Author Archives: Jody Peters

About Jody Peters

Ecological forecasting is going to transform our understanding of ecology. I am thrilled to have the opportunity to help coordinate efforts to improve and move the field forward.

EFI at AGU 2024

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November 26, 2024

Below is the list of poster and oral presentations for EFI’s hosted session at the American Geophysical Union (AGU) 2024 Conference in Washington, D.C., as well as other ecological forecasting-related talks, and talks by EFI community members that may be of interest. All times are listed in US Eastern Time.

EFI has name badges! EFI community members can find Mike Dietze at the Conference, during the EFI-hosted sessions, or at the Social to get a badge.

Tuesday EFI Social – Meet up with others in the EFI community on Tuesday evening, December 10 from 6:30-8:00 pm at The Delegate front bar in the Courtyard hotel just across the street from the convention center.

EFI’s Tuesday Poster and Oral Sessions – EFI’s oral and poster sessions on “Model-Data Integration and Novel Paradigms in Ecosystem Forecasting” will be held on Tuesday, December 10. The Poster Session is from 8:30am-12:20pm in Poster Hall B-C (Convention Center). The Oral session is from 14:10-15:40pm in 149A-B (Convention Center). We’re excited to have a great set of speakers that span cyberinfrastructure, decision making, and forecasts for coastal and terrestrial ecosystems and organisms. Come check out the following talks!

Tuesday Poster Session (8:30-12:20, Poster Hall B-C)

Tuesday Oral Session (14:10-15:40, 149 A-B (Convention Center))

Other Forecasting Presentations & Presentations by the EFI Community

If you are presenting an ecological forecasting-related talk or poster that you don’t see on the list, email EFI so we can get it added!

Monday

Tuesday

Wednesday

Thursday

Friday

Ecological forecasting is at a critical growth point – scientists call for new vision of data-driven environmental decision making

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November 8, 2024

Summary: Ecological forecasts can be used to predict changes in ecosystems and subsequent impacts on communities. In addition to long-term projections, there is an important need for forecasts in shorter-term decision-making time periods of weeks and months. Scientists at the Ecological Forecasting Initiative are working to advance the field through a process that enables them to continually update model predictions with observed data in order to improve our ability to foresee what may happen in the future. These scientists are calling on greater investment in these efforts, asking the scientific community to collectively commit to building the capacity to improve the field while calling on world nations, major corporations, and NGOs to integrate ecological forecasting into their climate adaptation and mitigation strategies.

Climate and biodiversity crises threaten our ability to manage and conserve natural resources, putting many ecosystems at risk of collapse. Ecological forecasts are a tool used to predict changes in ecosystems and how communities may be impacted. These forecasts can then be used to make decisions to mitigate environmental impacts and build a future that is climate resilient. But while many ecological forecasts have focused on predictions for 2100 and beyond, climate change is happening now and there is an urgent need for forecasts in shorter-term decision-making time periods of weeks and months.

This urgency has led scientists organized through the Ecological Forecasting Initiative (EFI) to call for increased infrastructure for predicting nature and environmental events. This call to action was recently published in Nature Climate Change. EFI is also urging world nations and UN bodies, major international corporations, and NGOs to integrate ecological forecasting into their climate adaptation and mitigation strategies while encouraging the scientific community to commit to building the technological and institutional capacity to respond to this urgent need.

Ecological forecasting is at a critical point for future growth according to Michael Dietze, EFI’s chair and lead of the Ecological Forecasting Laboratory at Boston University. “We have a lot of know-how and advances in sensing technologies, AI, and computing have opened new doors, but we now need to scale up the overall forecasting enterprise considerably to help society mitigate and adapt to widespread change in ecosystems in the face of climate change.”

Specifically, EFI scientists are talking about advancing the field of near-term iterative ecological forecasting, through which forecasts are regularly updated given new data. The forecasts can be compared to what actually happened, and the models can then be improved given what is learned to make more accurate predictions.

“Because you are constantly updating the forecasts as new information becomes available, near-term iterative ecological forecasting establishes a learning loop that is a win-win situation for ecology and decision making,” added Dietze. “The same forecasts that improve decision making on actionable timeframes also accelerate scientific understanding and help us probe new frontiers of discovery about how nature works.”

