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

Abstract

Misidentifying spatial population structure may result in harvest levels that are unable to achieve management goals. We developed a spatially explicit simulation model to determine how biological reference points differ among common population structures and to investigate the performance of management quantities that were calculated assuming incorrect spatial population dynamics. Simulated reference points were compared across a range of population structures and connectivity scenarios demonstrating the influence of spatial assumptions on management benchmarks. Simulations also illustrated that applying a harvest level based on misdiagnosed spatial structure leads to biased stock status indicators, overharvesting, or foregone yield. Across the scenarios examined, incorrectly specifying the connectivity dynamics (particularly misdiagnosing source–sink dynamics) was often more detrimental than ignoring spatial structure altogether. However, when the true dynamics exhibited spatial structure, incorrectly assuming panmictic structure resulted in severe depletion if harvesting concentrated on more productive population units (instead of being homogeneously distributed). Incorporating spatially generalized operating models, such as the one developed here, into management strategy evaluations will help develop management procedures that are more robust to spatial complexities.

Life history traits and temporal trends of abundance of the orange‐spotted trevally (Carangoides bajad) from Saudi waters of the Gulf

“…Although regional management would be desirable because of possible trans‐border population processes, management strategies should also be versatile to account for spatial differences in life‐history traits. For instance, recent research shows that wrongly assuming spatial homogeneity may lead to depletion of less productive sub‐populations within a stock (Goethel & Berger, ). In this connection, we found that the annual cycle of the gonado‐somatic index of C. bajad from Saudi Gulf waters differed considerably from the cycle in Emirati waters of the southern Gulf.…”

Genetic analyses reveal complex dynamics within a marine fish management area

“…Earlier work has used simulations to evaluate possible consequences of disregarding independent population dynamics of identified genetic units within a management area (Goethel & Berger, 2017;Heath et al, 2014). Typically, the main concern is overexploitation of weaker components if spatial sub-structuring is disregarded (Bonanomi et al, 2015;Goethel & Berger, 2017;Kerr et al, 2017;Reiss et al, 2009).…”

A novel spatiotemporal stock assessment framework to better address fine‐scale species distributions: Development and simulation testing

“…Spatially explicit population dynamics models are increasingly structured using multiple spatial strata (Goethel & Berger, ; Goethel et al, ). However, this approach requires extensive data to allow a fine spatial scale because (a) each stratum needs sufficient data so that model is tractable, and (b) additional data are often needed to estimate or predefine the connectivity among strata, for example movement.…”

“…These two types of model have fundamentally different structure, so have rarely been integrated into a single modelling framework. Consequently, natural resource management and conservation measures are often developed based on simplifying assumptions about, or implicit approximations to, population spatial structure, for example, management of marine fisheries resources (Goethel & Berger, 2017;Goethel, Quinn, & Cadrin, 2011;Punt, Haddon, & Tuck, 2015) and terrestrial wildlife (Adams et al, 2008;Bieber & Ruf, 2005). On the other hand, studies predicting effects of environmental changes have focused primarily on species' distributions (Ehrlén & Morris, 2015).…”