Risks to large marine protected areas posed by drifting fish aggregation devices

Abstract

Mapping and predicting the potential risk of fishing activities to large marine protected areas (MPAs), where management capacity is low but fish biomass may be globally important, is vital to prioritizing enforcement and maximizing conservation benefits. Drifting fish aggregating devices (dFADs) are a highly effective fishing method employed in purse seine fisheries that attract and accumulate biomass fish, making fish easier to catch. However, dFADs are associated with several negative impacts, including high bycatch rates and lost or abandoned dFADs becoming beached on sensitive coastal areas (e.g., coral reefs). Using Lagrangian particle modeling, we determined the potential transit of dFADs in a large MPA around the Chagos Archipelago in the central Indian Ocean. We then quantified the risk of dFADs beaching on the archipelago's reefs and atolls and determined the potential for dFADs to pass through the MPA, accumulate biomass while within, and export it into areas where it can be legally fished (i.e., transit). Over one-third (37.51%) of dFADs posed a risk of either beaching or transiting the MPA for >14 days, 17.70% posed a risk of beaching or transiting the MPA for >30 days, and 13.11% posed a risk of beaching or transiting the MPA for >40 days. Modeled dFADs deployed on the east and west of the perimeter were more likely to beach and have long transiting times (i.e., posed the highest risk). The Great Chagos Bank, the largest atoll in the archipelago, was the most likely site to be affected by dFADs beaching. Overall, understanding the interactions between static MPAs and drifting fishing gears is vital to developing suitable management plans to support enforcement of MPA boundaries and the functioning and sustainability of their associated biomass.

Surface habitat modification through industrial tuna fishery practices

“…The NLOG distributions obtained from these two sources independently were consistent (Supplementary Figure 11) and are in line with previously obtained results in the IO (Lebreton et al 2012;Van Sebille et al 2015;Viatte et al 2020). The timing of the particle releases can also have an influence on the simulation results (Siegel et al 2003;Curnick et al, 2021). Seasonal variability in the input of NLOGs from rivers has been reported (Caddy andMajkowski 1996, Hinojosa et al 2011) and there may also be a seasonal pattern in the drift of NLOGs away from mangroves and out into the open ocean.…”

“…Other important parameters which might influence the distributions calculated from the Lagrangian simulations are the location of NLOG inputs in the ocean and the magnitude and seasonality of this input. Some studies simulating FAD trajectories showed that these parameters could strongly influence the resulting FAD distributions (Curnick et al 2021;Davies et al 2017). Mangrove and rivers are the two most likely sources of NLOGs (Thiel and Gutow 2005;Caddy and Majkowski 1996;Krajick 2001).…”

SmallSats: a new technological frontier in ecology and conservation?

“…SmallSats are advantageous over drone applications that may be spatially restricted (e.g., geo‐fenced no‐fly zones) or temporally limited by battery life, range, flying conditions, costs and fieldwork logistics (Oleksyn et al., 2021). They could revolutionise fisheries surveillance by improving the detection of illegal, unreported and unregulated (IUU) fishing (Agnew et al., 2009; Tickler et al., 2019) and drifting gears (Curnick et al., 2020). Detecting vessels in a vast ocean is inherently difficult due to the sheer scale involved.…”

“…Satellites are a vital tool for ecologists and conservationists to monitor ecosystem structure, composition and functioning (Pettorelli, Laurance, et al, 2014;Pettorelli, Safi, et al, 2014); track human activities and impacts on the natural world (Biermann et al, 2020;Kroodsma et al, 2018); and relay data from instruments deployed on animals (e.g., Barkley et al, 2019;Curnick & Feary, 2020;Doherty et al, 2017). Continual increases in the spatial and temporal resolution of data freely available to researchers are being brought about by initiatives such as the Landsat, MODIS and Copernicus missions (Pettorelli, 2019;Williamson et al, 2019).…”

A standard processing framework for the location data of satellite-linked buoys on drifting fish aggregating devices

“…Gerritsen and Lordan 2011;Hintzen et al, 2012;Lambert et al, 2012;Lee et al, 2010), the design of a standardized framework to capture the heterogeneity that typifies DFAD-related data for their integration into research and management processes is now emerging as a key priority. Buoy location data, in particular, are critical to achieve some of the major objectives of DFAD management; including estimating and monitoring the actual number of DFADs at sea (Chassot et al, 2019;Escalle et al, 2021;Gershman et al, 2015), improving the conservation measures regarding DFAD limitations and their enforcement by fishermen (Goñi et al, 2017;Lennert-Cody et al, 2018), or defining strategies to mitigate their stranding in sensitive areas (Curnick et al, 2020;Davies et al, 2017;Escalle et al, 2021Escalle et al, , 2019Imzilen et al, 2022Imzilen et al, , 2021. Hence, a processing framework to transform this vast amount of industrial data into harmonized information, notably through standard procedures, independent of the characteristics of the databases, appears to be of primary importance.…”