This iterative approach has been used by atmospheric scientists for decades, who, helped by large-scale investments in weather monitoring, modeling, and data assimilation, were able to continuously improve weather forecasts. In the early days of numerical weather prediction,  weather forecasters had the choice between stepping away from forecasting until the mechanics of the atmosphere were better understood or stepping forward into an iterative forecast cycle of learning by doing – through choosing the latter, they achieved a critical win-win of relentless improvements.

Ecological forecasting is at a similar crossroads, though the challenge is more complicated because of the complexity of biodiversity and environmental systems. Scientists are seeing dramatic improvements in the field of ecological forecasting that were unimaginable even a decade ago, much of it fueled by advances in sensor technologies, satellites, computation methods, and machine learning. And, due to shifts towards large-scale networked science, data access has become more standardized and equitable. Thus, the time is right to accelerate our investment.

One of the ways that EFI has supported these advancements is through the ongoing National Ecological Observatory Network (NEON) Forecasting Challenge, which has the goal of predicting NEON data before it is collected. This challenge has involved over 200 teams and developed new educational resources and community cyberinfrastructure. “Ecological forecasting needs to build communities of practice to support its growth as a discipline to leverage these technical advances,” Quinn Thomas, who co-directs the Virginia Tech Center for Ecosystem Forecasting and leads the EFI NEON Forecasting Challenge.

One of the areas where there has been traction is exploring opportunities for cross-government agency collaborations on cyberinfrastructure. Cyberinfrastructure – such as models, community standards, consistent server time, and workflows – have community-scale benefits by reducing the costs, time, and learning curve involved in launching and maintaining forecasts. As an international grassroots consortium, EFI has taken a leadership role in piloting and hosting conversations about scaling cyberinfrastructure for the benefit of the larger community of practice.

“The field of ecological forecasting has included partnerships across many sectors –  academia, governmental agencies, industry, and NGOs,” stated Melissa Kenney, Director of Research at the University of Minnesota’s Institute on the Environment and a founding leader of EFI. “Such partnerships are critical to support the design and improvement of regularly updated forecasts that both improve the science and support decisions.”

“This is a new field of science that is at a transition point,” says Dietze. “Key investments in investments in education, training, community building, sensing and computational infrastructure, tool and model development, and research to operations is critical to rapidly scale forecast innovations in ways that will help address climate and biodiversity crises.”

Congratulations to Cerres Barros on the 2024 ESA Ecological Forecasting Award!

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The ESA Statistical Ecology section presented the 2024 Ecological Forecasting Outstanding Publication Award to Cerres Barros and co-authors, Yong Luo, Alex Chubaty, Ian Eddy, Tatiane Micheletti, Céline Boisvenue, David Andison, Steven Cumming, & Eliot McIntire, for their 2023 Methods in Ecology and Evolution paper:

“Empowering ecological modellers with a PERFICT workflow: Seamlessly linking data, parameterisation, prediction, validation and visualisation.”

The award committee was strongly impressed by the way this paper clearly organized and presented cyberinfrastructure principles that are an increasingly important part of doing ecology well. This paper helps to translate software engineering concepts into clear take home messages that are accessible to ecologists (e.g., predictions depend on workflows, not just models) and draws attention to the need for Interdisciplinary teamwork in successful ecological forecasting. The committee was further impressed by this team’s commitment to developing tools that are accessible, reusable, and multi-purpose, and that address the challenges of operationalizing ecological forecasts.

In addition to an award plaque and prize ($200), this year’s recipient will also receive a £120 credit from Routledge books.

