Estimating fishing effort from highly resolved geospatial data : focusing on passive gears

TM, JM and MJ appreciate the financial support provided by the University of St. Andrews Impact and Innovation Fund 2018. TM and MJ acknowledge financial support provided by the “Conserving Atlantic Biodiversity by Supporting Innovative Small-scale Fisheries Co-management” (CABFISHMAN) Project. This project is co-financed by the Interreg Atlantic Area Programme through the European Regional Development Fund. Project N°: EAPA_134/2018”.Increasing competition for marine space requires the appropriate development of indicators to best represent the use of marine areas and the value (whether economic, social and/or cultural) derived from such use. Fishers (the largest group of users) are often under-represented in marine spatial planning processes. Highly-resolved vessel tracking data provide opportunities to map the activities of fishing vessels at a level of detail never before available. Most effort mapping methods have focused on active gears such as trawls or dredges in large scale fisheries. For these fisheries, the time spent fishing at sea (hours) is usually a representative indicator of fishing effort, enabling a straightforward mapping of the most important fishing grounds. However, for passive gears generally used in small-scale fisheries, we show that spatial indicators of effort (here, length of vessel track) greatly outperform time-at-sea as an indicator of fishing effort. We further demonstrate and validate a method to estimate gear soak time from vessel tracking data and show how maps of effort that account for soak time can be different from those solely based on time spent fishing at sea. The development of adequate methods to quantify the spatial distribution of passive gear effort is particularly relevant to fisheries management because globally about a fifth of all catches (by weight) are landed by passive gears. Appropriate, fine scale effort maps will provide better tools for spatial planning to support sustainable fishing.Publisher PDFPeer reviewe

Maerl grounds : habitats of high biodiversity in European seas

The BIOMAERL programme is a 3-year collaborative programme between laboratories in UK, Spain, France and Malta which began in February 1996. Its main plans are described in the workplan. A full inventory of the biological composition (biodiversity) of maerl bed assemblages in these regions therefore has yet to be completed, but progress is outlined below.peer-reviewe

What’s the catch with lumpsuckers? A North Atlantic study of seabird bycatch in lumpsucker gillnet fisheries

Worldwide, incidental bycatch in fisheries is a conservation threat to many seabird species. Although knowledge on bycatch of seabirds has increased in the last decade, most stems from longline fisheries and the impacts of coastal gillnet fisheries are poorly understood. Gillnet fishing for North Atlantic lumpsucker (Cyclopterus lumpus) is one such fishery. We collated and synthesized the available information on seabird bycatch in lumpsucker gillnet fisheries across the entire geographical range to estimate and infer the magnitude of their impact on the affected seabird populations. Most birds killed were diving ducks, cormorants and auks, and each year locally high numbers of seabirds were taken as bycatch. We found large differences in bycatch rates among countries. The estimated mean bycatch in Iceland was 2.43 birds/trip, while the estimates in Norway was 0.44 and 0.39 birds/trip, respectively. The large disparities between estimates might reflect large spatial differences in bycatch rates, but could partly also arise due to distinctions in data recorded by onboard inspectors (Iceland), self-administered registration (Norway) and direct observations by cameras (Denmark). We show that lumpsucker gillnet fisheries might pose a significant risk to some populations of diving seabirds. However, a distinct data deficiency on seabird bycatch in terms of spatio-temporal coverage and the age and origins of the birds killed, limited our abilities to fully assess the extent and population consequences of the bycatch. Our results highlight the need for a joint effort among countries to standardize monitoring methods to better document the impact of these fisheries on seabirds.© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/). The attached file is the published pdf

A workflow for standardizing the analysis of highly resolved vessel tracking data

Knowledge on the spatial and temporal distribution of the activities carried out in the marine environment is key to manage available space optimally. However, frequently, little or no information is available on the distribution of the largest users of the marine space, namely fishers. Tracking devices are being increasingly used to obtain highly resolved geospatial data of fishing activities, at intervals from seconds to minutes. However, to date no standardized method is used to process and analyse these data, making it difficult to replicate analysis. We develop a workflow to identify individual vessel trips and infer fishing activities from highly resolved geospatial data, which can be applied for large-scale fisheries, but also considers nuances encountered when working with small-scale fisheries. Recognizing the highly variable nature of activities conducted by different fleets, this workflow allows the user to choose a path that best aligns with the particularities in the fishery being analysed. A new method to identify anchoring sites for small-scale fisheries is also presented. The paper provides detailed code used in each step of the workflow both in R and Python language to widen the application of the workflow in the scientific and stakeholder communities and to encourage its improvement and refinement in the future

A workflow for standardizing the analysis of highly resolved vessel tracking data

Knowledge on the spatial and temporal distribution of the activities carried out in the marine environment is key to manage available space optimally. However, frequently, little or no information is available on the distribution of the largest users of the marine space, namely fishers. Tracking devices are being increasingly used to obtain highly resolved geospatial data of fishing activities, at intervals from seconds to minutes. However, to date no standardized method is used to process and analyse these data, making it difficult to replicate analysis. We develop a workflow to identify individual vessel trips and infer fishing activities from highly resolved geospatial data, which can be applied for large-scale fisheries, but also considers nuances encountered when working with small-scale fisheries. Recognizing the highly variable nature of activities conducted by different fleets, this workflow allows the user to choose a path that best aligns with the particularities in the fishery being analysed. A new method to identify anchoring sites for small-scale fisheries is also presented. The paper provides detailed code used in each step of the workflow both in R and Python language to widen the application of the workflow in the scientific and stakeholder communities and to encourage its improvement and refinement in the future