more commonly referred to as an “integrated model.” This integrated modeling approach simultaneously analyzes all the available data within a single, and robust, statistical framework. This paper provides a statistical overview of ecological integrated models, with a focus on integrated population models (IPMs) which include abundance and demographic rates as quantities of interest. Four main challenges within this area are discussed, namely model specification, computational aspects, model assessment and forecasting. This should encourage researchers to explore further and develop new practical tools to ensure that full utility can be made of IPMs for future studies.
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MacNab YC. Adaptive Gaussian Markov random field spatiotemporal models for infectious disease mapping and forecasting. SPATIAL STATISTICS 2023; 53:100726. [PMID: 36713268 PMCID: PMC9859649 DOI: 10.1016/j.spasta.2023.100726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/02/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Recent disease mapping literature presents adaptively parameterized spatiotemporal (ST) autoregressive (AR) or conditional autoregressive (CAR) models for Bayesian prediction of COVID-19 infection risks. These models were motivated to capture complex spatiotemporal dynamics and heterogeneities of infection risks. In the present paper, we synthesize, generalize, and unify the ST AR and CAR model constructions for models augmented by adaptive Gaussian Markov random fields, with an emphasis on disease forecasting. A general convolution construction is presented, with illustrative models motivated to (i) characterize local risk dependencies and influences over both spatial and temporal dimensions, (ii) model risk heterogeneities and discontinuities, and (iii) predict and forecast areal-level disease risks and occurrences. The broadened constructions allow rich options of intuitive parameterization for disease mapping and spatial regression. Illustrative parameterizations are presented for Bayesian hierarchical models of Poisson, zero-inflated Poisson, and Bernoulli data models, respectively. They are also discussed in the context of quantifying time-varying or time-invariant effects of (omitted) covariates, with application to prediction and forecasting areal-level COVID-19 infection occurrences and probabilities of zero-infection. The model constructions presented herein have much wider scope in offering a flexible framework for modelling complex spatiotemporal data and for estimation, learning, and forecasting purposes.
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Affiliation(s)
- Ying C MacNab
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
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33
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Recknagel F. Cyberinfrastructure for sourcing and processing ecological data. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.102039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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34
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Record S, Boettiger C, Rollinson CR. Synthesizing forecasts to inform decision‐making and advance ecological theory. Methods Ecol Evol 2023. [DOI: 10.1111/2041-210x.14070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Affiliation(s)
- Sydne Record
- Department of Fisheries, Wildlife, and Conservation Biology University of Maine Orono Maine USA
| | - Carl Boettiger
- Department of Environmental Science, Policy, and Management University of California, Berkeley Berkeley California USA
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35
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Briscoe NJ, Morris SD, Mathewson PD, Buckley LB, Jusup M, Levy O, Maclean IMD, Pincebourde S, Riddell EA, Roberts JA, Schouten R, Sears MW, Kearney MR. Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology. GLOBAL CHANGE BIOLOGY 2023; 29:1451-1470. [PMID: 36515542 DOI: 10.1111/gcb.16557] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 11/10/2022] [Indexed: 05/20/2023]
Abstract
A core challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical ecology is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. It also illustrates the types of outputs that can be generated, and the data inputs required for different implementations. Examples range from simple calculations of body temperature at a particular site and time, to more complex analyses of species' distribution limits based on projected energy and water balances, accounting for behavior and phenology. We outline challenges that currently limit the widespread application of biophysical models relating to data availability, training, and the lack of common software ecosystems. We also discuss progress and future developments that could allow these models to be applied to many species across large spatial extents and timeframes. Finally, we highlight how biophysical models are uniquely suited to solve global change biology problems that involve predicting and interpreting responses to environmental variability and extremes, multiple or shifting constraints, and novel abiotic or biotic environments.
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Affiliation(s)
- Natalie J Briscoe
- School of Ecosystem and Forest Science, The University of Melbourne, Melbourne, Victoria, Australia
| | - Shane D Morris
- School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Paul D Mathewson
- Department of Zoology, University of Wisconsin Madison, Madison, Wisconsin, USA
| | - Lauren B Buckley
- Department of Biology, University of Washington, Seattle, Washington, USA
| | - Marko Jusup
- Fisheries Resources Research Institute, Fisheries Research Agency, Yokohama, Japan
| | - Ofir Levy
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Ilya M D Maclean
- School of Biosciences, Centre for Ecology and Conservation, Cornwall, UK
| | | | - Eric A Riddell
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa, USA
| | - Jessica A Roberts
- School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Rafael Schouten
- Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Michael W Sears
- Department of Biological Sciences, Clemson University, Clemson, South Carolina, USA
| | - Michael Ray Kearney
- School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia
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36
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Paniw M, García-Callejas D, Lloret F, Bassar RD, Travis J, Godoy O. Pathways to global-change effects on biodiversity: new opportunities for dynamically forecasting demography and species interactions. Proc Biol Sci 2023; 290:20221494. [PMID: 36809806 PMCID: PMC9943645 DOI: 10.1098/rspb.2022.1494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023] Open
Abstract
In structured populations, persistence under environmental change may be particularly threatened when abiotic factors simultaneously negatively affect survival and reproduction of several life cycle stages, as opposed to a single stage. Such effects can then be exacerbated when species interactions generate reciprocal feedbacks between the demographic rates of the different species. Despite the importance of such demographic feedbacks, forecasts that account for them are limited as individual-based data on interacting species are perceived to be essential for such mechanistic forecasting-but are rarely available. Here, we first review the current shortcomings in assessing demographic feedbacks in population and community dynamics. We then present an overview of advances in statistical tools that provide an opportunity to leverage population-level data on abundances of multiple species to infer stage-specific demography. Lastly, we showcase a state-of-the-art Bayesian method to infer and project stage-specific survival and reproduction for several interacting species in a Mediterranean shrub community. This case study shows that climate change threatens populations most strongly by changing the interaction effects of conspecific and heterospecific neighbours on both juvenile and adult survival. Thus, the repurposing of multi-species abundance data for mechanistic forecasting can substantially improve our understanding of emerging threats on biodiversity.
