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.

Transporting Models Between NEON and non-NEON Systems

September 5, 2023

Brendan Allison1, Olufemi Fatunsin2, Jeffrey Mintz3

1University of Texas, Austin, 2University of Alabama, 3University of Florida

AS NSF NEON data becomes more prominent in forecasting and other forms of ecological modeling, these models may potentially become products in themselves, distilling some important characteristic of this continental-scale network. A natural follow-up question as we seek to use not only the raw data but these derived products is that of model transportability. In this project group started at the 2023 Unconference, we asked: 

1) How can we take models trained on NEON data and refine them for use in another context?

2) How can we take models trained on non-NEON data and refine them on NEON data?

Doing this effectively can empower a range of applications, including local field studies, adaptive management, and data fusion from multiple monitoring networks, enabling greater statistical power for big ecological questions. We realized that, whether transporting a model to or from NEON, the vast majority of challenges are the same. These included unbalanced data, different monitoring protocols, different predictors, and different site selection criteria. As anyone who has fused multiple datasets together can tell you, even ostensibly identical data products can differ in both subtle and dramatic ways. Naturally, models trained on one data product will inherit the distinct characteristics of the monitoring networks they are derived from. There is nevertheless remarkable potential in being able to leverage what are in many cases continental-scale models for anything from fusion with another continental-scale network to an informed prior for a small-scale field study, dramatically increasing statistical power.

In framing the problem, we found it helpful to consider similarities with longstanding efforts to effectively downscale global climate models to local forecasts. Here, the two main classes of approach are dynamic (re-running a tuned version of the full model with nested local components, ensuring that known physics are respected) and statistical (examining correlations between global climate model outputs and local history, with the benefit of much faster performance). For the greatest flexibility, including compatibility with black-box machine learning approaches with unknown dynamics, we took inspiration from the statistical approaches.

Our only hard requirement as input for the modeling process was thus an existing model that can make predictions for an arbitrary set of sites, given a set of predictor covariates for these sites, alongside the ability to measure the true value of the predicted variable at these sites. From here, we can subtract prediction from true value, generating a dataset of residual errors. This sort of additive bias correction is not the only approach to the problem, but it is simple and effective. Any skill in predicting these errors represents an improved model: simply take the sum of the original model and the bias correction term to make new forecasts. At the same time, one may examine the various machinery of the residual error model alone to learn something about the gaps and biases in the original model. Allowing models to be incrementally improved in this fashion will enable teams to improve on existing models by tailoring them to their particular ecosystem of interest and the data they have at hand.

Case Study

To focus our efforts, we picked the particular case study of bringing together NEON forest data with similar datasets generated under the Forest Inventory Analysis (FIA) program. Because FIA has multiple hundreds of thousands of plots measured across decades of operation, while the more recent NEON network offers more intensive monitoring within a smaller set of sites, both networks have the potential to bolster one another. Our basic setup was to train a model predicting forest productivity at FIA sites from a small set of environmental covariates. Though we would train this ourselves, we would not take advantage of our knowledge of its structure or details. Instead, it serves as a stand-in for any existing black box model, which in some sense represents the distillation of a large body of FIA data. Typically, it would be a model generated by some other research group or forecasting team, which we seek to leverage in our own work. Using predictions from this FIA-trained model, we calculate a set of error residuals at both NEON and FIA sites, and ask: can we leverage this for a more robust multi-system model?

Our first product was the development of a draft Bayesian multilevel model that would be equally capable of integrating multiple sets of continental or global-scale monitoring networks as it would be of making predictions at a single site of interest, but informed by some larger statistical structure. We show our first/guiding version of this in the figure below; it changed a little over development, but the basic idea remained. Hierarchical structures such as this are commonly described as partial pooling approaches. This is because FIA and NEON systems are not assumed to operate under the same model. Nor are they assumed to be fully independent. Instead, they will flexibly share a set of data-driven parameters, in this case through the top layer of a global forestry model, while having the flexibility to stray a little from this global model to account for system-specific differences. 

