The EFI DEI Strategic Plan: What Have We Learned in 4 Years?

June 7, 2024

The Diversity, Equity, and Inclusion (DEI, see point 1 below) working group was created because EFI recognizes that we need involvement and collaboration with underrepresented groups to equitably meet the needs of diverse communities who are using and impacted by ecological forecasts. To this end, the EFI DEI working group, led by input from Diana Dalbotten (University of Minnesota) began in October 2019 to develop an EFI DEI Working Group Strategic Plan (Figure 1) with steps to broaden participation in EFI. At the time, the focus had mainly been on broadening racial diversity based on racial categories common within the United States. Yet other metrics of diversity (gender, nationality, career stage, academic discipline, occupation sector) are also important to EFI. By May 2020, the group posted the Strategic Plan on the EFI DEI working group page to publicly share the plan and use it as official guidance for group activities. 

Four years later, the DEI working group is revisiting the plan to remind ourselves of our activities and place them within the context of the Strategic Plan. Our goal is to consider which activities the group sees as successful and where there continue to be opportunities for growth.

Point 1Although the working group name uses the acronym DEI, we recognize that Justice is an integral part of DEI work. We therefore refer to the EFI Working Group as DEI and the activities of the group as DEIJ.

EFI Strategic Plan developed in 2021

Figure 1 . Steps in the original DEI Strategic Plan

The original DEI Strategic Plan highlighted that, just as the iterative approach is useful for ecological forecasting, likewise it is beneficial for the process of engaging with people from underrepresented backgrounds. However, during our reecnt review process, the DEI Strategic Plan instead appeared as a fairly linear process, which in practice, it has not been.  Instead, the group has worked on projects across all six steps as opportunities have become available or aligned with ongoing work. 

The working group is planning to revise the original Strategic Plan. To this end, the group envisions adding Diversity, Equity, Inclusion, and Justice (DEIJ) perspectives and actions into EFI activities as a spiral rather than a circle (Figure 2). The group will continue to refine the iterative nature of EFI’s DEIJ efforts and will share details about that in the future.  For this blog, however, our goal is to compile a record of what has been done to acknowledge past efforts and inspire and inform future efforts. Future steps will assess what has been successful, examine whether or not we have the metrics to measure success, and reflect on challenges that we can learn from to improve the iterative process in future efforts. 

EFI Strategic Plan as a spiral

Figure 2. A potential new way to think of the EFI Strategic Plan

Strategic Plan Steps

Below we list the six steps of the Strategic Plan and the DEI working group activities for each step.

Step 1:  Identify and clarify the problem.

  • JEDI Database – Dave Klinges (University of Florida) and Jody Peters (University of Notre Dame) have collaborated to develop a workflow to take anonymized EFI membership information and create plots of diversity metrics (see Figures 3-7 below) to assess how the community has changed through time, relative to external baselines.
  • Publication: Willson et al 2023. Assessing opportunities and inequities in undergraduate ecological forecasting education. Ecology and Evolution 13(5): e10001.
    • Willson et al. compiled resources for teaching and learning ecological forecasting at different curriculum levels and identified gaps between ecological forecasting courses offered at doctoral universities, in comparison to other colleges and universities. The authors also noted a general lack of high-level, quantitative forecasting opportunities for undergraduates. 

Step 2:  Identify barriers that may be preventing students from underrepresented groups to participate in ecological forecasting as a career.  

Step 3:  Identify possible solutions that could be taken. 

  • The DEI working group meets monthly, and conversations include identifying solutions for barriers identified in Step 2 and then working on solutions in Step 4. Meeting notes from the monthly calls can be found at the bottom of the DEI webpage.
  • DEI Book Club suggestions and action items
    • In October 2023, the DEI Working Group created this living document to provide a summary of action items suggested during book club calls. The living document is to be used as a reference to support and inspire activities for EFI that can be developed and led as individuals have time, availability, and interest in doing so. The living document also provides an opportunity to collate and celebrate activities that have taken place that have been inspired by or align with suggestions from the book clubs.
  • Based on Willson et al 2023, the group identified curriculum development at minority serving institutions as a solution to address the lack of ecological forecasting in course work and a path to introduce more Native American students to ecological forecasting topics. 

Step 4:  Identify which solutions from Step 3 make sense to work on now for the EFI group.  

Step 5:  Identify who else needs to be involved in the process and make a plan to bring them in. 

Note that this step might actually be better as Step 1–what groups should be involved in identifying the problem?  If the problem is that the right people are not involved, how can we adequately examine the barriers and identify solutions? We realize we cannot solve the problem without broader participation in identifying the issues, barriers, and potential strategies. 

  • Building relationships is a critical part of this step, and relationship building takes time and trust. Diana Dalbotten (University of Minnesota) has been largely influential in helping to set up collaborations with individuals at minority serving and Tribal institutions. These partnerships produced funding success from the Alfred P. Sloan Foundation and funding applications submitted to the National Science Foundation. As these relationships continue to develop, they inform actions to prioritize and relationship building efforts.
  • Identifying collaborators and building relationships can happen at any point in the process, but they are included as a step here to acknowledge that, as more work is done, it will inform which partners are still missing. We can then be intentional about bringing in more people to fill these gaps. In recognizing that this step links back to Step 1 (Identifying and clarifying problems), we again want to shift our thinking to follow a spiral rather than linear path, as a spiral builds on previous work as seen in Figure 2. 

Step 6:  Form collaborations and seek funding to carry out the plan. 

  • Successful: Alfred P. Sloan Foundation grant that brings together partners from Salish Kootenai College, Cal Poly Humboldt, University of New Mexico Gallup, University of Notre Dame, and the University of Minnesota
    • The goals of this grant include developing culturally responsive educational resources and supporting research efforts of underrepresented undergraduate and graduate students, creating actionable paths forward to address the problems and barriers identified in Steps 1-2. 
  • Unsuccessful: National Science Foundation

Opportunities for Growth

As the DEI working group considers our next steps and future activities, we first look back with pride at the range of activities highlighted here, noting that much of this has been done with volunteer efforts. Those in the DEI working group have also experienced personal growth in understanding racism and systemic barriers in science and ecological forecasting.  

In addition to having an impact within the group, the impact of the DEI working group has expanded to other EFI activities. Recent Education working group discussions have revolved around understanding the history of Tribal and Historically Black Colleges and Universities in the US. The Education working group-led paper on Ethics in Forecasting Educational Modules ( includes considerations for who participates, uses, or is impacted by forecasts and data science. Additionally, Design Justice Principles were a key component of a recent EFI Cyberinfrastructure Workshop. The Translation and Actionable Science working group has discussed topics such as the racial history of the term “stakeholder”, DEIJ implications of different types of collaborations (from community-led, to collaborative, to extractive), and how different situations can support or be detrimental to relationship building along the spectrum of collaborative efforts. As demonstrated, DEIJ issues transcend beyond the DEI working group, and we encourage other EFI subgroups to continue to consider DEIJ impacts of their discussions and efforts.

One of the high level objectives of the EFI Strategic Plan is to increase diversity, equity, and inclusion, and this has been an overarching component for the EFI Steering Committee as they make decisions. As noted above, the Steering Committee is dedicated to ensuring that at least one member is experienced in addressing DEIJ issues, and in the short-term, this goal seems accomplishable (see Engagement paragraph below for more long-term considerations). Similarly, EFI Conferences consistently include DEI workshops or sessions, and when considering whether to support workshop efforts by partners, we advocate for diverse representation in speakers or participants. Given lessons learned regarding the development of workshops and sessions, we see this as a relatively straightforward space where we can continue to participate and grow.

Engagement is an area where the DEI working group has struggled. We appreciate all the work that has been done through volunteers, but we also recognize that some people who may want to get involved do not have time to volunteer. The DEI working group has expanded over the past four years, but it is often a core group of individuals who participate in calls or book clubs. Continued engagement and growth will become especially important in five to ten years for maintaining a Steering Committee member with DEIJ experience, as this will be about the time when all of the individuals from the core group will have already served. If the group has not grown, unideal solutions may include second terms of service or recruitment of individuals with expertise in the DEIJ space but who have little knowledge of EFI. We see engagement as a struggle that is not unique to EFI and reflects, in part, patterns and issues in ecology and data science more broadly. We must continue to seek new perspectives and discuss solutions to this barrier.

The DEI working group activities that are most successful are ones that align with the research and activities that people are already doing. This likely reflects the above struggles with volunteer-based progress. When DEIJ efforts align with paid work, the projects tend to be prioritized and therefore actually come to fruition. Still, finding a good balance in overlap of current research programs and DEIJ work is hard in a geographically and topically distributed community. Relationships have been built (and continue to grow), but we also recognize that it takes a long time to gain trust and demonstrate reciprocity. 

The DEI working group is looking forward to revising the DEI Strategic Plan and working on the continual process of advancing DEIJ efforts within EFI.

JEDI Images

Figure 3. Changes in the discipline of EFI members.

Figure 4. Changes in the occupation section of EFI members.

Figure 5. Changes in the nationality of EFI members. 

Figure 6. Changes in the race of EFI members who indicated they are from the United States.

Figure 7. Gender composition of EFI members in June 2024.

From Communities to Topologies, Forecasting is a Social System

January 30, 2024

Post by: Nicholas R. Record, Tandy Center for Ocean Forecasting, Bigelow Laboratory for Ocean Sciences

May 21, 2024 Update. There is now a citable version of this post with a DOI and an updated Figure 1 HERE
Citation: Record NR (2024) From Communities to Topologies, Forecasting is a Social System. Technical report number: TCOF.2024.05.03. DOI: 10.13140/RG.2.2.29846.77120

Does the removal of urchins and perrywinkles lead to brown water?