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Affiliation(s)
- Maria Paniw
- Department of Conservation Biology and Global Change, Estación Biológica de Doñana (EBD-CSIC), Seville, 41001 Spain.,Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich 8057, Switzerland
| | - David García-Callejas
- Department of Integrative Ecology, Estación Biológica de Doñana (EBD-CSIC), Seville, 41001 Spain.,Instituto Universitario de Investigación Marina (INMAR), Departamento de Biología, Universidad de Cádiz, Campus Río San Pedro, 11510 Puerto Real, Spain
| | - Francisco Lloret
- Center for Ecological Research and Forestry Applications (CREAF), Cerdanyola del Vallès 08193, Spain.,Department Animal Biology, Plant Biology and Ecology, Universitat Autònoma Barcelona, Cerdanyola del Vallès 08193, Spain
| | - Ronald D Bassar
- Department of Biological Sciences, Auburn University, Auburn, AL 36849, USA
| | - Joseph Travis
- Department of Biological Science, Florida State University, Tallahassee, FL 32306, USA
| | - Oscar Godoy
- Instituto Universitario de Investigación Marina (INMAR), Departamento de Biología, Universidad de Cádiz, Campus Río San Pedro, 11510 Puerto Real, Spain
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37
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Jepsen JU, Vindstad OPL, Ims RA. Spatiotemporal dynamics of forest geometrid outbreaks. CURRENT OPINION IN INSECT SCIENCE 2023; 55:100990. [PMID: 36436809 DOI: 10.1016/j.cois.2022.100990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/12/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
We highlight recent developments and avenues for advancement, which can improve insight into the causes of changes in the spatiotemporal dynamics of forest Geometridea moth species (hereafter 'geometrids'). Some forest geometrids possess fundamental biological traits, which make them particularly liable to outbreak range expansions and host shifts mitigated by climate change. Indeed, recently observed changes in geometrid spatiotemporal dynamics represent both new research opportunities and challenges for empirically testing drivers of intra- and interspecific spatial synchrony, including the role of trophic interactions and biological traits (e.g. dispersal ability). We advocate that the emerging field of near-term ecological forecasting holds promise for studies of the spatiotemporal dynamics of forest geometrids and could be tailored to give both accurate predictions at management-relevant timescales and new insights into the mechanisms that underlie spatiotemporal population dynamics.
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Affiliation(s)
- Jane U Jepsen
- Norwegian Institute for Nature Research, Department of Arctic Ecology, Fram Centre, P.O. Box 6606 Langnes, 9296 Tromsø, Norway.
| | - Ole Petter L Vindstad
- UiT The Arctic University of Norway, Department of Arctic and Marine Biology, P.O. Box 6050 Langnes, 9037 Tromsø, Norway
| | - Rolf A Ims
- UiT The Arctic University of Norway, Department of Arctic and Marine Biology, P.O. Box 6050 Langnes, 9037 Tromsø, Norway
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38
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Scavia D, Wang YC, Obenour DR. Advancing freshwater ecological forecasts: Harmful algal blooms in Lake Erie. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 856:158959. [PMID: 36155036 DOI: 10.1016/j.scitotenv.2022.158959] [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: 06/01/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Ecological models help provide forecasts of ecosystem responses to natural and anthropogenic stresses. However, their ability to create reliable predictions requires forecasts with track records sufficiently long to build confidence, skill assessments, and treating uncertainty quantitatively. We use Lake Erie harmful algal blooms as a case study to help formalize ecological forecasting. Key challenges for models include uncertainty in the deterministic structure of the load-bloom relationship and the need to assess alternative drivers (e.g., biologically available phosphorus load, spring load, longer term cumulative load) with a larger dataset. We enhanced a Bayesian model considering new information and an expanded data set, test it through cross validation and blind forecasts, quantify and discuss its uncertainties, and apply it for assessing historical and future scenarios. Allowing a segmented relationship between bloom size and spring load indicates that loading above 0.15 Gg/month will have a substantially higher marginal impact on bloom size. The new model explains 84 % of interannual variability (9.09 Gg RMSE) when calibrated to the 19-year data set and 66 % of variability in cross validation (12.58 Gg RMSE). Blind forecasts explain 84 % of HAB variability between 2014 and 2020, which is substantially better than the actual forecast track record (R2 = 0.32) over this same period. Because of internal phosphorus recycling, represented by the long-term cumulative load, it could take over a decade for HABs to fully respond to loading reductions, depending on the pace of those reductions. Thus, the desired speed and endpoint of the lake's recovery should be considered when updating and adaptively managing load reduction targets. Results are discussed in the context of ecological forecasting best pactices: incorporate new knowledge and data in model construction; account for multiple sources of uncertainty; evaluate predictive skill through validation and hindcasting; and answer management questions related to both short-term forecasts and long-term scenarios.
