references.bib

@article{clarkEcologicalForecastsEmerging2001,
  title = {Ecological {{Forecasts}}: {{An Emerging Imperative}}},
  author = {Clark, James S and Carpenter, Steven R and Barber, Mary and Collins, Scott and Dobson, Andy and Foley, Jonathan A and Lodge, David M and Pascual, Mercedes and Pielke Jr, Roger and Pizer, William and Pringle, Cathy and Reid, Walter V and Rose, Kenneth A and Sala, Osvaldo and Schlesinger, William H and Wall, Diana H and Wear, David},
  date = {2001-07-27},
  journaltitle = {Science},
  volume = {293},
  number = {5530},
  pages = {657--660},
  doi = {10.1126/science.293.5530.657},
  url = {http://www.sciencemag.org/content/293/5530/657.full},
  abstract = {Abstract Planning and decision-making can be improved by access to reliable forecasts of ecosystem state, ecosystem services, and natural capital. Availability of new data sets, together with progress in computation and statistics, will increase our ability to forecast ...},
  langid = {english},
  file = {/Users/quinn/Zotero/storage/AYSMXJ44/Clark-2001.pdf}
}

@article{dietzeCommunityConventionEcological2023,
  title = {A Community Convention for Ecological Forecasting: {{Output}} Files and Metadata Version 1.0},
  shorttitle = {A Community Convention for Ecological Forecasting},
  author = {Dietze, Michael C. and Thomas, R. Quinn and Peters, Jody and Boettiger, Carl and Koren, Gerbrand and Shiklomanov, Alexey N. and Ashander, Jaime},
  date = {2023-11},
  journaltitle = {Ecosphere},
  shortjournal = {Ecosphere},
  volume = {14},
  number = {11},
  pages = {e4686},
  issn = {2150-8925, 2150-8925},
  doi = {10.1002/ecs2.4686},
  url = {https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.4686},
  urldate = {2024-01-24},
  abstract = {Abstract             This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast communication, distribution, validation, and synthesis. For output files, we first describe the convention conceptually in terms of global attributes, forecast dimensions, forecasted variables, and ancillary indicator variables. We then illustrate the application of this convention to the two file formats that are currently preferred by the EFI, netCDF (network common data form), and comma‐separated values (CSV), but note that the convention is extensible to future formats. For metadata, EFI's convention identifies a subset of conventional metadata variables that are required (e.g., temporal resolution and output variables) but focuses on developing a framework for storing information about forecast uncertainty propagation, data assimilation, and model complexity, which aims to facilitate cross‐forecast synthesis. The initial application of this convention expands upon the Ecological Metadata Language (EML), a commonly used metadata standard in ecology. To facilitate community adoption, we also provide a Github repository containing a metadata validator tool and several vignettes in R and Python on how to both write and read in the EFI standard. Lastly, we provide guidance on forecast archiving, making an important distinction between short‐term dissemination and long‐term forecast archiving, while also touching on the archiving of code and workflows. Overall, the EFI convention is a living document that can continue to evolve over time through an open community process.},
  langid = {english},
  file = {/Users/quinn/Zotero/storage/3KH4NDAZ/Dietze et al-2023-Ecosphere.pdf}
}

@book{dietzeEcologicalForecasting2017,
  title = {Ecological {{Forecasting}}},
  author = {Dietze, Michael C},
  date = {2017},
  publisher = {{Princeton University Press}},
  location = {{Princeton}}
}

@article{dietzeEcologicalForecasting2018,
  title = {Ecological {{Forecasting}}},
  author = {Dietze, Michael C. and Averill, Colin and Foster, John and Wheeler, Kathryn},
  date = {2018},
  journaltitle = {Oxford Bibliographies},
  pages = {657--660},
  doi = {10.1093/obo/9780199830060-0205}
}

@article{dietzeForecastingBrightFuture2019a,
  title = {Forecasting a Bright Future for Ecology},
  author = {Dietze, Michael and Lynch, Heather},
  date = {2019-02},
  journaltitle = {Frontiers in Ecology and the Environment},
  shortjournal = {Frontiers in Ecol \& Environ},
  volume = {17},
  number = {1},
  pages = {3--3},
  issn = {1540-9295, 1540-9309},
  doi = {10.1002/fee.1994},
  url = {https://esajournals.onlinelibrary.wiley.com/doi/10.1002/fee.1994},
  urldate = {2024-01-16},
  langid = {english},
  file = {/Users/quinn/Zotero/storage/CSQIT9TS/Dietze and Lynch - 2019 - Forecasting a bright future for ecology.pdf}
}