Figure 1 represents our original version of a partially pooled model. At the top layer, we have a global scientific model. This informs system-specific models in layer two, which then inherit ecoregion in layer three (eco_i) and plot-level parameters (p_j) in layer four. The bottom layer are the multiple observations at each plot. In this case, observations are not biomass or other direct data products, but residual error (e_k) after subtraction of predicted from actual values by some black-box model. The main modification to this model was to place the NEON and FIA models alongside the ecoregion layers as an additive source of variance, rather than nesting ecoregions inside the respective system models.

To account for spatial autocorrelation, we briefly considered Gaussian processes, but settled on building ecoregions into the model hierarchy for two main reasons. The first is that geographic distance is different from ecological distance: a mile might take you from prairie to mountaintop in one case, and from prairie to prairie in another case. The second is practical: because Gaussian processes and related kernel methods tend to scale poorly with increasing data, they tend to require extensive approximations for the fusion of multiple continental-scale datasets.

As mentioned, while this was our inspiration, we did make a few changes. The main change was to dial back on the nesting. In the above diagram, ecoregions are nested inside continental-scale monitoring networks, such that Level 3 – Ecoregion 1 – NEON plots are allowed to have different predictive models than Level 3 – Ecoregion 1 – FIA plots. This could be useful and true, but we realized that we were most interested in 1) the global forestry model at the top of the hierarchy, 2) any global differences between the types of sites or measurement protocols of NEON and FIA, and 3) any Level 3 Ecoregion variations in model structure. Thus the minor combinatorial explosion in parameters of nesting ecoregions inside monitoring systems seemed a cost without a corresponding benefit in scientific lessons learned. We decided to bring both ecoregion and monitoring system variables to the same level of the hierarchy. 

So far, the model has been tested on synthetic data, with code developed in Stan, using the CmdStanR library in R. Once we’ve had the chance to hone the model against real data, we’ll push all the code to a NEON repository for the community to begin playing around with it. From there, some version of the project is likely to continue and become a more formal manuscript. We may also reintroduce some complexity to the model in the form of nested ecoregions. 

With that said, we’d be interested in hearing from other members of the community. How to integrate NEON data with that of other monitoring networks, how to use it to inform local field studies, and how to leverage it for management goals at non-NEON sites are all open and important questions. What potential applications do you see? Are you working on similar projects?

Contact us at info@ecoforecast.org to share your interests in the project or similar work you are doing!

Reenvisioning EFI-RCN NEON Forecast Challenge Dashboard Visualization

August 22, 2023

Melissa Kenney1, Michael Gerst2, Toni Viskari3, Austin Delaney4, Freya Olsson4, Carl Boettiger5, Quinn Thomas4

1University of Minnesota, 2University of Maryland, 3Finnish Meteorological Institute,4Virginia Tech, 5University of California, Berkeley

With the growth of the EFI NEON Ecological Forecasting Challenge, we have outgrown the current Challenge Dashboard, which was designed to accommodate a smaller set of forecasts and synthesis questions. Thus, we have reenvisioned the next stage of the EFI-RCN NEON Forecast Challenge Dashboard in order to facilitate the ability to answer a wider range of questions that forecast challenge users would be interested in exploring. 

The main audience for this dashboard are NEON forecasters, EFI, Forecast Synthesizers, and students in classes or teams participating in the Forecast Challenge. Given this audience, we have identified 3 different dashboard elements that will be important to include: 

  1. forecast synthesis overview, 
  2. summary metrics about the Forecast challenge, and 
  3. self diagnostic platform.

During the June 2023 Unconference in Boulder, our team focused on scoping all three dashboard elements and prototyping the forecast synthesis overview. The objective of the synthesis overview visual platform is to support community learning and emergent theory development. Thus, the synthesis visualizations are aimed at creating a low bar entry for multi-model exploration to understand model performance, identify characteristics that lead to stronger performance than others, the spatial or ecosystems that are more predictable, and temporal forecast validity. 

You can view the prototype developed during the meeting HERE and in Figures 1 and 2. 

Figure 1.  Static image of an interactive map of aggregate forecast skill relative to climatology at each forecasted sites, here showing the water temperature forecasts for the aquatics theme. Bubble colour represents the continuous rank probability score (CRPS) skill relative to climatology with positive values (blues) showing submitted models on average perform better than climatology and negative values showing submitted models perform worse (reds). The size of the bubble represents the percentage of submitted models that outperformed the climatology null (i.e., larger sized bubbles have a higher percentage of skilled models). When hovered over, the bubbles show this percentage (perc_skilled), the site type (field_site_subtype), as well as the total number of models forecasting at that site (n_mod). 

