Why model assumptions matter for natural resource management: interactions between model structure and life histories in fishery models

Abstract

Summary1. Bioeconomic models are increasingly used to provide benchmarks for harvest levels in wildlife and natural resource management, yet uncertainties related to model structure are underexplored. We investigate the importance of a range of uncertainties with a focus on model structure and life histories when estimating bioeconomic target reference point (TRPs) and assess the policy implications of ignoring these uncertainties. 2. We use three contrasting case studies to investigate the interactions between model, observational and process errors related to life-history parameters: the short-lived Japanese common squid Todarodes pacificus and Pacific saury Cololabis saira, and the slow-growing Patagonian toothfish Dissostichus eleginoides. We developed a simulation framework to test the harvest strategies resulting from bioeconomic TRPs under various assumptions about model structures and parameters. 3. We found the relative importance of different types of uncertainties affecting precision and accuracy of the model outputs varied according to the life-history traits. Little difference in TRP estimates was found between simple vs. complex population models for saury, while large differences were found for toothfish. The assumptions made about stock structure for squid not only resulted in different TRP estimates (generally, smaller for the multistock models), but also different economic outcomes depending on the balance of effort allocation between stocks. 4. Synthesis and applications. We use models similar to those used in the actual management of three case study species to explore the effects of interacting uncertainties on the management advice. We show that the interactions between structural elements of the models lead to very different management advice, depending on the life history of the species concerned. For the long-lived toothfish, life-history and gear selectivity parameters interacted strongly. For the short-lived squid which is managed as two stocks, spatial fishing effort allocation, correlation of environmental drivers between stocks and differential stock productivity interacted, producing very poor economic performance if assumptions about stock structure are incorrect. The key message for model-based natural resource management is that it is vital to investigate the major uncertainties related to model structure, process and estimation errors simultaneously, because they interact to produce non-intuitive results.

Accounting for spatial complexities in the calculation of biological reference points: effects of misdiagnosing population structure for stock status indicators

“…Previous studies have suggested that ignoring spatial structure can lead to overharvesting and localized depletion of sub-population components (e.g., Fu and Fanning 2004;Ying et al 2011;Hoshino et al 2014). Our findings further support the general concept that ignoring spatial structure and connectivity dynamics can lead to unintended consequences, and expands upon the types of spatial scenarios for which that applies.…”

“…Accounting for only the biological aspects of spatial structure does not provide a complete overview of how spatial heterogeneity can impact estimation of biological reference points and related harvest strategies. The snapperlike results provide an indication of the increased complexities that result from spatial effort dynamics, which supports the findings of Hoshino et al (2014). A wide body of literature on Marine Protected Areas (MPAs) has demonstrated the importance of spatial harvest displacement for the determination of stock status indicators and achievement of conservation goals (e.g., Punt and Methot 2004;Pincin and Wilberg 2012;McGilliard et al 2015).…”

“…Over the last three decades, there has been increasing awareness that spatial population structure is an important facet of resilience for marine species (e.g., Sinclair 1988;Pelletier and Mahévas 2005;Kerr et al 2010a,b;Ciannelli et al 2013). However, little research has been devoted to describing how ignorance of spatial dynamics may impact biological reference points or the reliability of management strategies (e.g., Ying et al 2011;Hoshino et al 2014). Our results demonstrate that management benchmarks and the harvest levels required to attain them are strongly influenced by the underlying population structure and connectivity dynamics.…”

“…Although the results of this work provide a basic understanding of the interplay between complex spatial dynamics and estimates D r a f t of management benchmarks for marine resources, we acknowledge that it only represents a first step towards fully integrating spatial biological and fishery dynamics into fisheries policy. There is a clear need for fisheries scientists and managers to be aware of spatial population structure, because it can have strong implications for how to best manage a fishery to meet management objectives (Fahrig 1993;Benson et al 2015;Hoshino et al 2014). In addition, spatial heterogeneity due to fleet dynamics and regulatory measures (e.g., MPAs) only increases the importance of accounting for spatial processes across the assessment-management interface (Guan et al 2013;McGilliard et al 2015).…”

“…When management (e.g., setting of catch quotas) ignores population structure or connectivity among population units, there is increased potential for overharvesting and system productivity is often incorrectly estimated (Fu and Fanning 2004;de Moor and Butterworth 2015). Even when population structure is recognized and accounted for within the management framework, if the spatial dynamics of the fishery (e.g., gear selectivity or effort) are ignored, the possibility of overharvesting can remain (Fahrig 1993;Mchich et al 2006;Ling and Milner-Gulland 2008;Benson et al 2015;Hoshino et al 2014). Concomitantly, underharvesting can also occur when effort is not efficiently allocated between spatial units, resulting in foregone yield and lower net revenue for fishing fleets (Tuck and Possingham 1994).…”