This question is one of hundreds of hypotheses I’ve heard over the years working with community groups on forecasting projects. It’s one of the reasons that community-centered science is fun. The ideas I encounter in communities are far more wide ranging than what’s found among scientists, who are also interesting, but are often much more in lock-step with each other (McClenachan et al. 2022).

Questions and ideas that come from communities can broaden the scientific perspective, but they can also give an indication of what’s important to that community. To me, this resonates, at a time when we know science needs to be more equitable and inclusive. It’s especially pertinent to environmental forecasting, which can have direct impacts on communities.

So how can a community’s knowledge shape a forecasting system?

Thinking about this question took me down a strange rabbit hole recently. Or maybe it was more of a complex network of gopher holes. Or a termite mound. However esoteric, I popped out the other side with some new info.

The starting point was remembering that making a forecast is more than just solving a math problem. The techniques of forecasting might be learned in a quantitative context, but a forecasting system is a social system. As a social system, there can be complex social dynamics, like reflexivity and environmental justice (Record et al. 2021, Wilson et al. 2023). There’s lots of potential for unintended consequences and other ethical pitfalls if the social dimensions are brushed over (Boettiger 2022, Hobday et al. 2019). There are plenty of cases where well-intentioned forecasts have caused harm (see the aforementioned references).

But what does this forecasting / social system look like?

Most forecasting papers are mainly quantitative, but it’s common to include a figure that diagrams how the quantitative exercise–the forecasting algorithm–fits within a social context. For example, the “ecological forecasting cycle” traces steps from hypothesis generation to model building, uncertainty quantification, forecast generation and communication, assessment and updating, and back to hypothesis generation (Moore et al.2022). The cycle diagram codifies the implicit social system, which in turn shapes what we decide to forecast and how. This particular system has communication to and feedback from groups that are influenced by the forecasts (generally managers). The cycle is taught and learned by forecasters as a way to iteratively improve forecasting systems within their social contexts.

A cycle is not the only model for diagramming a forecasting system within its social network. Because I’m curious and a bit of a nerd, I recently reviewed a collection of papers with flowcharts diagramming their forecasting systems for ecological forecasting (Figure 1). You can see the “forecasting cycle” of Moore (2022) in the bottom center. There’s a range of configurations, including pure cycles, unidirectional flows, trees, meshes, nearly fully connected networks, and combinations of patterns. Personally, I like the one in the middle row, from Petchey, that looks like a flux capacitor.

Figure 1 Some examples of topologies of ecological forecasting systems, diagrammed from the figures provided in a subset of the papers reviewed. Each figure was distilled as a network graph based on nodes and edges indicated in the diagram.

I think I used the word “esoteric” earlier, but these so-called topologies of networks can be pretty informative. The shape of a network influences the flow of information and the resultant emergent knowledge. For example, stronger connectivity leads to faster consensus, and under certain conditions, regular networks (same number of edges and nodes) can increase the probability of reaching a less biased consensus (Fernandes 2023).

Some patterns emerged from an analysis of this collection of networks. For example, diameter appears to increase with network size at a rate similar to that for cyclic networks–i.e. networks that are basically circular, like the “forecasting cycle” (Figure 2). To find the diameter of a network, you look at the shortest distances between all pairs of vertices, and take the largest of those (wikipedia explains it in more detail). In principle, larger networks don’t necessarily have to have larger diameters. Fully-connected networks have small diameters no matter how large, and other shapes (e.g. tree-like networks) fall somewhere in between. But for this group of papers, larger networks had larger diameters, which means information has to pass through many steps to get through the social network. Multiple steps can lead to situations where the information is not well connected with other parts of the system. One potential explanation for this is that as we build larger forecasting systems that include more social components, we have a tendency to overlook the importance of the connectivity of these components.

Figure 2 The relationship between network size (edges) and network diameter (greatest shortest node-node distance) across eighteen forecasting topologies (r = 0.64, p < 0.005). In comparison are lines showing what this relationship would look like for cyclic (bi-directional) networks, tree-like networks, and fully connected networks.

To put this knowledge to use, we redrew the schematics that we use for our own forecasting projects at the Tandy Center for Ocean Forecasting (Figure 3). The idea was to have a system that is fully connected, without long chains of arrows that information has to follow to get from one place to another. This exercise emerged through the process of writing this EFI blog post on barriers to inclusivity. The system has properties that aim to reduce confirmation bias and to speed consensus (Fernandes 2023)–i.e. full connectivity and regularity (equal number of nodes and vertices). In practice, the idea is to include forecast users and those influenced by forecast-based decisions–i.e. communities–as participants throughout the process. More details on this approach are in a technical document (Record 2022).

We don’t know for sure that our approach will lead to more equitable outcomes in forecasting. That’s just the working theory. There’s an important trend in ocean science (and many geosciences) developing the role of co-producing knowledge through collaborations across social systems (Liboiron et al. 2021, Schreiber et al. 2022). The schematic devised here seeks to address issues of accessibility to forecasting science and practice, though there are tradeoffs to this approach (Record et al. 2022).

Ideas like, “Does the removal of urchins and perrywinkles lead to brown water?” should be able to propagate quickly through a well-connected network and be incorporated, or not, in a forecasting system. By centering communities of people who use or are influenced by forecasts, it can help to kickstart forecasts in places that might be under-resourced or otherwise outside of the mainstream of ocean forecasting applications. It might help avoid unintended consequences of forecasts. And it should get a community’s wide range of hypotheses and ideas into the mix faster.

Figure 3 Schematic diagram of the framework used by the Tandy Center for building forecasting systems. Arrows indicate two-way movement of information between any of the components of the framework, potentially multiple times.

Do you have your own sketch of what a forecasting system network should look like? Or are you encouraged to try to make one? I suspect there are lots of different configurations that could work in different circumstances.  If folks are willing to share, I’d be happy to collect them and post in a followup blog post. You can send it to, with a subject heading “topology”, or share on the EFI #inclusion Slack Channel (if you are not on the EFI Slack group and would like to join, reach out to to be added). 

Note: Some of this content appeared in a blog post from 2023 (

Acknowledgements: The EFI Diversity, Equity, and Inclusion Working Group provided helpful feedback on the post during the January 2024 call. Participants included: Alyssa Willson, Anna Sjodin, Antoinette Abeyta, Jason McLachlan, Jody Peters, John Zobitz, Rachel Torres, and Saeed Shafiei Sabet


Boettiger C. The forecast trap. Ecology Letters. 2022 Jul;25(7):1655-64.

Fernandes MR. Confirmation bias in social networks. Mathematical Social Sciences. 2023 May 1;123:59-76.

Hobday AJ, Hartog JR, Manderson JP, Mills KE, Oliver MJ, Pershing AJ, Siedlecki S. Ethical considerations and unanticipated consequences associated with ecological forecasting for marine resources. ICES Journal of Marine Science. 2019 Sep 1;76(5):1244-56.

Liboiron M, Zahara A, Hawkins K, Crespo C, de Moura Neves B, Wareham-Hayes V, Edinger E, Muise C, Walzak MJ, Sarazen R, Chidley J. Abundance and types of plastic pollution in surface waters in the Eastern Arctic (Inuit Nunangat) and the case for reconciliation science. Science of the Total Environment. 2021 Aug 15;782:146809.

McClenachan L, Record NR, Waller J. How do human actions affect fisheries? Differences in perceptions between fishers and scientists in the Maine lobster fishery. Facets. 2022 Feb 10;7(1):174-93.

Moore TN, Thomas RQ, Woelmer WM, Carey CC. Integrating ecological forecasting into undergraduate ecology curricula with an R shiny application-based teaching module. Forecasting. 2022 Jun 30;4(3):604-33.

Record NR, Pershing AJ. Facing the Forecaster’s Dilemma: Reflexivity in Ocean System Forecasting. Oceans 2021 Nov 12 (Vol. 2, No. 4, pp. 738-751). MDPI.

Record N. Early Warning Systems for Harmful Algae: A Stakeholder-Centered Framework. 2022.

Record NR, Evanilla J, Kanwit K, Burnell C, Cartisano C, Lewis BJ, MacLeod J, Tupper B, Miller DW, Tracy AT, White C, Moretti M, Hamilton B, Barner C, Archer SD (2022) Benefits and Challenges of a Stakeholder-Driven Shellfish Toxicity Forecast in Coastal Maine. Frontiers in Marine Science.

Schreiber MA, Chuenpagdee R, Jentoft S. Blue Justice and the co-production of hermeneutical resources for small-scale fisheries. Marine Policy. 2022 Mar 1;137:104959. AM, Gallo H, Peters JA, Abeyta A, Bueno Watts N, Carey CC, Moore TN, Smies G, Thomas RQ, Woelmer WM, McLachlan JS. Assessing opportunities and inequities in undergraduate ecological forecasting education. Ecology and Evolution. 2023 May;13(5):e10001.

EFI Book Club Suggestions & Actions Items

November 27, 2023

The following document was created by the DEI Working Group in October 2023 in response to the final discussion of the group that participated in the book club which read “97 Things About Ethics Everyone in Data Science Should Know”.  

