51
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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: 27] [Impact Index Per Article: 13.5] [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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52
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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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53
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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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54
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Applestein C, Caughlin TT, Germino MJ. Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation. AOB PLANTS 2022; 14:plac045. [PMID: 36380819 PMCID: PMC9661893 DOI: 10.1093/aobpla/plac045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Plant-population recovery across large disturbance areas is often seed-limited. An understanding of seed dispersal patterns is fundamental for determining natural-regeneration potential. However, forecasting seed dispersal rates across heterogeneous landscapes remains a challenge. Our objectives were to determine (i) the landscape patterning of post-disturbance seed dispersal, and underlying sources of variation and the scale at which they operate, and (ii) how the natural seed dispersal patterns relate to a seed augmentation strategy. Vertical seed trapping experiments were replicated across 2 years and five burned and/or managed landscapes in sagebrush steppe. Multi-scale sampling and hierarchical Bayesian models were used to determine the scale of spatial variation in seed dispersal. We then integrated an empirical and mechanistic dispersal kernel for wind-dispersed species to project rates of seed dispersal and compared natural seed arrival to typical post-fire aerial seeding rates. Seeds were captured across the range of tested dispersal distances, up to a maximum distance of 26 m from seed-source plants, although dispersal to the furthest traps was variable. Seed dispersal was better explained by transect heterogeneity than by patch or site heterogeneity (transects were nested within patch within site). The number of seeds captured varied from a modelled mean of ~13 m-2 adjacent to patches of seed-producing plants, to nearly none at 10 m from patches, standardized over a 49-day period. Maximum seed dispersal distances on average were estimated to be 16 m according to a novel modelling approach using a 'latent' variable for dispersal distance based on seed trapping heights. Surprisingly, statistical representation of wind did not improve model fit and seed rain was not related to the large variation in total available seed of adjacent patches. The models predicted severe seed limitations were likely on typical burned areas, especially compared to the mean 95-250 seeds per m2 that previous literature suggested were required to generate sagebrush recovery. More broadly, our Bayesian data fusion approach could be applied to other cases that require quantitative estimates of long-distance seed dispersal across heterogeneous landscapes.
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Affiliation(s)
| | - T Trevor Caughlin
- Department of Biology, Boise State University, 1910 W University Drive, Boise, ID 83725, USA
| | - Matthew J Germino
- US Geological Survey, Forest and Rangeland Ecosystem Science Center, 230 N Collins Rd, Boise, ID 83702, USA
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55
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Lippert F, Kranstauber B, Forré PD, van Loon EE. Learning to predict spatiotemporal movement dynamics from weather radar networks. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.14007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Fiona Lippert
- AI4Science Lab University of Amsterdam Amsterdam The Netherlands
- Amsterdam Machine Learning Lab University of Amsterdam Amsterdam The Netherlands
- Institute for Biodiversity and Ecosystem Dynamics University of Amsterdam Amsterdam The Netherlands
| | - Bart Kranstauber
- Institute for Biodiversity and Ecosystem Dynamics University of Amsterdam Amsterdam The Netherlands
| | - Patrick D. Forré
- AI4Science Lab University of Amsterdam Amsterdam The Netherlands
- Amsterdam Machine Learning Lab University of Amsterdam Amsterdam The Netherlands
| | - E. Emiel van Loon
- Institute for Biodiversity and Ecosystem Dynamics University of Amsterdam Amsterdam The Netherlands
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56
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Christie R, Stack Whitney K, Perrone J, Bahlai CA. Longer study length, standardized sampling techniques, and broader geographic scope leads to higher likelihood of detecting stable abundance patterns in long term black-legged tick studies. PeerJ 2022; 10:e13916. [PMID: 36281361 PMCID: PMC9587717 DOI: 10.7717/peerj.13916] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 07/28/2022] [Indexed: 01/19/2023] Open
Abstract
Background Understanding how study design and monitoring strategies shape inference within, and synthesis across, studies is critical across biological disciplines. Many biological and field studies are short term and limited in scope. Monitoring studies are critical for informing public health about potential vectors of concern, such as Ixodes scapularis (black-legged ticks). Black-legged ticks are a taxon of ecological and human health concern due to their status as primary vectors of Borrelia burgdorferi, the bacteria that transmits Lyme disease. However, variation in black-legged tick monitoring, and gaps in data, are currently considered major barriers to understanding population trends and in turn, predicting Lyme disease risk. To understand how variable methodology in black-legged tick studies may influence which population patterns researchers find, we conducted a data synthesis experiment. Materials and Methods We searched for publicly available black-legged tick abundance dataset that had at least 9 years of data, using keywords about ticks in internet search engines, literature databases, data repositories and public health websites. Our analysis included 289 datasets from seven surveys from locations in the US, ranging in length from 9 to 24 years. We used a moving window analysis, a non-random resampling approach, to investigate the temporal stability of black-legged tick population trajectories across the US. We then used t-tests to assess differences in stability time across different study parameters. Results All of our sampled datasets required 4 or more years to reach stability. We also found several study factors can have an impact on the likelihood of a study reaching stability and of data leading to misleading results if the study does not reach stability. Specifically, datasets collected via dragging reached stability significantly faster than data collected via opportunistic sampling. Datasets that sampled larva reached stability significantly later than those that sampled adults or nymphs. Additionally, datasets collected at the broadest spatial scale (county) reached stability fastest. Conclusion We used 289 datasets from seven long term black-legged tick studies to conduct a non-random data resampling experiment, revealing that sampling design does shape inferences in black-legged tick population trajectories and how many years it takes to find stable patterns. Specifically, our results show the importance of study length, sampling technique, life stage, and geographic scope in understanding black-legged tick populations, in the absence of standardized surveillance methods. Current public health efforts based on existing black-legged tick datasets must take monitoring study parameters into account, to better understand if and how to use monitoring data to inform decisioning. We also advocate that potential future forecasting initiatives consider these parameters when projecting future black-legged tick population trends.
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Affiliation(s)
- Rowan Christie
- Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY, United States of America
| | - Kaitlin Stack Whitney
- Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY, United States of America,Science, Technology & Society Department, Rochester Institute of Technology, Rochester, NY, United States of America
| | - Julia Perrone
- School of Information, Kent State University, Kent, OH, United States of America
| | - Christie A. Bahlai
- Department of Biological Sciences and Environmental Science and Design Research Initiative, Kent State University, Kent, OH, United States of America
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Abstract
AbstractRapid advances in hardware and software, accompanied by public- and private-sector investment, have led to a new generation of data-driven computational tools. Recently, there has been a particular focus on deep learning—a class of machine learning algorithms that uses deep neural networks to identify patterns in large and heterogeneous datasets. These developments have been accompanied by both hype and scepticism by ecologists and others. This review describes the context in which deep learning methods have emerged, the deep learning methods most relevant to ecosystem ecologists, and some of the problem domains they have been applied to. Deep learning methods have high predictive performance in a range of ecological contexts, leveraging the large data resources now available. Furthermore, deep learning tools offer ecosystem ecologists new ways to learn about ecosystem dynamics. In particular, recent advances in interpretable machine learning and in developing hybrid approaches combining deep learning and mechanistic models provide a bridge between pure prediction and causal explanation. We conclude by looking at the opportunities that deep learning tools offer ecosystem ecologists and assess the challenges in interpretability that deep learning applications pose.
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58
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Woelmer WM, Thomas RQ, Lofton ME, McClure RP, Wander HL, Carey CC. Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2642. [PMID: 35470923 PMCID: PMC9786628 DOI: 10.1002/eap.2642] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 03/07/2022] [Indexed: 06/01/2023]
Abstract
As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1-14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend that increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.
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Affiliation(s)
| | - R. Quinn Thomas
- Department of Forest Resources and Environmental ConservationVirginia TechBlacksburgVirginiaUSA
| | - Mary E. Lofton
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | - Ryan P. McClure
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | | | - Cayelan C. Carey
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
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59
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McDowell NG, Ball M, Bond‐Lamberty B, Kirwan ML, Krauss KW, Megonigal JP, Mencuccini M, Ward ND, Weintraub MN, Bailey V. Processes and mechanisms of coastal woody-plant mortality. GLOBAL CHANGE BIOLOGY 2022; 28:5881-5900. [PMID: 35689431 PMCID: PMC9544010 DOI: 10.1111/gcb.16297] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 05/24/2022] [Indexed: 05/26/2023]
Abstract
Observations of woody plant mortality in coastal ecosystems are globally widespread, but the overarching processes and underlying mechanisms are poorly understood. This knowledge deficiency, combined with rapidly changing water levels, storm surges, atmospheric CO2 , and vapor pressure deficit, creates large predictive uncertainty regarding how coastal ecosystems will respond to global change. Here, we synthesize the literature on the mechanisms that underlie coastal woody-plant mortality, with the goal of producing a testable hypothesis framework. The key emergent mechanisms underlying mortality include hypoxic, osmotic, and ionic-driven reductions in whole-plant hydraulic conductance and photosynthesis that ultimately drive the coupled processes of hydraulic failure and carbon starvation. The relative importance of these processes in driving mortality, their order of progression, and their degree of coupling depends on the characteristics of the anomalous water exposure, on topographic effects, and on taxa-specific variation in traits and trait acclimation. Greater inundation exposure could accelerate mortality globally; however, the interaction of changing inundation exposure with elevated CO2 , drought, and rising vapor pressure deficit could influence mortality likelihood. Models of coastal forests that incorporate the frequency and duration of inundation, the role of climatic drivers, and the processes of hydraulic failure and carbon starvation can yield improved estimates of inundation-induced woody-plant mortality.
