Bridging the divide between ecological forecasts and environmental decision making

Abstract

The rate of human-induced environmental change continues to accelerate, stimulating the need for rapid and science-based decision making. The recent availability of cyberinfrastructure, open-source data and novel techniques has increased opportunities to use ecological forecasts to predict environmental change. But to effectively inform environmental decision making, forecasts should not only be reliable, but should also be designed to address the needs of decision makers with their assumptions, uncertainties, and results clearly communicated. To help researchers better integrate forecasting into decision making, we outline ten practical guidelines to help navigate the interdisciplinary and collaborative nature of forecasting in social-ecological systems. Some guidelines focus on improving forecasting skills, including how to build better models, account for uncertainties and use technologies to improve their utility, while others are developed to facilitate the integration of forecasts with decision making, including how to form effective partnerships and how to design forecasts relevant to the specific decision being addressed. We hope these guidelines help researchers make forecasts more accurate, precise, transparent, and most pressingly, useful for informing environmental decisions.

Empowering ecological modellers with a PERFICT workflow: Seamlessly linking data, parameterisation, prediction, validation and visualisation

“…Establishing these direct links among data, models, and results also offers a solution for ecological modelling in light of recent calls for transparency and increased attention towards tampering and manipulating scientific results (Hopf et al, 2019). In applied contexts, rapid and constant flows among data, prediction and interpretable outputs are often necessary to meet stakeholder expectations (Bodner et al, 2021). Reliably achieving this requires transparency, reproducibility, and nimble workflows that enable the timely ingestion of new data and scientific advancements, while supporting uncertainty assessments.…”

“…Establishing these direct links among data, models, and results also offers a solution for ecological modelling in light of recent calls for transparency and increased attention towards tampering and manipulating scientific results (Hopf et al, 2019). In applied contexts, rapid and constant flows among data, prediction and interpretable outputs are often necessary to meet stakeholder expectations (Bodner et al, 2021) Automating the parameterisation and validation of predictive ecological models may also be important to support their uptake by ecology practitioners from nonmodelling backgrounds, to respond more quickly to stakeholder demands, and to address challenges associated with cross-model validation and tracking uncertainty of complex models (Fer et al, 2021). In our case, we linked our models with nation-wide data that allowed automatically estimating parameters and validating outputs across Canada.…”

“…Predictive models have been used by ecologists for decades and their utility is expanding to meet the increasing need for ecological forecasts that can inform ecosystem management and conservation (Bodner et al, 2021;Dietze, 2017). As a result, many applied ecological models have been developed using different approaches-for example statistical (Taylor & White, 2020), dynamic simulation (Seidl et al, 2011)-and scales-for example population ranges (Stewart et al, 2020), regional vegetation dynamics (Barros et al, 2017), biome productivity models (Boisvenue & Running, 2010), among others.…”

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

“…This iterative cycle enables the development of better ecological models and improved ecological understanding [12]. It also allows for proactive, rather than reactive, management for preparing for future environmental conditions, decisionmaking, and implementing policies [13]. Ecological forecasting offers a compelling and engaging tool for enriching undergraduate ecology education.…”

Existing caribou habitat and demographic models need improvement for Ring of Fire impact assessment: A roadmap for improving the usefulness, transparency, and availability of models for conservation

“…To demonstrate the utility of open source tools and reproducible workflows in this context, we used an R package framework (Wickham, 2015) to create an R package that reproduces the existing RSF and demographic models. Our package can be integrated into predictive frameworks to support resource use decisions or modified and advanced by other practitioners for other uses (Bodner et al, 2021b; McIntire et al, 2022; Micheletti et al, 2021; Miller and Frid, 2022). We identify and discuss challenges with using existing models for decision-support and opportunities for improving the usefulness, transparency, and availability of environmental impact assessment tools for SAR.…”