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Affiliation(s)
- Donald Scavia
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48103, USA.
| | - Yu-Chen Wang
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48103, USA
| | - Daniel R Obenour
- Department of Civil, Construction & Environmental Engineering, NC State University, Raleigh, NC 27695, USA
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39
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Slingsby JA, Wilson AM, Maitner B, Moncrieff GR. Regional ecological forecasting across scales: A manifesto for a biodiversity hotspot. Methods Ecol Evol 2023. [DOI: 10.1111/2041-210x.14046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Jasper A. Slingsby
- Department of Biological Sciences and Centre for Statistics in Ecology, Environment and Conservation University of Cape Town Cape Town South Africa
- Fynbos Node, South African Environmental Observation Network, Centre for Biodiversity Conservation Cape Town South Africa
| | - Adam M. Wilson
- Department of Geography, Department of Environment and Sustainability University at Buffalo Buffalo New York USA
| | - Brian Maitner
- Department of Geography, Department of Environment and Sustainability University at Buffalo Buffalo New York USA
| | - Glenn R. Moncrieff
- Fynbos Node, South African Environmental Observation Network, Centre for Biodiversity Conservation Cape Town South Africa
- Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences University of Cape Town Cape Town South Africa
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40
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Zhigang Y, Dayananda B, Popovic I, Xueli W, Dongwei K, Yubo Z, Guozhen S. Spatiotemporal evolution analysis of human disturbances on giant panda: A new approach to study cumulative influences with large spatial scales. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.102008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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41
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Mason NWH, Kirk NA, Price RJ, Law R, Bowman R, Sprague RI. Science for social licence to arrest an ecosystem-transforming invasion. Biol Invasions 2023; 25:873-888. [PMID: 36439632 PMCID: PMC9676737 DOI: 10.1007/s10530-022-02953-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 10/25/2022] [Indexed: 11/20/2022]
Abstract
The primary role for scientific information in addressing complex environmental problems, such as biological invasions, is generally assumed to be as a guide for management decisions. However, scientific information often plays a minor role in decision-making, with practitioners instead relying on professional experience and local knowledge. We explore alternative pathways by which scientific information could help reduce the spread and impacts of invasive species. Our study centred on attempts to understand the main motivations and constraints of three local governance bodies responsible for the management of invasive (wilding) conifer species in the southern South Island of New Zealand in achieving strategic and operational goals. We used a combination of workshop discussions, questionnaire responses and visits to field sites to elicit feedback from study participants. We applied a mixed inductive-deductive thematic analysis approach to derive themes from the feedback received. The three main themes identified were: (1) impacts of wilding conifers and goals for wilding conifer control, (2) barriers to achieving medium- and long-term goals, and (3) science needed to support wilding conifer control. Participants identified reversal and prevention of both instrumental (e.g. reduced water availability for agriculture) and intrinsic (e.g. loss of biodiversity and landscape values) impacts of wilding conifer invasions as primary motivators behind wilding conifer control. Barriers to achieving goals were overwhelmingly social, relating either to unwillingness of landowners to participate or poorly designed regulatory frameworks. Consequently, science needs related primarily to gaining social licence to remove wilding conifers from private land and for more appropriate regulations. Scientific information provided via spread and impacts forecasting models was viewed as a key source of scientific information in gaining social licence. International experience suggests that invasive species control programmes often face significant external social barriers. Thus, for many biological invasions, the primary role of science might be to achieve social licence and regulatory support for the long-term goals of invasive species control programmes and the management interventions required to achieve those goals.