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Affiliation(s)
- Nate G. McDowell
- Atmospheric Sciences and Global Change DivisionPacific Northwest National LabRichlandWashingtonUSA
- School of Biological SciencesWashington State UniversityPullmanWashingtonUSA
| | - Marilyn Ball
- Plant Science Division, Research School of BiologyThe Australian National UniversityActonAustralian Capital TerritoryAustralia
| | - Ben Bond‐Lamberty
- Joint Global Change Research Institute, Pacific Northwest National LaboratoryCollege ParkMarylandUSA
| | - Matthew L. Kirwan
- Virginia Institute of Marine Science, William & MaryGloucester PointVirginiaUSA
| | - Ken W. Krauss
- U.S. Geological Survey, Wetland and Aquatic Research CenterLafayetteLouisianaUSA
| | | | - Maurizio Mencuccini
- ICREA, Passeig Lluís Companys 23BarcelonaSpain
- CREAFCampus UAB, BellaterraBarcelonaSpain
| | - Nicholas D. Ward
- Marine and Coastal Research LaboratoryPacific Northwest National LaboratorySequimWashingtonUSA
- School of OceanographyUniversity of WashingtonSeattleWashingtonUSA
| | - Michael N. Weintraub
- Department of Environmental SciencesUniversity of ToledoToledoOhioUSA
- Biological Sciences DivisionPacific Northwest National LaboratoryWashingtonUSA
| | - Vanessa Bailey
- Biological Sciences DivisionPacific Northwest National LaboratoryWashingtonUSA
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60
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Kim AY, Herrmann V, Bareto R, Calkins B, Gonzalez‐Akre E, Johnson DJ, Jordan JA, Magee L, McGregor IR, Montero N, Novak K, Rogers T, Shue J, Anderson‐Teixeira KJ. Implementing GitHub Actions continuous integration to reduce error rates in ecological data collection. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Albert Y. Kim
- Program in Statistical and Data Sciences; Smith College Northampton Massachusetts USA
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
| | - Valentine Herrmann
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
| | - Ross Bareto
- School of Forest, Fisheries, & Geomatics Sciences University of Florida Gainesville Florida USA
| | - Brianna Calkins
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
| | - Erika Gonzalez‐Akre
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
| | - Daniel J. Johnson
- School of Forest, Fisheries, & Geomatics Sciences University of Florida Gainesville Florida USA
| | - Jennifer A. Jordan
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
| | - Lukas Magee
- School of Forest, Fisheries, & Geomatics Sciences University of Florida Gainesville Florida USA
| | - Ian R. McGregor
- Center for Geospatial Analytics North Carolina State University Raleigh North Carolina USA
| | - Nicolle Montero
- School of Forest, Fisheries, & Geomatics Sciences University of Florida Gainesville Florida USA
| | - Karl Novak
- School of Forest, Fisheries, & Geomatics Sciences University of Florida Gainesville Florida USA
| | - Teagan Rogers
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
| | - Jessica Shue
- Smithsonian Environmental Research Center Edgewater Maryland USA
| | - Kristina J. Anderson‐Teixeira
- Conservation Ecology Center Smithsonian National Zoo & Conservation Biology Institute Front Royal Virginia USA
- Forest Global Earth Observatory Smithsonian Tropical Research Institute Panama Republic of Panama
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61
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Clark NJ, Wells K. Dynamic generalised additive models (
DGAMs
) for forecasting discrete ecological time series. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Nicholas J. Clark
- School of Veterinary Science The University of Queensland Gatton QLD Australia
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62
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Fulford RS, Russell M, Myers M, Malish M, Delmaine A. Models help set ecosystem service baselines for restoration assessment. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 317:115411. [PMID: 35751248 DOI: 10.1016/j.jenvman.2022.115411] [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: 01/24/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
Coastal suburban watersheds are under heavy pressure from human activity. This pressure has yielded an extensive effort to protect, mitigate, and restore watershed ecosystem services. Assessment of restoration investments would be greatly improved by a standard approach for estimating change in ecosystem service production combined with a well-defined baseline for assessment of restoration effects. Here we take a model-based approach to both objectives by applying two established ecosystem service models in a representative coastal watershed. This watershed has undergone extensive suburbanization resulting in a loss of ecosystem services, which has resulted in heavy restoration investments. We used models to estimate loss of the ecosystem services; clean air, clean water, stable climate, and water storage resulting from suburbanization. We then applied these model-based estimates as a baseline for assessment of restoration focusing on the appropriate restoration scale and considering downstream watershed impacts. The results suggest that losses of ecosystem services, such as flood water storage, from suburbanization have been extensive since 2001, but a comparison of restoration value suggests that restoration has been effective in recouping ecosystem services in some but not all local regions suggesting there are trade-offs to be made in these efforts. These benefits were most evident for the services of clean water and water storage. Models can inform decisions by clarifying what has been lost and estimating what can be regained through restoration action. The former sets a baseline for the latter and allows for a functional equivalency approach to assessment.
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Affiliation(s)
- R S Fulford
- U.S. Environmental Protection Agency, Office of Research and Development, USA.
| | - M Russell
- U.S. Environmental Protection Agency, Office of Research and Development, USA.
| | - M Myers
- U.S. Environmental Protection Agency, Office of Research and Development, USA.
| | - M Malish
- U.S. Environmental Protection Agency, Office of Research and Development, USA.
| | - A Delmaine
- U.S. Environmental Protection Agency, Office of Research and Development, USA.
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63
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Abraham A, Duvall E, Ferraro K, Webster A, Doughty C, le Roux E, Ellis‐Soto D. Understanding anthropogenic impacts on zoogeochemistry is essential for ecological restoration. Restor Ecol 2022. [DOI: 10.1111/rec.13778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Andrew Abraham
- School of Informatics, Computing and Cyber Systems Northern Arizona University Flagstaff USA
| | - Ethan Duvall
- Department of Ecology and Evolutionary Biology Cornell University Ithaca USA
| | - Kristy Ferraro
- School of the Environment Yale University Connecticut USA
| | - Andrea Webster
- Mammal Research Institute University of Pretoria Pretoria South Africa
| | - Chris Doughty
- School of Informatics, Computing and Cyber Systems Northern Arizona University Flagstaff USA
| | - Elizabeth le Roux
- Mammal Research Institute University of Pretoria Pretoria South Africa
- Centre for Biodiversity Dynamics in a Changing World (BIOCHANGE), Section of EcoInformatics and Biodiversity, Department of Biology Aarhus University Denmark
- Environmental Change Institute, School of Geography and the Environment University of Oxford Oxford UK
| | - Diego Ellis‐Soto
- Department of Ecology and Evolutionary Biology Yale University Connecticut USA
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64
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Lewis ASL, Rollinson CR, Allyn AJ, Ashander J, Brodie S, Brookson CB, Collins E, Dietze MC, Gallinat AS, Juvigny‐Khenafou N, Koren G, McGlinn DJ, Moustahfid H, Peters JA, Record NR, Robbins CJ, Tonkin J, Wardle GM. The power of forecasts to advance ecological theory. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13955] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | | | | | - Jaime Ashander
- U.S. Geological Survey, Eastern Ecological Science Center, Patuxent Research Refuge Laurel MD USA
| | - Stephanie Brodie
- Institute of Marine Science University of California Santa Cruz Monterey CA USA
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration Monterey CA USA
| | - Cole B. Brookson
- Department of Biological Sciences University of Alberta Edmonton AB Canada
| | - Elyssa Collins
- Center for Geospatial Analytics North Carolina State University Raleigh NC USA
| | - Michael C. Dietze
- Department of Earth & Environment Boston University Boston MA United States
| | | | - Noel Juvigny‐Khenafou
- iES—Institute for Environmental Sciences University of Koblenz‐Landau Landau i. d. Pfalz Germany
| | - Gerbrand Koren
- Copernicus Institute of Sustainable Development Utrecht University Utrecht The Netherlands
| | | | | | | | | | - Caleb J. Robbins
- Department of Biology, Center for Reservoir and Aquatic Systems Research Baylor University Waco TX USA
| | - Jonathan Tonkin
- School of Biological Sciences University of Canterbury Christchurch New Zealand
- Te Pūnaha Matatini, Centre of Research Excellence in Complex Systems New Zealand
- Bioprotection Aotearoa, Centre of Research Excellence New Zealand
| | - Glenda M. Wardle
- School of Life and Environmental Sciences University of Sydney Sydney New South Wales Australia
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65
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Daugaard U, Munch SB, Inauen D, Pennekamp F, Petchey OL. Forecasting in the face of ecological complexity: Number and strength of species interactions determine forecast skill in ecological communities. Ecol Lett 2022; 25:1974-1985. [PMID: 35831269 PMCID: PMC9540476 DOI: 10.1111/ele.14070] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 06/12/2022] [Accepted: 06/17/2022] [Indexed: 11/28/2022]
Abstract
The potential for forecasting the dynamics of ecological systems is currently unclear, with contrasting opinions regarding its feasibility due to ecological complexity. To investigate forecast skill within and across systems, we monitored a microbial system exposed to either constant or fluctuating temperatures in a 5-month-long laboratory experiment. We tested how forecasting of species abundances depends on the number and strength of interactions and on model size (number of predictors). We also tested how greater system complexity (i.e. the fluctuating temperatures) impacted these relations. We found that the more interactions a species had, the weaker these interactions were and the better its abundance was predicted. Forecast skill increased with model size. Greater system complexity decreased forecast skill for three out of eight species. These insights into how abundance prediction depends on the connectedness of the species within the system and on overall system complexity could improve species forecasting and monitoring.
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Affiliation(s)
- Uriah Daugaard
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Stephan B Munch
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, California, USA
| | - David Inauen
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Frank Pennekamp
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Owen L Petchey
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
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66
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Lofton ME, Brentrup JA, Beck WS, Zwart JA, Bhattacharya R, Brighenti LS, Burnet SH, McCullough IM, Steele BG, Carey CC, Cottingham KL, Dietze MC, Ewing HA, Weathers KC, LaDeau SL. Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2590. [PMID: 35343013 PMCID: PMC9287081 DOI: 10.1002/eap.2590] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 08/16/2021] [Accepted: 09/16/2021] [Indexed: 06/01/2023]
Abstract
Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.
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Affiliation(s)
- Mary E. Lofton
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | - Jennifer A. Brentrup
- Department of Biological SciencesDartmouth CollegeHanoverNew HampshireUSA
- Present address:
Biology and Environmental Studies DepartmentSt. Olaf CollegeNorthfieldMinnesotaUSA
| | - Whitney S. Beck
- Department of Biology and Graduate Degree Program in EcologyColorado State UniversityFort CollinsColoradoUSA
- Present address:
U.S. Environmental Protection AgencyWashingtonDistrict of ColumbiaUSA
| | - Jacob A. Zwart
- U.S. Geological SurveyIntegrated Information Dissemination DivisionMiddletonWisconsinUSA
| | - Ruchi Bhattacharya
- Legacies of Agricultural Pollutants (LEAP)University of WaterlooWaterlooOntarioCanada
| | | | - Sarah H. Burnet
- Department of Fish and Wildlife ResourcesUniversity of IdahoMoscowIdahoUSA
| | - Ian M. McCullough
- Department of Fisheries and WildlifeMichigan State UniversityEast LansingMichiganUSA
| | | | - Cayelan C. Carey
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | | | - Michael C. Dietze
- Department of Earth and EnvironmentBoston UniversityBostonMassachusettsUSA
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67
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De Kauwe MG, Sabot MEB, Medlyn BE, Pitman AJ, Meir P, Cernusak LA, Gallagher RV, Ukkola AM, Rifai SW, Choat B. Towards species-level forecasts of drought-induced tree mortality risk. THE NEW PHYTOLOGIST 2022; 235:94-110. [PMID: 35363880 PMCID: PMC9321630 DOI: 10.1111/nph.18129] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 03/28/2022] [Indexed: 05/14/2023]
Abstract
Predicting species-level responses to drought at the landscape scale is critical to reducing uncertainty in future terrestrial carbon and water cycle projections. We embedded a stomatal optimisation model in the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model and parameterised the model for 15 canopy dominant eucalypt tree species across South-Eastern Australia (mean annual precipitation range: 344-1424 mm yr-1 ). We conducted three experiments: applying CABLE to the 2017-2019 drought; a 20% drier drought; and a 20% drier drought with a doubling of atmospheric carbon dioxide (CO2 ). The severity of the drought was highlighted as for at least 25% of their distribution ranges, 60% of species experienced leaf water potentials beyond the water potential at which 50% of hydraulic conductivity is lost due to embolism. We identified areas of severe hydraulic stress within-species' ranges, but we also pinpointed resilience in species found in predominantly semiarid areas. The importance of the role of CO2 in ameliorating drought stress was consistent across species. Our results represent an important advance in our capacity to forecast the resilience of individual tree species, providing an evidence base for decision-making around the resilience of restoration plantings or net-zero emission strategies.