Figure 2. a) Percentage of submitted models that are classed as ‘skillful’ (outperform the null climatology forecast based on the continuous rank probability score metric) at the river (n=27) and lake sites (n=6) for water temperature forecasts at each horizon from 1 to 30 days ahead. b)  Percentage of submitted models that are classed as ‘skillful’ for water temperature forecasts at six of the lake sites (https://www.neonscience.org/field-sites/explore-field-sites). 

Developing these graphics requires aggregation of skill scores. There are a multitude of metrics that can be used to calculate the skill score, which each have their own benefits and flaws. Thus, there should be multiple skill scores for different metrics with clear presentation of what metric is used at a given visualization. Additionally, in order to isolate what sites are more interesting from a model development perspective, there needs to be a comparison of how many of the models meet a baseline skill score at a given site at a chosen time frame. That allows isolating challenge areas and also easily informs which models really succeed at situations where others struggle. For better future analysis of how models perform at certain sites, we also envisage the visualization to include the skill scores for the relevant drivers (NOAA weather) for comparison. For example, if we see a drop in skill across models in water temperature projections after some time, there should be a direct method to assess if this reflects overall flawed model dynamics or if the weather forecast driving the water temperature loses its reliability. This also allows the user to approximate a maximum length in which the model performance analysis is at all useful.

In addition to the main synthesis overview, the goal of this platform is to support exploration of synthesis data. For all themes, there was general agreement that it would be useful to pull up at a glance, site characteristics, a photo, and basic summary statistics about the number of models and model performance. 

During the meeting, we worked with the Aquatics and Beetles Challenge teams to identify some of the key data aggregation groupings that will be important to facilitate exploration. One important distinction arose during the conversations – the baseline model, time scale, and data latency.  For Aquatics there is a long time series of data that create a climatology and data are provided relatively quickly via data loggers. For Beetles, there is a different null baseline model given the length of historic data that is different at each site and it takes a year to provide beetle abundance and richness assessment. There was also a desire to have specific types of synthesis visualizations including the species accumulation curve over years, 3-year running average, and indicating the lower and upper bounds of a particular variable (use in scale). Thus, for both Beetles and Aquatics there are similarities and differences in the types of groupings that would be most useful to support synthesis exploration. 

Table 1. Different data groupings that would be useful to facilitate easy-to-develop synthesis visualizations of the EFI-NEON Forecast Challenge models to facilitate learning and community theory development.

GroupingsAll ThemesAquaticsBeetles
Team / Challengetheme, site, model ID, customized classroom or team groupingsparticular variables (e.g., DO) within a theme
Spatial / Ecosystemssites, NEON domains, site type (river, stream, lake…), altitude (high vs lowlands)sites by distance, dominant NLCD classification
Temporal Scale average for past year, seasonal groupings,1 day, 5 days, 7 days, 15 days, 30 days14 days, growing season, multi-year (up to 5 year) forecasts
Modelsbest model at each site, model inputs, model structure, functional type, output uncertainty representationmodel run time, model computational requirements
Skill Scoringcurrent skill forecast approaches, better than climatology/null baseline, comparison of your model to the best forecast
Other Featuresenvironmental variables and weather forecast observationscomparison with weather/climate forecast skilldisturbance events (e.g., widlfire), growing season dates at each sites, site disturbance characteristics (e.g., mowing, fencing)

In addition to the synthesis overview, there were two complementary and linked platforms that will create the dashboard.  First, the objective of the forecast challenge overview is to provide a basic summary of metrics related to the overall EFI NEON Ecological Forecasting Challenge. Specifically, the metrics that would be included are: number of forecasts submitted, number of unique teams, percentage (or median of all) of models that are better than climatology or a null model per theme, and total forecast and observation pairs.

Second, the objective of the self-diagnositic platform is to provide an overview for individuals or team forecast contributions and performance. The types of summaries that will be provided for the forecasters are: confirmation of forecast submission, date of the most recent forecast submitted for a model, model performance relative to climatology or null model, model prediction versus observation, model performance vs other selected models, and model skill over a specific time horizon (to assess whether it performs better over time).

