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

Abstract

Quantitative models are powerful tools for informing conservation management and decision-making. As applied modeling is increasingly used to address conservation problems, guidelines are required to clarify the scope of modeling applications and to facilitate the impact and acceptance of models by practitioners. We identify three key roles for quantitative models in conservation management: (a) to assess the extent of a conservation problem; (b) to provide insights into the dynamics of complex social and ecological systems; and, (c) to evaluate the efficacy of proposed conservation interventions. We describe 10 recommendations to facilitate the acceptance of quantitative models in conservation management, providing a basis for good practice to guide their development and evaluation in conservation applications. We structure these recommendations within four established phases of model construction, enabling their integration within existing workflows: (a) design (two recommendations); (b) specification (two); (c) evaluation (one); and (d) inference (five). Quantitative modeling can support effective conservation management provided that both managers and modelers understand and agree on the place for models in conservation. Our concise review and recommendations will assist conservation managers and modelers to collaborate in the development of quantitative models that are fit-for-purpose, and to trust and use these models appropriately while understanding key drivers of uncertainty. K E Y W O R D Sapplied conservation, ecological models, prediction, projection, simulation model, statistical model, uncertainty GLOSSARY Conservation management: activities conducted with the primary aim of conserving species and systems to achieve maintenance or restoration of biodiversity features. Correlative or correlational model: a model representing noncausative associations between two or more variables.Differential equation: Mathematical equation that relates a function with one or more of its derivatives (i.e., the instantaneous rate of change of a function). For example, an expression for the rate of change (in space or time) of a population. Estimation: using data to approximate, with some degree of uncertainty, the parameter values of a model. Gene drive: a synthetic, self-replicating genetic element that can propagate one or more focal genes through a population.

A standard protocol for reporting species distribution models

“…Error propagation, for instance, is possible via bootstrapping or within Bayesian frameworks. García-Díaz et al (2019) recommend plotting (posterior) distributions of model outputs to give a measure of the likelihood of different values that can be readily interpreted in an ecological risk assessment context.…”

“…Key to this is communicating sufficient detail about the input data, the model implementation, its evaluation and validation, and output processing such that end-users (e.g. conservationist, evaluator) has enough information at hand to judge the model's reliability and relevance without personal communication with the authors (Araújo et al 2019, García-Díaz et al 2019, Rapacciuolo 2019.…”

“…Methods reproducibility is crucial for ensuring adherence to minimum standards and supporting the delivery of adequate outputs for policy decisions. Indeed, poor or inconsistent modelling practices can lead to inappropriate inference and misguided conservation actions (García-Díaz et al 2019). Recognizing the necessity for reproducibility and transparency, the recent IPBES (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services) methodological assessment report acknowledged the need for agreed-upon standards in biodiversity assessments (Ferrier et al 2016).…”

The ODMAP protocol: a new tool for standardized reporting that could revolutionize species distribution modeling

“…Second, and more importantly, SDMs are having substantial impact outside academia as demand has grown for spatial biodiversity predictions to inform real world problems like climate change, invasive species, disease and habitat loss. Given the potential of SDMs to (mis)inform applied science especially (García‐Díaz et al 2019), it is crucial that modeling protocols are clearly documented and communicated to ensure quality and reproducibility. This need for greater transparency and defined standards has been acknowledged by major players in biodiversity assessment and conservation such as the IPBES (Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services) and the IUCN (International Union for Conservation of Nature) (Ferrier et al 2016, IUCN Standards and Petitions Subcommittee 2017).…”

Strengthen causal models for better conservation outcomes for human well-being

“…Designing effective conservation increasingly requires thinking about how interventions are situated within linked social and ecological systems where pathways are often interconnected and synergistic [10,11]. Thus, in the face of complexity, there is a need for using more systems-based approaches that clearly articulate how components within social-ecological systems are connected [12]. This is especially important if we want to understand how changing one component can lead to cascading effects throughout a system, while also mitigating unintended consequences.…”