Abstract
In many developing countries, small-scale fisheries provide employment and important food security for local populations. To support resource management, the description of the spatiotemporal extent of fisheries is necessary, but often poorly understood due to the diffuse nature of effort, operated from numerous small wooden vessels. Here, in Gabon, Central Africa, we applied Hidden Markov Models to detect fishing patterns in seven different fisheries (with different gears) from GPS data. Models were compared to information collected by on-board observers (7 trips) and, at a larger scale, to a visual interpretation method (99 trips). Models utilizing different sampling resolutions of GPS acquisition were also tested. Model prediction accuracy was high with GPS data sampling rates up to three minutes apart. The minor loss of accuracy linked to model classification is largely compensated by the savings in time required for analysis, especially in a context of nations or organizations with limited resources. This method could be applied to larger datasets at a national or international scale to identify and more adequately manage fishing effort.
Highlights
In many developing countries, small-scale fisheries are the mainstay of the fisheries sector [1]
In a time series of locations, time interval standardized data are a prerequisite for Hidden Markov Models, GPS data for each fishing trip were standardized to 5-second intervals to avoid time lag and to eliminate missing locations from poor satellite coverage using speed-based linear interpolation
We investigated two approaches to identify fishing behavior within GPS tracking data (n = 99), using data from on-board observers (n = 7) regarding fisher behavioral changes as reference: (1) visual interpretation based on several characteristics of fishing behavior determined by on-board observers and (2) application of Hidden Markov Model to GPS tracking data
Summary
Small-scale fisheries are the mainstay of the fisheries sector [1]. 50 million people worldwide are employed directly in fishing, of which 22 million (44%) are associated with small-scale fisheries [2]. This sector, makes a considerable contribution to local and national economies due to its important role in food security, employment, and as a potential route to poverty alleviation. It has been demonstrated that in comparison to large-scale industrial fishing, small-scale fisheries provide more employment, have lower production costs, produce fewer discards [3], and may be more likely to promote the sustainable use of marine species, as they respond dynamically to resource fluctuations [4].
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