Overall, the goal of the re-envisioned visual dashboard is to create platforms that will allow us to track challenge engagement, individually or as a team diagnose any model submission problems and performance improvement opportunities, and support community theory development through a synthesis given the range of models submitted through the EFI NEON Ecological Forecasting Challenge.  Long-term, if this platform structure is useful and robust, it could be applied to other systems where there are multi-model predictions and there is a desire to collaboratively learn together to improve our theoretical understanding and forecasts to support decision-making.

We are looking for input from the EFI community on the synthesis dashboard for other themes, to discuss with individuals what synthesis would be most relevant to phenology, terrestrial, and ticks forecasters. Reach out to info@ecoforecast.org to share your thoughts or let us know you would like to join future conversations about updating the dashboard. 

EFI at the Ecological Society of America 2023 Conference

Date: July 26, 2023

EFI is excited about the opportunity to connect with the broader community through a number of events at ESA in Portland this year! Below are details about a workshop about the NEON Forecasting Challenge, the EFI Social, and the EFI organized oral session. Other ecological forecasting talks are also listed.
For the first time, we will also 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 or Anna Sjodin during the Conference or at the EFI-sponsored Organized Oral Session on Tuesday or the EFI Social on Wednesday to get a badge and look for others with the green badge!

EFI Social
Wednesday, August 9 at 6:30-8:00 PM

Meet up with others in the EFI community on Wednesday evening, August 9 from 6:30-8:00 pm at the Cartside Food Carts. Cartside has a range of food and drink options and is a less than 15-minute walk from the Convention Center.

Workshop:  Can You Predict the Future? Introducing the NEON Ecological Forecasting Challenge
Monday, August 7 at 11:45 AM – 1:15 PM; Location: C124

Freya Olsson (Virginia Tech) will be leading this 90-minute workshop that will be of interest to the EFI community. The workshop is perfect for those who want to know more about getting involved in the NEON Ecological Forecasting Challenge and will provide participants with materials and information to get them started. The primary goals of the session are to 1) introduce the Challenge and forecast themes; 2) familiarize participants with Challenge documentation as well as easy-to-use software, tools, and templates that have been developed in the R programming language; and 3) and facilitate participants in submitting their own forecast to the Challenge! We will provide a template forecasting workflow in R, using the daily terrestrial fluxes of carbon and evaporation theme as an example (neon4cast.org), and provide assistance to participants to set up their own forecasts. You can make sure you are ready to go for the workshop by looking at the draft materials here.

If you have questions about the workshop or set up instructions, please email freyao@vt.edu.

EFI Organized Oral Session: Ecological Forecasting: Applications, Discoveries, and Opportunities
Tuesday, August 8 at 1:30-3:00 PM; Location: 256

Other Forecasting Presentations

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!

Monday, August 7

Tuesday, August 8

Wednesday, August 9

Thursday, August 10

Collaborative Innovation and Skill-building at the 2023 Unconference: Empowering Ecological Forecasting with NEON Data

The 2023 EFI Unconference, hosted by the Ecological Forecasting Initiative Research Coordination Network (EFI RCN) and supported by the National Science Foundation, brought together 45 passionate individuals at the National Ecological Observatory Network (NEON) headquarters in Boulder, Colorado on June 21-23, 2023 to work on a diverse range of projects that were nominated and selected by the participants. With a focus on collaborative problem-solving, the Unconference fostered a unique environment for participants to exchange knowledge, generate new approaches, and advance the field of ecological forecasting.

In addition to project development, activities included a warm welcome from Kate Thibault, NEON Science Lead, icebreaker activities, expertly facilitated by Cayelan Carey from Virginia Tech that helped participants connect and form meaningful relationships, a tour of NEON facilities, and a poster session and social hour, where participants showcased their research and projects. Through these activities, Unconference participants and NEON staff were able to engage with one another, exchange feedback, and forge new collaborations.

To ensure a productive and focused Unconference, participants engaged in a review of project ideas and subsequent project selection. This process allowed attendees to propose projects aligned with their interests and expertise and fostered a sense of ownership and investment in the outcomes. Ten project groups developed out of the 24 that were initially proposed as part of the pre-meeting preparation.

