Space‐for‐time is not necessarily a substitution when monitoring the distribution of pelagic fishes in the San Francisco Bay‐Delta

Abstract

Occupancy models are often used to analyze long-term monitoring data to better understand how and why species redistribute across dynamic landscapes while accounting for incomplete capture. However, this approach requires replicate detection/non-detection data at a sample unit and many long-term monitoring programs lack temporal replicate surveys. In such cases, it has been suggested that surveying subunits within a larger sample unit may be an efficient substitution (i.e.

Imperfect detection and misidentification affect inferences from data informing water operation decisions

“…Since the vast majority (78%) of the sampling days had the occupancy state observed in just one replicate sample, we surmise that the detection and misidentification model estimated much fewer samples as truly occupied (i.e., it treated many of these single observations as false-positive detections), which would result in a higher detection rate, particularly under the baseline condition. Further, we found that the detection of juvenile winter-run salmon occupancy was spatially dependent among sampling locations and negatively related to clearer water and higher mean river flow, which is consistent with other fish studies conducted in the Estuary (Perry et al 2016;Peterson and Barajas 2018;Duarte and Peterson 2021;Mahardja et al 2021). Therefore, our study demonstrates that the false-negative and false-positive error rates in the juvenile winter-run salmon count data used to inform DCC gate operation decisions are capable of biasing occupancy estimates and thereby distorting our understanding of the factors influencing juvenile winterrun salmon distribution.…”

“…Further, we found that the detection of juvenile winter‐run salmon occupancy was spatially dependent among sampling locations and negatively related to clearer water and higher mean river flow, which is consistent with other fish studies conducted in the Estuary (Perry et al. 2016; Peterson and Barajas 2018; Duarte and Peterson 2021; Mahardja et al. 2021).…”

“…Fish detection varies based on sampling method and environmental conditions (Perry et al. 2016; Peterson and Barajas 2018; Duarte and Peterson 2021; Mahardja et al. 2021).…”

“…As a result, we estimated each of the detection probabilities as logit-linear functions of covariates including random intercepts as where η is the log-odds of the response (p 1 , p 2 , and δ), Y represents a predictor variable, β 0 represents a fixed intercept, β 0,h represents a randomly varying intercept that varied among sites, and β represents the effect of Y on the response variable. Fish detection varies based on sampling method and environmental conditions (Perry et al 2016;Peterson and Barajas 2018;Duarte and Peterson 2021;Mahardja et al 2021). Thus, we modeled p 1 , p 2 , and δ using the following predictor variables: sample volume, daily mean water clarity, daily mean Sacramento River flow at Wilkins Slough, beach seine sample method (coded as a binary indicator variable, with trawl as the baseline category), and interactions between the beach seine sample method and flow and sample volume.…”

Using passive acoustic monitoring to estimate northern spotted owl landscape use and pair occupancy

“…We assumed that repeated station‐level surveys within a hexagon were independent Bernoulli trials, and we considered individual stations and survey weeks to be replicate surveys (5 stations × 8 weeks = 40 survey occasions). This structure was chosen in order to incorporate conditions that influence detection at the level of individual ARU stations to ensure that their effects were accurately evaluated (e.g., Duarte & Peterson, 2021).…”