Detectability of Estuarine Fishes in a Beach Seine Survey of Tidal Tributaries of Lower Chesapeake Bay

Abstract

Detectability, the probability of encountering a species at a sampling site, is often overlooked in fisheries research despite its potential to obscure inferences on habitat use and lead to biased estimates of abundance. We used occupancy models to explore factors affecting detectability and occupancy (Ψ), the probability that a species inhabits a site, for three fishes frequently captured in Chesapeake Bay seine surveys: young‐of‐the‐year (age‐0) striped bass Morone saxatilis, yearling Atlantic croaker Micropogonias undulatus, and spottail shiner Notropis hudsonius. Twelve repeat‐sampling events occurred during the summers of 2008 and 2009 at 20 sites in the Mattaponi and Pamunkey rivers, Virginia. Although the environmental factors that influenced detection probabilities varied with species, the detectability of all species was positively related to effective net length (i.e., the maximum distance from shore that the seine was deployed). The mean detectability of age‐0 striped bass, which occupied nearly every site (Ψ = 0.99, SE = 0.01), was 0.62 (SE = 0.06) and positively related to mean water temperature during sampling. The detectability of yearling Atlantic croakers was negatively related to water temperature at time of capture and was significantly greater in early summer than in late summer. In early summer, Atlantic croakers occupied all sampling sites; occupancy decreased in late summer (Ψ = 0.86, SE = 0.08) and was positively related to the mean salinity at the site. Unlike with Atlantic croakers, the mean detectability of spottail shiners was significantly greater in late summer than in early summer; detectability was positively related to turbidity at the time of sampling and reflected the increased availability of recently recruited individuals. Spottail shiners occupied fewer sites than did the two other species (Ψ = 0.59, SE = 0.21). Seasonal changes in detectability appeared to reflect changes in catchability associated with fish growth. Determining factors that influence detection probabilities is vital to improving our understanding of habitat use by fish and reducing the variation in fisheries sampling.

Sampling Little Fish in Big Rivers: Larval Fish Detection Probabilities in Two Lake Erie Tributaries and Implications for Sampling Effort and Abundance Indices

“…Thus, abundance, occupancy, and gear vulnerability are likely dynamic through the larval stage. Our results suggest that ontogenetic changes during early life history stages influence detection probabilities and occupancy, as has been shown by other studies (Falke et al 2010;Williams and Fabrizio 2011). As a result, estimates of occupancy and detection probability may be improved if they are allowed to vary through space and time (MacKenzie et al 2003).…”

“…Detection probabilities of larval and adult fishes in lotic systems vary across species (e.g., Falke et al 2010;Peoples and Frimpong 2011;McManamay et al 2014) and in this study we found detection probabilities of larval fishes ranged from <0.1 to >0.9. Although occupancy modeling has been conducted for adult and juvenile fishes of many species in a large variety of systems (e.g., Burdick et al 2008;Williams and Fabrizio 2011;McManamay et al 2014), few examples of occupancy models exist for larval freshwater fishes (but see Falke et al 2010Falke et al , 2012. We suggest that detection probabilities of larval fish is greater than those for juvenile and adult fish because larval fish are likely more abundant (as a function of the high fecundity of most fishes) and are less able to avoid sampling gears.…”

Understanding imperfect detection in a San Francisco Estuary long‐term larval and juvenile fish monitoring programme

“…In recent years, statistical methods have been developed to estimate the probability of detecting species at occupied sites based on multiple site visits (MacKenzie et al., , ). This approach has provided valuable information for fisheries management, including describing species distribution for rare species (Albanese, Litts, Camp & Weiler, ; Albanese, Peterson, Freeman & Weiler, ), assessing habitat preferences (Falke, Fausch, Bestgen & Bailey, ), evaluating sampling protocols (Peoples & Frimpong, ; Williams & Fabrizio, ) and improving the precision of abundance indices (Pritt, DuFour, Mayer, Roseman & DeBruyne, ). Although site occupancy models have been applied to the sampling of larval or juvenile fishes (Falke et al., ; Pritt et al., ), none have used a long‐term monitoring data set that spans decades and is comprises over 20,000 individual sampling events.…”

“…& Frimpong, 2011; Williams & Fabrizio, 2011) and improving the precision of abundance indices (Pritt, DuFour, Mayer, Roseman & DeBruyne, 2014). Although site occupancy models have been applied to the sampling of larval or juvenile fishes (Falke et al, 2010;Pritt et al, 2014), none have used a long-term monitoring data set that spans decades and is comprises over 20,000 individual sampling events.…”

An individual-based approach to year-class strength estimation

“…Williams and Fabrizio, 2011;Kotwicki et al, 2015). Using the method presented in this study, such processes could be included in computations at the individual level.…”