A substantial increase in predictive capacity is needed to anticipate and mitigate the widespread change in ecosystems and their services in the face of climate and biodiversity crises. In this era of accelerating change, we cannot rely on historical patterns or focus primarily on long-term projections that extend decades into the future. In this Perspective, we discuss the potential of near-term (daily to decadal) iterative ecological forecasting to improve decision-making on actionable time frames. We summarize the current status of ecological forecasting and focus on how to scale up, build on lessons from weather forecasting, and take advantage of recent technological advances. We also highlight the need to focus on equity, workforce development, and broad cross-disciplinary and non-academic partnerships. In this Perspective, the authors discuss the current status of ecological forecasting research, its role in helping to address the climate and biodiversity crises facing society and potential future directions, with a central focus on how to scale up ecological forecasting capabilities.

AbstractModels have become a key component of scientific hypothesis testing and climate and sustainability planning, as enabled by increased data availability and computing power. As a result, understanding how the perceived ‘complexity’ of a model corresponds to its accuracy and predictive power has become a prevalent research topic. However, a wide variety of definitions of model complexity have been proposed and used, leading to an imprecise understanding of what model complexity is and its consequences across research studies, study systems, and disciplines. Here, we propose a more explicit definition of model complexity, incorporating four facets—model class, model inputs, model parameters, and computational complexity—which are modulated by the complexity of the real‐world process being modelled. We illustrate these facets with several examples drawn from ecological literature. Overall, we argue that precise terminology and metrics of model complexity (e.g., number of parameters, number of inputs) may be necessary to characterize the emergent outcomes of complexity, including model comparison, model performance, model transferability and decision support.

Conducting ecological research in a way that addresses complex, real-world problems requires a diverse, interdisciplinary and quantitatively trained ecology and environmental science workforce. This begins with equitably training students in ecology, interdisciplinary science, and quantitative skills at the undergraduate level. Understanding the current undergraduate curriculum landscape in ecology and environmental sciences allows for targeted interventions to improve equitable educational opportunities. Ecological forecasting is a sub-discipline of ecology with roots in interdisciplinary and quantitative science. We use ecological forecasting to show how ecology and environmental science undergraduate curriculum could be evaluated and ultimately restructured to address the needs of the 21(st) century workforce. To characterize the current state of ecological forecasting education, we compiled existing resources for teaching and learning ecological forecasting at three curriculum levels: online resources; US university courses on ecological forecasting; and US university courses on topics related to ecological forecasting. We found persistent patterns (1) in what topics are taught to US undergraduate students at each of the curriculum levels; and (2) in the accessibility of resources, in terms of course availability at higher education institutions in the United States. We developed and implemented programs to increase the accessibility and comprehensiveness of ecological forecasting undergraduate education, including initiatives to engage specifically with Native American undergraduates and online resources for learning quantitative concepts at the undergraduate level. Such steps enhance the capacity of ecological forecasting to be more inclusive to undergraduate students from diverse backgrounds and expose more students to quantitative training.

