TY - JOUR
T1 - Estimating fishing effort in small-scale fisheries using high-resolution spatio-temporal tracking data (an implementation framework illustrated with case studies from Portugal)
AU - Rufino, Marta M.
AU - Mendo, Tania
AU - Samarão, João
AU - Gaspar, Miguel B.
N1 - Funding Information:
The authors would like to acknowledge all colleagues working on tracking SSF, namely the participants in WSSFGEO and WSSFGEO2 that provided valuable discussions on this subject and André Carvalho (IPMA) for sending the data from the GPRS for the case study. Marta M. Rufino is funded by a DL57 contract (junior researcher) awarded by IPMA within the project “Real-time monitoring of bivalve dredge fisheries” (MONTEREAL, MAR-01.03.02-FEAMP-0022), funded by the Fisheries Operational Programme (MAR 2020) and co-financed by the European Maritime and Fisheries Fund (EMFF 2014–2020). João Samarão received a research grant (Ref: IPMA-2022-015-BII) awarded by IPMA within the framework of the project PESCAPANHA. The authors also acknowledge the two anonymous referees for their useful comments on the manuscript.
Funding Information:
The authors would like to acknowledge all colleagues working on tracking SSF, namely the participants in WSSFGEO and WSSFGEO2 that provided valuable discussions on this subject and André Carvalho (IPMA) for sending the data from the GPRS for the case study. Marta M. Rufino is funded by a DL57 contract (junior researcher) awarded by IPMA within the project “Real-time monitoring of bivalve dredge fisheries” (MONTEREAL, MAR-01.03.02-FEAMP-0022), funded by the Fisheries Operational Programme (MAR 2020) and co-financed by the European Maritime and Fisheries Fund (EMFF 2014–2020). João Samarão received a research grant (Ref: IPMA-2022-015-BII) awarded by IPMA within the framework of the project PESCAPANHA. The authors also acknowledge the two anonymous referees for their useful comments on the manuscript.
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/10
Y1 - 2023/10
N2 - Small-scale fisheries (SSF, boats < 12 m) represent 90% of this sector at a worldwide scale and 84% of the EU fleet. Mapping the areas and intensity where the fishing operations occur is essential for spatial planning, safety, fisheries sustainability and biodiversity conservation. The EU is currently regulating position tracking of SSF fishing vessels requiring precision resolved geo-positional data (sec to min resolution). Here we developed a series of procedures aimed at categorizing fishing boats behaviour using high resolution data. Our integrated approach involve novel routines aimed at (i) produce an expert validated data set, (ii) pre-processing of positional data, (iii) establishing minimal required temporal resolution, and (iv) final assessment of an optimized classification model. Objective (iv) was implemented by using statistical and machine learning (ML) routines, using novel combinations of fixed thresholds estimates using regression trees and classification methods based on anti-mode, Gaussian Mixture Models (GMM), Expectation Maximisation (EM) algorithms, Hidden Markov Models (HMM) and Random Forest (RF). Of relevance, the final evaluation framework incorporates both error quantification and fishing effort indicators. We tested the method by running through four SSF fisheries from Portugal recorded every 30 sec, with 183 boat trips validated, and concluded that the more robust time interval for data acquisition in these metiers should be <2 min and that mode and random forest methods with pre-data treatment gave the best results. A special effort was concentrated in a visual support provided by the results produced by this new method, making its interpretation easier, thus facilitating transference and translation into other fishery levels. After the current validation in the Portuguese SSF fleet, we posit that our novel procedure has the potential to serve as an integrated quantitative approach to the EU SSF management.
AB - Small-scale fisheries (SSF, boats < 12 m) represent 90% of this sector at a worldwide scale and 84% of the EU fleet. Mapping the areas and intensity where the fishing operations occur is essential for spatial planning, safety, fisheries sustainability and biodiversity conservation. The EU is currently regulating position tracking of SSF fishing vessels requiring precision resolved geo-positional data (sec to min resolution). Here we developed a series of procedures aimed at categorizing fishing boats behaviour using high resolution data. Our integrated approach involve novel routines aimed at (i) produce an expert validated data set, (ii) pre-processing of positional data, (iii) establishing minimal required temporal resolution, and (iv) final assessment of an optimized classification model. Objective (iv) was implemented by using statistical and machine learning (ML) routines, using novel combinations of fixed thresholds estimates using regression trees and classification methods based on anti-mode, Gaussian Mixture Models (GMM), Expectation Maximisation (EM) algorithms, Hidden Markov Models (HMM) and Random Forest (RF). Of relevance, the final evaluation framework incorporates both error quantification and fishing effort indicators. We tested the method by running through four SSF fisheries from Portugal recorded every 30 sec, with 183 boat trips validated, and concluded that the more robust time interval for data acquisition in these metiers should be <2 min and that mode and random forest methods with pre-data treatment gave the best results. A special effort was concentrated in a visual support provided by the results produced by this new method, making its interpretation easier, thus facilitating transference and translation into other fishery levels. After the current validation in the Portuguese SSF fleet, we posit that our novel procedure has the potential to serve as an integrated quantitative approach to the EU SSF management.
KW - Fishing effort estimation
KW - Highly resolved boat tracks
KW - Modelling track data
KW - Small scale fisheries
UR - http://www.scopus.com/inward/record.url?scp=85166665092&partnerID=8YFLogxK
U2 - 10.1016/j.ecolind.2023.110628
DO - 10.1016/j.ecolind.2023.110628
M3 - Article
AN - SCOPUS:85166665092
SN - 1470-160X
VL - 154
JO - Ecological Indicators
JF - Ecological Indicators
M1 - 110628
ER -