Summaries provided by each project working group are listed below. Some groups will provide additional details in forthcoming blog posts, so be sure to watch for those future posts.

This was the first in-person EFI event since 2019 and it was absolutely lovely to be in the same room to meet new people and to see in-person people we had only seen on Zoom before.  We appreciate the Unconference participants’ willingness to share their time, talents, and perspectives.  As you will read below, there were a number of accomplishments over the three days of the meeting and we look forward to seeing future outcomes from what was developed at the Unconference!

Unconference participants. Photo courtesy Quinn Thomas

List of Projects

Spatially Explicit Forecasting

Participants: John Smith, David Durden, Emma Mendelsohn, Carl Boettiger

To date, the NEON Ecological Forecasting Challenge has been focused on generating near term forecasts for specific sites. However, many interesting ecological phenomena occur across both time and space. At the EFI 2023 Unconference, our group prototyped a forecasting challenge that is also spatially explicit. For our prototype forecasting challenge, we focused on Leaf Area Index (LAI) recovery in post-burn areas. Our focal sites so far include the California August complex fire and the Colorado East Troublesome fire. Our work at the Unconference focused on building cyber-infrastructure to ingest and aggregate data, build target files, assess models using proper scoring rules, and build baseline climatological forecasts. Current progress, including an example notebook and a detailed workflow diagram, are available on GitHub: https://github.com/eco4cast/modis-lai-forecast/. Current and future work includes building additional baseline models, setting up a submission portal using GitHub actions, and integrating additional sites to include a variety of ecoclimatic domains.

Go back to the list of projects.

Forecast uncertainty

Participants: Noam Ross, Eli Horner, Ashley Bonner, Mike Dietze, Chris Jones

Interest and use of ecological forecasting have increased in recent years due in large part to the efforts of EFI, including the NEON Ecological Forecasting Challenge. However, only a small percentage of ecological forecasts published have fully quantified and partitioned their forecast uncertainties. Quantifying and validating model predictions and uncertainties allows for understanding the degree of uncertainty in forecasts and how much we understand the underlying ecological system (our ability to predict them). Partitioning forecast uncertainties allows for increased focus on data collection efforts that could lead to improved model performance and reduction in uncertainty. Our group worked toward creating a tutorial for how to quantify and partition forecast uncertainties and validate model predictions with uncertainty by using the NEON Phenology Forecasting Challenge. We are using an ARIMA model and a random forest model as examples. During the Unconference we were able to get both models working and partition uncertainties. We are finishing up the code base, tutorial, and discussing challenges with each type of model when it comes to performing uncertainty quantification and partition.

Go back to the list of projects.

Forecasting Impacts: Measuring the Current and Future Impacts of EFI

Participants: Rebecca Finger-Higgens, Jessica Burnett, Alexis O’Callahan, Ayanna St. Rose

It turns out getting-to-know-you style ice breakers can provide more than just a few new friends, they can also demonstrate group priorities and motivations for coming together in the first place. On a sunny morning at the NEON Headquarters in Boulder, CO, Cayelan Carey (Virginia Tech) asked the group of EFI Unconference participants to organize themselves based on whether they individually felt that the goal of forecasts were for understanding or decision making. As the participants shuffled around and considered the question before them, the final results revealed a pattern among the group that resembled a skewed desire for forecasts to inform decision making versus broadening the understanding of ecological systems. However, the ability of ecological forecasts to effectively inform decision making has not clearly been measured. Besides directly impacting decision making processes, how do we, as a grassroots organization, recognize and measure the other societal impacts that EFI might be, or capable, of creating?

This led our group to think through ways that EFI could measure impacts, to ask: what are the impact goals and achievements of EFI, what does the community want out of EFI, and what is the best way to measure these often hard to measure metrics? Using five categories of societal impacts (instrumental applications, connectivity impacts, conceptual impacts, capacity building, and socio-ecological impacts), we developed a poll for Unconference participants to assess the priorities and current thoughts of this representative group. The poll results suggest that EFI community goals emphasize conceptual impacts (i.e. improve ecological understanding), connectivity impacts (i.e. maintaining and developing community and partnerships) and instrumental applications (i.e., applications for decision making). We also found that EFI has made the greatest advancements in capacity building (i.e., curriculum development, short courses), conceptual impacts (i.e., working groups), and connectivity impacts (i.e., newsletters and conference sessions).  These discoveries have allowed us to identify a space for the creation of a concrete link between the connectivity of forecasting and the desired application outcomes of the group. It has allowed us to develop a number of recommendations for the steering committee and the EFI community. Some of these recommendations include focusing on if, how, and why a created forecast product achieves one of the five predefined societal benefits. Together, we hope to continue to build on the vision statement of EFI to build forecasts to understand, manage, and conserve ecosystems in a measurable and remarkable way.