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Affiliation(s)
| | | | | | - Richard Law
- Manaaki Whenua – Landcare Research, Palmerston North, New Zealand
| | - Richard Bowman
- New Zealand Wilding Conifer Group, 200 Tuam St, Christchurch Central City, Christchurch, 8011 New Zealand
| | - Rowan I. Sprague
- New Zealand Wilding Conifer Group, 200 Tuam St, Christchurch Central City, Christchurch, 8011 New Zealand
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Halpern BS, Boettiger C, Dietze MC, Gephart JA, Gonzalez P, Grimm NB, Groffman PM, Gurevitch J, Hobbie SE, Komatsu KJ, Kroeker KJ, Lahr HJ, Lodge DM, Lortie CJ, Lowndes JSS, Micheli F, Possingham HP, Ruckelshaus MH, Scarborough C, Wood CL, Wu GC, Aoyama L, Arroyo EE, Bahlai CA, Beller EE, Blake RE, Bork KS, Branch TA, Brown NEM, Brun J, Bruna EM, Buckley LB, Burnett JL, Castorani MCN, Cheng SH, Cohen SC, Couture JL, Crowder LB, Dee LE, Dias AS, Diaz‐Maroto IJ, Downs MR, Dudney JC, Ellis EC, Emery KA, Eurich JG, Ferriss BE, Fredston A, Furukawa H, Gagné SA, Garlick SR, Garroway CJ, Gaynor KM, González AL, Grames EM, Guy‐Haim T, Hackett E, Hallett LM, Harms TK, Haulsee DE, Haynes KJ, Hazen EL, Jarvis RM, Jones K, Kandlikar GS, Kincaid DW, Knope ML, Koirala A, Kolasa J, Kominoski JS, Koricheva J, Lancaster LT, Lawlor JA, Lowman HE, Muller‐Karger FE, Norman KEA, Nourn N, O'Hara CC, Ou SX, Padilla‐Gamino JL, Pappalardo P, Peek RA, Pelletier D, Plont S, Ponisio LC, Portales‐Reyes C, Provete DB, Raes EJ, Ramirez‐Reyes C, Ramos I, Record S, Richardson AJ, Salguero‐Gómez R, Satterthwaite EV, Schmidt C, Schwartz AJ, See CR, Shea BD, Smith RS, Sokol ER, Solomon CT, Spanbauer T, Stefanoudis PV, Sterner BW, Sudbrack V, Tonkin JD, Townes AR, Valle M, Walter JA, Wheeler KI, Wieder WR, Williams DR, Winter M, Winterova B, Woodall LC, Wymore AS, Youngflesh C. Priorities for synthesis research in ecology and environmental science. Ecosphere 2023. [DOI: 10.1002/ecs2.4342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Affiliation(s)
- Benjamin S. Halpern
- National Center for Ecological Analysis and Synthesis University of California Santa Barbara California USA
- Bren School of Environmental Science and Management University of California Santa Barbara California USA
| | - Carl Boettiger
- Department of Environmental Science, Policy, and Management University of California Berkeley California USA
| | - Michael C. Dietze
- Department of Earth & Environment Boston University Boston Massachusetts USA
| | - Jessica A. Gephart
- Department of Environmental Science American University Washington District of Columbia USA
| | - Patrick Gonzalez
- Department of Environmental Science, Policy, and Management University of California Berkeley California USA
- Institute for Parks, People, and Biodiversity University of California Berkeley California USA
| | - Nancy B. Grimm
- School of Life Sciences Arizona State University Tempe Arizona USA
| | - Peter M. Groffman
- City University of New York Advanced Science Research Center at the Graduate Center New York New York USA
- Cary Institute of Ecosystem Studies Millbrook New York USA
| | - Jessica Gurevitch
- Department of Ecology and Evolution Stony Brook University Stony Brook New York USA
| | - Sarah E. Hobbie
- Department of Ecology, Evolution and Behavior University of Minnesota St. Paul Minnesota USA
| | | | - Kristy J. Kroeker
- Department of Ecology and Evolutionary Biology University of California Santa Cruz Santa Cruz California USA
| | - Heather J. Lahr
- National Center for Ecological Analysis and Synthesis University of California Santa Barbara California USA
| | - David M. Lodge
- Cornell Atkinson Center for Sustainability Cornell University Ithaca New York USA
- Department of Ecology and Evolutionary Biology Cornell University Ithaca New York USA
| | - Christopher J. Lortie
- National Center for Ecological Analysis and Synthesis University of California Santa Barbara California USA
- Department of Biology York University Toronto Ontario Canada
| | - Julie S. S. Lowndes
- National Center for Ecological Analysis and Synthesis University of California Santa Barbara California USA
| | - Fiorenza Micheli
- Hopkins Marine Station, Oceans Department Stanford University Pacific Grove California USA
- Stanford Center for Ocean Solutions Pacific Grove California USA
| | - Hugh P. Possingham
- Centre for Biodiversity and Conservation Science (CBCS) The University of Queensland Brisbane Queensland Australia
| | | | - Courtney Scarborough
- National Center for Ecological Analysis and Synthesis University of California Santa Barbara California USA
| | - Chelsea L. Wood
- School of Aquatic and Fishery Sciences University of Washington Seattle Washington USA
| | - Grace C. Wu
- Environmental Studies University of California Santa Barbara California USA
| | - Lina Aoyama
- Environmental Studies Program and Department of Biology University of Oregon Eugene Oregon USA
| | - Eva E. Arroyo
- Department of Ecology Evolution and Environmental Biology New York New York USA
| | | | - Erin E. Beller
- Real Estate and Workplace Services Sustainability Team Google Inc. Mountain View California USA
| | | | | | - Trevor A. Branch
- School of Aquatic and Fishery Sciences University of Washington Seattle Washington USA
