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Comparing and Combining Effort and Catch Estimates from Aerial–Access Designs as Applied to a Large‐Scale Angler Survey in the Delaware River
Author(s) -
Vølstad Jon H.,
Pollock Kenneth H.,
Richkus William A.
Publication year - 2006
Publication title -
north american journal of fisheries management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 72
eISSN - 1548-8675
pISSN - 0275-5947
DOI - 10.1577/m04-146.1
Subject(s) - alosa , shore , fishery , environmental science , aerial survey , recreation , sampling (signal processing) , statistics , geography , mathematics , ecology , fish <actinopterygii> , cartography , computer science , fish migration , biology , filter (signal processing) , computer vision
We used probability‐based aerial−access surveys to estimate effort, catch, and harvest of American shad Alosa sapidissima and striped bass Morone saxatilis by recreational anglers in the Delaware River and upper estuary in 2002. Sampling of anglers at access points and flights over the river were conducted weekly from mid‐March through October. Daily flight times were randomly selected; probabilities were proportional to the observed distribution of daily angler effort in a prior aerial−access survey (random count). Additional experimental flights were scheduled to occur at the time of day with expected peak effort (maximum count). Effort estimates derived from these maximum counts were more precise than estimates derived from the random flights, but the maximum‐count observations caused bias except when the daily count expansions were based on effort distributions from the concurrent access survey. The aerial and access surveys produced similar estimates of boat angler effort and little evidence of bias, but shore anglers were undercounted in the aerial survey. We maximized the precision and minimized bias in total effort estimates by combining the estimates of boat angler effort and shore angler access. An estimated sevenfold increase in the access survey sampling effort (at nearly five times the cost) would be required to achieve the same precision in the total effort estimate produced by the aerial–access survey. Effective stratification and the use of efficient model‐based estimators helped us to achieve the target precision of 20% in relative standard error (RSE) for estimated recreational catch of American shad (mean = 26,885 fish; RSE = 16%) and striped bass (mean = 47,671 fish; RSE = 15%). A single access survey during the American shad run would have required a 10‐fold increase in sampling effort to achieve the same precision in estimated catch at six times the cost of the complemented surveys.