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Affiliation(s)
| | - Manon E. B. Sabot
- ARC Centre of Excellence for Climate ExtremesSydneyNSW2052Australia
- Climate Change Research CentreUniversity of New South WalesSydneyNSW2052Australia
| | - Belinda E. Medlyn
- Hawkesbury Institute for the EnvironmentWestern Sydney UniversityLocked Bag 1797PenrithNSW2751Australia
| | - Andrew J. Pitman
- ARC Centre of Excellence for Climate ExtremesSydneyNSW2052Australia
- Climate Change Research CentreUniversity of New South WalesSydneyNSW2052Australia
| | - Patrick Meir
- School of GeosciencesThe University of EdinburghEdinburghEH9 3FFUK
| | - Lucas A. Cernusak
- College of Science and EngineeringJames Cook UniversityCairnsQld4878Australia
| | - Rachael V. Gallagher
- Hawkesbury Institute for the EnvironmentWestern Sydney UniversityLocked Bag 1797PenrithNSW2751Australia
| | - Anna M. Ukkola
- ARC Centre of Excellence for Climate ExtremesSydneyNSW2052Australia
- Climate Change Research CentreUniversity of New South WalesSydneyNSW2052Australia
| | - Sami W. Rifai
- ARC Centre of Excellence for Climate ExtremesSydneyNSW2052Australia
- Climate Change Research CentreUniversity of New South WalesSydneyNSW2052Australia
| | - Brendan Choat
- Hawkesbury Institute for the EnvironmentWestern Sydney UniversityLocked Bag 1797PenrithNSW2751Australia
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68
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Beringer J, Moore CE, Cleverly J, Campbell DI, Cleugh H, De Kauwe MG, Kirschbaum MUF, Griebel A, Grover S, Huete A, Hutley LB, Laubach J, Van Niel T, Arndt SK, Bennett AC, Cernusak LA, Eamus D, Ewenz CM, Goodrich JP, Jiang M, Hinko‐Najera N, Isaac P, Hobeichi S, Knauer J, Koerber GR, Liddell M, Ma X, Macfarlane C, McHugh ID, Medlyn BE, Meyer WS, Norton AJ, Owens J, Pitman A, Pendall E, Prober SM, Ray RL, Restrepo‐Coupe N, Rifai SW, Rowlings D, Schipper L, Silberstein RP, Teckentrup L, Thompson SE, Ukkola AM, Wall A, Wang Y, Wardlaw TJ, Woodgate W. Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network. GLOBAL CHANGE BIOLOGY 2022; 28:3489-3514. [PMID: 35315565 PMCID: PMC9314624 DOI: 10.1111/gcb.16141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/30/2022] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.
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69
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McIntire EJB, Chubaty AM, Cumming SG, Andison D, Barros C, Boisvenue C, Haché S, Luo Y, Micheletti T, Stewart FEC. PERFICT: A Re-imagined foundation for predictive ecology. Ecol Lett 2022; 25:1345-1351. [PMID: 35315961 PMCID: PMC9310704 DOI: 10.1111/ele.13994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 02/10/2022] [Indexed: 12/02/2022]
Abstract
Making predictions from ecological models-and comparing them to data-offers a coherent approach to evaluate model quality, regardless of model complexity or modelling paradigm. To date, our ability to use predictions for developing, validating, updating, integrating and applying models across scientific disciplines while influencing management decisions, policies, and the public has been hampered by disparate perspectives on prediction and inadequately integrated approaches. We present an updated foundation for Predictive Ecology based on seven principles applied to ecological modelling: make frequent Predictions, Evaluate models, make models Reusable, Freely accessible and Interoperable, built within Continuous workflows that are routinely Tested (PERFICT). We outline some benefits of working with these principles: accelerating science; linking with data science; and improving science-policy integration.
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Affiliation(s)
- Eliot J. B. McIntire
- Pacific Forestry CentreCanadian Forest ServiceNatural Resources CanadaVictoriaBritish ColumbiaCanada
- Faculty of ForestryForest Resources ManagementThe University of British ColumbiaVancouverBritish ColumbiaCanada
- Département des sciences du bois et de la forêtPavillon Abitibi‐Price, 2405, rue de la TerrasseUniversité LavalQuébec CityQuébecCanada
| | - Alex M. Chubaty
- Pacific Forestry CentreCanadian Forest ServiceNatural Resources CanadaVictoriaBritish ColumbiaCanada
- Département des sciences du bois et de la forêtPavillon Abitibi‐Price, 2405, rue de la TerrasseUniversité LavalQuébec CityQuébecCanada
- FOR‐CAST Research & AnalyticsCalgaryAlbertaCanada
| | - Steven G. Cumming
- Département des sciences du bois et de la forêtPavillon Abitibi‐Price, 2405, rue de la TerrasseUniversité LavalQuébec CityQuébecCanada
| | - Dave Andison
- Faculty of ForestryForest Resources ManagementThe University of British ColumbiaVancouverBritish ColumbiaCanada
- Bandaloop Landscape‐Ecosystem Services Ltd.NelsonBritish ColumbiaCanada
| | - Ceres Barros
- Faculty of ForestryForest Resources ManagementThe University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Céline Boisvenue
- Pacific Forestry CentreCanadian Forest ServiceNatural Resources CanadaVictoriaBritish ColumbiaCanada
- Faculty of ForestryForest Resources ManagementThe University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Samuel Haché
- Canadian Wildlife ServiceEnvironment and Climate Change CanadaYellowknifeNorthwest TerritoriesCanada
| | - Yong Luo
- Pacific Forestry CentreCanadian Forest ServiceNatural Resources CanadaVictoriaBritish ColumbiaCanada
- Forest Analysis and Inventory BranchBC Ministry of ForestsVictoriaBritish ColumbiaCanada
| | - Tatiane Micheletti
- Faculty of ForestryForest Resources ManagementThe University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Frances E. C. Stewart
- Pacific Forestry CentreCanadian Forest ServiceNatural Resources CanadaVictoriaBritish ColumbiaCanada
- University of VictoriaSchool of Environmental StudiesVictoriaBritish ColumbiaCanada
- Department of BiologyWilfrid Laurier UniversityWaterlooOntarioCanada
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70
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71
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Boettiger C. The forecast trap. Ecol Lett 2022; 25:1655-1664. [PMID: 35635782 DOI: 10.1111/ele.14024] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/23/2022] [Accepted: 04/15/2022] [Indexed: 11/26/2022]
Abstract
Encouraged by decision makers' appetite for future information on topics ranging from elections to pandemics, and enabled by the explosion of data and computational methods, model-based forecasts have garnered increasing influence on a breadth of decisions in modern society. Using several classic examples from fisheries management, I demonstrate that selecting the model or models that produce the most accurate and precise forecast (measured by statistical scores) can sometimes lead to worse outcomes (measured by real-world objectives). This can create a forecast trap, in which the outcomes such as fish biomass or economic yield decline while the manager becomes increasingly convinced that these actions are consistent with the best models and data available. The forecast trap is not unique to this example, but a fundamental consequence of non-uniqueness of models. Existing practices promoting a broader set of models are the best way to avoid the trap.
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Affiliation(s)
- Carl Boettiger
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, California, USA
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72
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Five decades (1972-2020) of zooplankton monitoring in the upper San Francisco Estuary. PLoS One 2022; 17:e0265402. [PMID: 35286360 PMCID: PMC8920236 DOI: 10.1371/journal.pone.0265402] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 03/01/2022] [Indexed: 11/19/2022] Open
Abstract
We present the longest available dataset (by 15 years) of estuarine zooplankton abundance worldwide. Zooplankton have been monitored throughout the upper San Francisco Estuary from 1972 –present due to its status as a central hub of California water delivery and home to commercially important and endangered fishes. We integrated data from five monitoring programs, including over 300 locations, three size-classes of zooplankton targeted with different gears, over 80,000 samples, and over two billion sampled organisms. Over the duration of this dataset, species invasions have driven community turnover, periodic droughts have occurred, and important fishes have declined, likely due in part to reduced food supply from zooplankton. Data from the individual surveys have been used in prior studies on issues related to species invasions, flows, fish diets and population dynamics, zooplankton population dynamics, and community ecology. Our integrated dataset offers unparalleled spatio-temporal scope to address these and other fundamental ecological questions.
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73
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Randon M, Dowd M, Joy R. A real-time data assimilative forecasting system for animal tracking. Ecology 2022; 103:e3718. [PMID: 35405019 PMCID: PMC9541799 DOI: 10.1002/ecy.3718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/20/2022] [Accepted: 02/16/2022] [Indexed: 11/25/2022]
Abstract
Monitoring technologies now provide real‐time animal location information, which opens up the possibility of developing forecasting systems to fuse these data with movement models to predict future trajectories. State‐space modeling approaches are well established for retrospective location estimation and behavioral inference through state and parameter estimation. Here we use a state‐space model within a comprehensive data assimilative framework for probabilistic animal movement forecasting. Real‐time location information is combined with stochastic movement model predictions to provide forecasts of future animal locations and trajectories, as well as estimates of key behavioral parameters. Implementation uses ensemble‐based sequential Monte Carlo methods (a particle filter). We first apply the framework to an idealized case using a nondimensional animal movement model based on a continuous‐time random walk process. A set of numerical forecasting experiments demonstrates the workflow and key features, such as the online estimation of behavioral parameters using state augmentation, the use of potential functions for habitat preference, and the role of observation error and sampling frequency on forecast skill. For a realistic demonstration, we adapt the framework to short‐term forecasting of the endangered southern resident killer whale (SRKW) in the Salish Sea using visual sighting information wherein the potential function reflects historical habitat utilization of SRKW. We successfully estimate whale locations up to 2.5 h in advance with a moderate prediction error (<5 km), providing reasonable lead‐in time to mitigate vessel–whale interactions. It is argued that this forecasting framework can be used to synthesize diverse data types and improve animal movement models and behavioral understanding and has the potential to lead to important advances in movement ecology.
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Affiliation(s)
- Marine Randon
- Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada
| | - Michael Dowd
- Department of Mathematics and Statistics, Dalhousie University, 6316 Coburg Road, PO Box 15000, Halifax, Nova Scotia, Canada
| | - Ruth Joy
- Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada.,School of Environmental Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada
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74
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Heilman KA, Dietze MC, Arizpe AA, Aragon J, Gray A, Shaw JD, Finley AO, Klesse S, DeRose RJ, Evans MEK. Ecological forecasting of tree growth: Regional fusion of tree-ring and forest inventory data to quantify drivers and characterize uncertainty. GLOBAL CHANGE BIOLOGY 2022; 28:2442-2460. [PMID: 35023229 DOI: 10.1111/gcb.16038] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 10/26/2021] [Accepted: 10/31/2021] [Indexed: 06/14/2023]
Abstract
Robust ecological forecasting of tree growth under future climate conditions is critical to anticipate future forest carbon storage and flux. Here, we apply three ingredients of ecological forecasting that are key to improving forecast skill: data fusion, confronting model predictions with new data, and partitioning forecast uncertainty. Specifically, we present the first fusion of tree-ring and forest inventory data within a Bayesian state-space model at a multi-site, regional scale, focusing on Pinus ponderosa var. brachyptera in the southwestern US. Leveraging the complementarity of these two data sources, we parsed the ecological complexity of tree growth into the effects of climate, tree size, stand density, site quality, and their interactions, and quantified uncertainties associated with these effects. New measurements of trees, an ongoing process in forest inventories, were used to confront forecasts of tree diameter with observations, and evaluate alternative tree growth models. We forecasted tree diameter and increment in response to an ensemble of climate change projections, and separated forecast uncertainty into four different causes: initial conditions, parameters, climate drivers, and process error. We found a strong negative effect of fall-spring maximum temperature, and a positive effect of water-year precipitation on tree growth. Furthermore, tree vulnerability to climate stress increases with greater competition, with tree size, and at poor sites. Under future climate scenarios, we forecast increment declines of 22%-117%, while the combined effect of climate and size-related trends results in a 56%-91% decline. Partitioning of forecast uncertainty showed that diameter forecast uncertainty is primarily caused by parameter and initial conditions uncertainty, but increment forecast uncertainty is mostly caused by process error and climate driver uncertainty. This fusion of tree-ring and forest inventory data lays the foundation for robust ecological forecasting of aboveground biomass and carbon accounting at tree, plot, and regional scales, including iterative improvement of model skill.