| | - Norah E. M. Brown
- Department of Biology University of Victoria Victoria British Columbia Canada
| | - Julien Brun
- National Center for Ecological Analysis and Synthesis University of California Santa Barbara California USA
| | - Emilio M. Bruna
- Department of Wildlife Ecology & Conservation University of Florida Gainesville Florida USA
| | - Lauren B. Buckley
- Department of Biology University of Washington Seattle Washington USA
| | - Jessica L. Burnett
- Core Science Systems Science Analytics and Synthesis U.S. Geological Survey, 8th and Kipling, Denver Federal Center Lakewood Colorado USA
| | - Max C. N. Castorani
- Department of Environmental Sciences University of Virginia Charlottesville Virginia USA
| | - Samantha H. Cheng
- Center for Biodiversity and Conservation American Museum of Natural History New York New York USA
| | - Sarah C. Cohen
- Estuary and Ocean Science Center, Biology Department San Francisco State University San Francisco California USA
| | | | - Larry B. Crowder
- Hopkins Marine Station, Oceans Department Stanford University Pacific Grove California USA
| | - Laura E. Dee
- Department of Ecology and Evolutionary Biology University of Colorado Boulder Colorado USA
| | - Arildo S. Dias
- Department of Physical Geography (IPG) Goethe‐Universität Frankfurt (Campus Riedberg) Frankfurt am Main Germany
| | | | - Martha R. Downs
- National Center for Ecological Analysis and Synthesis University of California Santa Barbara California USA
| | - Joan C. Dudney
- Department of Plant Sciences UC Davis Davis California USA
| | - Erle C. Ellis
- Geography & Environmental Systems University of Maryland Baltimore Maryland USA
| | - Kyle A. Emery
- Department of Geography UC Los Angeles Los Angeles California USA
| | | | - Bridget E. Ferriss
- Resource Ecology and Fisheries Management Division Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA Seattle Washington USA
| | - Alexa Fredston
- Department of Ocean Sciences University of California Santa Cruz California USA
| | - Hikaru Furukawa
- School of Earth and Space Exploration Arizona State University Tempe Arizona USA
| | - Sara A. Gagné
- Department of Geography and Earth Sciences University of North Carolina at Charlotte Charlotte North Carolina USA
| | | | - Colin J. Garroway
- Department of Biological Sciences University of Manitoba Winnipeg Manitoba Canada
| | - Kaitlyn M. Gaynor
- Departments of Zoology and Botany University of British Columbia Vancouver British Columbia Canada
| | - Angélica L. González
- Department of Biology & Center for Computational and Integrative Biology Rutgers University Camden New Jersey USA
| | - Eliza M. Grames
- Department of Biology University of Nevada, Reno Reno Nevada USA
| | - Tamar Guy‐Haim
- National Institute of Oceanography Israel Oceanographic and Limnological Research (IOLR) Haifa Israel
| | - Ed Hackett
- School of Human Evolution & Social Change Arizona State University Tempe Arizona USA
| | - Lauren M. Hallett
- Environmental Studies Program and Department of Biology University of Oregon Eugene Oregon USA
| | - Tamara K. Harms
- Institute of Arctic Biology and Department of Biology & Wildlife University of Alaska Fairbanks Fairbanks Alaska USA
| | - Danielle E. Haulsee
- Hopkins Marine Station, Oceans Department Stanford University Pacific Grove California USA
| | - Kyle J. Haynes
- Blandy Experimental Farm University of Virginia Boyce Virginia USA
| | - Elliott L. Hazen
- Department of Ecology and Evolutionary Biology University of California Santa Cruz Santa Cruz California USA
| | - Rebecca M. Jarvis
- School of Science Auckland University of Technology Auckland New Zealand
| | | | - Gaurav S. Kandlikar
- Division of Biological Sciences & Division of Plant Sciences University of Missouri Columbia Missouri USA
| | - Dustin W. Kincaid
- Vermont EPSCoR and Gund Institute for Environment University of Vermont Burlington Vermont USA
| | - Matthew L. Knope
- Department of Biology University of Hawai'i at Hilo Hilo Hawaii USA
| | - Anil Koirala
- Warnell School of Forestry and Natural Resources University of Georgia Athens Georgia USA
| | - Jurek Kolasa
- Department of Biology McMaster University Hamilton Ontario Canada
| | - John S. Kominoski
- Institute of Environment Florida International University Miami Florida USA
| | - Julia Koricheva
- Department of Biological Sciences Royal Holloway University of London Surrey UK
| | | | - Jake A. Lawlor
- Department of Biology McGill University Montreal Quebec Canada
| | - Heili E. Lowman
- Department of Natural Resources and Environmental Science University of Nevada, Reno Reno Nevada USA
| | | | - Kari E. A. Norman
- Département de sciences biologiques Université de Montréal Montréal Québec Canada
| | - Nan Nourn
- Department of Fisheries and Wildlife Michigan State University East Lansing Michigan USA
| | - Casey C. O'Hara
- Bren School of Environmental Science and Management University of California Santa Barbara California USA
| | - Suzanne X. Ou
- Department of Biology Stanford University Stanford California USA
| | | | - Paula Pappalardo
- Marine Invasions Laboratory Smithsonian Environmental Research Center Tiburon California USA
| | - Ryan A. Peek
- Center for Watershed Sciences University of California Davis California USA
| | - Dominique Pelletier