Nominate Papers for the 2025 Award

Individuals wishing to nominate papers published in the past  3 years for the 2025 award are encouraged to do so by the March 1, 2025 deadline. Additional information can be found at https://www.esa.org/stats/awards/ecological-forecasting-outstanding-publication-award/

Full List of Award Winners & Citations

2024 – Cerres Barros (University of British Columbia)
Barros, C., Luo, Y., Chubaty, A. M., Eddy, I. M. S., Micheletti, T., Boisvenue, C., Andison, D. W., Cumming, S. G., & McIntire, E. J. B. (2023). Empowering ecological modellers with a PERFICT workflow: Seamlessly linking data, parameterisation, prediction, validation and visualisation. Methods in Ecology and Evolution, 14, 173–188. https://doi.org/10.1111/2041-210X.14034

2023 – Jacob Zwart (USGS)
Zwart, J.A., Oliver, S.K., Watkins, W.D., Sadler, J.M., Appling, A.P., Corson-Dosch, H.R., Jia, X., Kumar, V., and Read, J.S. 2023. “Near-Term Forecasts of Stream Temperature Using Deep Learning and Data Assimilation in Support of Management Decisions.” JAWRA Journal of the American Water Resources Association 59 (2): 317–37. https://doi.org/10.1111/1752-1688.13093.

2022 – Kelly Heilman (University of Arizona)
Heilman, K.A., Dietze, M.C., Arizpe, A.A., Aragon, J., Gray, A., Shaw, J.D., Finley, A.O., Klesse, S., DeRose, R.J., & Evans, M.E.K. (2022). Ecological forecasting of tree growth: Regional fusion of tree-ring and forest inventory data to quantify drivers and characterize uncertainty. Global Change Biology 28(7):2442-2460 doi.org/10.1111/gcb.16038

2021 – Sarah Saunders (National Audubon Society)
Saunders, S.P., F.J. Cuthbert, and E.F. Zipkin. “Evaluating Population Viability and Efficacy of Conservation Management Using Integrated Population Models.” Journal of Applied Ecology 55, no. 3 (2018): 1380–92. https://doi.org/10.1111/1365-2664.13080.

2020 –  Paige Howell (USGS)
Howell, P.E., B.R. Hossack, E. Muths, B.H. Sigafus, A. Chenevert‐Steffler, and R.B. Chandler. “A Statistical Forecasting Approach to Metapopulation Viability Analysis.” Ecological Applications 30, no. 2 (2020): e02038. https://doi.org/10.1002/eap.2038.

2019 – Maria Paniw (CREAF, Ecological and Forestry Applications Research Centre)
Paniw, M., N. Maag, G. Cozzi, T. Clutton-Brock, and A. Ozgul. “Life History Responses of Meerkats to Seasonal Changes in Extreme Environments.” Science 363, no. 6427 (February 8, 2019): 631–35. https://doi.org/10.1126/science.aau5905.

2018 – Quinn Thomas (Virginia Tech)
Thomas, R.Q., E.B. Brooks, A.L. Jersild, E.J. Ward, R.H. Wynne, T.J. Albaugh, H. Dinon-Aldridge, et al. “Leveraging 35 Years of Pinus Taeda Research in the Southeastern US to Constrain Forest Carbon Cycle Predictions: Regional Data Assimilation Using Ecosystem Experiments.” Biogeosciences 14, no. 14 (2017): 3525–47. https://doi.org/10.5194/bg-14-3525-2017.

EFI at the Ecological Society of America 2024 Conference

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Date: July 25, 2023

EFI is excited about the opportunity to connect with the broader ecological forecasting community through a number of events at ESA in Long Beach, California this year! Below are details about the EFI Social, a workshop about the NEON Forecasting Challenge, multiple sessions organized by EFI and others about forecasting EFI-organized and other forecasting, and a Career Central panel. If you are presenting an ecological forecasting-related talk or poster that you don’t see on the list, reach out so we can get it added to this list!

We will again have EFI badges to add to your name tags!
We will continue to make updates to this page prior to ESA. All times listed below are in US Pacific Time.

EFI Badges

We will have EFI badges that can be attached to the ESA name tags available for individuals who are part of the Ecological Forecasting Initiative community. Find Mike Dietze throughout the week or at the EFI Social on Wednesday to get a badge and look for others with the green badge!

Schedule Summary

Schedule Details

EFI Social
Wednesday, August 7 at 6:30-8:30 PM

Connect and network with others in the EFI community over food, including vegan and vegetarian options at The Auld Dubliner. The Auld Dubliner is just 400 feet from the Convention Center.