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Affiliation(s)
- Kelly A Heilman
- Laboratory of Tree Ring Research, University of Arizona, Tucson, Arizona, USA
| | - Michael C Dietze
- Department of Earth & Environment, Boston University, Boston, Massachusetts, USA
| | - Alexis A Arizpe
- Austrian Academy of Sciences, Gregor Mendel Institute, Vienna, Austria
| | - Jacob Aragon
- Laboratory of Tree Ring Research, University of Arizona, Tucson, Arizona, USA
| | - Andrew Gray
- Laboratory of Tree Ring Research, University of Arizona, Tucson, Arizona, USA
- Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, Michigan, USA
| | - John D Shaw
- Rocky Mountain Research Station, USDA Forest Service, Ogden, Utah, USA
| | - Andrew O Finley
- Department of Forestry, Michigan State University, East Lansing, Michigan, USA
| | - Stefan Klesse
- Department of Forest Dynamics, Department of Forest Resources and Management, Swiss Federal Institute for Forest, Snow, and Landscape Research WSL, Birmensdorf, Switzerland
| | - R Justin DeRose
- Department of Wildland Resources and Ecology Center, Utah State University, Logan, Utah, USA
| | - Margaret E K Evans
- Laboratory of Tree Ring Research, University of Arizona, Tucson, Arizona, USA
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75
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Muthukrishnan R, Hayes K, Bartowitz K, Cattau ME, Harvey BJ, Lin Y, Lunch C. Harnessing
NEON
to evaluate ecological tipping points: Opportunities, challenges, and approaches. Ecosphere 2022. [DOI: 10.1002/ecs2.3989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- Ranjan Muthukrishnan
- Environmental Resilience Institute Indiana University Bloomington Indiana USA
- Department of Biology Boston University Boston Massachusetts USA
| | - Katherine Hayes
- Department of Integrative Biology University of Colorado Denver Colorado USA
| | - Kristina Bartowitz
- Department of Forest Rangeland and Fire Sciences University of Idaho Moscow Idaho USA
| | - Megan E. Cattau
- Human–Environment Systems Boise State University Boise Idaho USA
| | - Brian J. Harvey
- School of Environmental and Forest Sciences University of Washington Seattle Washington USA
| | - Yang Lin
- Department of Soil and Water Sciences University of Florida Gainesville Florida USA
| | - Claire Lunch
- Battelle National Ecological Observatory Network Boulder Colorado USA
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76
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Lewis ASL, Woelmer WM, Wander HL, Howard DW, Smith JW, McClure RP, Lofton ME, Hammond NW, Corrigan RS, Thomas RQ, Carey CC. Increased adoption of best practices in ecological forecasting enables comparisons of forecastability. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2500. [PMID: 34800082 PMCID: PMC9285336 DOI: 10.1002/eap.2500] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/21/2021] [Accepted: 10/05/2021] [Indexed: 05/24/2023]
Abstract
Near-term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross-ecosystem analysis of near-term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near-term (≤10-yr forecast horizon) ecological forecasting papers to understand the development and current state of near-term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near-term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near-term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1-7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.
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Affiliation(s)
| | | | | | - Dexter W. Howard
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | - John W. Smith
- Department of StatisticsVirginia TechBlacksburgVirginiaUSA
| | - Ryan P. McClure
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | - Mary E. Lofton
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | | | - Rachel S. Corrigan
- Department of Forest Resources and Environmental ConservationVirginia TechBlacksburgVirginiaUSA
| | - R. Quinn Thomas
- Department of Forest Resources and Environmental ConservationVirginia TechBlacksburgVirginiaUSA
| | - Cayelan C. Carey
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
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77
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Howard L, van Rees CB, Dahlquist Z, Luikart G, Hand BK. A review of invasive species reporting apps for citizen science and opportunities for innovation. NEOBIOTA 2022. [DOI: 10.3897/neobiota.71.79597] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Smartphone apps have enhanced the potential for monitoring of invasive alien species (IAS) through citizen science. They now have the capacity to massively increase the volume and spatiotemporal coverage of IAS occurrence data accrued in centralised databases. While more reporting apps are developed each year, innovation across diverse functionalities and data management in this field are occurring separately and simultaneously amongst numerous research groups with little attention to trends, priorities and opportunities for improvement. This creates the risk of duplication of effort and missed opportunities for implementing new and existing functionalities that would directly benefit IAS research and management. Using a literature search of Early Detection and Rapid Response implementation, smartphone app development and invasive species reporting apps, we developed a rubric for quantitatively assessing the functionality of IAS reporting apps and applied this rubric to 41 free, English-language IAS reporting apps, available via major mobile app stores in North America. The five highest performing apps achieved scores of 61.90% to 66.35% relative to a hypothetical maximum score, indicating that many app features and functionalities, acknowledged to be useful for IAS reporting in literature, are not present in sampled apps. This suggests that current IAS reporting apps do not make use of all available and known functionalities that could maximise their efficacy. Major implementation gaps, highlighted by this rubric analysis, included limited implementation in user engagement (particularly gamification elements and social media compatibility), ancillary information on search effort, detection method, the ability to report absences and local habitat characteristics. The greatest advancement in IAS early detection would likely result from app gamification. This would make IAS reporting more engaging for a growing community of non-professional contributors and encourage frequent and prolonged participation. We discuss these implementation gaps in relation to the increasingly urgent need for Early Detection and Rapid Response frameworks. We also recommend future innovations in IAS reporting app development to help slow the spread of IAS and curb the global economic and biodiversity extinction crises. We also suggest that further funding and investment in this and other implementation gaps could greatly increase the efficacy of current IAS reporting apps and increase their contributions to addressing the contemporary biological invasion threat.
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Clark NJ, Proboste T, Weerasinghe G, Soares Magalhães RJ. Near-term forecasting of companion animal tick paralysis incidence: An iterative ensemble model. PLoS Comput Biol 2022; 18:e1009874. [PMID: 35171905 PMCID: PMC8887734 DOI: 10.1371/journal.pcbi.1009874] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 03/01/2022] [Accepted: 01/27/2022] [Indexed: 11/18/2022] Open
Abstract
Tick paralysis resulting from bites from Ixodes holocyclus and I. cornuatus is one of the leading causes of emergency veterinary admissions for companion animals in Australia, often resulting in death if left untreated. Availability of timely information on periods of increased risk can help modulate behaviors that reduce exposures to ticks and improve awareness of owners for the need of lifesaving preventative ectoparasite treatment. Improved awareness of clinicians and pet owners about temporal changes in tick paralysis risk can be assisted by ecological forecasting frameworks that integrate environmental information into statistical time series models. Using an 11-year time series of tick paralysis cases from veterinary clinics in one of Australia's hotspots for the paralysis tick Ixodes holocyclus, we asked whether an ensemble model could accurately forecast clinical caseloads over near-term horizons. We fit a series of statistical time series (ARIMA, GARCH) and generative models (Prophet, Generalised Additive Model) using environmental variables as predictors, and then combined forecasts into a weighted ensemble to minimise prediction interval error. Our results indicate that variables related to temperature anomalies, levels of vegetation moisture and the Southern Oscillation Index can be useful for predicting tick paralysis admissions. Our model forecasted tick paralysis cases with exceptional accuracy while preserving epidemiological interpretability, outperforming a field-leading benchmark Exponential Smoothing model by reducing both point and prediction interval errors. Using online particle filtering to assimilate new observations and adjust forecast distributions when new data became available, our model adapted to changing temporal conditions and provided further reduced forecast errors. We expect our model pipeline to act as a platform for developing early warning systems that can notify clinicians and pet owners about heightened risks of environmentally driven veterinary conditions.
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Affiliation(s)
- Nicholas J. Clark
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, the University of Queensland, Gatton, Queensland, Australia
| | - Tatiana Proboste
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, the University of Queensland, Gatton, Queensland, Australia
| | - Guyan Weerasinghe
- Department of Agriculture, Water and the Environment, Canberra, Australia
| | - Ricardo J. Soares Magalhães
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, the University of Queensland, Gatton, Queensland, Australia
- Children’s Health and Environment Program, UQ Child Health Research Centre, the University of Queensland, Gatton, Australia
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79
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Newcomb K, Smith ME, Donohue RE, Wyngaard S, Reinking C, Sweet CR, Levine MJ, Unnasch TR, Michael E. Iterative data-driven forecasting of the transmission and management of SARS-CoV-2/COVID-19 using social interventions at the county-level. Sci Rep 2022; 12:890. [PMID: 35042958 PMCID: PMC8766467 DOI: 10.1038/s41598-022-04899-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 12/23/2021] [Indexed: 12/24/2022] Open
Abstract
The control of the initial outbreak and spread of SARS-CoV-2/COVID-19 via the application of population-wide non-pharmaceutical mitigation measures have led to remarkable successes in dampening the pandemic globally. However, with countries beginning to ease or lift these measures fully to restart activities, concern is growing regarding the impacts that such reopening of societies could have on the subsequent transmission of the virus. While mathematical models of COVID-19 transmission have played important roles in evaluating the impacts of these measures for curbing virus transmission, a key need is for models that are able to effectively capture the effects of the spatial and social heterogeneities that drive the epidemic dynamics observed at the local community level. Iterative forecasting that uses new incoming epidemiological and social behavioral data to sequentially update locally-applicable transmission models can overcome this gap, potentially resulting in better predictions and policy actions. Here, we present the development of one such data-driven iterative modelling tool based on publicly available data and an extended SEIR model for forecasting SARS-CoV-2 at the county level in the United States. Using data from the state of Florida, we demonstrate the utility of such a system for exploring the outcomes of the social measures proposed by policy makers for containing the course of the pandemic. We provide comprehensive results showing how the locally identified models could be employed for accessing the impacts and societal tradeoffs of using specific social protective strategies. We conclude that it could have been possible to lift the more disruptive social interventions related to movement restriction/social distancing measures earlier if these were accompanied by widespread testing and contact tracing. These intensified social interventions could have potentially also brought about the control of the epidemic in low- and some medium-incidence county settings first, supporting the development and deployment of a geographically-phased approach to reopening the economy of Florida. We have made our data-driven forecasting system publicly available for policymakers and health officials to use in their own locales, so that a more efficient coordinated strategy for controlling SARS-CoV-2 region-wide can be developed and successfully implemented.
