Connectivity, persistence, and loss of high abundance areas of a recovering marine fish population in the Northwest Atlantic Ocean

Abstract

In the early 1990s, the Northwest Atlantic Ocean underwent a fisheries‐driven ecosystem shift. Today, the iconic cod (Gadus morhua) remains at low levels, while Atlantic halibut (Hippoglossus hippoglossus) has been increasing since the mid‐2000s, concomitant with increasing interest from the fishing industry. Currently, our knowledge about halibut ecology is limited, and the lack of recovery in other collapsed groundfish populations has highlighted the danger of overfishing local concentrations. Here, we apply a Bayesian hierarchical spatiotemporal approach to model the spatial structure of juvenile Atlantic halibut over 36 years and three fisheries management regimes using three model parameters to characterize the resulting spatiotemporal abundance structure: persistence (similarity of spatial structure over time), connectivity (coherence of temporal pattern over space), and spatial variance (variation across the seascape). Two areas of high juvenile abundance persisted through three decades whereas two in the northeast are now diminished, despite the increased abundance and landings throughout the management units. The persistent areas overlap with full and seasonal area closures, which may act as refuges from fishing. Connectivity was estimated to be 250 km, an order of magnitude less than the distance assumed by the definition of the Canadian management units (~2,000 km). The underlying question of whether there are distinct populations within the southern stock unit cannot be answered with this model, but the smaller ~250 km scale of coherent temporal patterns suggests more complex population structure than previously thought, which should be taken into consideration by fishery management.

Spatial modeling with R‐INLA: A review

“…A collection of some recent examples of spatial applications with the R‐INLA software, intended as a source of inspiration for the reader, follows; environmental risk factors to liver Fluke in cattle (Innocent et al, ) using a spatial random effect to account for regional residual effects; modeling fish populations that are recovering (Boudreau, Shackell, Carson, & den Heyer, ) with a separable space–time model; mapping gender‐disaggregated development indicators (Bosco et al, ) using a spatial model for the residual structure; environmental mapping of soil (Huang, Malone, Minasny, McBratney, & Triantafilis, ) comparing a spatial model in R‐INLA with “REML‐LMM”; changes in fish distributions (Thorson, Ianelli, & Kotwicki, ); febrile illness in children (Dalrymple et al, ); dengue disease in Malaysia (Naeeim & Rahman, ); modeling pancreatic cancer mortality in Spain using a spatial gender‐age‐period‐cohort model (Etxeberria, Goicoa, López‐Abente, Riebler, & Ugarte, ); soil properties in forest (Beguin, Fuglstad, Mansuy, & Paré, ) comparing spatial and nonspatial approaches; ethanol and gasoline pricing (Laurini, ) using a separable space–time model; fish diversity (Fonseca, Pennino, de Nóbrega, Oliveira, & de Figueiredo Mendes, ) using a spatial GRF to account for unmeasured covariates; a spatial model of unemployment (Pereira, Turkman, Correia, & Rue, ); distance sampling of blue whales (Yuan et al, ) using a likelihood for point processes; settlement patterns and reproductive success of prey (Morosinotto, Villers, Thomson, Varjonen, & Korpimäki, ); cortical surface fMRI data (Mejia, Yue, Bolin, Lindren, & Lindquist, ) computing probabilistic activation regions; distribution and drivers of bird species richness (Dyer et al, ) with a global model, and comparing several different likelihoods; socioenvironmental factors in influenza‐like illness (Lee, Arab, Goldlust, Viboud, & Bansal, ); global distributions of Lygodium microphyllum under projected climate warming (Humphreys, Elsner, Jagger, & Pau, ) using a spatial model on the globe; logging and hunting impacts on large animals (Roopsind, Caughlin, Sambhu, Fragoso, & Putz, ); sociodemographic and geographic impact of HPV vaccination (Rutten et al, ); a combined analysis of point‐ and area‐level data (Moraga, Cramb, Mengersen, & Pagano, ); probabilistic prediction of wind power (Lenzi, Pinson, Clemmensen, & Guillot, ); animal tuberculosis (Gortázar, Fernández‐Calle, Collazos‐Martínez, Mínguez‐González, & Acevedo, ); poliovirus eradication in Pakistan (Mercer et al, ) with a Poisson hurdle model; detecting local overfishing (Carson, Shackell, & Flemming, ) from the posterior spatial effect; joint modeling of presence–absence and abundance of hake Paradinas, Conesa, López‐Quílez, and Bellido (); topsoil metals and cancer mortality ...…”

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

“…There is extensive evidence suggesting that marine and terrestrial populations are spatially patchy and locally structured (e.g. Boudreau, Shackell, Carson, & Heyer, ; Ehrlén & Morris, ; Elith & Leathwick, ). In marine systems, local population processes are obscured, for example local depletion of weaker subpopulation or persistent high fishing pressure on local concentrations, if fine‐scale population spatial structure is overlooked (Benson, Cox, & Cleary, ; Boudreau et al, ), which may lead to over‐exploitation of local fish populations.…”

“…Boudreau, Shackell, Carson, & Heyer, ; Ehrlén & Morris, ; Elith & Leathwick, ). In marine systems, local population processes are obscured, for example local depletion of weaker subpopulation or persistent high fishing pressure on local concentrations, if fine‐scale population spatial structure is overlooked (Benson, Cox, & Cleary, ; Boudreau et al, ), which may lead to over‐exploitation of local fish populations. Locally depleted populations may not be easily replenished by recolonization (Boudreau et al, ; Kuo, Mandal, Yamauchi, & Hsieh, ).…”

“…In marine systems, local population processes are obscured, for example local depletion of weaker subpopulation or persistent high fishing pressure on local concentrations, if fine‐scale population spatial structure is overlooked (Benson, Cox, & Cleary, ; Boudreau et al, ), which may lead to over‐exploitation of local fish populations. Locally depleted populations may not be easily replenished by recolonization (Boudreau et al, ; Kuo, Mandal, Yamauchi, & Hsieh, ). Therefore, it is critical to understand spatial population structure and address the spatial heterogeneity in population density, productivity and fishing pressure to prevent overfishing more vulnerable local subpopulations.…”

“…Statistical methods and computational approaches for spatiotemporal models have seen tremendous advances in recent years (Cressie, Calder, Clark, Hoef, & Wikle, ). It is increasingly possible to fit a spatiotemporal population model directly to available fishery and survey data at the scale they were collected (Boudreau et al, ; Kristensen, Thygesen, Andersen, & Beyer, ; Thorson, Ianelli, Munch, Ono, & Spencer, ). Spatiotemporal models define how population variables, for example density, vary continuously across space (Kristensen et al, ), or in practice at hundreds of small‐scale strata, while estimating spatial variation as a random effect (Thorson et al, ).…”