Abstract
Many small-scale fisheries are remote in nature, making data collection logistically difficult. Thus, there is a need for accessible solutions that address the data gaps present in these fisheries. One possible solution is to incorporate photography into community- or harvest-based monitoring frameworks and employ these images to estimate biological data. Here, we test this approach using łuk dagaii, or broad whitefish, Coregonus nasus (Pallus, 1776) in the Gwich'in Settlement Area, a remote region in the Mackenzie River system in Canada's Northwest Territories. We used photographs taken by Gwich'in collaborators using a simple, standardized set-up to ask the question: how accurately can weight be estimated from a photo? Using random forest models based on morphometric photograph measurements as well as season and location of harvest, we predicted broad whitefish weight to within 13% of true weight (257 g, for fish weighing an average of 2036 g). The model predictions were well distributed in their residuals for most fish, though we discuss biases at low and high weights. Image analysis is a simple, low cost, and accessible method that may contribute to ongoing, community/harvest-based fishery data collection where fish length (measured) and weight (predicted) can be tracked through time.
Highlights
Collecting fisheries data can be difficult for small-scale fisheries in remote regions because of financial and logistical constraints
To evaluate an accessible data collection tool that could be utilized by a community-based monitoring program, we explored the feasibility of predicting broad whitefish weight from fish measurements derived from photographs
We found that whitefish weight can be estimated from photographs using morphometric measurements paired with information on sampling location and season of capture
Summary
Collecting fisheries data can be difficult for small-scale fisheries in remote regions because of financial and logistical constraints. In northern Canada, there has been very limited effort to document small-scale fishery catches (Zeller et al 2011); yet, it is a system where basic biological data are considerably lacking for many species (Dey et al 2018). Canadian Science Publishing is harvest- or community-based monitoring, where trained fishers gather information (e.g., record measurements) from their catch, creating the opportunity for long-term biological data sets while a species is harvested, often for subsistence (Bell and Harwood 2012). Cameras have been trialed in small-scale fisheries to identify species (Bartholomew et al 2018), to monitor regulation compliance (Pitcher et al 2009), and as a means for trained community members to report shark landings (Jeffers et al 2019). Image analysis has been used to discern fish morphometrics in large-scale fishing and aquaculture industries (Balaban et al 2010)
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