Workshop:  An Introduction to the NEON Ecological Forecasting Challenge: A Hands-On Example Using Ground Beetle Abundance and Richness
Thursday, August 8 at 11:45 AM – 1:15 PM; Location: 104B

This workshop will provide an overview of the EFI RCN NEON Ecological Forecasting Challenge theme forecasting ground beetle abundance and richness across NEON terrestrial sites. The workshop will include code-along instructions to help participants create and submit a simple forecast to the Forecasting Challenge platform as well as interpret metrics of forecast skill. Participants will gain a foundation that can be built upon to create more sophisticated predictions about ecological communities and use the EFI RCN resources in future forecasting applications.

EFI Organized Oral Session: Ecological Forecasting for Research and Decision Making
Tuesday. August 6 at 8-9:30 AM; Location 104C

Contributed Oral Session: Back and Forecasting in Ecology
Tuesday, August 6 at 10:00-11:30 AM; Location: 102C

Organized Oral Session: What is Model Complexity? Defining Complexity Across Systems in Ecology
Tuesday, August 6 at 10:00-11:30 AM; Location: 101A

This session, organized by Charlotte Malmborg (Boston University) and R. Alex Thompson (Washington State University), has been in the works since ESA 2023 with conversations about the then-blog post, now-published paper about model complexity (https://doi.org/10.1002/met.2202). There will be an audience discussion following the panelists and it would be great to have fellow forecasters join that discussion.

Organized Oral Session: Climate Change Impacts on Biodiversity Through the Lens of Climate-Explicit Demographic Modeling
Thursday, August 8 at 8:00-9:30 AM; Location: 104A

Special Session: Toward Understanding and Anticipating Extreme Weather Effects on Biodiversity, Phenology, and Ecosystems
Monday, August 5 at 10:30-11:30 AM; Location: 104C

Extreme weather events, including heat waves, droughts, flooding, and unusual cold periods, are a major component of global climate change. Despite increases in the frequency and magnitude of extreme weather events, direct and indirect effects on biodiversity, phenology, and ecosystem services remain poorly understood. Assessing and anticipating impacts of extreme weather presents unique obstacles across systems, geographies, and scales. Key challenges include availability of and accessibility to relevant data resources, development of methods and approaches to characterize extreme weather events, and obstacles to predicting and forecasting. Solutions require multidisciplinary collaborations across the ecological community to develop effective tools and resources to address these challenges. We propose a session to convene a panel of experts spanning multiple career stages in the fields of biodiversity informatics, plant and insect phenology, and vector borne disease systems to discuss key challenges to understanding and anticipating extreme weather effects across organisms, phenology, and ecosystems. Session hosts will provide a talk outlining work within their field of expertise, followed by an interactive panel and audience discussion. The session will culminate in a dedicated 10-minute discussion, facilitated by the organizer, focusing on facilitating cross-disciplinary collaborations to leverage knowledge and approaches applicable to addressing challenges across domains.  

Speakers: Robert Guralnick (University of Florida), Michael Belitz (Michigan State University), Daijiang Li (Louisiana State University), Assaf Anyamba (Oak Ridge National Laboratory)

Organized Oral Session: Collaborative Conservation in the Face of Changing Climate
Thursday, August 8 at 8:00-9:30 AM; Location: 104B

Career Central – The Ecology Entrepreneur: Insights from Industry Leaders
Monday, Aug 5 at noon-1pm, Location: Exhibit Hall Career Central Room 1

Join this insightful session with founders of ecological organizations, consulting services, and innovative initiatives. Panelists are entrepreneurs who have demonstrated initiative, leadership, and innovation in creating and leading their communities of practice. If you identify with these qualities, you too can become an entrepreneur.

Panelists: Tim Nuttle (Oikos Ecology), Michael Dietze (Ecological Forecasting initiative), Shah Selbe (Conservify and FieldKit)

EFI Futures Outstanding Student Presentation Award 2024 Results

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The EFI2024 Conference provided the second opportunity for EFI to give out the EFI Futures Outstanding Student Presentation Award. This award is given to promote, recognize, and reward an outstanding student presenter and provides valuable feedback to student presenters on their research and presentation skills.  Awards were given to students who gave both Posters and Oral Presentations. Poster or oral presentations were anonymously reviewed by three volunteer reviewers with no conflicts of interest with the presenters. In addition to being recognized for their outstanding work, award winners get to select any item from the EFI shop. We thank all the students who presented and the volunteers who reviewed the presentations!