Goals of this document:

  1. Provide a summary of action items suggested during book clubs. The summary is a living document kept on the EFI Book Club webpage and action items will be added from future book clubs. This document is to be used as a reference to support and inspire activities for EFI that can be developed/led as individuals have time, availability, and interest in doing so
  2. Provide an opportunity to collate and celebrate activities that have taken place and those activities that will take place in the future that have been inspired by or align with suggestions from the book clubs.

Books Read

Note: There was a group within EFI that read Spatio-Temporal Statistics with R. That book group was not led by the DEI working group and was not included in this assessment.

Have we done any of the stuff we planned to do?

  1. From 97 Things About Ethics
    1. Summarize ideas we’ve had in previous book clubs and do self-assessment
      1. This document!
  2. From Braiding Sweetgrass
    1. Gratitude statement regarding data as a gift from the community, posted in:
      1. Newsletter
      2. Book club page
    2. The book club included student participants from the Sustainable Land and Water Resources (SLAWR) REU which provided opportunities for discussions across career stages and sectors
  3. From Weapons of Math Destruction
    1. JEDI (justice, equity, diversity, inclusion) database assessing who is in EFI is a good place to start to contribute to the self-assessment
      1. Dave Klinges (University of Florida) has worked with EFI Community Manager, Jody Peters (University of Notre Dame) to develop a workflow to assess the demographics of EFI members and how that changes through time
  4. From The Years That Matter Most and from Caste
    1. DEI Workshop ideas – webinar series leading up to workshops. Whose voices do we want to hear ahead of time
    2. Culturally competent training
      1. In late 2020/early 2021 the EFI DEI working group hosted 4 panels for the “Conversations on Diversity and Data Seminar Series.” See the links below for the recordings for each panel and the resources shared.
      2. What do diverse students need to thrive in data- and computer-heavy settings? November 19, 2020
      3. What does culturally competent data science training look like? January 20, 2021
      4. How can 4-year data-driven institutions and programs support students transferring from community colleges? February 19, 2021 
      5. What kind of online tools are useful and work well for remote learning and teaching? March 25, 2021 
  5. From Caste:
    1. Mentorship networks and having a series of mentors
      1. EFISA co-working session
      2. Developing a mentoring network for minority students is one of the goals of the Alfred P. Sloan Foundation Equitable Pathways funding from the project “A Generation of Native American Earth and Environmental Data Scientists” that EFI is involved with
        1. Still working on developing this, but the idea is for BIPOC students in research to have mentors from their own institution as well as mentors from the institutions they are doing research with and that the mentors will continue to support the students beyond the original activity
  6. From the Years That Matter Most
    1. Train the trainers
      1. This is something that continues to be included in EFI-led proposals.  Freya Olsson is also thinking about updating her NEON Forecasting Challenge workshop material which is currently geared towards participants wanting to learn how to submit forecasts to the Challenge to be applicable for people want to teach others how to submit to the Forecasting Challenge
      2. On the October 2023 Education working group call, the group discussed the following two papers. The first is about the needs for instructor training for people teaching data science at the collegiate level. The second provides papers, discussion prompts, and R code materials that can be used or modified for teaching ecological forecasting at the upper undergrad/early grad student level
        1. Emery et al 2021: Instructor Training Needs for Data Science
        2. Ernest et al 2023: Ecological Forecasting & Dynamics Course
    2. Highlight the #jobs and #funding Slack channels in the newsletter and in the welcome letter for new EFI members.

Do any of the ideas overlap between books? 

  1. The Years That Matter Most, Caste, and 97 Things: mentorship, supporting involvement, inclusive trainings
    1. Ongoing EFI efforts with potential overlap:
      1. EFISA efforts to share grant applications
  2. Braiding Sweetgrass, Weapons of Math Destruction, and 97 Things: addressing unintended consequences of forecasts and taking time for self reflection
    1. Ongoing EFI efforts with potential overlap:
      1. Examining EFI efforts from the lens of Design Justice Principles
      2. Social scientist-ecologist matchmaking

A full list of the notes and recommendations compiled from the book clubs can be found in the living document on the EFI Book Club webpage.

Barriers to Inclusivity in Ecological Forecasting

May 17, 2023; updated June 9, 2023 with the Spanish Translation

Antoinette Abeyta1, Jason McLachlan2, Jody Peters2, Nicholas R. Record3, Anna R. Sjodin, Olivia Tabares5, Alyssa M. Willson2
Co-authors are listed alphabetically since all contributed substantially to this project.

1University of New Mexico, Gallup, 2University of Notre Dame, 3Tandy Center for Ocean Forecasting, Bigelow Laboratory for Ocean Sciences, 4Environmental Protection Agency (EPA), 5Universidad Nacional Autónoma de México
§These views are my own and do not reflect the opinions or beliefs of the EPA.

Spanish Translation/Traducción al Español
Following our own recommendations in the Language section, we have provided a Spanish translation for the entire post. Translation by Yerania Serrato-Bucio, University of Notre Dame. Click here to access the Spanish Translation.
Siguiendo nuestras propias recomendaciones en la sección Idioma, hemos proporcionado una traducción al español para la publicación completa. Traducción por Yerania Serrato-Bucio, University of Notre Dame. Haga clic aquí para acceder a la traducción al español.


Here, we introduce a way to evaluate barriers to inclusion in ecological forecasting and environmental sciences using the iterative forecasting and adaptive management cycle, and suggest ways to extend our understanding of ecological forecasting beyond this cycle. We begin by highlighting three examples of barriers to inclusivity (i.e., hypotheses, models, and language). Next, in an attempt to make ecological forecasting a more inclusive discipline through the very ways we conceptualize its component steps, we reimagine the iterative forecasting cycle to emphasize and center marginalized groups. Finally, we provide suggestions for next steps that focus on working with students and reversing marginalization of historically excluded individuals. 

We invite anyone with an interest in participating to join these efforts. If you have comments or suggestions or would like to participate as a co-author in a manuscript that builds from these ideas, reach out to us in the comments below, at, on the #inclusion channel on the EFI Slack group, through Twitter (@eco4cast) or directly contact any of the authors of the blog post.


Ecology, one of the pillars of ecological forecasting, is fraught with experiences of racism and sexism, despite concerted efforts to improve inclusion and reduce barriers to entry (Martínez-Blancas et al., 2023). One of the tasks from the DEI Strategic Plan (developed by the Ecological Forecasting Initiative’s (EFI’s) Diversity, Equity, and Inclusion (DEI) Working Group) is to identify barriers hindering historically excluded individuals’ participation in ecological forecasting and other quantitative environmental sciences. The EFI DEI working group has been discussing this in monthly meetings, at the EFI annual meetings in 2021 and 2022, and at national meetings (e.g., Geoscience Alliance and the Ecological Society of America meetings in 2022). Our goal in this blog post is to spark perhaps new considerations for the EFI community about what the barriers to ecological forecasting are and ways to begin addressing those barriers.  We want to open up a discussion with the broader EFI community, and those interested in ecological forecasting, and ask for input on the barriers and the next steps identified, and ask for input about barriers and solutions beyond those that we have already considered.

Iterative ecological forecasting in the tradition promoted by EFI is often conceptualized as a cycle that has focused on the technical requirements or research outcomes of forecasting (e.g., Dietze et al., 2018) (shared here in Figure 1). We used this conceptual diagram of the iterative forecasting and adaptive management cycle as a starting point for thinking about the human components required for forecasting and to identify where the cycle presented barriers to entry and persistence in ecological forecasting and in what ways.  ​​Often existing forecasting cycles center research outcomes, leaving community as an afterthought. To make ecological forecasting more reflective of the communities we work with, we have to structure our forecasting cycles to center values that are important to the communities we serve. 

For example, at the Geoscience Alliance meeting in 2022, we spent considerable time discussing how marginalized communities often do not have access to the internet, computer hardware, and cyberinfrastructure tools that are often taken for granted in the ecological forecasting community (Federal Communications Commission, 2012), which corresponds to part B of the iterative forecasting and adaptive management cycle. The list of barriers to persistence in ecological forecasting quickly became too long for a blog post, as we realized that the forecasting cycle reflects the systemic barriers to inclusivity that persist in ecological forecasting and quantitative environmental sciences more generally. This led to the question: Can we re-envision a forecasting cycle that incorporates inclusion throughout the forecasting enterprise? 

Figure 1. Iterative ecological forecasting and adaptive management cycle figure from Dietze et al. 2018.


Hypotheses – Who gets to decide what gets forecasted, and how?

Hypotheses, as they are imagined in the Western scientific process, originate within a specific epistemology, or way of knowing and understanding. However, there are many epistemologies, and without engagement with communities, forecasts developed from and motivated by historical ecological literature will be biased towards what has historically been studied and the tried and true approaches to scientific inquiry. In ecology as in many scientific disciplines, the historical literature is dominated by White men. Additionally, the topics and studies that have produced the most amount of data and positive results (Nakagawa et al. 2022), or the data streams that provide consistently collected, archived, and available data, will be the most represented and continually reinforced. An alternative is co-produced forecasts, which center the assumptions, knowledge, and ideas most valuable to a community’s needs (Nyadzi et al., 2022; Record et al., 2022).

Funding similarly reinforces perpetual barriers to entry for historically marginalized communities. Research funds that continually reward novelty and the bleeding edge of science often don’t align with local or community needs (Flagg, 2022; Van Horne et al., 2023), and there is often no incentive to continue to work with local communities on persistent problems after the grant cycle ends. Moreover, researchers from historically excluded groups, often more in touch with local or community needs or working in the Global South, get their work published and showcased less than scientists in Global North institutions (Smith et al., 2023), thus perpetuating barriers to publishing and funding research related to community problems.

