Making ecological models adequate

Abstract

Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems' responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.

Understanding extinction debts: spatio–temporal scales, mechanisms and a roadmap for future research

“…Considering the spatio-temporal scales that extinction debts can reach (Fig. At the same time, the more mechanistically complex a model is, the harder it is to parameterize (Getz et al 2018) and to interpret emergent patterns. At the same time, the more mechanistically complex a model is, the harder it is to parameterize (Getz et al 2018) and to interpret emergent patterns.…”

“…Statistical models can detect extinction debts by verifying whether current species richness exceeds or corresponds to expected values under current habitat conditions (Kuussaari et al 2009). Theoretical models, however, provide insights into relevant processes but the development of such models is slow and data-dependent for parameterization and verification (Getz et al 2018). Theoretical models, however, provide insights into relevant processes but the development of such models is slow and data-dependent for parameterization and verification (Getz et al 2018).…”

A concise guide to developing and using quantitative models in conservation management

“…Model evaluation and validation against adequate suitability indicators, such as the percentage of variance and deviance explained (R 2 and D 2 , respectively) or the area under the receiver operative curve (however see Lobo, Jiménez-Valverde, & Real, 2008), will likely improve the confidence in its appropriateness to inform conservation management. In the case of simulation models, validation may not always be possible, but global sensitivity analyses can provide information on whether model outputs are robust to uncertainty in parameter inputs (Dietze, 2017;Getz et al, 2018;Prowse et al, 2016;Saltelli et al, 2008). It is important to keep in mind that P-values and information criteria scores such as Akaike's Information Criterion are not measures of model fit (Mac Nally, Duncan, Thomson, & Yen, 2018;Wasserstein & Lazar, 2016).…”

“…Modeling tools are being used by specialists but also by non-modelers and conservation managers with little quantitative training (Barraquand et al, 2014;Conroy & Peterson, 2013;Dietze et al, 2018;Schmolke, Thorbek, DeAngelis, & Grimm, 2010;Touchon & McCoy, 2016;Yackulic et al, 2013). As a result, quantitative models are rapidly becoming entrenched in the toolbox of conservation practice, policy, and management (Akçakaya et al, 2016;Conroy & Peterson, 2013;Getz et al, 2018;Guisan et al, 2013;Law et al, 2017;Nicholson et al, 2018;Schmolke et al, 2010). In fact, quantitative models are fundamental components of some formal conservation decision-making frameworks (Conroy & Peterson, 2013;Schwartz et al, 2018).…”

“…Poor modeling practices can result in inappropriate inferences and serious unintended, potentially detrimental, consequences for conservation management (Addison et al, 2013;Barraquand et al, 2014;Bestelmeyer, 2006;Coulson, Mace, Hudson, & Possingham, 2001;Harihar, Chanchani, Pariwakam, Noon, & Goodrich, 2017;Moilanen, 2011;Sofaer, Jarnevich, & Flather, 2018;Touchon & McCoy, 2016;Wilson, Westphal, Possingham, & Elith, 2005). The effective uptake and application of quantitative models in conservation management requires both sound modeling practices and substantial trust from conservation practitioners that such models are reliable and valuable tools for informing their time-and cost-critical tasks (Addison et al, 2013;Conroy & Peterson, 2013;Dietze, 2017;Getz et al, 2018;Holden & Ellner, 2016;Nicholson et al, 2018;Parrott, 2017;Schmolke et al, 2010). The effective uptake and application of quantitative models in conservation management requires both sound modeling practices and substantial trust from conservation practitioners that such models are reliable and valuable tools for informing their time-and cost-critical tasks (Addison et al, 2013;Conroy & Peterson, 2013;Dietze, 2017;Getz et al, 2018;Holden & Ellner, 2016;Nicholson et al, 2018;Parrott, 2017;Schmolke et al, 2010).…”