Congratulations to this year’s Outstanding Presentation Award recipients!

  • Gabrielle Koerich (University of Canterbury) won for her oral presentation, “Modelling bryophyte distributions in Antarctica: unveiling the influence of water availability, sampling bias, and spatially complex dynamics” and
  • Nima Farchadi (San Diego State University) won for his poster “Integrating diverse data for robust species distribution models in a dynamic ocean.”

See Gabrielle and Nima’s presentation details below.

Modelling bryophyte distributions in Antarctica: unveiling the influence of water availability, sampling bias, and spatially complex dynamics

Gabrielle Koerich1 , Hao Ran Lai1 , Grant Duffy2 , Eva B. Nielsen1 , Jonathan D. Tonkin1
1University of Canterbury, Christchurch, New Zealand. 2University of Otago, Dunedin, New Zealand

Abstract: In Antarctica, observations of biodiversity are often incomplete and commonly restricted to presence-only data, as sampling in the continent is difficult. Thus, understanding and forecasting the range dynamics of key taxa, such as bryophytes, requires a modelling framework capable of dealing with such challenges. Here, we developed log-Gaussian Cox process models of bryophytes’ across the entire Antarctic continent to (1) assess whether broad-scale bryophytes distributions are driven by water availability, as widely hypothesized; (2) map and forecast their distributions and identify under sampled regions; and (3) determine if there’s a spatial dependency between “patches” of bryophytes, which may be related to their limited dispersal in Antarctica. Results show that the main driver of bryophytes distributions in Antarctica is indeed related to areas where water tends to accumulate (97.5% CI:-0.886;-0.797). Maximum temperature was the second most important predictor (97.5% CI: 0.511; 0.681), signalling the importance of elevated temperatures for bryophytes’ restricted metabolism in this extreme environment. The covariate related to human activity showed a high level of sampling bias, and by accounting for this covariate in predictions, we detected habitat suitability for bryophytes in two under-sampled mountain ranges. Finally, the inclusion of a Gaussian random field to account for spatial autocorrelation increased model performance (pseudo R2 and mapped mean estimated density), indicating a spatial dependency between the presence of mosses. Our study demonstrates that a spatially structured modelling framework can provide robust results and allow for valuable forecasts of biodiversity change in data-poor regions.

Integrating diverse data for robust species distribution models in a dynamic ocean

Nima Farchadi1 , Camrin Braun2 , Martin Arostegui2 , Rebecca Lewison1
1San Diego State University, San Diego, USA. 2Woods Hole Oceanographic Institution, Woods Hole, USA

Abstract: Species distribution models (SDMs) are an important tool for marine conservation and management. Despite the burgeoning use of SDMs, limited guidance is available on how to leverage distinct data types to build robust models. Here we assess whether an integrative model framework improves performance over traditional data pooling or ensemble approaches when synthesizing multiple data types. We trained traditional, correlative SDMs and integrative SDMs (iSDMs) with three distinct data types that represent the distribution of a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the North Atlantic. Weevaluated data pooling and ensembling approaches in a correlative SDM framework and compared performance to a model-based data integration approach designed to explicitly account for data-specific biases while retaining the strengths of each dataset. We found that while each integration approach can result in robust models, there was variation in predictive accuracy among data types, with all models predicting fishery-dependent data more accurately than fishery-independent data. Differences in performance were primarily attributed to each model’s ability to explain the spatiotemporal dynamics of the training data, with iSDMs including spatiotemporal terms to have the most accurate and ecological realistic estimates. Our findings reveal trade-offs in the current techniques for integrating data in SDMs between accurately estimating species distributions, generating ecologically realistic predictions, and practical feasibility. With increasing access to growing and diverse data sources, comparing integration approaches can provide valuable guidance for practitioners navigating diverse data types in SDM development and will help users better understand model biases and estimate error.