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Affiliation(s)
- Ken Newcomb
- Center for Global Health Infectious Disease Research, University of South Florida, Tampa, FL, USA
| | - Morgan E Smith
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | - Rose E Donohue
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | - Sebastian Wyngaard
- Center for Research Computing, University of Notre Dame, Notre Dame, IN, USA
| | - Caleb Reinking
- Center for Research Computing, University of Notre Dame, Notre Dame, IN, USA
| | - Christopher R Sweet
- Center for Research Computing, University of Notre Dame, Notre Dame, IN, USA
| | - Marissa J Levine
- Center for Leadership in Public Health Practice, University of South Florida, Tampa, FL, USA
| | - Thomas R Unnasch
- Center for Global Health Infectious Disease Research, University of South Florida, Tampa, FL, USA
| | - Edwin Michael
- Center for Global Health Infectious Disease Research, University of South Florida, Tampa, FL, USA.
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80
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Neupane N, Zipkin EF, Saunders SP, Ries L. Grappling with uncertainty in ecological projections: a case study using the migratory monarch butterfly. Ecosphere 2022. [DOI: 10.1002/ecs2.3874] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Affiliation(s)
- Naresh Neupane
- Department of Biology Georgetown University Washington D.C. 20057 USA
| | - Elise F. Zipkin
- Department of Integrative Biology Michigan State University East Lansing Michigan 48824 USA
| | | | - Leslie Ries
- Department of Biology Georgetown University Washington D.C. 20057 USA
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81
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Zimmerman GS, Millsap BA, Abadi F, Gedir JV, Kendall WL, Sauer JR. Estimating allowable take for an increasing bald eagle population in the United States. J Wildl Manage 2022. [DOI: 10.1002/jwmg.22158] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Guthrie S. Zimmerman
- U.S. Fish and Wildlife Service, Division of Migratory Bird Management 3020 State University Drive East Modoc Hall, Suite 2007 Sacramento CA 95819 USA
| | - Brian A. Millsap
- U.S. Fish and Wildlife Service, Division of Migratory Bird Management 2105 Osuna NE Albuquerque NM 87113 USA
| | - Fitsum Abadi
- Department of Fish Wildlife, and Conservation Ecology, New Mexico State University P. O. Box 30003, MSC 4901 Las Cruces NM 88003 USA
| | - Jay V. Gedir
- Department of Fish Wildlife, and Conservation Ecology, New Mexico State University P. O. Box 30003, MSC 4901 Las Cruces NM 88003 USA
| | - William L. Kendall
- U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit Colorado State University 1484 Campus Delivery Fort Collins CO 80523 USA
| | - John R. Sauer
- U.S. Geological Survey, Eastern Ecological Science Center 12100 Beech Forest Road Laurel MD 20708 USA
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82
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Holmquist JR, Windham-Myers L. A Conterminous USA-Scale Map of Relative Tidal Marsh Elevation. ESTUARIES AND COASTS : JOURNAL OF THE ESTUARINE RESEARCH FEDERATION 2022; 45:1596-1614. [PMID: 35903080 PMCID: PMC9309155 DOI: 10.1007/s12237-021-01027-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 10/26/2021] [Accepted: 11/08/2021] [Indexed: 06/15/2023]
Abstract
Tidal wetlands provide myriad ecosystem services across local to global scales. With their uncertain vulnerability or resilience to rising sea levels, there is a need for mapping flooding drivers and vulnerability proxies for these ecosystems at a national scale. However, tidal wetlands in the conterminous USA are diverse with differing elevation gradients, and tidal amplitudes, making broad geographic comparisons difficult. To address this, a national-scale map of relative tidal elevation (Z*MHW), a physical metric that normalizes elevation to tidal amplitude at mean high water (MHW), was constructed for the first time at 30 × 30-m resolution spanning the conterminous USA. Contrary to two study hypotheses, watershed-level median Z*MHW and its variability generally increased from north to south as a function of tidal amplitude and relative sea-level rise. These trends were also observed in a reanalysis of ground elevation data from the Pacific Coast by Janousek et al. (Estuaries and Coasts 42 (1): 85-98, 2019). Supporting a third hypothesis, propagated uncertainty in Z*MHW increased from north to south as light detection and ranging (LiDAR) errors had an outsized effect under narrowing tidal amplitudes. The drivers of Z*MHW and its variability are difficult to determine because several potential causal variables are correlated with latitude, but future studies could investigate highest astronomical tide and diurnal high tide inequality as drivers of median Z*MHW and Z*MHW variability, respectively. Watersheds of the Gulf Coast often had propagated Z*MHW uncertainty greater than the tidal amplitude itself emphasizing the diminished practicality of applying Z*MHW as a flooding proxy to microtidal wetlands. Future studies could focus on validating and improving these physical map products and using them for synoptic modeling of tidal wetland carbon dynamics and sea-level rise vulnerability analyses. Supplementary Information The online version contains supplementary material available at 10.1007/s12237-021-01027-9.
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Affiliation(s)
- James R. Holmquist
- Smithsonian Environmental Research Center, 647 Contees Wharf Road, Edgewater, MD 21037 USA
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83
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Aoki LR, Brisbin MM, Hounshell AG, Kincaid DW, Larson EI, Sansom BJ, Shogren AJ, Smith RS, Sullivan-Stack J. OUP accepted manuscript. Bioscience 2022; 72:508-520. [PMID: 35677292 PMCID: PMC9169894 DOI: 10.1093/biosci/biac020] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Extreme events have increased in frequency globally, with a simultaneous surge in scientific interest about their ecological responses, particularly in sensitive freshwater, coastal, and marine ecosystems. We synthesized observational studies of extreme events in these aquatic ecosystems, finding that many studies do not use consistent definitions of extreme events. Furthermore, many studies do not capture ecological responses across the full spatial scale of the events. In contrast, sampling often extends across longer temporal scales than the event itself, highlighting the usefulness of long-term monitoring. Many ecological studies of extreme events measure biological responses but exclude chemical and physical responses, underscoring the need for integrative and multidisciplinary approaches. To advance extreme event research, we suggest prioritizing pre- and postevent data collection, including leveraging long-term monitoring; making intersite and cross-scale comparisons; adopting novel empirical and statistical approaches; and developing funding streams to support flexible and responsive data collection.
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Affiliation(s)
| | | | - Alexandria G Hounshell
- Biological Sciences Department, Virginia Tech, Blacksburg, Virginia
- National Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science, Silver Spring, Maryland, United States
| | - Dustin W Kincaid
- Vermont EPSCoR and Gund Institute for Environment, University of Vermont, Burlington, Vermont, United States
| | - Erin I Larson
- Institute of Culture and Environment, Alaska Pacific University, Anchorage, Alaska, United States
| | - Brandon J Sansom
- Department of Geography, State University of New York University, Buffalo, Buffalo, New York
- US Geological Survey's Columbia Environmental Research Center, Columbia, Missouri, United States
| | - Arial J Shogren
- Department of Earth and Environmental Sciences, Michigan State University, East Lansing Michigan
- Department of Biological Sciences, University of Alabama, Tuscaloosa Alabama, United States
| | - Rachel S Smith
- Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, United States
| | - Jenna Sullivan-Stack
- Department of Integrative Biology, Oregon State University, Corvallis, Oregon, United States
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84
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Koons DN, Riecke TV, Boomer GS, Sedinger BS, Sedinger JS, Williams PJ, Arnold TW. A niche for null models in adaptive resource management. Ecol Evol 2022; 12:e8541. [PMID: 35127044 PMCID: PMC8794763 DOI: 10.1002/ece3.8541] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 11/17/2021] [Accepted: 12/22/2021] [Indexed: 11/07/2022] Open
Abstract
As global systems rapidly change, our collective ability to predict future ecological dynamics will become increasingly important for successful natural resource management. By merging stakeholder objectives with system uncertainty, and by adapting actions to changing systems and knowledge, adaptive resource management (ARM) provides a rigorous platform for making sound decisions in a changing world. Critically, however, applications of ARM could be improved by employing benchmarks (i.e., points of reference) for determining when learning is occurring through the cycle of monitoring, modeling, and decision-making steps in ARM. Many applications of ARM use multiple model-based hypotheses to identify and reduce systematic uncertainty over time, but generally lack benchmarks for gauging discovery of scientific evidence and learning. This creates the danger of thinking that directional changes in model weights or rankings are indicative of evidence for hypotheses, when possibly all competing models are inadequate. There is thus a somewhat obvious, but yet to be filled niche for including benchmarks for learning in ARM. We contend that carefully designed "ecological null models," which are structured to produce an expected ecological pattern in the absence of a hypothesized mechanism, can serve as suitable benchmarks. Using a classic case study of mallard harvest management that is often used to demonstrate the successes of ARM for learning about ecological mechanisms, we show that simple ecological null models, such as population persistence (Nt +1 = Nt ), provide more robust near-term forecasts of population abundance than the currently used mechanistic models. More broadly, ecological null models can be used as benchmarks for learning in ARM that trigger the need for discarding model parameterizations and developing new ones when prevailing models underperform the ecological null model. Identifying mechanistic models that surpass these benchmarks will improve learning through ARM and help decision-makers keep pace with a rapidly changing world.
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Affiliation(s)
- David N. Koons
- Department of Fish, Wildlife, and Conservation BiologyGraduate Degree Program in EcologyColorado State UniversityFort CollinsColoradoUSA
| | - Thomas V. Riecke
- Department of Natural Resources and Environmental ScienceUniversity of NevadaRenoNevadaUSA
- Program in Ecology, Evolution, and Conservation BiologyUniversity of NevadaRenoNevadaUSA
| | - G. Scott Boomer
- Division of Migratory Bird ManagementU.S. Fish and Wildlife ServiceLaurelMarylandUSA
| | - Benjamin S. Sedinger
- College of Natural ResourcesUniversity of Wisconsin – Stevens PointStevens PointWisconsinUSA
| | - James S. Sedinger
- Department of Natural Resources and Environmental ScienceUniversity of NevadaRenoNevadaUSA
| | - Perry J. Williams
- Department of Natural Resources and Environmental ScienceUniversity of NevadaRenoNevadaUSA
| | - Todd W. Arnold
- Department of Fisheries, Wildlife and Conservation BiologyUniversity of MinnesotaSt. PaulMinnesotaUSA
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85
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Bodner K, Rauen Firkowski C, Bennett JR, Brookson C, Dietze M, Green S, Hughes J, Kerr J, Kunegel‐Lion M, Leroux SJ, McIntire E, Molnár PK, Simpkins C, Tekwa E, Watts A, Fortin M. Bridging the divide between ecological forecasts and environmental decision making. Ecosphere 2021. [DOI: 10.1002/ecs2.3869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Affiliation(s)
- Korryn Bodner
- Department of Ecology and Evolution University of Toronto Toronto Ontario Canada
- Department of Biological Sciences University of Toronto Scarborough Toronto Ontario Canada
| | - Carina Rauen Firkowski
- Department of Ecology and Evolution University of Toronto Toronto Ontario Canada
- Department of Biology McGill University Montreal Quebec Canada
| | | | - Cole Brookson
- Department of Biological Sciences University of Alberta Edmonton Alberta Canada
| | - Michael Dietze
- Department of Earth & Environment Boston University Boston Massachusetts USA
| | - Stephanie Green
- Department of Biological Sciences University of Alberta Edmonton Alberta Canada
| | - Josie Hughes
- National Wildlife Research Centre Environment and Climate Change Canada Ottawa Ontario Canada
| | - Jeremy Kerr
- Department of Biology University of Ottawa Ottawa Ontario Canada
| | - Mélodie Kunegel‐Lion
- Canadian Forest Service Northern Forestry Centre Natural Resources Canada Edmonton Alberta Canada
| | - Shawn J. Leroux
- Department of Biology Memorial University of Newfoundland St. John’s Newfoundland Canada
| | - Eliot McIntire
- Canadian Forest Service Pacific Forestry Centre Natural Resources Canada Victoria British Columbia Canada
- Faculty of Forestry Forest Resources Management University of British Columbia Vancouver British Columbia Canada
| | - Péter K. Molnár
- Department of Ecology and Evolution University of Toronto Toronto Ontario Canada
- Department of Biological Sciences University of Toronto Scarborough Toronto Ontario Canada
| | - Craig Simpkins
- School of Environment University of Auckland Auckland New Zealand
- Department of Biology Wilfrid Laurier University Waterloo Ontario Canada
- Department of Ecological Modelling Georg‐August University of Goettingen Goettingen Germany
| | - Edward Tekwa
- Department of Zoology University of British Columbia Vancouver British Columbia Canada
| | | | - Marie‐Josée Fortin
- Department of Ecology and Evolution University of Toronto Toronto Ontario Canada
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86
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Evans MEK, DeRose RJ, Klesse S, Girardin MP, Heilman KA, Alexander MR, Arsenault A, Babst F, Bouchard M, Cahoon SMP, Campbell EM, Dietze M, Duchesne L, Frank DC, Giebink CL, Gómez-Guerrero A, García GG, Hogg EH, Metsaranta J, Ols C, Rayback SA, Reid A, Ricker M, Schaberg PG, Shaw JD, Sullivan PF, GaytÁn SAV. Adding Tree Rings to North America's National Forest Inventories: An Essential Tool to Guide Drawdown of Atmospheric CO2. Bioscience 2021; 72:233-246. [PMID: 35241971 PMCID: PMC8888126 DOI: 10.1093/biosci/biab119] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Tree-ring time series provide long-term, annually resolved information on the growth of trees. When sampled in a systematic context, tree-ring data can be scaled to estimate the forest carbon capture and storage of landscapes, biomes, and—ultimately—the globe. A systematic effort to sample tree rings in national forest inventories would yield unprecedented temporal and spatial resolution of forest carbon dynamics and help resolve key scientific uncertainties, which we highlight in terms of evidence for forest greening (enhanced growth) versus browning (reduced growth, increased mortality). We describe jump-starting a tree-ring collection across the continent of North America, given the commitments of Canada, the United States, and Mexico to visit forest inventory plots, along with existing legacy collections. Failing to do so would be a missed opportunity to help chart an evidence-based path toward meeting national commitments to reduce net greenhouse gas emissions, urgently needed for climate stabilization and repair.