- UMR DECOD, HALGO, Département Ressources Biologiques et Environnement Institut Français de Recherche pour l'Exploitation de la Mer Lorient France
| | - Stephen Plont
- Department of Biological Sciences Virginia Polytechnic Institute and State University Blacksburg Virginia USA
| | - Lauren C. Ponisio
- Institute of Ecology and Evolution, Department of Biology University of Oregon Eugene Oregon USA
| | | | - Diogo B. Provete
- Instituto de Biociências Universidade Federal de Mato Grosso do Sul Campo Grande Brazil
| | - Eric J. Raes
- Minderoo Foundation, Flourishing Oceans Nedlands Western Australia Australia
| | | | - Irene Ramos
- Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO) Mexico City Mexico
| | - Sydne Record
- Department of Wildlife, Fisheries, and Conservation Biology University of Maine Orono Maine USA
| | - Anthony J. Richardson
- School of Mathematics and Physics University of Queensland St Lucia Queensland Australia
| | | | - Erin V. Satterthwaite
- California Sea Grant Scripps Institution of Oceanography, University of California, San Diego La Jolla California USA
| | - Chloé Schmidt
- Department of Biological Sciences University of Manitoba Winnipeg Manitoba Canada
| | - Aaron J. Schwartz
- Department of Ecology and Evolutionary Biology University of Colorado Boulder Colorado USA
| | - Craig R. See
- Center for Ecosystem Science and Society Northern Arizona University Flagstaff Arizona USA
| | - Brendan D. Shea
- Department of Fish and Wildlife Conservation Virginia Tech Blacksburg Virginia USA
| | - Rachel S. Smith
- Department of Environmental Sciences University of Virginia Charlottesville Virginia USA
| | - Eric R. Sokol
- Battelle, National Ecological Observatory Network (NEON) Boulder Colorado USA
| | | | - Trisha Spanbauer
- Department of Environmental Sciences/Lake Erie Center University of Toledo Toledo Ohio USA
| | | | | | - Vitor Sudbrack
- Department of Ecology and Evolution University of Lausanne Lausanne Switzerland
| | - Jonathan D. Tonkin
- School of Biological Sciences University of Canterbury Christchurch New Zealand
| | - Ashley R. Townes
- School of Aquatic and Fishery Sciences University of Washington Seattle Washington USA
| | - Mireia Valle
- AZTI, Marine Research, Basque Research and Technology Alliance (BRTA) Sukarrieta Spain
| | - Jonathan A. Walter
- Center for Watershed Sciences University of California Davis California USA
| | - Kathryn I. Wheeler
- Department of Earth & Environment Boston University Boston Massachusetts USA
| | - William R. Wieder
- Climate and Global Dynamics Laboratory, Terrestrial Sciences Section National Center for Atmospheric Research Boulder Colorado USA
| | - David R. Williams
- Sustainability Research Institute, School of Earth and Environment University of Leeds Leeds UK
| | - Marten Winter
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Leipzig Germany
| | - Barbora Winterova
- Department of Botany and Zoology, Faculty of Science Masaryk University Brno Czech Republic
| | - Lucy C. Woodall
- School of Aquatic and Fishery Sciences University of Washington Seattle Washington USA
| | - Adam S. Wymore
- Department of Natural Resources and the Environment University of New Hampshire Durham New Hampshire USA
| | - Casey Youngflesh
- Ecology, Evolution, and Behavior Program Michigan State University East Lansing Michigan USA
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Duarte HO, Siqueira PG, Oliveira ACA, Moura MDC. A probabilistic epidemiological model for infectious diseases: The case of COVID-19 at global-level. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2023; 43:183-201. [PMID: 35589673 PMCID: PMC9347552 DOI: 10.1111/risa.13950] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This study has developed a probabilistic epidemiological model a few weeks after the World Health Organization declared COVID-19 a pandemic (based on the little data available at that time). The aim was to assess relative risks for future scenarios and evaluate the effectiveness of different management actions for 1 year ahead. We quantified, categorized, and ranked the risks for scenarios such as business as usual, and moderate and strong mitigation. We estimated that, in the absence of interventions, COVID-19 would have a 100% risk of explosion (i.e., more than 25% infections in the world population) and 34% (2.6 billion) of the world population would have been infected until the end of simulation. We analyzed the suitability of model scenarios by comparing actual values against estimated values for the first 6 weeks of the simulation period. The results proved to be more suitable with a business-as-usual scenario in Asia and moderate mitigation in the other continents. If everything went on like this, we would have 55% risk of explosion and 22% (1.7 billion) of the world population would have been infected. Strong mitigation actions in all continents could reduce these numbers to, 7% and 3% (223 million), respectively. Although the results were based on the data available in March 2020, both the model and probabilistic approach proved to be practicable and could be a basis for risk assessment in future pandemic episodes with unknown virus, especially in the early stages, when data and literature are scarce.