The 21st century continues to be characterized by major changes to the environment and the ecosystem services upon which society depends. Anticipating and responding to these changes requires that scientists explicitly forecast future conditions in real time (Dietze et al. 2018). Ecological forecasting, like weather and epidemiological forecasting, involves integrating data and models to generate quantitative predictions of the future state of ecological systems before observations are collected. The iterative cycle of creating forecasts, evaluating them with new observations, updating the models, and then making new forecasts has the potential to accelerate learning across many ecological subdisciplines. This cycle builds on openly available data, often published soon after collection, as is increasingly common in ecological observatory networks, such as the National Ecological Observatory Network (NEON). To accelerate improvements in ecological forecasting, we designed and launched the NEON Ecological Forecasting Challenge (hereafter, “Challenge”) (Figure 1), an open platform for the ecological and data science communities to forecast NEON data before they are collected. The ecological forecasting community is interested in using forecasts to advance theory (Lewis et al. 2023) and in translating forecasts for natural resource management (Enquist et al. 2017). By analyzing a catalog of forecasts developed for a range of ecological systems, spatiotemporal scales, and environmental gradients, scientists can begin to address fundamental questions in ecology. The Ecological Forecasting Initiative Research Coordination Network (EFI-RCN) – funded by the US National Science Foundation (NSF) – invites the broad ecology community to help build this catalog by forecasting NEON data. NEON is a powerful platform to support such a challenge because it provides standardized data with reported uncertainties that span a range of environmental conditions and levels of biological organization across terrestrial and freshwater systems in the US. The Challenge was designed on input from academic, government, and private sectors through workshops and working groups. We call it a “Challenge” because, despite its similarities to data science competitions (Makridakis et al. 2021), we are empowering the community to do more than just submit forecasts – we are also collaboratively developing software, training materials, and best practices. In May 2020, we launched the Challenge's design at a virtual conference with over 200 attendees (Peters and Thomas 2021). Attendees prioritized five forecasting “themes” that draw on NEON data, address open science questions, and have potential to support decision making for resource management: (1) freshwater temperature, dissolved oxygen, and chlorophyll-a; (2) terrestrial carbon fluxes and evapotranspiration; (3) plant canopy phenology; (4) tick populations; and (5) beetle communities. Themes were identified at the virtual meeting, after which smaller design teams developed detailed theme-specific protocols. The protocols defined the timing of forecast submissions (when and how often forecasts are due) and forecast horizons (how far forecasts extend into the future). With these protocols in place, team participants developed code to convert NEON data products into standardized time-series that are ready for modeling and evaluation. Simultaneously, the EFI-RCN standards working group was assembled to define the format of forecast submissions and metadata across themes (Dietze et al. 2023). Likewise, the EFI-RCN steering committee worked with each design team to ensure the developed protocols were consistent across themes (eg all forecasts quantify uncertainty in predictions). To support the Challenge, we created software and workflows for provisioning model inputs and processing model outputs that leverage modern cloud storage and computing (Figure 1). We also developed software to improve the efficiency of downloading NEON data while facilitating the analysis of data that exceed computer memory (Boettiger et al. 2021). Other end-user tools convert NEON data into easy-to-use time-series, process submitted forecasts, score probabilistic forecasts, and visualize submissions. Every day we are automatically downloading, processing, and sharing NOAA numerical ensemble weather forecasts for all NEON sites, eliminating the need for users to have to do so themselves. All of these technologies are open source and generalized to be applicable beyond the Challenge. We hope everyone who is interested in participating in the Challenge feels empowered to submit forecasts as individuals or teams. To reduce barriers, we have curated resources (documentation, workflow code examples, videos) to train teams in the computational skills needed for model development and submission. Participants can contribute forecasts from any NEON site or theme, using any type of modeling framework (eg empirical, process-based, machine learning). We also created a set of simple forecasts to serve as benchmarks for submissions. The Challenge provides a foundation for training in ecological modeling and forecasting. Teaching ecological modeling in undergraduate classrooms improves students’ systems-level thinking skills and quantitative literacy (Carey et al. 2020). Similarly, integrating ecological forecasting into classrooms expands students’ understanding of complex ecological concepts (Moore et al. 2022). Challenge submissions are an ideal project for undergraduate and graduate students in courses and workshops. The rapid, iterative feedback inherent to the Challenge design inspires student engagement and improvement – submissions are evaluated daily as new submissions are accepted and as new data become available. The Challenge can help transform ecology into a more predictive science while providing feedback to NEON that improves data generation and delivery. Forecasting is part of NEON's mission, and the Challenge empowers the ecological forecasting community to lead the charge in accomplishing that mission. However, the Challenge extends well beyond NEON, engaging researchers who have not previously considered forecasting or who are seeking new data analysis and modeling approaches. It is a testing ground for developing novel forecasting techniques that can be rapidly applied across broad environmental gradients, with products and outcomes that have the potential to support decision making in environmental management and conservation. It fosters the creation of a more quantitative workforce and can be an inspiration and blueprint for other environmental observatory networks across the globe. In 2021, we launched a beta round of the Challenge that resulted in 2516 forecast submissions contributed by 54 different teams, ranging in composition from students to researchers at private companies. At that stage of the Challenge, contributions were critical to refining protocols and to identifying needed software and educational materials. Today, the Challenge is fully operational and actively seeking contributions. If you are interested in becoming involved or learning more, see www.neon4cast.org (Thomas et al. 2021). We aim for the Challenge to further enable new forecast innovations, provide valuable training, and spark broad engagement among ecologists. The EFI-RCN is supported by NSF (DEB-1926388) with computational support provided by NSF-funded Jetstream2 (OAC-2005506). NEON is a program sponsored by NSF and operated under cooperative agreement by Battelle. This material is based in part upon work supported by the NSF through NEON. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government. WebPanel 1 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Data on open-access, online resources used to analyze the distribution of ecological forecasting-related resources in the associated manuscript. Rows are individual resources. Resources can be repeated if they fall into multiple forecasting topics. Columns are as follows: Link: URL for the resource Title: Title of the resource Resource type: Type of resource (e.g., video). Resource types are defined in the appendix of the associated manuscript. Category: Forecasting topic to which the resource corresponds Author: Author(s) of the resource. Institutions were named if no individual author could be identified Education level: Undergraduate, graduate, or both. Estimated by the authors by experience level required Resource level: Introductory or advanced. An alternative way of classifying the experience level required Discipline: Discipline the resource is most closely related to Only the Category column is used in our analysis, but all columns are provided for future analyses.

