January 30, 2024

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

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

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

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

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

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

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

But what does this forecasting / social system look like?

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

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

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

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

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

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

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

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

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

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

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

Note: Some of this content appeared in a blog post from 2023 (https://seascapescience.github.io/posts/2023/10/topologies/)

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

References

Boettiger C. The forecast trap. Ecology Letters. 2022 Jul;25(7):1655-64.  https://doi.org/10.1111/ele.14024

Fernandes MR. Confirmation bias in social networks. Mathematical Social Sciences. 2023 May 1;123:59-76. http://dx.doi.org/10.2139/ssrn.3504342

Hobday AJ, Hartog JR, Manderson JP, Mills KE, Oliver MJ, Pershing AJ, Siedlecki S. Ethical considerations and unanticipated consequences associated with ecological forecasting for marine resources. ICES Journal of Marine Science. 2019 Sep 1;76(5):1244-56. https://doi.org/10.1093/icesjms/fsy210

Liboiron M, Zahara A, Hawkins K, Crespo C, de Moura Neves B, Wareham-Hayes V, Edinger E, Muise C, Walzak MJ, Sarazen R, Chidley J. Abundance and types of plastic pollution in surface waters in the Eastern Arctic (Inuit Nunangat) and the case for reconciliation science. Science of the Total Environment. 2021 Aug 15;782:146809. https://doi.org/10.1016/j.scitotenv.2021.146809

McClenachan L, Record NR, Waller J. How do human actions affect fisheries? Differences in perceptions between fishers and scientists in the Maine lobster fishery. Facets. 2022 Feb 10;7(1):174-93. https://doi.org/10.1139/facets-2021-0030

Moore TN, Thomas RQ, Woelmer WM, Carey CC. Integrating ecological forecasting into undergraduate ecology curricula with an R shiny application-based teaching module. Forecasting. 2022 Jun 30;4(3):604-33. https://doi.org/10.3390/forecast4030033

Record NR, Pershing AJ. Facing the Forecaster’s Dilemma: Reflexivity in Ocean System Forecasting. Oceans 2021 Nov 12 (Vol. 2, No. 4, pp. 738-751). MDPI. https://doi.org/10.3390/oceans2040042

Record N. Early Warning Systems for Harmful Algae: A Stakeholder-Centered Framework. 2022. http://dx.doi.org/10.13140/RG.2.2.24501.14568

Record NR, Evanilla J, Kanwit K, Burnell C, Cartisano C, Lewis BJ, MacLeod J, Tupper B, Miller DW, Tracy AT, White C, Moretti M, Hamilton B, Barner C, Archer SD (2022) Benefits and Challenges of a Stakeholder-Driven Shellfish Toxicity Forecast in Coastal Maine. Frontiers in Marine Science. https://doi.org/10.3389/fmars.2022.923738

Schreiber MA, Chuenpagdee R, Jentoft S. Blue Justice and the co-production of hermeneutical resources for small-scale fisheries. Marine Policy. 2022 Mar 1;137:104959. https://doi.org/10.1016/j.marpol.2022.104959Willson AM, Gallo H, Peters JA, Abeyta A, Bueno Watts N, Carey CC, Moore TN, Smies G, Thomas RQ, Woelmer WM, McLachlan JS. Assessing opportunities and inequities in undergraduate ecological forecasting education. Ecology and Evolution. 2023 May;13(5):e10001. https://doi.org/10.1002/ece3.10001