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Affiliation(s)
- Heitor Oliveira Duarte
- Departamento de Engenharia Mecânica, Coordenação de Engenharia NavalUniversidade Federal de PernambucoRecifePernambucoBrazil
| | - Paulo Gabriel Siqueira
- Programa de Pós‐Graduação em Engenharia de Produção, Centro de Estudos e Ensaios em Risco e Modelagem Ambiental (CEERMA)Universidade Federal de PernambucoRecifePernambucoBrazil
| | | | - Márcio das Chagas Moura
- Programa de Pós‐Graduação em Engenharia de Produção, Centro de Estudos e Ensaios em Risco e Modelagem Ambiental (CEERMA)Universidade Federal de PernambucoRecifePernambucoBrazil
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44
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Becker DJ, Eby P, Madden W, Peel AJ, Plowright RK. Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts. Ecol Lett 2023; 26:23-36. [PMID: 36310377 DOI: 10.1111/ele.14007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 03/09/2022] [Indexed: 12/27/2022]
Abstract
The ecological conditions experienced by wildlife reservoirs affect infection dynamics and thus the distribution of pathogen excreted into the environment. This spatial and temporal distribution of shed pathogen has been hypothesised to shape risks of zoonotic spillover. However, few systems have data on both long-term ecological conditions and pathogen excretion to advance mechanistic understanding and test environmental drivers of spillover risk. We here analyse three years of Hendra virus data from nine Australian flying fox roosts with covariates derived from long-term studies of bat ecology. We show that the magnitude of winter pulses of viral excretion, previously considered idiosyncratic, are most pronounced after recent food shortages and in bat populations displaced to novel habitats. We further show that cumulative pathogen excretion over time is shaped by bat ecology and positively predicts spillover frequency. Our work emphasises the role of reservoir host ecology in shaping pathogen excretion and provides a new approach to estimate spillover risk.
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Affiliation(s)
- Daniel J Becker
- Department of Microbiology and Immunology, Montana State University, Bozeman, Montana, USA.,Department of Biology, University of Oklahoma, Norman, Oklahoma, USA
| | - Peggy Eby
- School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, Australia.,Centre for Planetary Health and Food Security, Griffith University, Queensland, Australia
| | - Wyatt Madden
- Department of Microbiology and Immunology, Montana State University, Bozeman, Montana, USA
| | - Alison J Peel
- Centre for Planetary Health and Food Security, Griffith University, Queensland, Australia
| | - Raina K Plowright
- Department of Microbiology and Immunology, Montana State University, Bozeman, Montana, USA
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45
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Koerich G, Fraser CI, Lee CK, Morgan FJ, Tonkin JD. Forecasting the future of life in Antarctica. Trends Ecol Evol 2023; 38:24-34. [PMID: 35934551 DOI: 10.1016/j.tree.2022.07.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 12/24/2022]
Abstract
Antarctic ecosystems are under increasing anthropogenic pressure, but efforts to predict the responses of Antarctic biodiversity to environmental change are hindered by considerable data challenges. Here, we illustrate how novel data capture technologies provide exciting opportunities to sample Antarctic biodiversity at wider spatiotemporal scales. Data integration frameworks, such as point process and hierarchical models, can mitigate weaknesses in individual data sets, improving confidence in their predictions. Increasing process knowledge in models is imperative to achieving improved forecasts of Antarctic biodiversity, which can be attained for data-limited species using hybrid modelling frameworks. Leveraging these state-of-the-art tools will help to overcome many of the data scarcity challenges presented by the remoteness of Antarctica, enabling more robust forecasts both near- and long-term.
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Affiliation(s)
- Gabrielle Koerich
- School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
| | - Ceridwen I Fraser
- Department of Marine Science, University of Otago, PO Box 56, Dunedin 9054, New Zealand
| | - Charles K Lee
- International Centre for Terrestrial Antarctic Research, School of Science, University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand
| | - Fraser J Morgan
- Manaaki Whenua - Landcare Research, Auckland 1072, New Zealand; Te Pūnaha Matatini, Centre of Research Excellence in Complex Systems, Auckland, New Zealand
| | - Jonathan D Tonkin
- School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand; Te Pūnaha Matatini, Centre of Research Excellence in Complex Systems, Auckland, New Zealand; Bioprotection Aotearoa, Centre of Research Excellence, Canterbury, New Zealand.