Models – Who generates the model, and who has access to the models and data?

Models are, by definition, simplifications of the truth. The aspects of truth that are simplified are inherently biased by those generating the models, and models are often developed to reflect a researcher’s hypothesis about how a system works. The more aspects that must be omitted to create a working model, the more opportunities exist for removing perspectives and truths understood by marginalized people. Generation of models is subject to the same epistemological weaknesses as hypotheses because models in many ways are our hypotheses. For ecological systems, specifically, mechanisms are complex, and inclusion of epistemological diversity becomes increasingly important for contextual understanding in such complex systems (Page, 2014). Importantly, mechanistic understanding and decisions based on information produced in partnership among epistemologies are more robust (Berkes, 2009; Schuttenberg and Guth, 2015; Wheeler and Root-Bernstein, 2020). So, in addition to the inherent value of a more inclusive process (Morrison and Steltzer, 2021; NASEM, 2022), incorporating different perspectives can provide a more holistic, and arguably stronger, understanding of natural systems. 

Additionally, creation of the models that produce forecasts is influenced by who has access to use or run the models. Not all models are open source and many have a historical legacy of ownership which can make it more difficult for individuals new to the field to learn how to run a certain model. Even models that can be built from scratch (e.g., linear regression models) require statistical knowledge that is often inaccessible to scholars outside of research intensive academic settings.

Data ownership can also be a barrier to creating forecasting models. On one hand, there are many open source data sets (e.g., NEON). However, these data may be too big to access on personal laptops, the data are often not collected equally across locations (e.g., NEON), and the data that have been collected will be biased towards the interests of those who have set up the collection processes. There are also cases where data sovereignty needs to be considered (Vera et al., 2019) and where data should be kept proprietary (e.g., working with culturally relevant data or human subjects). Similarly, running models and making probabilistic forecasts requires access to computational resources and expertise. Basic quantitative training is not universal nor created equal (Willson et al., 2023).   


El uso del inglés como lingua franca en el contexto científico, impone barreras a quienes no son hablantes nativos del idioma en diferentes aspectos. Desde la dificultad para adquirir nuevos conocimientos en una lengua diferente a la materna, pasando por limitación de oportunidades de estudio y laborales, hasta sesgos de publicación  y experiencias de discriminación en instituciones académicas (Woolston & Osorio 2019). 

A lo largo del siglo XX se ha observado que el inglés pasó de ser parte de un modelo plurilingüe de comunicación científica internacional, en el que otros idiomas, como el francés y alemán tambien eran comúnmente utilizados, a tener un dominio casi absoluto en la comunicación de las ciencias naturales y sociales, lo que limita cómo y quienes pueden comunicar ciencia e incluso aprenderla (Hamer, 2013; Amano et al 2021).

En el caso particular del Ecological Forecasting, al aprendizaje de tópicos complejos como la estadística, modelación matemática y programación, se añade la dificultad de hacerlo en inglés para hablantes ESL (English as a Second Language), ya que la mayoría de los textos, artículos y recursos de apoyo (e.g. foros, videos, tutoriales) están en este idioma (Amano et al. 2021). Prueba de ello es el repositorio que como Education Section de EFI hemos colectado, con materiales exclusivamente en inglés. Si bien esto está sesgado por la composición lingüistica de nuestro grupo, es un reflejo de muchos tópicos en ciencias.

Si bien la enseñanza del idioma inglés suele estar en la curricula de la gran mayoría de países no angloparlantes desde la educación básica, y un 20% de la población global se encuentra estudiando inglés como segunda lengua (TEFL Academy 2020), la comprensión necesaria del idioma para poder aprender y producir predicciones ecologicas dificilmente puede ser garantizada para personas que no hayan recibido instrucción privada y vengan de contextos socioeconómicos provilegiados. Esto impone dificultades a las personas interesadas en investigación que no hablen inglés tanto para adquirir nuevos conocimientos como para hacer posgrados, colaboraciones de investigación y publicar en revistas internacionales (Woolston & Osorio 2019; Amano et al 2021).


The forecasting cycle shown in Figure 1 has appeared in different iterations in subsequent studies (e.g. Moore et al. 2022). We’ve added two alternative forecasting “cycles”, described below, to this list, with a goal of reducing barriers to inclusivity. As we considered alternative figures we asked, “What should it look like? Should it be a cycle at all, or some other conceptual model?” The two figures presented here should be considered as first drafts and provide some food for thought on barriers to inclusivity in the forecasting process and how to address them. Moving forward, we hope that the redrawing of the ecological forecasting framework could, itself, be an inclusive process. We present figures depicting forecasting cycles in this slide deck, where we welcome additional contributions from readers and invite anyone interested to join our efforts to write a manuscript associated with this project.

Community-Centered Forecasting Cycle – Example 1

Science is often lauded for its objectivity, but each hypothesis or question carries with it inherent biases that reflect the values, thought processes, and experiences of the researcher(s), as discussed above in The Barriers section. These values show when scientific findings are applied, as in forecast-based decision-making, and the end user is often different from the researcher, with different values, thought processes, and experiences. In the DEI working group at EFI, we think a lot about these differences and how to unite them for the common goals of improving the field of ecological forecasting and promoting informed ecological decision-making. We recognize not only the inherent value of inclusivity (Morrison and Steltzer, 2021), but also the benefit that different experiences and expertise can have on an emerging field like ours (Woelmer et al. 2021, Willson et al., 2023).

Figure 2. One imperfect attempt at visualizing a diversity-focused forecasting cycle. In this cycle, whether the end goal is systemic understanding or decision-making, forecaster biases and goals are explicitly considered.

With inclusion front of mind, we created figures such as Figure 2 to focus the forecasting cycle within a diverse community of practice. Figure 2 stresses centering project goals within the values of the researchers and end users (blue circle). Ethics, therefore, cannot be an afterthought: the continuous evaluation and re-evaluation of goals, values, and ethics should be a discrete action item. These biases are then explicitly acknowledged as informing the scientific process (the outer red circle), and vice-versa (purple arrows). Importantly, those involved in the forecasting research effort can join the process from either the blue or ourter red circle, demonstrating the interdisciplinary qualities of forecasting. And finally, decisions can be made without science, but they cannot be made independently of personal values and biases (black internal arrows).

1 The value of diversity and inclusivity goes far beyond just ecological forecasting. Also, see the DEI statement on the EFI DEI Working Group webpage.

Community-Centered Forecasting Cycle – Example 2, model of practice at the Tandy Center for Ocean Forecasting

The Tandy Center for Ocean Forecasting works on developing forecasts for communities, industries, and other users. In thinking about inclusivity, individuals at the Tandy Center are thinking about those communities that are affected by the forecasts they create and the decisions based on them. In the conventional forecasting cycle (Figure 1), scientists are centered. However, some of the lessons that ocean ecosystem forecasters have learned and have been generous to share over the years have had to do with unintended consequences and accidental harms caused by well-intentioned forecasting programs (Hobday et al., 2019). Being a forecaster can be precarious, and it’s probably impossible to avoid every pitfall, but hopefully, we can learn from some of these lessons. The Center works under the belief that being more inclusive can help align the needs of communities using, or affected by, the forecast with the design of the forecasting program. The schematic included below (Figure 3) comes from the Tandy Center’s guidance documentation (Record, 2022). It’s not exactly a cycle, but more like a map to guide dialogues with different community groups so that they can collaborate at each step. There’s also a video that walks through the figure. In short, communities should be partners throughout the process, contributing to the design of the system. By centering forecast users rather than forecasters, we hope to help forecast development be more accessible and reach new people and places that might be under-resourced or otherwise excluded from the mainstream of forecasting applications.

Figure 3. Schematic diagram of the stakeholder-based framework for building forecasting systems (Record 2022). Arrows indicate two-way movement between any of the components of the framework, potentially multiple times.

Notable weaknesses that still remain

Of course, including everything in a single figure is challenging (hence this blog post). Continued discourse about how to address ongoing weaknesses is shared in Next Steps. Additionally, salient ideas have been left out of or de-emphasized in the above figures but have been discussed throughout the EFI DEI working group meetings. For example, diversity, equity, inclusion, and justice (DEIJ) efforts are often listed in grant applications as Broader Impacts but are not considered to contribute to intellectual merit. A more thorough list, including some more inclusive alternatives, can be found in Table 1.


Thinking about students

The legacy of barriers to inclusivity has been carried on through science education, and breaking this legacy needs to include thinking about education. Today’s students are highly motivated, eager, and ready to tackle broadly defined diversity, equity, inclusion and justice issues. This passion is shaped from a range of experiences and identities. Good education and pedagogical approaches are attentive to the diversity of learners (Harris et al., 2020; Miriti, 2019; Rawlings-Goss et al., 2018). Teaching takes many forms, but can consist of (1) active and collaborative learning activities (Corwin et al., 2018; Graham et al., 2013), (2) providing student agency and voice (they are co-creators in results), (3) honoring existing knowledge, (4) avoiding deficit language when teaching. Additional resources for inclusive teaching can be found in the Inclusive Pedagogy Resources compiled by EFI as well as this extensive list of inclusive teaching resources and strategies from the University of Michigan’s Center for Research on Learning and Teaching.  There are also efforts within the EFI community and the EFI Education Working Group to compile open educational resources (Willson and Peters, 2021; Willson et al., 2023, Table 1), as well as to develop educational modules in collaboration with faculty at Minority Serving Institutions to teach data science tools that incorporate Traditional Ecological Knowledge and cultural values. 

