Toward a Model of Age Inclusivity in Higher Education

The AFU principles clearly state the aspiration of promoting age inclusivity in higher education within the context of the UN Sustainability Development Goals. With these principles as a starting point, the Age-Friendly Campus Climate Inventory and Survey were developed to assess the extent to which AFU principles are put into practice (Inventory) and how campus constituencies perceive these practices. Based on social ecological models, a framework for measuring age inclusivity was developed in which practices ("objective environment") are compared to perceptions ("subjective environment"). Participating campuses (N=29) completed the inventory for each major executive unit, providing scores that were grouped by major campus functions, including research, teaching, community engagement, and support. By comparing these scores with perceptions of each function by samples of constituencies of faculty, staff, and students, it is possible to test the person-environment match as conceptualized by social ecological models providing important clarification for the AFU principles.

Feasibility conditions of ecological models: Unfolding links between model parameters

Over more than 100 years, ecological research has been striving to derive internal and external conditions compatible with the coexistence of a given group of interacting species. To address this challenge, numerous studies have focused on developing ecological models and deriving the necessary conditions for species coexistence under equilibrium dynamics, namely feasibility. However, due to mathematical limitations, it has been impossible to derive analytic expressions if the isocline equations have five or more roots, which can be easily reached even in 2-species models. Here, we propose a general formalism to obtain the set of analytical conditions of feasibility for any polynomial population dynamics model of any dimension without the need to solve for the equilibrium locations. We additionally illustrate the application of our methodology by showing how it is possible to derive mathematical relationships between model parameters, while maintaining feasibility in modified Lotka-Volterra models with functional responses and higher-order interactions (model systems with at least five equilibrium points)—a task that is impossible to do with simulations. This work unlocks the opportunity to increase our systematic understanding of species coexistence.

Why Do We Model?

The chapter “Why Do We Model?” addresses the question of why it is important to use models. It is not possible to represent all of reality with a series of mathematical or statistical expressions. Luckily, modelers do not intend to do this. Rather, modelers simplify reality on purpose, so that they can better understand it. However, models must be faithful to reality. In addition, they must be purposeful, that is, they must guide experience in very specific ways. This chapter covers the epistemological basis of ecological models, introduces the core concept of “describe, explain, and interpret” as the core steps of learning from models, and then walks the reader through the process of model development.

PERFICT: a Re-imagined Foundation for Predictive Ecology

Making predictions from ecological models – and comparing these predictions to data – offers a coherent approach to objectively evaluate model quality, regardless of model complexity or modeling paradigm. To date, our ability to use predictions for developing, validating, updating, integrating and applying models across scientific disciplines while influencing management decisions, policies and the public has been hampered by disparate perspectives on prediction and inadequate integrated approaches. We present an updated foundation for Predictive Ecology that is based on 7 principles applied to ecological models: make frequent Predictions, Evaluate models, make models Reusable, Freely accessible and Interoperable, built within Continuous workflows, that are routinely Tested (PERFICT). We outline some benefits of working with these principles: 1) accelerating science; 2) bridging to data science; and 3) improving science-policy integration.

A model-independent data assimilation (MIDA) module and its applications in ecology

Models are an important tool to predict Earth system dynamics. An accurate prediction of future states of ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications of data assimilation to ecology are restricted by highly technical requirements such as model-dependent coding. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module. MIDA works in three steps including data preparation, execution of data assimilation, and visualization. The first step prepares prior ranges of parameter values, a defined number of iterations, and directory paths to access files of observations and models. The execution step calibrates parameter values to best fit the observations and estimates the parameter posterior distributions. The final step automatically visualizes the calibration performance and posterior distributions. MIDA is model independent, and modelers can use MIDA for an accurate and efficient data assimilation in a simple and interactive way without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC) model, a surrogate-based energy exascale earth system model: the land component (ELM), nine phenological models and a stand-alone biome ecological strategy simulator (BiomeE). The applications indicate that MIDA can effectively solve data assimilation problems for different ecological models. Additionally, the easy implementation and model-independent feature of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting.