Combining multiple data sets to unravel the spatiotemporal dynamics of a data-limited fish stock

Abstract

The biological status of many commercially exploited fishes remains unknown, mostly due to a lack of data necessary for their assessment. Investigating the spatiotemporal dynamics of such species can lead to new insights into population processes and foster a path towards improved spatial management decisions. Here, we focused on striped red mullet (Mullus surmuletus), a widespread yet data-limited species of high commercial importance. Aiming to quantify range dynamics in this data-poor scenario, we combined fishery-dependent and -independent data sets through a series of Bayesian mixed-effects models designed to capture monthly and seasonal occurrence patterns near the species’ northern range limit across 20 years. Combining multiple data sets allowed us to cover the entire distribution of the northern population of M. surmuletus, exploring dynamics at different spatiotemporal scales and identifying key environmental drivers (i.e., sea surface temperature, salinity) that shape occurrence patterns. Our results demonstrate that even when process and (or) observation uncertainty is high, or when data are sparse, if we combine multiple data sets within a hierarchical modelling framework, accurate and useful spatial predictions can still be made.

Bridging the gap between commercial fisheries and survey data to model the spatiotemporal dynamics of marine species

“…These approaches use statistical methods such as Bayesian hierarchical models, multivariate autoregressive state-space models and delta-generalized linear models. Nevertheless, we found some shortcomings that could restrict their applicability into real case studies, such as (1) summarizing the response variable into a presence/absence format (e.g., Pennino et al 2016); (2) conduct a comparative analysis between the commercial fishery and survey data via separate models (e.g., Pennino et al 2016, Bourdaud et al 2017); (3) not addressing spatiotemporal dependencies simultaneously (e.g., Bourdaud et al 2017, Pinto et al 2018); (4) not explicitly modeling the differences in terms of fishing catchability and effort (e.g., Pennino et al 2016, Bourdaud et al 2017, Pinto et al 2018, Zhu et al 2018); (5) omission of the preferential sampling (PS) nature of the commercial fishery data when possibly present (e.g., Sant'Ana et al 2017, Pinto et al 2018, Thorson 2019, and (6) omission of the spatial extent of the sampling unit (all aforementioned studies).…”

Response to RC Hagen Radtke

“…In contrast to the Norwegian Sea, where distinct feeding migrations are observed for the pelagic fish component (Nøttestad et al, 2011) following the northward progressing zooplankton blooms and light conditions, the North Sea and the Baltic Sea exhibit a relatively constant spatial pattern of system productivity, with highly productive areas along the coast and less productivity in the central seasonally stratified regions. Hence, the migratory movements of North Sea and Baltic Sea fish stocks might not be based solely on large feeding migrations, but may also related to temperature and salinity changes and spawning behaviour (Hinrichsen et al, 2016;Hunter et al, 2003;Pinto et al, 2018;Radtke et al, 2013). Additionally, fish migrate into the area from the North Atlantic (e.g.…”

“…Daewel et al: ECOSMO E2E_v1.0 ter mainly simulate fish on the species level, including both single-species individual-based models (IBMs; e.g. Daewel et al, 2008;Megrey et al, 2007;Politikos et al, 2018;Vikebø et al, 2007) and multi-species models. Although some of these models are complex and already include many food web components such as OSMOSE Cury, 2004, 2001) and ERSEM (Butenschön et al, 2016), the separation of trophic levels often constrains such models' ability to simulate and distinguish between major control mechanisms on marine ecosystems (Cury and Shannon, 2004).…”

Are we ready to track climate‐driven shifts in marine species across international boundaries? ‐ A global survey of scientific bottom trawl data

“…Such sources could include fisherydependent data (such as observer, landings, vessel report trip data) that are able to report species occurrences and in some cases catch-perunit-effort. Data derived from the fisheries industry have the potential to: (a) indicate the presence and abundance of species where scientific surveys are not conducted (Hilborn & Walters, 2013); derive abundance estimates and spatial coverage by combining fishery-dependent and fishery-independent data (Nielsen et al, 2019;Pennino et al, 2016;Pinto et al, 2018); (c) provide global information on marine species from all types of habitats (for instance, http://www.seaar oundus.org/, used in Pinsky et al, 2018or Watson, 2017; (d) understand socio-ecological fisheries systems under shifting resources (Greenan et al, 2019;Pinsky & Fogarty, 2012;Young et al, 2019). Other promising sources of data could be derived from environmental DNA (eDNA; Pikitch, 2018;Salter et al, 2019).…”