Reversing marginalization

Data science continues to have tremendously low rates of representation from historically excluded and marginalized groups. Consequently, data and computational tools are often created from a narrow world view of priorities, values, and practices (see The Barriers section for more details). When these tools are used on marginalized communities, they are often limited to the interpretations, biases, and preconceived notions of the creators of these tools (David-Chavez and Gavin, 2018). Without centering the knowledge, experience, and perspectives of marginalized groups in the creation of these tools, they become tools of oppression, promoting erasure, perpetuating stereotypes, and continuing violence and harm to communities. If we want ecological forecasting to become a tool to enact meaningful and just change, we have to structurally alter our research methods and practices through a lens of intersectionality to center the voices of marginalized communities, make them leaders in the creation of tools, and coproduce models and tools with communities (Crenshaw, 2014). In Table 1, we demonstrate how structural modifications to research practices can improve engagement with marginalized groups, showing the shift of power towards community benefit.  By encouraging the forecasting community to thoughtfully consider how to develop collaborations, we hope future research will center and bring in the perspectives of historically excluded individuals.

Connect with us!

Developing this post has been a learning process for the entire EFI DEI Working Group and we acknowledge that this is an ongoing and iterative process where it is important to hear from additional voices and to continue to learn.  We hope that this post can help the scientific and EFI community continue to think about barriers to participating in forecasting and environmental sciences and solutions for overcoming those barriers. We welcome comments, suggestions, and feedback on these ideas presented. Our goal is to turn this post into a manuscript that builds from these ideas and we invite anyone that would like to share comments or participate as a co-author in the manuscript effort to reach out to us in the comments below, at, on the #inclusion channel on the EFI Slack group, or through Twitter (@eco4cast).

Table 1. We provide a list of different topics related to forecasting (and data science and science in general) that demonstrate how structural modifications to research practices can improve the inclusion of historically marginalized groups. The three columns represent situations where there is the most room for improvement in connecting to and centering marginalized groups, where there is room for improvement, and a column with situations that are most beneficial to the most people. It is important to note that this table is written as a generalization, and all research practices in a project should be decided collaboratively with the community and be aligned with their values. This table was inspired by The Wheel of Power and Privilege and other related work (Hierarchy of Indigenous Data, the Global Indigenous Data Alliance, and models for decolonizing science research, e.g., David-Chavez 2019, David-Chavez et al. 2020). You can also view a PDF of the Table HERE.

TopicSituations with the Most Room for ImprovementSituations with Room for ImprovementSituations that are the Most Beneficial and Works to Center and Bring People in
Community Involvement– Research is done on communities without input or with limited input from the community
– Research dollars are not directed to the community
– Researchers are outsiders to the community
– Coproduction of knowledge between researchers and the community
– Research dollars are distributed to work with the community
– Researchers include community members
– Addresses generational and long-scale impacts
– Marginalized communities are leading research initiatives
– Research dollars go directly toward marginalized groups
– Community members are PIs on research projects
– Research involves children and younger generations in the development and execution of projects
Education – Eurocentric education practices
– Value is placed on credentialed programming only
– Programs limited to R1 institutions and primarily white institutions
– High cost of tuition is inaccessible
– Materials are often presented in English only
– Education emphasizes different cultures and values
– Improved access to higher education
– Programs available to public, community, and tribal institutions. 
– Inclusion of bilingual materials
– Curriculum material is culturally informed and relevant
– Recognition of the impact of non-credentialed programs
– Improving access to credentialed/advanced degree programs
– Education programs span the spectrum of education (K-12 through post-graduate programs) to promote generational learning
– Provides education outside of academic institutions
– Traditional ecological knowledge incorporated into education
– Materials are created and translated into multiple languages used by the community
Benefits & Harms – Using tools or data that misrepresent a community
– Science is done on others without consent
– Perpetuating violence, harm, and erasure of marginalized groups
– Communities and people are an afterthought in projects
– Values are centered on Western ideals
– Does not acknowledge the communities where research comes from
– Science is done with the consent of the community but without input in the design
– Research provides knowledge for the community
– Prioritizes and centers most privileged communities or most represented communities
– Land acknowledgments recognize the legacy of colonization
– Working on science in tandem with communities to benefit communities
– Giving agency to marginalized communities to define and access research
– Centers people and communities in projects
– Values are centered on communities ideals
– Land back, or land acknowledgments address the continued harm and benefit to the institution
Computation &
– Requiring subscriptions for software 
– Limited training options
– Limited computational and internet access
– Using low-cost and accessible tools
– Providing access to computers or mobile devices
– Use of universal design in materials
– Resources developed for individuals without internet access in mind
– Open-source materials are used or generated
– Computational resources are readily accessible
Data Availability (including journals & tools) – Data is only available through private access or behind a paywall
– Communities and individuals are unable to control data or access to data
– Requires specialized software, tools, knowledge of where to access data
– Data collected without consent of community or individuals
– Data available via request rather than open online access
– Data and tools are published for sharing for further research and collaborations
– Data is collected with community knowledge and consent
– Promotes ethical use of data
– Acknowledges and supports data sovereignty
– Broad education on how to access tools, resources, and data
– Data collected with respect to cultural values and practices
– Improves the ease of accessing public data
– Data is made open upon the wishes and needs of the community
Diversity & Justice – Homogenous racial or cultural research teams
– Research perpetuates harm to marginalized groups or maintains the status quo
– Funding is focused on short-term impacts
– Projects ends when funding ends
– Researchers work with students from diverse backgrounds
– Improved representation of marginalized groups in research spaces
– Diverse teams with agency
– Research enacts meaningful social change
– Funding and research acknowledge the importance of long-term impacts
– Project implementation continues after funding ends

Click here to see the Citations.

Barreras a la inclusión en la predicción ecológica

Antoinette Abeyta1, Jason McLachlan2, Jody Peters2, Nicholas R. Record3, Anna R. Sjodin, Olivia Tabares5, Alyssa M. Willson2
Los coautores se enumeran alfabéticamente ya que todos contribuyeron sustancialmente a este proyecto.

1University of New Mexico, Gallup, 2University of Notre Dame, 3Tandy Center for Ocean Forecasting, Bigelow Laboratory for Ocean Sciences, 4Environmental Protection Agency, 5Universidad Nacional Autónoma de México
§Estas opiniones son mías y no reflejan las opiniones o creencias de la EPA.
Traducción por Yerania Serrato-Bucio, University of Notre Dame


Aquí, presentamos una forma de evaluar las barreras para la inclusión en el pronóstico ecológico (Ecological Forecasting) y las ciencias ambientales, utilizando el ciclo de pronóstico iterativo y gestión adaptativa,  también sugerimos formas de ampliar nuestra comprensión del pronóstico ecológico más allá de este ciclo. Comenzamos  acentuando tres ejemplos de barreras a la inclusión: hipótesis, modelos y lenguaje. A continuacion, reimaginamos el ciclo de pronónstico iterativo enfatizando y centrando a los grupos marginados, en un intento por hacer a la predicción ecológica una disciplina más inclusiva desde su conceptualización. Finalmente, proporcionamos sugerencias para los próximos pasos que se enfocan en trabajar con estudiantes y revertir la marginación de personas históricamente excluidas.

Invitamos a cualquier persona interesada en participar a unirse a estos esfuerzos. Si tiene comentarios o sugerencias o le gustaría participar como coautor en un manuscrito que se basa en estas ideas, comuníquese con nosotros en los comentarios a continuación, en, en el canal #inclusion en EFI Slack grupo, a través de Twitter (@eco4cast) o contactar directamente cualquiera de los autores de la publicación del blog.


La ecología, uno de los pilares del pronóstico ecológico, está plagada de experiencias de racismo y sexismo, a pesar de los esfuerzos concertados para mejorar la inclusión y reducir las barreras de entrada (Martínez-Blancas et al., 2023). Una de las tareas del Plan Estratégico DEI (desarrollado por el Grupo de Trabajo de Diversidad, Equidad e Inclusión (DEI) de la Iniciativa de Pronóstico Ecológico (EFI)) es identificar las barreras que obstaculizan la participación de las personas históricamente excluidas en el pronóstico ecológico y otras ciencias ambientales cuantitativas. El grupo de trabajo de EFI DEI ha estado discutiendo esto en reuniones mensuales, asi como en las reuniones anuales de EFI en 2021 y 2022; y en reuniones nacionales (por ejemplo, reuniones de Geoscience Alliance y la Ecological Society of America en 2022). Nuestro objetivo en esta publicación de blog es  incitar a la reflexión y al diálogo dentro de la comunidad EFI sobre  cuáles son las barreras para el pronóstico ecológico y las formas de comenzar a enfrentarlas . Queremos comenzar una discusión con la comunidad EFI más amplia y todos aquellos interesados ​​en el pronóstico ecológico para obtener información sobre las barreras al construir un pronóstico ecológico y los próximos pasos a seguir para enfrentarlas ,así como soluciones más allá de las que ya hemos considerado.