@article{dietzeIterativeNeartermEcological2018,
  title = {Iterative Near-Term Ecological Forecasting: {{Needs}}, Opportunities, and Challenges},
  author = {Dietze, M.C. and Fox, A. and Beck-Johnson, L. M. and Betancourt, J. L. and Hooten, M. B. and Jarnevich, C. S. and Keitt, T. H. and Kenney, M. A. and Laney, C. M. and Larsen, L. G. and Loescher, H. W. and Lunch, C. K. and Pijanowski, B. C. and Randerson, J. T. and Read, E. K. and Tredennick, A. T. and Vargas, R. and Weathers, K. C. and White, E. P.},
  date = {2018-02-13},
  journaltitle = {Proc Natl Acad Sci U S A},
  edition = {2018/02/01},
  volume = {115},
  number = {7},
  eprint = {29382745},
  eprinttype = {pubmed},
  pages = {1424--1432},
  issn = {1091-6490 (Electronic) 0027-8424 (Linking)},
  doi = {10.1073/pnas.1710231115},
  url = {https://www.ncbi.nlm.nih.gov/pubmed/29382745},
  abstract = {Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.},
  keywords = {ecology,forecast,prediction},
  file = {/Users/quinn/Zotero/storage/86QE7YEM/Dietze et al. - 2018 - Iterative near-term ecological forecasting Needs,.pdf}
}

@article{gneitingStrictlyProperScoring2007,
  title = {Strictly {{Proper Scoring Rules}}, {{Prediction}}, and {{Estimation}}},
  author = {Gneiting, Tilmann and Raftery, Adrian E},
  date = {2007-03},
  journaltitle = {Journal of the American Statistical Association},
  shortjournal = {Journal of the American Statistical Association},
  volume = {102},
  number = {477},
  pages = {359--378},
  issn = {0162-1459, 1537-274X},
  doi = {10.1198/016214506000001437},
  url = {http://www.tandfonline.com/doi/abs/10.1198/016214506000001437},
  urldate = {2023-08-31},
  langid = {english},
  file = {/Users/quinn/Zotero/storage/FDLU6ZAK/Gneiting and Raftery - 2007 - Strictly Proper Scoring Rules, Prediction, and Est.pdf}
}

@article{harrisForecastingBiodiversityBreeding2018,
  title = {Forecasting Biodiversity in Breeding Birds Using Best Practices},
  author = {Harris, David J. and Taylor, Shawn D. and White, Ethan P.},
  date = {2018},
  journaltitle = {PeerJ},
  volume = {6},
  issn = {2167-8359},
  doi = {10.7717/peerj.4278},
  file = {/Users/quinn/Zotero/storage/NREJF3PY/Harris-2018-Forecasting biodiversity in breedi.pdf}
}

@article{hobdayEthicalConsiderationsUnanticipated2019,
  title = {Ethical Considerations and Unanticipated Consequences Associated with Ecological Forecasting for Marine Resources},
  author = {Hobday, Alistair J. and Hartog, Jason R. and Manderson, John P. and Mills, Katherine E. and Oliver, Matthew J. and Pershing, Andrew J. and Siedlecki, Samantha},
  date = {2019},
  journaltitle = {ICES Journal of Marine Science},
  issn = {1054-3139 1095-9289},
  doi = {10.1093/icesjms/fsy210},
  file = {/Users/quinn/Zotero/storage/B4B4NJKX/Hobday-2019-Ethical considerations and unantic.pdf}
}

@article{lewisIncreasedAdoptionBest2022,
  title = {Increased Adoption of Best Practices in Ecological Forecasting Enables Comparisons of Forecastability},
  author = {Lewis, Abigail S. L. and Woelmer, Whitney M. and Wander, Heather L. and Howard, Dexter W. and Smith, John W. and McClure, Ryan P. and Lofton, Mary E. and Hammond, Nicholas W. and Corrigan, Rachel S. and Thomas, R. Quinn and Carey, Cayelan C.},
  date = {2022-03},
  journaltitle = {Ecological Applications},
  shortjournal = {Ecological Applications},
  volume = {32},
  number = {2},
  pages = {e02500},
  issn = {1051-0761, 1939-5582},
  doi = {10.1002/eap.2500},
  url = {https://onlinelibrary.wiley.com/doi/10.1002/eap.2500},
  urldate = {2022-06-17},
  langid = {english},
  file = {/Users/quinn/Zotero/storage/NZALVF9T/Lewis et al-2022-Ecological Applications.pdf}
}