The opportunity to participate in and contribute to emerging fields is increasingly prevalent in science. However, simply thinking about stepping outside of your academic silo can leave many students reeling from the uncertainty. Here, we describe 10 simple rules to successfully train yourself in an emerging field, based on our experience as students in the emerging field of ecological forecasting. Our advice begins with setting and revisiting specific goals to achieve your academic and career objectives and includes several useful rules for engaging with and contributing to an emerging field.

Lianas, or woody vines, are abundant throughout forests worldwide, but are especially common in the tropics. Their presence can strongly suppress tree wood production, and presumably also reduce the strength of the tropical forest carbon sink. In intact neotropical forests, liana presence has been increasing over the past few decades, though the mechanisms remain under debate. Vexingly, lianas are not represented at all in current-day climate models. Better knowledge of liana morphology and allocation is required to unravel the mechanisms of below- and aboveground liana-tree competition in tropical forests. Such knowledge is also an essential step toward incorporating lianas into mechanistic forest dynamics models. To address these liana knowledge gaps, we have initiated a new project that integrates empirical and modeling work. Our objectives in this presentation are to compare observed liana allocation patterns to allocation patterns predicted by theory, and then to demonstrate how these results can be integrated into a numerical model. Empirical measurements are being carried out in tropical dry forests in Guanacaste, Costa Rica. These measurements will eventually include excavations of ~80 entire trees and lianas, which will enable measurements of belowground and aboveground biomass of co-occurring trees and lianas, coarse and fine root vertical distribution, and lateral root spread. Also being measured are liana traits (including several critical hydraulic traits), above- and belowground productivity, and species-level fine root productivity. The modeling work includes the incorporation of lianas into the TROLL model, which is a mechanistic, individual-based forest dynamics model. The model will simulate the unique features of lianas, accounting for their structural parasitism and their different allocation strategies and morphology compared to trees. The simulated trees and lianas will compete aboveground for light and belowground for water. Thus, the model will integrate above- and belowground processes and couple the carbon and water cycles. Traits measured as part of this project are being used to parameterize the model. Thus far, 33 mature, canopy-exposed individuals (18 trees and 15 lianas) have been harvested and analyzed. For both trees and lianas, biomass partitioning to roots, stems, and leaves were consistent with the predictions of allometric biomass partitioning theory. This result thwarted our initial expectation that lianas, with their narrow-diameter stems, would allocate proportionally less to stems than trees. We also found that vertical root profiles varied across life forms: lianas had the shallowest roots, evergreen trees had the deepest roots, and deciduous trees had intermediate rooting depths. The liana root systems also had notably broader lateral extents than the tree root systems. These results run contrary to previous work that reported that lianas were relatively deeply-rooted. Our empirical results have helped to motivate model development. Each of our modeled liana individuals is assigned a laterally-widespread root system that can potentially extend beneath many trees. The liana root system is then permitted to put up aboveground shoots that associate with trees within the footprint of the root system. Comparisons of simulated and observed above- and belowground productivity are currently being conducted to help evaluate model assumptions.

Lianas, or woody vines, and trees dominate the canopy of tropical forests and comprise the majority of tropical aboveground carbon storage. These growth forms respond differently to contemporary variation in climate and resource availability, but their responses to future climate change are poorly understood because there are very few predictive ecosystem models representing lianas. We compile a database of liana functional traits (846 species) and use it to parameterize a mechanistic model of liana-tree competition. The substantial difference between liana and tree hydraulic conductivity represents a critical source of inter-growth form variation. Here, we show that lianas are many times more sensitive to drying atmospheric conditions than trees as a result of this trait difference. Further, we use our competition model and projections of tropical hydroclimate based on Representative Concentration Pathway 4.5 to show that lianas are more susceptible to reaching a hydraulic threshold for viability by 2100.