El pronóstico ecológico iterativo en la tradición promovida por EFI, muchas veces se conceptualiza como un ciclo que se ha centrado en los requisitos técnicos o los resultados de investigación del pronóstico (p. ej., Dietze et al., 2018) (compartido aquí en la Figura 1). Usamos este diagrama conceptual del ciclo de pronóstico iterativo y manejo adaptativo como punto de partida para pensar en los componentes humanos requeridos para el pronóstico, e identificar dónde el ciclo presenta barreras para la entrada y persistencia de la en el pronóstico ecológico y de qué manera. ​​Muchas veces los ciclos de pronóstico existentes centran los resultados de la investigación, dejando a la comunidad como una idea de último momento. Para que el pronóstico ecológico refleje mejor las comunidades con las que trabajamos, debemos estructurar nuestros ciclos de pronóstico para centrar los valores que son importantes para las comunidades a las que servimos.

Por ejemplo, en la reunión de Geoscience Alliance en 2022, dedicamos un tiempo considerable a discutir cómo las comunidades marginadas seguido no tienen acceso a Internet, hardware informático y herramientas de infraestructura cibernética que muchas veces se dan por hecho en la comunidad de pronósticos ecológicos (Comisión Federal de Comunicaciones, 2012), que corresponde a la parte B del ciclo de pronóstico iterativo y gestión adaptativa. La lista de barreras para la persistencia en el pronóstico ecológico rápidamente se volvió demasiado larga para una publicación de blog, ya que nos dimos cuenta de que el ciclo de pronóstico refleja las barreras sistémicas para la inclusión que persisten en el pronóstico ecológico y las ciencias ambientales cuantitativas en general. Esto llevó a la pregunta: ¿Podemos reimaginar un ciclo de pronóstico que incorpore la inclusión en toda la empresa de pronóstico?

Figura 1. Pronóstico ecológico iterativo y figura del ciclo de manejo adaptativo de Dietze et al. 2018.


Hipótesis: ¿Quién decide qué se pronostica y cómo?

Las hipótesis, tal como son imaginadas en el proceso científico occidental, se originan dentro de una epistemología específica, o forma de conocer y comprender. Sin embargo, hay muchas epistemologías, y sin compromiso con las comunidades, los pronósticos desarrollados y motivados por la literatura ecológica histórica estarán sesgados hacia lo que se ha estudiado históricamente y los enfoques probados y verdaderos de la investigación científica. En ecología como en muchas disciplinas científicas, la literatura histórica está dominada por hombres blancos. Además, los temas y estudios que han producido la mayor cantidad de datos y resultados positivos (Nakagawa et al. 2022), o los flujos de datos que producen datos consistentemente colectados, archivados y disponibles, serán los más representados y reforzados continuamente. Una alternativa son los pronósticos coproducidos, que centran los supuestos, el conocimiento y las ideas más valiosas para las necesidades de una comunidad (Nyadzi et al., 2022; Record et al., 2022).

De manera similar, la financiación refuerza las barreras perpetuas de entrada para las comunidades históricamente marginadas. Los fondos de investigación que recompensan continuamente la novedad y la vanguardia de la ciencia muchas veces no se alinean con las necesidades locales o comunitarias (Flagg, 2022; Van Horne et al., 2023), y muchas veces no hay incentivos para continuar trabajando con las comunidades locales en problemas persistentes después de que termine el ciclo de subvenciones. Además, los investigadores de grupos históricamente excluidos, muchas veces más conscientes de las necesidades locales o comunitarias o que trabajan en el Sur Global, tienen su trabajo publicado y exhibido menos que los científicos en las instituciones del Norte Global (Smith et al., 2023), perpetuando así las barreras para publicar y financiar investigaciones relacionadas con los problemas de la comunidad.

Modelos: ¿Quién genera el modelo y quién tiene acceso a los modelos y datos?

Los modelos son, por definición, simplificaciones de la verdad. Los aspectos de la verdad que se simplifican están inherentemente sesgados por aquellos que generan los modelos, y los modelos frecuentemente son desarrollados para reflejar la hipótesis de un investigador sobre cómo funciona un sistema. Cuantos más aspectos se omiten para crear un modelo de trabajo, más oportunidades existen para eliminar perspectivas y verdades entendidas por las personas marginadas. La generación de modelos está sujeta a las mismas debilidades epistemológicas que las hipótesis porque los modelos en muchos sentidos son nuestras hipótesis. Para los sistemas ecológicos, específicamente, los mecanismos son complejos, y la inclusión de la diversidad epistemológica se vuelve cada vez más importante para la comprensión contextual en sistemas tan complejos (Page, 2014). Importantemente, la comprensión mecanicista y las decisiones basadas en información producida en asociación entre epistemologías son más sólidas (Berkes, 2009; Schuttenberg y Guth, 2015; Wheeler y Root-Bernstein, 2020). Entonces, además del valor inherente de un proceso más inclusivo (Morrison y Steltzer, 2021; NASEM, 2022), incorporando diferentes perspectivas puede proporcionar una comprensión más holística y posiblemente más sólida de los sistemas naturales.

Además, la creación de los modelos que producen pronósticos está influenciada por quién tiene acceso para usar o ejecutar los modelos. No todos los modelos son de código abierto y muchos tienen un legado histórico de propiedad que puede dificultar que las personas nuevas en esta área aprendan a ejecutar un cierto modelo. Incluso los modelos que se pueden construir desde cero (por ejemplo, los modelos de regresión lineal) requieren conocimientos estadísticos que mayormente son inaccesibles para los académicos fuera de los entornos académicos intensivos en investigación.

La propiedad de los datos también puede ser una barrera para crear modelos de pronóstico. Por un lado, hay muchos conjuntos de datos de código abierto (por ejemplo, NEON). Sin embargo, estos datos pueden ser demasiado grandes para obtener acceso usando computadoras portátiles personales, los datos muchas veces no son colectados por las mismas maneras en todas las ubicaciones (p. ej., NEON), y los datos que se han colectado estarán sesgados hacia los intereses de quienes han configurado el proceso de coleccion. También hay casos en los que se debe considerar la soberanía de los datos (Vera et al., 2019) y en los que los datos deben mantenerse propietarios (p. ej., trabajar con datos culturalmente relevantes o sujetos humanos). De manera similar, ejecutar modelos y hacer pronósticos probabilísticos requiere acceso a recursos computacionales y experiencia. El entrenamiento cuantitativo básico no es universal ni creado igual (Willson et al., 2023).


El uso del inglés como lingua franca en el contexto científico, impone barreras a quienes no son hablantes nativos del idioma en diferentes aspectos. Desde la dificultad para adquirir nuevos conocimientos en una lengua diferente a la materna, pasando por limitación de oportunidades de estudio y laborales, hasta sesgos de publicación  y experiencias de discriminación en instituciones académicas (Woolston & Osorio 2019). 

A lo largo del siglo XX se ha observado que el inglés pasó de ser parte de un modelo plurilingüe de comunicación científica internacional, en el que otros idiomas, como el francés y alemán también eran comúnmente utilizados, a tener un dominio casi absoluto en la comunicación de las ciencias naturales y sociales, lo que limita cómo y quiénes pueden comunicar ciencia e incluso aprenderla (Hamer, 2013; Amano et al 2021).

En el caso particular del Ecological Forecasting, al aprendizaje de tópicos complejos como la estadística, modelación matemática y programación, se añade la dificultad de hacerlo en inglés para hablantes ESL (English as a Second Language), ya que la mayoría de los textos, artículos y recursos de apoyo (e.g. foros, videos, tutoriales) están en este idioma (Amano et al. 2021). Prueba de ello es el repositorio que como Education Section de EFI hemos colectado, con materiales exclusivamente en inglés. Si bien esto está sesgado por la composición lingüística de nuestro grupo, es un reflejo de muchos tópicos en ciencias.

Si bien la enseñanza del idioma inglés suele estar en la currícula de la gran mayoría de países no angloparlantes desde la educación básica, y un 20% de la población global se encuentra estudiando inglés como segunda lengua (TEFL Academy 2020), la comprensión necesaria del idioma para poder aprender y producir predicciones ecológicas difícilmente puede ser garantizada para personas que no hayan recibido instrucción privada y vienen de contextos socioeconómicos privilegiados. Esto impone dificultades a las personas interesadas en investigación que no hablen inglés tanto para adquirir nuevos conocimientos como para hacer posgrados, colaboraciones de investigación y publicar en revistas internacionales (Woolston & Osorio 2019; Amano et al 2021).


El ciclo de pronóstico que se muestra en la Figura 1 ha aparecido en diferentes iteraciones en estudios subsecuentes (por ejemplo, Moore et al. 2022). Hemos agregado dos “ciclos” de pronóstico alternativos, que se describen a continuación, a esta lista, con el objetivo de reducir las barreras a la inclusión. Mientras considerábamos figuras alternativas, preguntamos: “¿Cómo deben ser? ¿Debería ser un ciclo o algún otro modelo conceptual?” Las dos figuras que se presentan aquí deben considerarse como primeros borradores y dan algunos elementos de reflexión sobre las barreras a la inclusión en el proceso de pronóstico y cómo enfrentarlas. En el futuro, esperamos que el rediseño del marco de pronóstico ecológico pueda ser, en sí mismo, un proceso inclusivo. Presentamos figuras que representan los ciclos de pronóstico en este paquete de diapositivas, donde agradecemos las contribuciones adicionales de los lectores e invitamos a cualquier persona interesada a unirse a nuestros esfuerzos para escribir un manuscrito asociado con este proyecto.

