Developing an automated iterative near‐term forecasting system for an ecological study

Abstract

1. Most forecasts for the future state of ecological systems are conducted once and never updated or assessed. As a result, many available ecological forecasts are not based on the most up-to-date data, and the scientific progress of ecological forecasting models is slowed by a lack of feedback on how well the forecasts perform. Iterative near-term ecological forecasting involves repeated daily to annual scaleforecasts of an ecological system as new data becomes available and regular assessment of the resulting forecasts. We demonstrate how automated iterative near-term forecasting systems for ecology can be constructed by building one to conduct monthly forecasts of rodent abundances at the Portal Project, a longterm study with over 40 years of monthly data. This system automates most aspects of the six stages of converting raw data into new forecasts: data collection, data sharing, data manipulation, modelling and forecasting, archiving, and presentation of the forecasts.3. The forecasting system uses R code for working with data, fitting models, making forecasts, and archiving and presenting these forecasts. The resulting pipeline is automated using continuous integration (a software development tool) to run the entire pipeline once a week. The cyberinfrastructure is designed for long-term maintainability and to allow the easy addition of new models. Constructing this forecasting system required a team with expertise ranging from field site experience to software development.4. Automated near-term iterative forecasting systems will allow the science of ecological forecasting to advance more rapidly and provide the most up-to-date forecasts possible for conservation and management. These forecasting systems will also accelerate basic science by allowing new models of natural systems to be quickly implemented and compared to existing models. Using existing technology, and teams with diverse skill sets, it is possible for ecologists to build automated forecasting systems and use them to advance our understanding of natural systems. K E Y W O R D Sforecasting, iterative forecasting, mammals, Portal Project, predictionThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

End‐user involvement to improve predictions and management of populations with complex dynamics and multiple drivers

“…One of the main needs arising from the foresight process was to assess the performance of models in making anticipatory predictions (Bradford et al 2018, White et al 2019); i.e., based on the desire of managers and hunters to have near‐term forecast of Ptarmigan dynamics prior to the line transect census in late summer. Predictive performance was fairly good compared to what can be theoretically expected given a “perfect” Poisson model, even though predictions in some years were not as good as might be desired (cf.…”

Beyond Modeling: A Roadmap to Community Cyberinfrastructure for Ecological Data-Model Integration

“…Making a forecast operational also requires a higher level of repeatability and efficient scheduling of cyclic workflows where large number of jobs are executed at regular intervals and each forecast cycle depends on previous ones (Oliver et al, 2019). Among the tasks required, open archiving, community standards, and a full uncertainty accounting and propagation have proven to be prohibitively difficult (White et al, 2019). Overall, the breadth of expertise and investment of resources needed to set up a forecasting pipeline using state-of-the-art data assimilation methods often exceeds the limits of individualistic efforts (White et al, 2019).…”

“…Among the tasks required, open archiving, community standards, and a full uncertainty accounting and propagation have proven to be prohibitively difficult (White et al, 2019). Overall, the breadth of expertise and investment of resources needed to set up a forecasting pipeline using state-of-the-art data assimilation methods often exceeds the limits of individualistic efforts (White et al, 2019).…”

Iterative near-term forecasting of the transmission and management of SARS-CoV-2/COVID-19 using social interventions at the county-level in the United States

“…Third, general models, even if supported by the available historical data, do not address the need for data assimilation in real-time forecasting. Such data assimilation, in which information regarding the extant transmission processes that are embedded in observational data is used to iteratively update the underlying dynamical principles represented by the structure and parameters of a model, has been shown to provide near-term forecasts of the state of a dynamical system which are better than could be obtained with just data or the model alone [17][18][19][20][21] . For forecasting epidemics over the near future, this data-model assimilation framework will allow shrinkage of forecast variance while also correcting for model bias and drift 22 .…”

“…In parallel, major progress has also been made in the development of iterative statistical data-model assimilation techniques, whereby data of diverse types and prior information regarding model structures and parameters can be used reliably to constrain model parameters or states in a setting, including supporting evaluations of forecast uncertainty over time 16,17,19,[28][29][30][31][32][33][34][35] . Lastly, developments in cyberinfrastructures to automate the dynamic integration of new data and information to facilitate regular assessment of forecasts and active updating of models mean that the practical implementation of iterative data-driven epidemic forecasting is now increasingly becoming possible 21,27,29,30,[36][37][38][39][40][41][42][43][44] .…”