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46
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Mühlbauer LK, Harpole WS, Clark AT. Differences in initial abundances reveal divergent dynamic structures in Gause's predator-prey experiments. Ecol Evol 2022; 12:e9638. [PMID: 36545367 PMCID: PMC9760897 DOI: 10.1002/ece3.9638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 10/25/2022] [Accepted: 11/23/2022] [Indexed: 12/23/2022] Open
Abstract
Improved understanding of complex dynamics has revealed insights across many facets of ecology, and has enabled improved forecasts and management of future ecosystem states. However, an enduring challenge in forecasting complex dynamics remains the differentiation between complexity and stochasticity, that is, to determine whether declines in predictability are caused by stochasticity, nonlinearity, or chaos. Here, we show how to quantify the relative contributions of these factors to prediction error using Georgii Gause's iconic predator-prey microcosm experiments, which, critically, include experimental replicates that differ from one another only in initial abundances. We show that these differences in initial abundances interact with stochasticity, nonlinearity, and chaos in unique ways, allowing us to identify the impacts of these factors on prediction error. Our results suggest that jointly analyzing replicate time series across multiple, distinct starting points may be necessary for understanding and predicting the wide range of potential dynamic types in complex ecological systems.
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Affiliation(s)
| | - William Stanley Harpole
- Department of Physiological DiversityHelmholtz Centre for Environmental Research (UFZ)LeipzigGermany,German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany,Institute of BiologyMartin Luther UniversityHalleGermany
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47
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Life History of the Arctic Squid Gonatus fabricii (Cephalopoda: Oegopsida) Reconstructed by Analysis of Individual Ontogenetic Stable Isotopic Trajectories. Animals (Basel) 2022; 12:ani12243548. [PMID: 36552473 PMCID: PMC9774963 DOI: 10.3390/ani12243548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/17/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
Cephalopods are important in Arctic marine ecosystems as predators and prey, but knowledge of their life cycles is poor. Consequently, they are under-represented in the Arctic ecosystems assessment models. One important parameter is the change in ecological role (habitat and diet) associated with individual ontogenies. Here, the life history of Gonatus fabricii, the most abundant Arctic cephalopod, is reconstructed by the analysis of individual ontogenetic trajectories of stable isotopes (δ13C and δ15N) in archival hard body structures. This approach allows the prediction of the exact mantle length (ML) and mass when the species changes its ecological role. Our results show that the life history of G. fabricii is divided into four stages, each having a distinct ecology: (1) epipelagic squid (ML < 20 mm), preying mostly on copepods; (2) epi- and occasionally mesopelagic squid (ML 20−50 mm), preying on larger crustaceans, fish, and cephalopods; (3) meso- and bathypelagic squid (ML > 50 mm), preying mainly on fish and cephalopods; and (4) non-feeding bathypelagic gelatinous females (ML > 200 mm). Existing Arctic ecosystem models do not reflect the different ecological roles of G. fabricii correctly, and the novel data provided here are a necessary baseline for Arctic ecosystem modelling and forecasting.
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48
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Besson M, Alison J, Bjerge K, Gorochowski TE, Høye TT, Jucker T, Mann HMR, Clements CF. Towards the fully automated monitoring of ecological communities. Ecol Lett 2022; 25:2753-2775. [PMID: 36264848 PMCID: PMC9828790 DOI: 10.1111/ele.14123] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/09/2022] [Accepted: 09/06/2022] [Indexed: 01/12/2023]
Abstract
High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.
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Affiliation(s)
- Marc Besson
- School of Biological SciencesUniversity of BristolBristolUK,Sorbonne Université CNRS UMR Biologie des Organismes Marins, BIOMBanyuls‐sur‐MerFrance
| | - Jamie Alison
- Department of EcoscienceAarhus UniversityAarhusDenmark,UK Centre for Ecology & HydrologyBangorUK
| | - Kim Bjerge
- Department of Electrical and Computer EngineeringAarhus UniversityAarhusDenmark
| | - Thomas E. Gorochowski
- School of Biological SciencesUniversity of BristolBristolUK,BrisEngBio, School of ChemistryUniversity of BristolCantock's CloseBristolBS8 1TSUK
| | - Toke T. Høye
- Department of EcoscienceAarhus UniversityAarhusDenmark,Arctic Research CentreAarhus UniversityAarhusDenmark
| | - Tommaso Jucker
- School of Biological SciencesUniversity of BristolBristolUK
| | - Hjalte M. R. Mann
- Department of EcoscienceAarhus UniversityAarhusDenmark,Arctic Research CentreAarhus UniversityAarhusDenmark
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49
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Lapeyrolerie M, Boettiger C. Limits to ecological forecasting: Estimating uncertainty for critical transitions with deep learning. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.14013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Marcus Lapeyrolerie
- Department of Environmental Science, Policy, and Management University of California, Berkeley Berkeley California USA
| | - Carl Boettiger
- Department of Environmental Science, Policy, and Management University of California, Berkeley Berkeley California USA
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50
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Cameron D, Hartig F, Minnuno F, Oberpriller J, Reineking B, Van Oijen M, Dietze M. Issues in calibrating models with multiple unbalanced constraints: the significance of systematic model and data errors. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.14002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- David Cameron
- UK Centre for Ecology and Hydrology Bush Estate Penicuik UK
| | - Florian Hartig
- Theoretical Ecology University of Regensburg Regensburg Germany
| | - Francesco Minnuno
- Department of Forest Sciences University of Helsinki Helsinki Finland
| | | | | | | | - Michael Dietze
- Department of Earth & Environment Boston University Boston Massachusetts USA
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