Figure 1: Word cloud generated from Unconference participant responses to the question “describe the potential, importance, or value of the community of EFI”.

Go back to the list of projects.

Reenvisioning the NEON Ecological Forecasting Challenge Dashboard Visualization

Participants: Melissa Kenney, Michael Gerst, Toni Viskari, Austin Delaney, Freya Olsson, Carl Boettiger, Quinn Thomas

With the growth of the NEON Ecological Forecasting Challenge, we have outgrown the current Challenge Dashboard, which was designed to accommodate a smaller set of forecasts and synthesis questions. Thus, we have reenvisioned the next stage NEON Forecast Challenge Dashboard in order to facilitate the ability to answer a wider range of questions that forecast challenge users would be interested in exploring. The main audience for this dashboard is NEON forecasters, the EFI community, Forecast Synthesizers, and students in classes or teams participating in the NEON Ecological Forecasting Challenge. Given this audience, we have identified 3 different dashboard elements that will be important to include: 

  1. forecast synthesis overview, 
  2. summary metrics about the Forecast Challenge, and 
  3. self diagnostic platform.

To learn more about the dashboard redesign approach, see the prototypes here. Find more details about this project in this blog post.

Go back to the list of projects.

Transporting Models Between NEON and non-NEON Systems

Participants: Brendan Allison, Olufemi Fatunsin, & Jeff Mintz

A community of practice is increasingly active in developing models and forecasts for NEON sites. We asked: how can we take models trained on NEON data and refine them for use in another context? Similarly, how can we take models trained on non-NEON data and refine them on NEON data? This goal of transplanting models can empower a range of applications, including local field studies, adaptive management, and data fusion from multiple monitoring networks, enabling greater statistical power for big ecological questions. Whether transporting a model to or from NEON, the challenges are effectively the same. These included unbalanced data, different monitoring protocols, different predictors, and different site selection criteria. To focus our efforts, we picked the particular case study of bringing together NEON vegetation survey data with similar datasets generated under the Forest Inventory Analysis (FIA) program. Our first product was the development of a Bayesian multilevel model with the capacity to scale to the integration of multiple sets of continental or global-scale monitoring networks, or shrink to the job of predicting outcomes at a single site, but informed by a shared global layer. With this case study in mind, we have been building a codebase for processing the relevant NEON and FIA forestry data and for joint modeling of residual error across monitoring systems in Stan, a popular probabilistic programming language. Find more details about this project in this blog post.

Go back to the list of projects.

ML-based Uncertainty in the NEON Ecological Forecasting Challenge

Participants: Marcus Lapeyrolerie, Caleb Robbins

How can machine learning (ML) provide a solution to estimating forecast uncertainty across NEON Ecological Forecasting Challenge? We generated a proof-of-concept workflow combining two machine learning approaches to make probabilistic forecasts. Random forests were used to learn relationships between forecast challenge variables and past NOAA weather data and to make predictions. While these models were able to make forecasts that perform well in approximating the future target time series, they were not implemented to provide estimates of uncertainty. We explored how we could use past data along with these deterministic forecasts to generate probabilistic forecasts. Our approach was to train another machine learning model to make probabilistic forecasts on the residual errors from the previous Random Forest models. We then used these predicted residual error forecasts to modify the Random Forest-based forecasts. This combined approach holds potential as it could be used in a plug-n-play manner, where this method could correct the deterministic (or even probabilistic) forecasts from any model to account for temporal trends in the residual error and provide uncertainty estimates. In our next steps, we will work on creating an automated workflow to generate residual error forecasts for the Eco4Cast challenge.  

Go back to the list of projects.