Ciclo de Pronóstico Centrado en la Comunidad – Ejemplo 1

La ciencia se alaba mucho por su objetividad, pero cada hipótesis o pregunta conlleva sesgos inherentes que reflejan los valores, los procesos de pensamiento y las experiencias de los investigadores, como se discutió anteriormente en la sección Las Barreras. Estos valores se muestran cuándo se aplican los hallazgos científicos, como en la toma de decisiones basada en pronósticos, y el usuario final usualmente es diferente que el investigador, con diferentes valores, procesos de pensamiento, y experiencias. En el grupo de trabajo DEI de EFI, pensamos mucho en estas diferencias y en cómo unirlas para los objetivos comunes de mejorar la área de la predicción ecológica y promover la toma de decisiones ecológicas informadas. Reconocemos no solo el valor inherente de la inclusión (Morrison y Steltzer, 2021), sino también el beneficio que las diferentes experiencias y conocimientos pueden tener en una área emergente como el nuestro (Woelmer et al. 2021, Willson et al., 2023).1

Figura 2. Un intento imperfecto de visualizar un ciclo de pronóstico centrado en la diversidad. En este ciclo, ya sea que el objetivo final sea la comprensión sistémica o la toma de decisiones, los sesgos y objetivos del pronosticador se consideran explícitamente.

Con la inclusión en mente, creamos figuras como la Figura 2 para enfocar el ciclo de pronóstico dentro de una comunidad de práctica diversa. La Figura 2 enfatiza centrar los objetivos del proyecto dentro de los valores de los investigadores y usuarios finales (círculo azul). La ética, por lo tanto, no puede ser una idea de último momento: la evaluación y reevaluación continua de las metas, los valores y la ética debe ser un elemento de acción discreto. Estos sesgos luego se reconocen explícitamente como información del proceso científico (círculo rojo) y viceversa (flechas moradas). Importantemente, los que están involucrados en el esfuerzo de investigación de pronósticos pueden unirse al proceso desde el círculo azul o rojo, lo que demuestra las cualidades interdisciplinarias de los pronósticos. Y finalmente, las decisiones se pueden tomar sin ciencia, pero no se pueden tomar independientemente de los valores y sesgos personales (flechas negras internas).

1 El valor de la diversidad y la inclusión va mucho más allá de la previsión ecológica. Además, consulte la declaración de DEI en la página web del Grupo de trabajo de EFI DEI.

Ciclo de pronóstico centrado en la comunidad – Ejemplo 2, modelo de práctica en el Tandy Center for Ocean Forecasting

El Tandy Center for Ocean Forecasting trabaja en el desarrollo de pronósticos para comunidades, industrias y otros usuarios. Al pensar en la inclusión, las personas del Centro Tandy están pensando en aquellas comunidades que se ven afectadas por los pronósticos que crean y las decisiones basadas en ellos. En el ciclo de pronóstico convencional (Figura 1), los científicos están centrados. Sin embargo, algunas de las lecciones que los pronosticadores de ecosistemas oceánicos han aprendido y han tenido la generosidad de compartir a lo largo de los años han tenido que ver con las consecuencias no deseadas y los daños accidentales causados por programas de pronóstico bien intencionados (Hobday et al., 2019). Ser pronosticador puede ser precario, y probablemente sea imposible evitar todos los escollos, pero con suerte, podemos aprender de algunas de estas lecciones. El Centro trabaja bajo la creencia de que ser más inclusivo puede ayudar a alinear las necesidades de las comunidades que usan, o se ven afectadas por, el pronóstico con el diseño del programa de pronóstico. El esquema que se incluye a continuación (Figura 3) proviene de la documentación de orientación del Tandy Center (Record, 2022). No es exactamente un ciclo, sino más como un mapa para guiar los diálogos con diferentes grupos comunitarios para que puedan colaborar en cada paso. También hay un video que recorre la figura. En resumen, las comunidades deben ser socios durante todo el proceso, contribuyendo al diseño del sistema. Al centrarnos en los usuarios de pronósticos en vez de los pronosticadores, esperamos ayudar a que el desarrollo de pronósticos sea más accesible y llegue a nuevas personas y lugares que podrían ser de bajos recursos o están excluidos de la corriente principal de las aplicaciones de pronósticos.

Figura 3. Diagrama esquemático del marco basado en los depositarios para construir sistemas de pronóstico (Registro 2022). Las flechas indican el movimiento bidireccional entre cualquiera de los componentes del marco, potencialmente varias veces.

Debilidades notables que aún permanecen

Por supuesto, incluir todo en una sola figura es un desafío (por eso la publicación de este blog). Discurso continuado sobre cómo afrontar las debilidades se comparte en Próximos Pasos. Además, las ideas más notables se han omitido o se les ha quitado énfasis en las figuras anteriores, pero se han discutido a lo largo de las reuniones del grupo de trabajo. Por ejemplo, los esfuerzos de diversidad, equidad, inclusión y justicia (DEIJ) se enumeran muchas veces en las solicitudes de subvenciones como impactos más amplios, pero no se considera que contribuyan al mérito intelectual. En la Tabla 1 se puede encontrar una lista más completa, que incluye algunas alternativas más inclusivas.


Pensando en los estudiantes

El legado de las barreras a la inclusión se ha llevado a cabo a través de la educación científica, y romper este legado debe incluir el pensamiento sobre la educación. Los estudiantes de hoy están muy motivados, ansiosos y listos para enfrentar asuntos generalmente definidos sobre diversidad, equidad, inclusión y justicia. Esta pasión se forma a partir de una variedad de experiencias e identidades. La buena educación y los enfoques pedagógicos están atentos de la diversidad de los alumnos (Harris et al., 2020; Miriti, 2019; Rawlings-Goss et al., 2018). La enseñanza toma muchas formas, pero puede consistir en (1) actividades de aprendizaje activas y colaborativas (Corwin et al., 2018; Graham et al., 2013), (2) proporcionar agencia y voz a los estudiantes (son co-creadores en los resultados) , (3) honrar los conocimientos existentes, (4) evitar el lenguaje deficitario al enseñar. Se pueden encontrar recursos adicionales para la enseñanza inclusiva en los Recursos de pedagogía inclusiva compilados por EFI, así como en esta extensa lista de recursos y estrategias de enseñanza inclusiva del Centro de Investigación sobre el Aprendizaje y la Enseñanza de la Universidad de Michigan. También hay esfuerzos dentro de la comunidad de EFI y el Grupo de Trabajo de Educación de EFI para compilar recursos educativos abiertos (Willson and Peters, 2021; Willson et al., 2023, Tabla 1), así como para desarrollar módulos educativos en colaboración con profesores de Instituciones al Servicio de las Minorías para enseñar herramientas de ciencia de datos que incorporan conocimientos ecológicos tradicionales y valores culturales.

Revertir la marginación

La ciencia de datos continúa teniendo tasas de representación tremendamente bajas de grupos históricamente excluidos y marginados. En consecuencia, los datos y las herramientas informáticas muchas veces se crean a partir de una visión limitada del mundo de prioridades, valores y prácticas (consulte la sección Las barreras para obtener más detalles). Cuando estas herramientas se utilizan en comunidades marginadas, muchas veces son limitadas a las interpretaciones, sesgos y nociones preconcebidas de los creadores de estas herramientas (David-Chavez y Gavin, 2018). Sin centrar el conocimiento, la experiencia y las perspectivas de los grupos marginados en la creación de estas herramientas, se convierten en herramientas de opresión, que promueven el borrado, la perpetuación de estereotipos, y la continuación de la violencia y el daño a las comunidades. Si queremos que el pronóstico ecológico se convierta en una herramienta para promulgar un cambio significativo y justo, tenemos que alterar estructuralmente nuestros métodos y prácticas de investigación a través de una lente de interseccionalidad para centrar las voces de las comunidades marginadas, convertirlas en líderes en la creación de herramientas, y coproducir modelos y herramientas con las comunidades (Crenshaw, 2014). En la Tabla 1, mostramos cómo las modificaciones estructurales a las prácticas de investigación pueden mejorar el compromiso con los grupos marginados, mostrando la transferencia de poder hacia el beneficio de la comunidad. Al animar a la comunidad de pronósticos a considerar cuidadosamente cómo desarrollar colaboraciones, esperamos que las investigaciones futuras centren y traigan las perspectivas de las personas históricamente excluidas. 

¡Conéctate con nosotros!

El desarrollo de esta publicación ha sido un proceso de aprendizaje para todo el grupo de trabajo EFI DEI y reconocemos que se trata de un proceso continuo e iterativo en el que es importante escuchar voces adicionales y seguir aprendiendo. Esperamos que esta publicación pueda ayudar a la comunidad científica y de EFI a seguir pensando en las barreras para participar en la predicción y las ciencias ambientales y las soluciones para superar esas barreras. Damos la bienvenida a los comentarios, sugerencias y reacciones sobre estas ideas presentadas. Nuestro objetivo es convertir esta publicación en un manuscrito que se base en estas ideas e invitamos a cualquier persona que desee compartir comentarios o participar como coautor en el esfuerzo del manuscrito a comunicarse con nosotros en los comentarios a continuación, en, en el canal #inclusion del grupo EFI Slack, o a través de Twitter (@eco4cast).

