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

Abstract

Marine biota is redistributing at a rapid pace in response to climate change and shifting seascapes. While changes in fish populations and community structure threaten the sustainability of fisheries, our capacity to adapt by tracking and projecting marine species remains a challenge due to data discontinuities in biological observations, lack of data availability, and mismatch between data and real species distributions. To assess the extent of this challenge, we review the global status and accessibility of ongoing scientific bottom trawl surveys. In total, we gathered metadata for 283,925 samples from 95 surveys conducted regularly from 2001 to 2019. 59% of the metadata collected are not publicly available, highlighting that the availability of data is the most important challenge to assess species redistributions under global climate change. We further found that single surveys do not cover the full range of the main commercial demersal fish species and that an average of 18 surveys is needed to cover at least 50% of species ranges, demonstrating the importance of combining multiple surveys to evaluate species range shifts. We assess the potential for combining surveys to track transboundary species redistributions and show that differences in sampling schemes and inconsistency in sampling can be overcome with vector autoregressive spatio-temporal modeling to follow species density redistributions. In light of our global assessment, we establish a framework for improving the management and conservation of transboundary and migrating marine demersal species. We provide directions to improve data availability and encourage countries to share survey data, to assess species vulnerabilities, and to support management adaptation in a time of climate-driven ocean changes.

Improving estimates of the state of global fisheries depends on better data

“…(www.FishS tats.org). Recent research confirms that there are bottom trawl data to support analysis of biomass-trends since 2001 and potentially earlier in many regions(Maureaud et al, 2020), and survey data are available for more stocks than have stock assessments. Effort reconstructions such as those utilized here may help create fishery-dependent abundance indices in some instances, and going forward data sets such as those compiled by Global Fishing Watch (https://globa lfish ingwa tch.org/) in combination with the reconstruction approaches ofRousseau et al (2019) might allow us to construct and use time series of fishing effort specific to particular areas, fleets and species complexes.…”

“…SAR is a measure of the intensity of trawl fishing within a particular area, which sraplus uses to construct an informative prior on the magnitude of fishing mortality. While fishery-independent surveys are becoming increasingly available (Maureaud et al, 2020), they are not yet sufficiently distributed or accessible to serve as a foundation for global assessments of unassessed fisheries. As an alternative, Rousseau et al (2019) present a global reconstruction of fishing effort for countries around the world.…”

Timing and magnitude of climate driven range shifts in transboundary fish stocks challenge their management

“…Finally, changes in policy can be sluggish (e.g., it took the PST about 10 years to agree on a conservation fund) compared to the timeframe over which species shift (Pinsky & Fogarty, 2012). This is specifically true as in many cases we lack standardized data to track shifts of species across international jurisdictions (Maureaud et al, 2021) as these might already be happening. Addressing shifting transboundary stocks is also challenged by an overall lack of knowledge of how many international agreements exist or what transboundary stocks are jointly managed.…”

sdmTMB: An R Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields

“…or in situations up to mid-resolution meshes (~500 knots), which in practice can cover fairly complex spatial processes on large datasets (e.g., Maureaud et al 2021;English et al 2022).…”