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Affiliation(s)
- Margaret E K Evans
- Assistant professor, University of Arizona, Tucson, Arizona, United States
| | - R Justin DeRose
- Quinney College of Natural Resources, Utah State University, Logan, Utah, United States
| | - Stefan Klesse
- Swiss Federal Institute for Forest, Snow, and Landscape Research, Zürich, Switzerland
| | - Martin P Girardin
- Canadian Forest Service, Laurentian Forestry Centre, Québec, Québec, Canada
| | - Kelly A Heilman
- Postdoctoral researcher, University of Arizona, Tucson, Arizona, United States
| | | | - André Arsenault
- Canadian Forest Service, Atlantic Forestry Centre, Natural Resources Canada, Corner Brook, Labrador, Canada
| | - Flurin Babst
- School of Natural Resources, Environment at University of Arizona, Tucson, Arizona, United States
| | - Mathieu Bouchard
- Department of Wood Science and Forestry, Laval University, Québec, Québec, Canada
| | - Sean M P Cahoon
- USDA Forest Service, Pacific Northwest Research Station, Anchorage, Alaska, United States
| | - Elizabeth M Campbell
- Canadian Forest Service, Pacific Forestry Centre, Victoria, British Columbia, Canada
| | - Michael Dietze
- Department of Earth and Environment, Boston University, Boston, Massachusetts, United States
| | - Louis Duchesne
- Direction de la Recherche Forestière, Ministère des Forêts, de la Faune, et des Parcs du Québec, Quebec, Québec, Canada
| | - David C Frank
- Professor and the director, University of Arizona, Tucson, Arizona, United States
| | - Courtney L Giebink
- Graduate student, Laboratory of Tree-Ring Research, University of Arizona, Tucson, Arizona, United States
| | | | - Genaro Gutiérrez García
- Departamento de Ciencias Ambientales y del Suelo, Instituto de Geología, Universidad Nacional Autónoma de México, Ciudad Universitaria, Ciudad de México, Mexico
| | - Edward H Hogg
- Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, Canada
| | - Juha Metsaranta
- Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, Canada
| | - Clémentine Ols
- Institut National de l'Information Géographique et Forestière, Nancy, France
| | - Shelly A Rayback
- Department of Geography, University of Vermont, Burlington, Vermont, United States
| | - Anya Reid
- British Columbia Ministry of Forests, Victoria, British Columbia, Canada
| | - Martin Ricker
- Instituto de Biología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
| | - Paul G Schaberg
- USDA Forest Service, Northern Research Station, Burlington, Vermont, United States
| | - John D Shaw
- USDA Forest Service, Rocky Mountain Research Station, Ogden, Utah, United States
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87
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Gaydos DA, Jones CM, Jones SK, Millar GC, Petras V, Petrasova A, Mitasova H, Meentemeyer RK. Evaluating online and tangible interfaces for engaging stakeholders in forecasting and control of biological invasions. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02446. [PMID: 34448316 PMCID: PMC9285687 DOI: 10.1002/eap.2446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 02/25/2021] [Accepted: 04/16/2021] [Indexed: 06/13/2023]
Abstract
Ecological forecasts will be best suited to inform intervention strategies if they are accessible to a diversity of decision-makers. Researchers are developing intuitive forecasting interfaces to guide stakeholders through the development of intervention strategies and visualization of results. Yet, few studies to date have evaluated how user interface design facilitates the coordinated, cross-boundary management required for controlling biological invasions. We used a participatory approach to develop complementary tangible and online interfaces for collaboratively forecasting biological invasions and devising control strategies. A diverse group of stakeholders evaluated both systems in the real-world context of controlling sudden oak death, an emerging forest disease killing millions of trees in California and Oregon. Our findings suggest that while both interfaces encouraged adaptive experimentation, tangible interfaces are particularly well suited to support collaborative decision-making. Reflecting on the strengths of both systems, we suggest workbench-style interfaces that support simultaneous interactions and dynamic geospatial visualizations.
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Affiliation(s)
- Devon A. Gaydos
- United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service (APHIS)Plant Protection and Quarantine (PPQ)4700 River RoadRiverdaleMaryland20737USA
| | - Chris M. Jones
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNorth Carolina27695USA
| | - Shannon K. Jones
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNorth Carolina27695USA
| | - Garrett C. Millar
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNorth Carolina27695USA
| | - Vaclav Petras
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNorth Carolina27695USA
| | - Anna Petrasova
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNorth Carolina27695USA
| | - Helena Mitasova
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNorth Carolina27695USA
- Department of Marine, Earth, and Atmospheric SciencesNorth Carolina State UniversityRaleighNorth Carolina27695USA
| | - Ross K. Meentemeyer
- Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNorth Carolina27695USA
- Department of Forestry and Environmental ResourcesNorth Carolina State UniversityRaleighNorth Carolina27596USA
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88
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Albery GF, Becker DJ, Brierley L, Brook CE, Christofferson RC, Cohen LE, Dallas TA, Eskew EA, Fagre A, Farrell MJ, Glennon E, Guth S, Joseph MB, Mollentze N, Neely BA, Poisot T, Rasmussen AL, Ryan SJ, Seifert S, Sjodin AR, Sorrell EM, Carlson CJ. The science of the host-virus network. Nat Microbiol 2021; 6:1483-1492. [PMID: 34819645 DOI: 10.1038/s41564-021-00999-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 10/18/2021] [Indexed: 01/21/2023]
Abstract
Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics.
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Affiliation(s)
- Gregory F Albery
- Department of Biology, Georgetown University, Washington DC, USA.
| | - Daniel J Becker
- Department of Biology, University of Oklahoma, Norman, OK, USA
| | - Liam Brierley
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Cara E Brook
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
| | | | - Lily E Cohen
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tad A Dallas
- Department of Biological Sciences, University of South Carolina, Columbia, SC, USA
| | - Evan A Eskew
- Department of Biology, Pacific Lutheran University, Tacoma, WA, USA
| | - Anna Fagre
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, USA
| | - Maxwell J Farrell
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Emma Glennon
- Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Sarah Guth
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Maxwell B Joseph
- Earth Lab, Cooperative Institute for Research in Environmental Science, University of Colorado Boulder, Boulder, CO, USA
| | - Nardus Mollentze
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK.,MRC - University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Benjamin A Neely
- National Institute of Standards and Technology, Charleston, SC, USA
| | - Timothée Poisot
- Québec Centre for Biodiversity Sciences, Montréal, Québec, Canada.,Département de Sciences Biologiques, Université de Montréal, Montréal, Québec, Canada
| | - Angela L Rasmussen
- Vaccine and Infectious Disease Organization, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.,Department of Biochemistry, Microbiology, and Immunology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Sadie J Ryan
- Department of Geography, University of Florida, Gainesville, FL, USA.,Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.,School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Stephanie Seifert
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, USA
| | - Anna R Sjodin
- Department of Biological Sciences, University of Idaho, Moscow, ID, USA
| | - Erin M Sorrell
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC, USA.,Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC, USA
| | - Colin J Carlson
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC, USA. .,Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC, USA.
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89
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Civantos-Gómez I, García-Algarra J, García-Callejas D, Galeano J, Godoy O, Bartomeus I. Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble. PLoS Comput Biol 2021; 17:e1008906. [PMID: 34871304 PMCID: PMC8675934 DOI: 10.1371/journal.pcbi.1008906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 12/16/2021] [Accepted: 11/12/2021] [Indexed: 11/19/2022] Open
Abstract
Prediction is one of the last frontiers in ecology. Indeed, predicting fine-scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand, there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models to predict species abundances using reasonably easy to obtain data. We propose a sequential data-driven modeling approach that in a first step predicts the potential species abundances based on abiotic variables, and in a second step uses these predictions to model the realized abundances once accounting for species competition. Using a curated data set over five years we predict fine-scale species abundances in a highly diverse annual plant community. Our models show a remarkable spatial predictive accuracy using only easy-to-measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggests that predicting future abundances requires longer time series analysis to capture enough variability. In addition, we show that these data-driven models can also suggest how to improve mechanistic models by adding missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Robust models for predicting fine-scale species composition informed by the mechanistic understanding of the underlying abiotic and biotic processes can be a pivotal tool for conservation, especially given the human-induced rapid environmental changes we are experiencing. This objective can be achieved by promoting the knowledge gained with classic modelling approaches in ecology and recently developed data-driven models.
