Expanding Aerial–Roving Surveys to Include Counts of Shore‐Based Recreational Fishers from Remotely Operated Cameras: Benefits, Limitations, and Cost Effectiveness

Abstract

Information on shore‐based recreational fishing is essential for the sustainable management of nearshore fish stocks. However, obtaining estimates of catch and effort from such fishing activity can be complex and expensive due to the large spatial scales over which surveys are typically conducted and the fine‐scale temporal resolution that is desired. Complementary surveys are one option for improving the accuracy of estimates. A pilot study was conducted in Perth, Western Australia, from April to June 2010 to test an expanded aerial–roving survey design that incorporated remotely operated cameras. Cameras recorded the distribution of shore‐based fishing activity across a 24‐h day, highlighting an afternoon peak as well as some nighttime activity, which is rarely captured in existing survey designs. This information was combined with instantaneous counts of shore fishers from aerial surveys and trip length data that were obtained from 1,194 incomplete trip interviews conducted during roving creel surveys; the resulting estimate of fishing effort was 213,460 angler‐hours (SE = 18,141; relative SE [RSE] = 8%). Catch rates, which were calculated from roving creel survey data on the numbers of retained fish, were combined with fishing effort to estimate a total retained catch of 355,801 fish (SE = 41,446; RSE = 12%). The Australian herring Arripis georgianus was the dominant species, with a retained catch of 229,779 fish (SE = 39,007; RSE = 17%). In comparison with other on‐site techniques, the incorporation of cameras into an aerial–roving survey design provides a generic, cost‐effective method for measuring the distribution of shore‐based fishing activity across a 24‐h day. Our findings improve the understanding of the exploitation of nearshore fish stocks along the Perth coast. Our method has broad application to many other recreational fisheries around the world, especially where nighttime fishing is popular. Received June 28, 2012; accepted August 24, 2012

Imputing recreational angling effort from time-lapse cameras using an hierarchical Bayesian model

“…Additionally, this bias increases as either total effort declines or the proportion of total effort seen by cameras declines due to an increased frequency of zero camera observations (Sunger et al, 2012) and could therefore lead to bias in trends in estimated total effort. Estimates of total effort were the least precise in false zero situations further emphasizing the need for careful consideration of camera placement (Smallwood et al, 2012). This may be addressed by targeting high-use areas, ensuring the view is clear of obstructions or by using multiple cameras to cover a higher proportion of the total fishing area.…”

“…The most appropriate use of cameras for fisheries management and monitoring may come when combining this technology with other monitoring methods, such as creel surveys and aerial surveys (Parnell et al, 2010;Smallwood et al, 2012;Steffe et al, 2008). Smallwood et al (2012) demonstrated how cameras can be used to compliment other methods to capture times (e.g., night) when other methods are not feasible.…”

“…Creel and aerial surveys of fishing effort are often too costly for all but the highest priority, most accessible water bodies, and are generally not intended to cover the entire fishing day or season (Smallwood et al, 2012). Time-lapse digital cameras or digital video cameras (cameras) are increasingly being adopted as an alternative method to monitor angling effort in a range of settings, including small lakes (Patterson and Sullivan, 2013;van Poorten, 2010;Ward et al, 2013a) and coastal marine fisheries (Parnell et al, 2010;Smallwood et al, 2012).…”

“…Time-lapse digital cameras or digital video cameras (cameras) are increasingly being adopted as an alternative method to monitor angling effort in a range of settings, including small lakes (Patterson and Sullivan, 2013;van Poorten, 2010;Ward et al, 2013a) and coastal marine fisheries (Parnell et al, 2010;Smallwood et al, 2012). Cameras have proven particularly useful in quantifying temporal and spatial trends in angling effort (Parnell et al, 2010;Smallwood et al, 2012) that were previously obtained using creel surveys 1 . Moreover, the frequency of observations obtained with cameras should permit them greater power to detect shifts in effort that may be difficult to achieve with other methods, such as creel and aerial surveys (Parkinson et al, 1988).…”

“…It is therefore necessary to uprate (bias correct) camera observations of angling effort (camera effort) to provide estimates of total angling effort (total effort). This uprating is not as simple as dividing by the proportion of fishing area seen by the camera, because angler density on lakes is rarely homogeneous (Smallwood et al, 2012). Given that the degree of bias correction varies among lakes and also potentially due to other factors such as time of day, it is necessary to calibrate the uprating model on a lake-by-lake basis.…”