Forecasting Ground Beetles: Avoiding Pitfalls

Participants: Eric Sokol, Glenda Wardle, Vihanga Gunadasa, Juniper Simonis, Alison Gerken, Meghan Beatty, Khum Thapa-Magar

Ground beetles are a versatile species with which to measure biodiversity, yet they lack behind other EFI NEON Ecological Forecasting Challenge themes in terms of forecasts and models. Our group at the Unconference wanted to figure out why forecasters were not submitting to the NEON Ecological Forecasting Challenge Beetle Communities Theme and how we could remove those barriers to increase forecast submission. We created a tutorial  (in progress) that describes general goals for forecasting ecological communities, a how-to on submitting a forecast, some of the challenges in forecasting ecological community data, and examples of forecasts people might submit to begin to address those challenges. We first reviewed the underlying data structure of the pre-made targets file that had been developed for the forecasting challenge. We then combined currently available code for a null model, an ARIMA model, and an available tutorial for working with data from the Aquatics Challenge into a workable tutorial to prepare and submit forecasts to the Beetle Challenge. Our goal is to finalize the tutorial by adding a random walk model and more detail on how to add additional covariates to the model, including climate variables. We are also designing a new targets file that has different variables of interest at finer spatial scales at a given NEON site (e.g. plot or habitat information, survey effort). The beetles community data provides an example of when patterns in non-continuous or seasonal data may be poorly capture by a simple model (e.g., ARIMA). When there is latency or gaps in the data more data processing is often required than when using continuous sensor-captured data. Knowing the experimental design is also critical to be able to design a model to build understanding. We hope that this tutorial increases overall interest in submitting forecasts to the beetle forecasting challenge and removes barriers that may prevent forecasters at all levels from submitting. Further information and development on community ecology and biodiversity data is critical for understanding many different biological systems, can help researchers broaden their understanding of how and why communities change over time, and can better provide decision-making tools for ecosystem monitoring.

Go back to the list of projects.

Towards Principles for Designing Inclusive Ecological Forecasts

Participants: Anna Sjodin, Mary Lofton, Sean Dorr, Jody Peters, Jason McLachlan, Cazimir Kowalski, Melissa Kenney, Katie Jones

Our group is interested in exploring opportunities for improving inclusivity in ecological forecasting. Through discussion, we identified the ten principles of Design Justice (Box. 1) as a potential mechanism for evaluating the inclusivity of forecast products, services, and systems.

Box 1: Design Justice Network Principles, reproduced from https://designjustice.org/read-the-principles, license CC BY-ND 4.0.
To learn more, please visit the Design Justice Network website (https://designjustice.org) or see Design Justice: Community led practices to build the world we need by Sasha Costanza-Chock (https://designjustice.mitpress.mit.edu/;
open access pdf version is available here:  https://library.oapen.org/bitstream/handle/20.500.12657/43542/1/external_content.pdf).

1We use design to sustain, heal, and empower our communities, as well as to seek liberation from exploitative and oppressive systems.
2We center the voices of those who are directly impacted by the outcomes of the design process.
3We prioritize design’s impact on the community over the intentions of the designer.
4We view change as emergent from an accountable, accessible, and collaborative process, rather than as a point at the end of a process.
5We see the role of the designer as a facilitator rather than an expert.
6We believe that everyone is an expert based on their own lived experience, and that we all have unique and brilliant contributions to bring to a design process.
7We share design knowledge and tools with our communities.
8We work towards sustainable, community-led and -controlled outcomes.
9We work towards non-exploitative solutions that reconnect us to the earth and to each other.
10Before seeking new design solutions, we look for what is already working at the community level. We honor and uplift traditional, indigenous, and local knowledge and practices.

As a first step towards applying the Design Justice principles to EFI-created products, services, and systems, our team evaluated to what degree the ten principles were evident in the design of the NEON Ecological Forecasting Challenge. We identified several ways in which the design of the Challenge was well-aligned with Design Justice Principles (e.g., Principle 4: We view change as emergent from an accountable, accessible, and collaborative process, rather than as a point at the end of a process.), as well as areas in which we thought we could improve (e.g., Principle 3: We prioritize design’s impact on the community over the intentions of the designer). 

