Scenarios and Models to Support Global Conservation Targets

Abstract

Global biodiversity targets have far-reaching implications for nature conservation worldwide. Scenarios and models hold unfulfilled promise for ensuring such targets are well founded and implemented; here, we review how they can and should inform the Aichi Targets of the Strategic Plan for Biodiversity and their reformulation. They offer two clear benefits: providing a scientific basis for the wording and quantitative elements of targets; and identifying synergies and trade-offs by accounting for interactions between targets and the actions needed to achieve them. The capacity of scenarios and models to address complexity makes them invaluable for developing meaningful targets and policy, and improving conservation outcomes. The Potential of Scenarios and Models to Inform Conservation TargetsThe Aichi Targets of the Strategic Plan for Biodiversity 2011-2020 i provide an agreed set of conservation aspirations for the international community, as well as explicit targets that countries have committed to achieve [1]. Justified and compelling targets have the power to shape policy and activity within and beyond the environment sector [2]. Knock-on effects of environmental targets, such as the 2015 global target of less than 2 C warming [3], can be profound, but the ways in which they are realised are complex. Feedbacks and trade-offs between sectors and policies, in particular, are challenging to characterise, understand, and navigate [4,5]. A poor understanding of the potential consequences of conservation targets, the interactions between targets, and the actions needed to achieve them, can lead to unexpectedly poor conservation outcomes, inefficient actions, and lost opportunities for meeting commitments. For example, some of the easiest pathways towards achieving the global target to protect 17% of the terrestrial ecosystems on Earth would not adequately safeguard the biodiversity (see Glossary) this target is intended to conserve [6][7][8][9]. Scenarios depict plausible futures and alternative policies and management strategies that may affect the achievement of conservation goals [10,11]. Models represent simplified and idealised understandings of a system, and can describe or predict (or forecast) conservation outcomes under a range of alternative scenarios. They range from qualitative conceptual models describing relationships between elements of a system, to quantitative models, built from either a principled understanding of the mechanics of a system, or through analysis of the emergent patterns observed in data [11,12]. Here, we focus primarily on quantitative correlative and process-based models dealing with biodiversity and ecosystem services, such as biophysical, ecological, and socio-ecological models. Together, scenarios and models provide a powerful means of characterising, understanding, and projecting the conservation implications of targets, and the positive and negative consequences of actions aimed at achieving them [11,13]; for example, scenarios and models have underpinned climate change target...

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

“…In general, quantitative models can fulfill two purposes in conservation management and policy; namely to diagnose the magnitude of a conservation issue and to assess the effectiveness of ongoing or future interventions (Cairney, 2016;Conroy & Peterson, 2013;Nicholson et al, 2018). In general, quantitative models can fulfill two purposes in conservation management and policy; namely to diagnose the magnitude of a conservation issue and to assess the effectiveness of ongoing or future interventions (Cairney, 2016;Conroy & Peterson, 2013;Nicholson et al, 2018).…”

“…First, the characteristics of the input data, including data sparseness in statistical models and the input data quality in simulation models, typically propagate through the model and produce uncertain parameter estimates. Furthermore, presenting the results of simulation models as a set of scenarios representing alternative uncertain species and system conditions is a good way to be explicit about uncertainty in conservation management (Akçakaya et al, 2016;Groves & Game, 2016;Mahmoud et al, 2009;Nicholson et al, 2018;Peterson, Cumming, & Carpenter, 2003). Model and parameter uncertainty measures complement other measures of centrality (e.g., mean or median).…”

“…Thresholds can sometimes be arbitrary and misleading when they are used in the context of conservation management, and it always is important to explain and justify their use (Bestelmeyer, 2006;Field, Tyre, Jonzén, Rhodes, & Possingham, 2004;Liu, Berry, Dawson, & Pearson, 2005;Wasserstein & Lazar, 2016) The development of optimal thresholds to discontinue surveillance or the removal of invasive species, by estimating the costs and benefits associated with deploying different surveying efforts, is a good example of a welldesigned and justifiable threshold in the context of conservation management (Gormley,Anderson,& Nugent,FIGURE 3 Two histograms illustrating recommendation 7 ("communicating the uncertainty in model results to end-users broadens its utility"). Nicholson et al, 2018;Schmolke et al, 2010). The histograms were obtained by plotting 1,000 random values drawn from a Poisson distribution with a mean of five (left panel) and 1,000 hand-picked values (right panel).…”

“…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).…”