Increased adoption of best practices in ecological forecasting enables comparisons of forecastability

Abstract

Near‐term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross‐ecosystem analysis of near‐term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near‐term (≤10‐yr forecast horizon) ecological forecasting papers to understand the development and current state of near‐term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near‐term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near‐term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1–7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.

Near‐term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability

“…While the number of ecological forecasts is increasing (Lewis et al, 2021 ; Luo et al, 2011 ; Rousso et al, 2020 ), many questions remain regarding the appropriate time scale at which to develop ecological models for forecasting and management applications, the time horizon and conditions under which ecological variables are predictable, the major sources of uncertainty in forecasts, and their scalability across waterbodies (Clark et al, 2001 ; Dietze et al, 2018 ; Petchey et al, 2015 ). Our work indicates that forecast accuracy varies with model time step and forecast horizon, and that weekly and fortnightly chl a forecast models can outperform null estimates under most conditions.…”

Citizen science across two centuries reveals phenological change among plant species and functional groups in the Northeastern US

“…Understanding how plant phenological responses to warming vary among species, across regions, and over long time scales is an important step in predicting future ecological responses to climate change (Lewis et al, 2022;Piao et al, 2019). At regional and landscape scales, as in this study, plant phenology can influence migration, pollination and ecosystem processes such as carbon, water and nutrient cycling (Peñuelas et al, 2009).…”

Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module

“…Rapid changes in many ecological populations, communities, and ecosystems due to land use and climate change has motivated the emerging discipline of near-term, iterative ecological forecasting [1][2][3][4][5]. We define near-term ecological forecasting as the prediction of future (day to decade) environmental conditions with quantified uncertainty [6]. Ecological forecasting has much potential for advancing ecology as a discipline because it can both improve decision-making related to natural resource management and expand knowledge of ecological systems [2,[7][8][9].…”

“…(3) quantifying uncertainty in predictions; (4) generating a forecast with quantified uncertainty; (5) communicating the forecast to stakeholders; (6) assessing the forecast when new observational data are available; and (7) updating the forecast with new data (e.g., changing model parameters, initial conditions) to improve the model for the next forecast. This cycle is then repeated each time a new forecast is made [11].…”