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Affiliation(s)
- Icíar Civantos-Gómez
- Universidad Pontificia Comillas, Faculty of Economics and Business Administration, Madrid, Spain
- Complex Systems Group, Universidad Politécnica de Madrid, Madrid, Spain
| | | | - David García-Callejas
- Departamento de Biología, Instituto Universitario de Investigación Marina (INMAR), Universidad de Cádiz, Puerto Real, Spain
- Estación Biológica de Doñana (EBD-CSIC), Sevilla, Spain
| | - Javier Galeano
- Complex Systems Group, Universidad Politécnica de Madrid, Madrid, Spain
| | - Oscar Godoy
- Departamento de Biología, Instituto Universitario de Investigación Marina (INMAR), Universidad de Cádiz, Puerto Real, Spain
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90
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Horton KG, Van Doren BM, Albers HJ, Farnsworth A, Sheldon D. Near-term ecological forecasting for dynamic aeroconservation of migratory birds. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2021; 35:1777-1786. [PMID: 33826183 PMCID: PMC9290813 DOI: 10.1111/cobi.13740] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 03/06/2021] [Accepted: 03/12/2021] [Indexed: 05/08/2023]
Abstract
Near-term ecological forecasting has the potential to mitigate negative impacts of human modifications on wildlife by directing efficient action through relevant and timely predictions. We used the U.S. avian migration system to highlight ecological forecasting applications for aeroconservation. We used millions of observations from 143 weather surveillance radars to construct and evaluate a migration forecasting system for nocturnal bird migration over the contiguous United States. We identified the number of nights of mitigation required to reduce the risk of aerial hazards to 50% of avian migrants passing a given area in spring and autumn based on dynamic forecasts of migration activity. We also investigated an alternative approach, that is, employing a fixed conservation strategy based on time windows that historically capture 50% of migratory passage. In practice, during both spring and autumn, dynamic forecasts required fewer action nights compared with fixed window selection at all locations (spring: mean of 7.3 more alert days; fall: mean of 12.8 more alert days). This pattern resulted in part from the pulsed nature of bird migration captured in the radar data, where the majority (54.3%) of birds move on 10% of a migration season's nights. Our results highlight the benefits of near-term ecological forecasting and the potential advantages of dynamic mitigation strategies over static ones, especially in the face of increasing risks to migrating birds from light pollution, wind energy infrastructure, and collisions with structures.
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Affiliation(s)
- Kyle G. Horton
- Department of Fish, Wildlife, and Conservation BiologyColorado State UniversityFort CollinsColoradoUSA
| | - Benjamin M. Van Doren
- Edward Grey Institute, Department of ZoologyUniversity of OxfordOxfordUK
- Cornell Lab of OrnithologyCornell UniversityIthacaNew YorkUSA
| | - Heidi J. Albers
- Department of EconomicsUniversity of WyomingLaramieWyomingUSA
| | | | - Daniel Sheldon
- College of Information and Computer SciencesUniversity of MassachusettsAmherstMassachusettsUSA
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91
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Townsend PA, Clare JDJ, Liu N, Stenglein JL, Anhalt‐Depies C, Van Deelen TR, Gilbert NA, Singh A, Martin KJ, Zuckerberg B. Snapshot Wisconsin: networking community scientists and remote sensing to improve ecological monitoring and management. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02436. [PMID: 34374154 PMCID: PMC9286556 DOI: 10.1002/eap.2436] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 03/25/2021] [Accepted: 04/21/2021] [Indexed: 06/13/2023]
Abstract
Biological data collection is entering a new era. Community science, satellite remote sensing (SRS), and local forms of remote sensing (e.g., camera traps and acoustic recordings) have enabled biological data to be collected at unprecedented spatial and temporal scales and resolution. There is growing interest in developing observation networks to collect and synthesize data to improve broad-scale ecological monitoring, but no examples of such networks have emerged to inform decision-making by agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin, which links synoptic environmental data derived from SRS to biodiversity observations collected continuously from a trail camera network to support management decision-making. We use several examples to illustrate that Snapshot Wisconsin improves the spatial, temporal, and biological resolution and extent of information available to support management, filling gaps associated with traditional monitoring and enabling consideration of new management strategies. JONs like Snapshot Wisconsin further strengthen monitoring inference by contributing novel lines of evidence useful for corroboration or integration. SRS provides environmental context that facilitates inference, prediction, and forecasting, and ultimately helps managers formulate, test, and refine conceptual models for the monitored systems. Although these approaches pose challenges, Snapshot Wisconsin demonstrates that expansive observation networks can be tractably managed by agencies to support decision making, providing a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making.
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Affiliation(s)
- Philip A. Townsend
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
| | - John D. J. Clare
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
| | - Nanfeng Liu
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
| | | | - Christine Anhalt‐Depies
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
- Wisconsin Department of Natural ResourcesMadisonWisconsin53707USA
| | - Timothy R. Van Deelen
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
| | - Neil A. Gilbert
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
| | - Aditya Singh
- Department of Agricultural and Biological EngineeringUniversity of FloridaGainesvilleFlorida32603USA
| | - Karl J. Martin
- Division of ExtensionUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
| | - Benjamin Zuckerberg
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
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92
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Townsend PA, Clare JDJ, Liu N, Stenglein JL, Anhalt-Depies C, Van Deelen TR, Gilbert NA, Singh A, Martin KJ, Zuckerberg B. Snapshot Wisconsin: networking community scientists and remote sensing to improve ecological monitoring and management. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021. [PMID: 34374154 DOI: 10.5281/zenodo.4716378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Biological data collection is entering a new era. Community science, satellite remote sensing (SRS), and local forms of remote sensing (e.g., camera traps and acoustic recordings) have enabled biological data to be collected at unprecedented spatial and temporal scales and resolution. There is growing interest in developing observation networks to collect and synthesize data to improve broad-scale ecological monitoring, but no examples of such networks have emerged to inform decision-making by agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin, which links synoptic environmental data derived from SRS to biodiversity observations collected continuously from a trail camera network to support management decision-making. We use several examples to illustrate that Snapshot Wisconsin improves the spatial, temporal, and biological resolution and extent of information available to support management, filling gaps associated with traditional monitoring and enabling consideration of new management strategies. JONs like Snapshot Wisconsin further strengthen monitoring inference by contributing novel lines of evidence useful for corroboration or integration. SRS provides environmental context that facilitates inference, prediction, and forecasting, and ultimately helps managers formulate, test, and refine conceptual models for the monitored systems. Although these approaches pose challenges, Snapshot Wisconsin demonstrates that expansive observation networks can be tractably managed by agencies to support decision making, providing a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making.
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Affiliation(s)
- Philip A Townsend
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - John D J Clare
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Nanfeng Liu
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | | | - Christine Anhalt-Depies
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
- Wisconsin Department of Natural Resources, Madison, Wisconsin, 53707, USA
| | - Timothy R Van Deelen
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Neil A Gilbert
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Aditya Singh
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida, 32603, USA
| | - Karl J Martin
- Division of Extension, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Benjamin Zuckerberg
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
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93
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Raiho AM, Nicklen EF, Foster AC, Roland CA, Hooten MB. Bridging implementation gaps to connect large ecological datasets and complex models. Ecol Evol 2021; 11:18271-18287. [PMID: 35003672 PMCID: PMC8717344 DOI: 10.1002/ece3.8420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 11/12/2021] [Accepted: 11/16/2021] [Indexed: 11/09/2022] Open
Abstract
Merging robust statistical methods with complex simulation models is a frontier for improving ecological inference and forecasting. However, bringing these tools together is not always straightforward. Matching data with model output, determining starting conditions, and addressing high dimensionality are some of the complexities that arise when attempting to incorporate ecological field data with mechanistic models directly using sophisticated statistical methods. To illustrate these complexities and pragmatic paths forward, we present an analysis using tree-ring basal area reconstructions in Denali National Park (DNPP) to constrain successional trajectories of two spruce species (Picea mariana and Picea glauca) simulated by a forest gap model, University of Virginia Forest Model Enhanced-UVAFME. Through this process, we provide preliminary ecological inference about the long-term competitive dynamics between slow-growing P. mariana and relatively faster-growing P. glauca. Incorporating tree-ring data into UVAFME allowed us to estimate a bias correction for stand age with improved parameter estimates. We found that higher parameter values for P. mariana minimum growth under stress and P. glauca maximum growth rate were key to improving simulations of coexistence, agreeing with recent research that faster-growing P. glauca may outcompete P. mariana under climate change scenarios. The implementation challenges we highlight are a crucial part of the conversation for how to bring models together with data to improve ecological inference and forecasting.
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Affiliation(s)
- Ann M. Raiho
- Department of Fish, Wildlife, and Conservation BiologyColorado State UniversityFort CollinsColoradoUSA
| | - E. Fleur Nicklen
- Denali National Park and PreserveNational Park ServiceFairbanksAlaskaUSA
| | - Adrianna C. Foster
- School of Informatics, Computing, and Cyber SystemsNorthern Arizona UniversityFlagstaffArizonaUSA
| | - Carl A. Roland
- Denali National Park and PreserveNational Park ServiceFairbanksAlaskaUSA
| | - Mevin B. Hooten
- Department of Fish, Wildlife, and Conservation BiologyColorado State UniversityFort CollinsColoradoUSA
- Department of StatisticsColorado State UniversityFort CollinsColoradoUSA
- Colorado Cooperative Fish and Wildlife Research UnitU.S. Geological SurveyFort CollinsColoradoUSA
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94
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Applestein C, Caughlin TT, Germino MJ. Bayesian modeling can facilitate adaptive management in restoration. Restor Ecol 2021. [DOI: 10.1111/rec.13596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Cara Applestein
- Department of Biological Sciences Boise State University 1910 University Drive Boise ID 83725 U.S.A
| | - T. Trevor Caughlin
- Department of Biological Sciences Boise State University 1910 University Drive Boise ID 83725 U.S.A
| | - Matthew J. Germino
- US Geological Survey, Forest and Rangeland Ecosystem Science Center 970 S Lusk Street Boise ID 83706 U.S.A
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95
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Strydom T, Catchen MD, Banville F, Caron D, Dansereau G, Desjardins-Proulx P, Forero-Muñoz NR, Higino G, Mercier B, Gonzalez A, Gravel D, Pollock L, Poisot T. A roadmap towards predicting species interaction networks (across space and time). Philos Trans R Soc Lond B Biol Sci 2021; 376:20210063. [PMID: 34538135 PMCID: PMC8450634 DOI: 10.1098/rstb.2021.0063] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/29/2021] [Indexed: 11/12/2022] Open
Abstract
Networks of species interactions underpin numerous ecosystem processes, but comprehensively sampling these interactions is difficult. Interactions intrinsically vary across space and time, and given the number of species that compose ecological communities, it can be tough to distinguish between a true negative (where two species never interact) from a false negative (where two species have not been observed interacting even though they actually do). Assessing the likelihood of interactions between species is an imperative for several fields of ecology. This means that to predict interactions between species-and to describe the structure, variation, and change of the ecological networks they form-we need to rely on modelling tools. Here, we provide a proof-of-concept, where we show how a simple neural network model makes accurate predictions about species interactions given limited data. We then assess the challenges and opportunities associated with improving interaction predictions, and provide a conceptual roadmap forward towards predictive models of ecological networks that is explicitly spatial and temporal. We conclude with a brief primer on the relevant methods and tools needed to start building these models, which we hope will guide this research programme forward. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.