Moving forward, we are soliciting broader participation from all EFI community members in small focus groups to continue our internal evaluation of the inclusivity of current EFI products, with the ultimate goal of furthering the inclusivity of ecological forecasting by developing recommendations towards a more complete alignment of EFI-designed products with design justice principles. If you are interested in participating in such a focus group, please provide your contact information in the Google Form linked here.

Go back to the list of projects.

A proactive step toward decision-ready forecasts: Fusing iterative, near-term ecological forecasting and adaptive management

Participants: Jaime Ashander, LM Bradley, Mark Buckner, Nathan Byer, Cayelan Carey, Michael Gerst

This group aimed to improve the conceptual tools for co-production of ecological forecasts that aid in decision making. We identified that there is a need for tighter conceptual integration of the iterative, near-term ecological forecasting cycle (as practiced by the EFI community) with the adaptive management cycle (as practiced by communities of natural resource managers) and the broader context for management decisions. While prior frameworks have treated the iterative, near-term forecasting and adaptive management cycles as independent, with limited points of contact, a careful fusion of these processes may increase conceptual utility for co-production. As a first step towards a more useful framework, we then located iterative, near-term forecasting activities within the management decision making process, using the PrOACT (Problem, Objectives, Alternative Actions, Consequences, and Tradeoffs) tool from structured decision making. After creating this draft version of a framework, we explored several targeted case studies in ecological forecasting and adaptive management to evaluate its efficacy as a tool for fusing forecasting and adaptive management efforts. We will continue meeting to develop these ideas and work towards a manuscript.

Go back to the list of projects.

Disease Forecasting

Participants: Janet Agbaje, Kayode Oshinubi, Ethan Deyle (and thanks Ayanna St. Rose!)

Developing models to understand the transmission of pathogens in disease ecology is critical to understanding the spread of diseases and how to prevent them. A model study is relied on to simulate the spread of disease and predict the effectiveness of different control strategies. Model forecasting is also critical, both for planning and enacting public health interventions but also for building our understanding of the sometimes complex drivers of disease dynamics across space and time. Vector-transmitted diseases (e.g.,mosquito- or tick-borne) represent an exceptionally difficult case since key processes affecting spread and transmission are not directly reflected in typical public health monitoring. For example, the presence and behavior of the vector species themselves, but often there are infection reservoirs in wildlife populations as well.  In this way, connecting ecological forecasts to human epidemiological forecasts is an important challenge to tackle.

The NEON Ecological Forecasting Challenge has already included a tick forecast challenge, although it has not yet been tied directly to tick pathogen status monitoring or human health. In this project, we worked on the West Nile Virus (WNV), which is a mosquito-borne disease in the family of flaviviruses. The primary host is birds (across a wide range of species), while humans are the dead-end host. WNV occurs and is commonly spread, especially in the summer, through mosquito bites. Our goal at the EFI Unconference was to examine the opportunities that NEON data could provide to create impactful forecasts for the public’s health from vector-borne diseases, focusing on WNV. Especially since humans are a dead-end host, understanding and forecasting the disease dynamics demands ecological, human, and human data. We intend to forecast the mosquito abundance as well as the infection rate in humans over time, incorporating the mosquito abundance, seasonality, drivers, and co-occurring bird abundances.

We built a preliminary bridge that connects National Ecological Observatory Network (NEON) and Center for Disease Control (CDC) data and, through preliminary visualization, demonstrated the potential to match between the NEON mosquito data (abundance and pathogen status) and CDC-reported human cases on a year-by-year and county-by-county level for 14 NEON sites located in counties with reported cases of WNV. A first look at the collected data set showed a relationship between the NEON bird and mosquito abundance that suggests large bird presence is one driving condition of large mosquito abundance in a summer sampling season. Given the relative rarity of WNV compared to some other vector-borne illnesses like Lyme disease, there are definitely some challenges to setting up a forecasting challenge for the full disease dynamics, although we may be able to cast a wider net for human cases in counties adjacent to NEON site counties. We’re excited to build this preliminary effort into a new neon4cast theme, and we’re also eager to dive into the lessons learned from one of the other Unconference projects that examined pitfalls in recruiting broad engagement in the beetle forecasting challenge. 

Go back to the list of projects.

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 info@ecoforecast.org, 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 info@ecoforecast.org, 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 info@ecoforecast.org, 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 info@ecoforecast.org, 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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