@article{mooreIntegratingEcologicalForecasting2022,
  title = {Integrating {{Ecological Forecasting}} into {{Undergraduate Ecology Curricula}} with an {{R Shiny Application-Based Teaching Module}}},
  author = {Moore, Tadhg N. and Thomas, R. Quinn and Woelmer, Whitney M. and Carey, Cayelan C.},
  date = {2022-06-30},
  journaltitle = {Forecasting},
  shortjournal = {Forecasting},
  volume = {4},
  number = {3},
  pages = {604--633},
  issn = {2571-9394},
  doi = {10.3390/forecast4030033},
  url = {https://www.mdpi.com/2571-9394/4/3/33},
  urldate = {2022-11-16},
  abstract = {Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts.},
  langid = {english},
  file = {/Users/quinn/Zotero/storage/7U7WQV7R/Moore et al-2022-Forecasting.pdf}
}

@article{niuRoleDataAssimilation2014,
  title = {The Role of Data Assimilation in Predictive Ecology},
  author = {Niu, Shuli and Luo, Yiqi and Dietze, Michael C and Keenan, Trevor F and Shi, Zheng and Li, Jianwei and III, F Stuart Chapin},
  date = {2014-05},
  journaltitle = {Ecosphere},
  volume = {5},
  number = {5},
  pages = {art65-16},
  doi = {10.1890/ES13-00273.1},
  url = {http://doi.wiley.com/10.1890/ES13-00273.1},
  langid = {english},
  file = {/Users/quinn/Zotero/storage/NHYNS324/Niu et al. - 2014 - The role of data assimilation in predictive ecolog.pdf}
}

@article{simonisEvaluatingProbabilisticEcological2021,
  title = {Evaluating Probabilistic Ecological Forecasts},
  author = {Simonis, Juniper L. and White, Ethan P. and Ernest, S. K. Morgan},
  date = {2021-08},
  journaltitle = {Ecology},
  shortjournal = {Ecology},
  volume = {102},
  number = {8},
  issn = {0012-9658, 1939-9170},
  doi = {10.1002/ecy.3431},
  url = {https://onlinelibrary.wiley.com/doi/10.1002/ecy.3431},
  urldate = {2023-08-14},
  langid = {english},
  file = {/Users/quinn/Zotero/storage/SYBE2DH6/Simonis et al-2021-Ecology.pdf}
}

@article{spiegelhalterVisualizingUncertaintyFuture2011,
  title = {Visualizing {{Uncertainty About}} the {{Future}}},
  author = {Spiegelhalter, David and Pearson, Mike and Short, Ian},
  date = {2011-09-09},
  journaltitle = {Science},
  shortjournal = {Science},
  volume = {333},
  number = {6048},
  pages = {1393--1400},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.1191181},
  url = {https://www.science.org/doi/10.1126/science.1191181},
  urldate = {2024-01-16},
  abstract = {We are all faced with uncertainty about the future, but we can get the measure of some uncertainties in terms of probabilities. Probabilities are notoriously difficult to communicate effectively to lay audiences, and in this review we examine current practice for communicating uncertainties visually, using examples drawn from sport, weather, climate, health, economics, and politics. Despite the burgeoning interest in infographics, there is limited experimental evidence on how different types of visualizations are processed and understood, although the effectiveness of some graphics clearly depends on the relative numeracy of an audience. Fortunately, it is increasingly easy to present data in the form of interactive visualizations and in multiple types of representation that can be adjusted to user needs and capabilities. Nonetheless, communicating deeper uncertainties resulting from incomplete or disputed knowledge—or from essential indeterminacy about the future—remains a challenge.},
  langid = {english},
  file = {/Users/quinn/Zotero/storage/SBKWQBL3/Spiegelhalter et al. - 2011 - Visualizing Uncertainty About the Future.pdf}
}

@article{thomasNEONEcologicalForecasting2023,
  title = {The {{NEON Ecological Forecasting Challenge}}},
  shorttitle = {The},
  author = {Thomas, R Quinn and Boettiger, Carl and Carey, Cayelan C and Dietze, Michael C and Johnson, Leah R and Kenney, Melissa A and McLachlan, Jason S and Peters, Jody A and Sokol, Eric R and Weltzin, Jake F and Willson, Alyssa and Woelmer, Whitney M and {Challenge contributors}},
  date = {2023-04},
  journaltitle = {Frontiers in Ecology and the Environment},
  shortjournal = {Frontiers in Ecol \& Environ},
  volume = {21},
  number = {3},
  pages = {112--113},
  issn = {1540-9295, 1540-9309},
  doi = {10.1002/fee.2616},
  url = {https://esajournals.onlinelibrary.wiley.com/doi/10.1002/fee.2616},
  urldate = {2023-04-03},
  langid = {english},
  file = {/Users/quinn/Zotero/storage/RSYY76HL/Thomas et al-2023-Frontiers in Ecology and the Environment.pdf}
}