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Affiliation(s)
- Tanya Strydom
- Sciences Biologiques, Université de Montréal, Montréal, Canada H2V 0B3
- Québec Centre for Biodiversity Sciences, Montréal, Canada
| | - Michael D. Catchen
- Québec Centre for Biodiversity Sciences, Montréal, Canada
- McGill University, Montréal, Canada
| | - Francis Banville
- Sciences Biologiques, Université de Montréal, Montréal, Canada H2V 0B3
- Québec Centre for Biodiversity Sciences, Montréal, Canada
- Université de Sherbrooke, Sherbrooke, Canada
| | - Dominique Caron
- Québec Centre for Biodiversity Sciences, Montréal, Canada
- McGill University, Montréal, Canada
| | - Gabriel Dansereau
- Sciences Biologiques, Université de Montréal, Montréal, Canada H2V 0B3
- Québec Centre for Biodiversity Sciences, Montréal, Canada
| | - Philippe Desjardins-Proulx
- Sciences Biologiques, Université de Montréal, Montréal, Canada H2V 0B3
- Québec Centre for Biodiversity Sciences, Montréal, Canada
| | - Norma R. Forero-Muñoz
- Sciences Biologiques, Université de Montréal, Montréal, Canada H2V 0B3
- Québec Centre for Biodiversity Sciences, Montréal, Canada
| | | | - Benjamin Mercier
- Québec Centre for Biodiversity Sciences, Montréal, Canada
- Université de Sherbrooke, Sherbrooke, Canada
| | - Andrew Gonzalez
- Québec Centre for Biodiversity Sciences, Montréal, Canada
- McGill University, Montréal, Canada
| | - Dominique Gravel
- Québec Centre for Biodiversity Sciences, Montréal, Canada
- Université de Sherbrooke, Sherbrooke, Canada
| | - Laura Pollock
- Québec Centre for Biodiversity Sciences, Montréal, Canada
- McGill University, Montréal, Canada
| | - Timothée Poisot
- Sciences Biologiques, Université de Montréal, Montréal, Canada H2V 0B3
- Québec Centre for Biodiversity Sciences, Montréal, Canada
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96
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The Future Is Big-and Small: Remote Sensing Enables Cross-Scale Comparisons of Microbiome Dynamics and Ecological Consequences. mSystems 2021; 6:e0110621. [PMID: 34726484 PMCID: PMC8562476 DOI: 10.1128/msystems.01106-21] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Coupling remote sensing with microbial omics-based approaches provides a promising new frontier for scientists to scale microbial interactions across space and time. These data-rich, interdisciplinary methods allow us to better understand interactions between microbial communities and their environments and, in turn, their impact on ecosystem structure and function. Here, we highlight current and novel examples of applying remote sensing, machine learning, spatial statistics, and omics data approaches to marine, aquatic, and terrestrial systems. We emphasize the importance of integrating biochemical and spatiotemporal environmental data to move toward a predictive framework of microbiome interactions and their ecosystem-level effects. Finally, we emphasize lessons learned from our collaborative research with recommendations to foster productive and interdisciplinary teamwork.
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97
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Urban MC, Travis JMJ, Zurell D, Thompson PL, Synes NW, Scarpa A, Peres-Neto PR, Malchow AK, James PMA, Gravel D, De Meester L, Brown C, Bocedi G, Albert CH, Gonzalez A, Hendry AP. Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity. Bioscience 2021. [DOI: 10.1093/biosci/biab099] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Time is running out to limit further devastating losses of biodiversity and nature's contributions to humans. Addressing this crisis requires accurate predictions about which species and ecosystems are most at risk to ensure efficient use of limited conservation and management resources. We review existing biodiversity projection models and discover problematic gaps. Current models usually cannot easily be reconfigured for other species or systems, omit key biological processes, and cannot accommodate feedbacks with Earth system dynamics. To fill these gaps, we envision an adaptable, accessible, and universal biodiversity modeling platform that can project essential biodiversity variables, explore the implications of divergent socioeconomic scenarios, and compare conservation and management strategies. We design a roadmap for implementing this vision and demonstrate that building this biodiversity forecasting platform is possible and practical.
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Affiliation(s)
- Mark C Urban
- University of Connecticut, Storrs, Connecticut, United States
| | | | | | | | | | - Alice Scarpa
- University of Aberdeen, Aberdeen, Scotland, United Kingdom
| | | | | | | | | | - Luc De Meester
- Laboratory of Aquatic Ecology, Evolution, and Conservation, KU Leuven, Leuven, Belgium, with the Leibniz-Institut für Gewässerökologie und Binnenfischerei, Berlin, Germany, and with the Institute of Biology, Freie Universität Berlin, Berlin, Germany
| | - Calum Brown
- IMK-IFU, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany
| | - Greta Bocedi
- University of Aberdeen, Aberdeen, Scotland, United Kingdom
| | - Cécile H Albert
- Aix Marseille Univ, CNRS, Univ Avignon, IRD, IMBE, Marseille, France
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98
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Myers BJE, Weiskopf SR, Shiklomanov AN, Ferrier S, Weng E, Casey KA, Harfoot M, Jackson ST, Leidner AK, Lenton TM, Luikart G, Matsuda H, Pettorelli N, Rosa IMD, Ruane AC, Senay GB, Serbin SP, Tittensor DP, Beard TD. A New Approach to Evaluate and Reduce Uncertainty of Model-Based Biodiversity Projections for Conservation Policy Formulation. Bioscience 2021. [DOI: 10.1093/biosci/biab094] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Biodiversity projections with uncertainty estimates under different climate, land-use, and policy scenarios are essential to setting and achieving international targets to mitigate biodiversity loss. Evaluating and improving biodiversity predictions to better inform policy decisions remains a central conservation goal and challenge. A comprehensive strategy to evaluate and reduce uncertainty of model outputs against observed measurements and multiple models would help to produce more robust biodiversity predictions. We propose an approach that integrates biodiversity models and emerging remote sensing and in-situ data streams to evaluate and reduce uncertainty with the goal of improving policy-relevant biodiversity predictions. In this article, we describe a multivariate approach to directly and indirectly evaluate and constrain model uncertainty, demonstrate a proof of concept of this approach, embed the concept within the broader context of model evaluation and scenario analysis for conservation policy, and highlight lessons from other modeling communities.
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Affiliation(s)
- Bonnie J E Myers
- National Climate Adaptation Science Center, Reston, Virginia, United States
| | - Sarah R Weiskopf
- National Climate Adaptation Science Center, Reston, Virginia, United States
| | | | - Simon Ferrier
- Commonwealth Scientific and Industrial Research Organisation (CSIRO) Canberra, Australia
| | - Ensheng Weng
- NASA Goddard Institute for Space Studies and Columbia University, New York, New York, United States
| | - Kimberly A Casey
- US Geological Survey's National Land Imaging Program, Reston, Virginia, United States
| | - Mike Harfoot
- UN Environment Programme World Conservation Monitoring Centre, Cambridge, England, United Kingdom
| | | | - Allison K Leidner
- NASA Headquarters/Biological Diversity Program, Washington, DC, United States
| | - Timothy M Lenton
- Global Systems Institute, University of Exeter, Exeter, England, United Kingdom
| | - Gordon Luikart
- University of Montana Flathead Lake Biological Station, Polson, Montana, United States
| | | | - Nathalie Pettorelli
- Institute for Zoology, Zoological Society of London, Regent's Park, England, United Kingdom
| | - Isabel M D Rosa
- School of Natural Sciences, Bangor University, Bangor, Wales, United Kingdom
| | - Alex C Ruane
- NASA Goddard Institute for Space Studies, New York, New York, United States
| | - Gabriel B Senay
- US Geological Survey Earth Resources Observation Science Center, North Central Climate Adaptation Science Center, Fort Collins, Colorado, United States
| | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, United States
| | - Derek P Tittensor
- UN Environment Programme World Conservation Monitoring Centre, Cambridge, England, United Kingdom
- Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - T Douglas Beard
- National Climate Adaptation Science Center, Reston, Virginia, United States
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99
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Micheletti T, Stewart FEC, Cumming SG, Haché S, Stralberg D, Tremblay JA, Barros C, Eddy IMS, Chubaty AM, Leblond M, Pankratz RF, Mahon CL, Van Wilgenburg SL, Bayne EM, Schmiegelow F, McIntire EJB. Assessing Pathways of Climate Change Effects in SpaDES: An Application to Boreal Landbirds of Northwest Territories Canada. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.679673] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Distributions of landbirds in Canadian northern forests are expected to be affected by climate change, but it remains unclear which pathways are responsible for projected climate effects. Determining whether climate change acts indirectly through changing fire regimes and/or vegetation dynamics, or directly through changes in climatic suitability may allow land managers to address negative trajectories via forest management. We used SpaDES, a novel toolkit built in R that facilitates the implementation of simulation models from different areas of knowledge to develop a simulation experiment for a study area comprising 50 million ha in the Northwest Territories, Canada. Our factorial experiment was designed to contrast climate effects pathways on 64 landbird species using climate-sensitive and non-climate sensitive models for tree growth and mortality, wildfire, and landbirds. Climate-change effects were predicted to increase suitable habitat for 73% of species, resulting in average net gain of 7.49 million ha across species. We observed higher species turnover in the northeastern, south-central (species loss), and western regions (species gain). Importantly, we found that most of the predicted differences in net area of occupancy across models were attributed to direct climate effects rather than simulated vegetation change, despite a similar relative importance of vegetation and climate variables in landbird models. Even with close to a doubling of annual area burned by 2100, and a 600 kg/ha increase in aboveground tree biomass predicted in this region, differences in landbird net occupancy across models attributed to climate-driven forest growth were very small, likely resulting from differences in the pace of vegetation and climate changes, or vegetation lags. The effect of vegetation lags (i.e., differences from climatic equilibrium) varied across species, resulting in a wide range of changes in landbird distribution, and consequently predicted occupancy, due to climate effects. These findings suggest that hybrid approaches using statistical models and landscape simulation tools could improve wildlife forecasts when future uncoupling of vegetation and climate is anticipated. This study lays some of the methodological groundwork for ecological adaptive management using the new platform SpaDES, which allows for iterative forecasting, mixing of modeling paradigms, and tightening connections between data, parameterization, and simulation.
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100
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Woelmer WM, Bradley LM, Haber LT, Klinges DH, Lewis ASL, Mohr EJ, Torrens CL, Wheeler KI, Willson AM. Ten simple rules for training yourself in an emerging field. PLoS Comput Biol 2021; 17:e1009440. [PMID: 34710084 PMCID: PMC8553149 DOI: 10.1371/journal.pcbi.1009440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The opportunity to participate in and contribute to emerging fields is increasingly prevalent in science. However, simply thinking about stepping outside of your academic silo can leave many students reeling from the uncertainty. Here, we describe 10 simple rules to successfully train yourself in an emerging field, based on our experience as students in the emerging field of ecological forecasting. Our advice begins with setting and revisiting specific goals to achieve your academic and career objectives and includes several useful rules for engaging with and contributing to an emerging field.
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Affiliation(s)
- Whitney M. Woelmer
- Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, United States of America
| | - L. M. Bradley
- Department of Biology, Emory University, Atlanta, Georgia, United States of America
| | - Lisa T. Haber
- Integrative Life Sciences, Virginia Commonwealth University, Richmond, Virginia, United States of America
| | - David H. Klinges
- School of Natural Resources and Environment, University of Florida, Gainesville, Florida, United States of America
| | - Abigail S. L. Lewis
- Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Elizabeth J. Mohr
- Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, United States of America
| | - Christa L. Torrens
- Institute of Arctic and Alpine Research, University of Colorado at Boulder, Boulder, Colorado, United States of America
| | - Kathryn I. Wheeler
- Department of Earth and Environment, Boston University, Boston, Massachusetts, United States of America
| | - Alyssa M. Willson
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
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