Tabla 1. Esta tabla proporciona una lista de diferentes temas relacionados con el pronóstico (y la ciencia de datos y la ciencia en general) que demuestran cómo las modificaciones estructurales a las prácticas de investigación pueden mejorar la inclusión de grupos históricamente marginados. Las tres columnas representan situaciones en las que hay mayor margen de mejora para conectar y centrar a los grupos marginados, en las que hay espacio para mejorar, y una columna con situaciones que son más beneficiosas para la mayoría de las personas. Es importante señalar que esta tabla está escrita como una generalización, y todas las prácticas de investigación en un proyecto deben decidirse en colaboración con la comunidad y estar alineadas con sus valores. Esta tabla se inspiró en The Wheel of Power and Privilege y otros trabajos relacionados (Jerarquía de datos indígenas, Global Indigenous Data Alliance y modelos para descolonizar la investigación científica, por ejemplo, David-Chavez 2019, David-Chavez et al. 2020). También puede ver un PDF de la Tabla AQUÍ.

TemaSituaciones con mayor margen de mejoraSituaciones con espacio para mejorarSituaciones que son las más beneficiosas y funcionan para centrar y atraer a las personas
Participación de la comunidad– La investigación se realiza en comunidades sin contribuciones o con contribuciones limitadas de la comunidad
– Los dólares de investigación no están dirigidos a la comunidad
– Los investigadores son desconocidos a la comunidad
– Coproducción de conocimiento entre investigadores y la comunidad
– Los dólares de investigación se distribuyen para trabajar con la comunidad
– Los investigadores incluyen miembros de la comunidad
– Reconocer y afrontar los impactos generacionales y a largo plazo
– Comunidades marginadas dirigen iniciativas de investigación
– Los dólares de investigación van directamente a los grupos marginados
– Los miembros de la comunidad son IPs en proyectos de investigación
– La investigación involucra a niños y jóvenes en el desarrollo y ejecución de proyectos
Educación – Prácticas educativas eurocéntricas
– El valor se asigna solo a la programación con credenciales
– Programas limitados a instituciones R1 y principalmente instituciones blancas
– El alto costo de la matrícula es inaccesible
– Los materiales mayormente se presentan sólo en inglés
– La educación enfatiza diferentes culturas y valores
– Mejor acceso a la educación superior
– Programas disponibles para instituciones públicas, comunitarias y tribales 
– Inclusión de materiales bilingües
– El material del plan de estudios es culturalmente informado y relevante
– Reconocimiento del impacto de los programas no acreditados
– Improving access to credentialed/advanced degree programs
– Mejorar el acceso a programas acreditados/de grado avanzado
– Los programas educativos abarcan el espectro de la educación (K-12 hasta programas de posgrado) para promover el aprendizaje generacional
– Proporciona educación fuera de las instituciones académicas
– Conocimientos ecológicos tradicionales incorporados en la educación
– Los materiales se crean y traducen en los varios idiomas usados por la comunidad
Beneficios y daños – Usar herramientas o datos que mal representan una comunidad
– La ciencia se lleva a cabo sobre personas sin consentimiento
– Perpetuación de la violencia, el daño y el borrado de grupos marginados
– Las comunidades y las personas son consideradas como una idea de último momento en los proyectos
– Los valores se centran en los ideales occidentales
– No reconoce a las comunidades de donde proviene la investigación
– La ciencia se lleva a cabo con el consentimiento de la comunidad pero sin sus contribuciones en el diseño
– La investigación proporciona conocimiento a la comunidad
– Prioriza y centra las comunidades más privilegiadas o las comunidades más representadas
– Reconocimientos de tierras reconocen el legado de la colonización
– Trabajando en ciencia en conjunto con las comunidades para beneficiar a las comunidades
– Dar agencia a las comunidades marginadas para definir y tener acceso a la investigación
– Centra a las personas y las comunidades en los proyectos
– Los valores se centran en los ideales de las comunidades
– Devolución de tierras, o los reconocimientos de tierras reconociendo el daño y el beneficio continuos para la institución
Computación y
– Requerir suscripciones para software 
– Opciones limitadas de entrenamiento
– Acceso computacional y a internet limitado
– Uso de herramientas accesibles y de bajo costo
– Proporcionar acceso a computadoras o dispositivos móviles
– Uso del diseño universal en materiales
– Recursos desarrollados para personas sin acceso a Internet en mente
– Se utilizan o generan materiales de código abierto
– Los recursos computacionales son fácilmente accesibles
Disponibilidad de datos (incluidas revistas y herramientas) – Los datos solo están disponibles a través del acceso privado o detrás de un muro de pago
– Las comunidades y los individuos no pueden controlar los datos o el acceso de datos
– Requiere software especializado, herramientas, y conocimiento de dónde acceder a los dato
– Datos colectados sin el consentimiento de la comunidad o los individuos
– Datos disponibles por solicitud en lugar de acceso abierto en línea
– Los datos y las herramientas se publican para compartir en futuras investigaciones y colaboraciones
– Los datos son colectados con el conocimiento y consentimiento de la comunidad
– Promueve el uso ético de los datos
– Reconoce y apoya la soberanía de datos
– Amplia educación sobre cómo obtener acceso a herramientas, recursos y datos
– Datos colectados con respecto a los valores y prácticas culturales
– Mejora la facilidad de acceso a datos públicos
– Los datos se abren según los deseos y necesidades de la comunidad
Diversidad y Justicia – Equipos de investigacion homogéneos en terminos de raza o cultura
– La investigación perpetúa el daño a los grupos marginados o mantiene el statu quo
– La financiación se enfoca en los impactos a corto plazo
– Los proyectos finalizan cuando finaliza la financiación
– Los investigadores trabajan con estudiantes de orígenes diversos
– Mejora la representación de los grupos marginados en los espacios de investigación
– Equipos diversos con agencia
– La investigación promulga un cambio social significativo
– La financiación y la investigación reconocen la importancia de los impactos a largo plazo
– La implementación del proyecto continúa después de que finaliza la financiación


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Diversity with EFI and How You Can Get Involved

Date: June 18, 2020

Post by: The EFI Diversity, Equity, and Inclusion Working Group

The Ecological Forecasting Initiative, like many other organizations, calls for justice for George Floyd and countless other Black individuals and persons of color, and we stand in solidarity with our Black colleagues and friends saying #BlackLivesMatter. Our EFI Diversity, Equity, and Inclusion (DEI) Working Group is committed to listening, learning, and exploring ways to promote anti-racism and to make EFI, and STEM fields more broadly, a welcoming environment. In regards to ecological forecasting, as a first step, we need the input and experience from people of all backgrounds at all stages of the ecological forecasting process, from forecast development and implementation to stakeholder decisions. Ecological forecasting as a field is relatively new; creating an inclusive, anti-racist field starts with understanding the lived experiences of all types of forecasters, end-users, and community stakeholders. We have more in-depth initiatives and tasks associated with the current NSF funded EFI-RCN grant and other proposals submitted for review, but for now, the following are four ways we invite you to get involved by joining the group, using or adding to our Bibliography of resources, filling out a short 5-minute survey, or joining our book club.

Of all the EFI Working Groups, our DEI Group has the smallest number of participants. We welcome anyone who is interested in participating to learn more about ways to expand diversity and inclusion as well as brainstorming ways to increase diversity within the ecological forecasting field. Our next call is June 30 and upcoming monthly meetings are posted on the EFI’s DEI webpage, as is our Strategic Plan, which is a living document that provides an overview of the steps the DEI Working Group is taking to promote diversity, accessibility, and inclusion in EFI. Email to be added to the mailing list for this group.

If you are not able to join the Working Group calls at this time, there are additional ways to get involved. We are compiling a Bibliography that provides resources for learning more about anti-racism and the diversity status of fields relevant to ecological forecasting. These resources include lists of minority supporting associations, links to diversity and inclusion plans from professional societies, blog posts, publications, and compiled lists of resources from other organizations. This is also a living document, to which we will add additional documents moving forward. If there are additional resources you have found useful, they can be submitted through this Google form.

As part of Step 1 of our Strategic Plan, “Identify and clarify the problem”, we are working to identify the current status of diversity within fields relevant to ecological forecasting as a baseline to assess the current status of diversity within the Ecological Forecasting Initiative, specifically. Once we assess the current diversity status of EFI, our next goal is to provide suggestions to ecological forecasting labs about ways to recruit more diverse students into our undergraduate and graduate programs.
To assess the current status of diversity within fields relevant to ecological forecasting we are using the NSF funded NCSES Interactive Data Tool IPEDS database of the racial and ethnic backgrounds of students that have graduated from US institutions in over 400 academic programs. We have narrowed down the list to 29 academic degrees and are asking for your help to rank the relevance of these degrees to ecological forecasting in this short survey ( Once we know which academic degrees are most relevant to ecological forecasting, we can assess the current diversity of those degrees relative to EFI. We will then work on Step 2 of our Strategic Plan, “Identify barriers that may prevent students from underrepresented groups from participating in ecological forecasting.”

To encourage open, honest conversation and anti-racist thinking, EFI will host its first virtual book club. We will begin with The Years That Matter Most: How College Makes or Breaks Us by Paul Tough. Tough’s book explores privilege in higher education, from the application process to the classroom. As many forecasters are educators and participants in higher education, we believe this book will serve the interests of EFI’s mission while helping participants grow in anti-racist values. The book club is open to all participants, regardless of EFI membership, race, ethnicity, gender, religion, or any other personal identity – we ask only that you participate with an open mind and a willingness for vulnerability. For those who would like to participate but need help acquiring the book, we have a limited amount of financial assistance available. Email for more info.
Logistics: The book club will meet weekly, in the evenings, starting the week of July 13th, with about 40-70 pages of reading per meeting (although meeting frequency and page counts can be adjusted to meet the needs of the group). If you are interested in participating, email so we can send you the doodle poll to find a day/time for the group to meet.