@article{wheelerPredictingSpringPhenology2024b,
  title = {Predicting Spring Phenology in Deciduous Broadleaf Forests: {{NEON}} Phenology Forecasting Community Challenge},
  shorttitle = {Predicting Spring Phenology in Deciduous Broadleaf Forests},
  author = {Wheeler, Kathryn I. and Dietze, Michael C. and LeBauer, David and Peters, Jody A. and Richardson, Andrew D. and Ross, Arun A. and Thomas, R. Quinn and Zhu, Kai and Bhat, Uttam and Munch, Stephan and Buzbee, Raphaela Floreani and Chen, Min and Goldstein, Benjamin and Guo, Jessica and Hao, Dalei and Jones, Chris and Kelly-Fair, Mira and Liu, Haoran and Malmborg, Charlotte and Neupane, Naresh and Pal, Debasmita and Shirey, Vaughn and Song, Yiluan and Steen, McKalee and Vance, Eric A. and Woelmer, Whitney M. and Wynne, Jacob H. and Zachmann, Luke},
  date = {2024-02},
  journaltitle = {Agricultural and Forest Meteorology},
  shortjournal = {Agricultural and Forest Meteorology},
  volume = {345},
  pages = {109810},
  issn = {01681923},
  doi = {10.1016/j.agrformet.2023.109810},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0168192323005002},
  urldate = {2023-12-07},
  langid = {english},
  file = {/Users/quinn/Zotero/storage/L7H2IA34/Wheeler et al-2024-Agricultural and Forest Meteorology.pdf}
}

@article{woelmerEmbeddingCommunicationConcepts2023,
  title = {Embedding Communication Concepts in Forecasting Training Increases Students' Understanding of Ecological Uncertainty},
  author = {Woelmer, Whitney M. and Moore, Tadhg N. and Lofton, Mary E. and Thomas, R. Quinn and Carey, Cayelan C.},
  date = {2023-08},
  journaltitle = {Ecosphere},
  shortjournal = {Ecosphere},
  volume = {14},
  number = {8},
  pages = {e4628},
  issn = {2150-8925, 2150-8925},
  doi = {10.1002/ecs2.4628},
  url = {https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.4628},
  urldate = {2023-12-13},
  abstract = {Abstract                            Communicating and interpreting uncertainty in ecological model predictions is notoriously challenging, motivating the need for new educational tools, which introduce ecology students to core concepts in uncertainty communication. Ecological forecasting, an emerging approach to estimate future states of ecological systems with uncertainty, provides a relevant and engaging framework for introducing uncertainty communication to undergraduate students, as forecasts can be used as decision support tools for addressing real‐world ecological problems and are inherently uncertain. To provide critical training on uncertainty communication and introduce undergraduate students to the use of ecological forecasts for guiding decision‐making, we developed a hands‐on teaching module within the Macrosystems Environmental Data‐Driven Inquiry and Exploration (EDDIE;               MacrosystemsEDDIE.org               ) educational program. Our module used an active learning approach by embedding forecasting activities in an R Shiny application to engage ecology students in introductory data science, ecological modeling, and forecasting concepts without needing advanced computational or programming skills. Pre‐ and post‐module assessment data from more than 250 undergraduate students enrolled in ecology, freshwater ecology, and zoology courses indicate that the module significantly increased students' ability to interpret forecast visualizations with uncertainty, identify different ways to communicate forecast uncertainty for diverse users, and correctly define ecological forecasting terms. Specifically, students were more likely to describe visual, numeric, and probabilistic methods of uncertainty communication following module completion. Students were also able to identify more benefits of ecological forecasting following module completion, with the key benefits of using forecasts for prediction and decision‐making most commonly described. These results show promise for introducing ecological model uncertainty, data visualizations, and forecasting into undergraduate ecology curricula via software‐based learning, which can increase students' ability to engage and understand complex ecological concepts.},
  langid = {english},
  file = {/Users/quinn/Zotero/storage/6IM2AZER/Woelmer et al. - 2023 - Embedding communication concepts in forecasting tr.pdf}
}