Predation of study fish is a common occurrence and is likely the proximate cause of mortality in some studies of juvenile fish. Although some studies assume negligible mortality whether from predation or other sources (e.g., behavior studies), survival studies are designed to characterize the magnitude of mortality and the factors that contribute to it. The large majority of tagging studies require that the measures in the data set represent only live study fish rather than a mix of live study fish, predators, and deposited tags. While a variety of methods have been used previously to diagnose tag predation, this is the first study to compare results of different methods and assess the sensitivity of study results to the diagnosis process used. For this data set, we observed considerable variability in predator status classification depending on the predator filter, but minimal effect on survival estimates (e.g., Figure 4) and measures of average travel time. The largest effects were seen in estimates of the upper quantiles of the travel time distribution (e.g., Figure 3).
Despite the variability in predated tag classifications among the filters, the resulting absolute differences in survival estimates tended to be small and within the sampling error. When translated to the relative scale, however, the differences between the various filtered and unfiltered estimates of cumulative survival were larger, ranging up to 19% for survival to the Turner Cut Junction (A20/T1) and 31% for survival to the Navy Bridge/Rough and Ready Island region (A17/R1) (pattern recognition filters, Figure 4). Survival to A17/R1 also had the largest variability among filter estimates (CV = 16.7%). These results are comparable to the large differences in relative survival observed in a 2010 study of subyearling Chinook salmon in the region, in which the estimate of total Delta survival from Mossdale to Delta exit at Chipps Island (55 km downstream of A20/T1) was reduced by 50% when a similar complex rule-based filter was applied to the data set (0.11 vs 0.05; Buchanan et al. 2018).
The four predator filters considered here entailed different tradeoffs in their level of complexity, subjectivity, and interpretability and in the effort required by the researcher. Higher levels of complexity resulted from more data (e.g., multispecies versus smolt-only tag data), increased degrees of freedom represented by more metrics (e.g., complex versus simple rule-based filters), or more decisions that also represented a higher level of effort and subjectivity (e.g., complex rule-based filter compared to the others). On the other hand, the rule-based filters resulted in clearly defined state assignments whereas the cluster analysis in the pattern recognition filters required further assumptions and interpretations to yield state assignments (e.g., the ROE method).
The issue of subjectivity looms large in adapting any predator filter approach and making inferences from its results. In some data sets, there are cases in which particular tag movements are well beyond the swimming capabilities of the focal species and the choice to omit some or all of a tag record is obvious. However, there are likely to be many more cases where subjective judgements can significantly influence findings. The two primary filtering approaches considered here varied in their demands on subject matter knowledge by the researcher and their degree of subjectivity. The metrics and criteria in the rule-based filters were based entirely upon expert knowledge gleaned from literature review, consultation with salmon and bass biologists, and personal experience with similar data sets. Pattern recognition methods that use multivariate or machine learning, on the other hand, prioritize letting signals emerge from the data rather than requiring explicit biological judgements from the researcher. A distinct benefit of the pattern recognition approach lies in its ability to isolate sets of abnormal behavior metrics without requiring the researcher to identify precise numeric criteria associated with smolt-like behavior. Thus, it may appear that pattern recognition filters or similar statistics-based filters remove all subjectivity from the exercise. However, there remains subjectivity in selecting the metrics to include, how to scale or transform the metrics, how many clusters to use, and how to interpret the clusters identified. In our analysis, we were confronted with several options of distance measures, clustering algorithms, statistics for choosing an optimal number of clusters, and ordination approaches. Thus, although pattern recognition approaches remove the researcher’s responsibility for setting numeric thresholds, there remain a multitude of decisions, each of which may affect the outcome.
There are limitations to any predator filter’s ability to correctly identify all predator detections. Even a well-designed predator filter based on realistic and defensible understanding of the behavior of the focal species and predators may miss some predator detections and/or misclassify some valid detections. A predated tag that has a short detection history may have too few opportunities to demonstrate aberrant behavior to trigger a predator classification. Highly variable behavior in the focal species will also make it difficult to accurately distinguish between predated and non-predated tags. Other factors contributing to variability in filter outcomes include the composition, mobility, and home range of the predator community, the density of the telemetry network and the spatiotemporal scale of inference, and the amount of data available. The availability of predator data in particular may affect the outcome of the multispecies pattern recognition filter. This case study used opportunistic detections of four species of piscivorous fish that had been tagged with long-lived acoustic tags one or two years prior to this study; only 37 individual predators were represented, 24 (65%) of which were largemouth bass and most of which were detected in the segment between the A6 and A12 arrays. A larger and more representative data set of tagged predators captured, released, and detected in a more variable spatial region may have resulted in different predator classifications from the multispecies filter.
Our case study findings suggest that a predator filter may be more important when studying survival over smaller spatial scales, for intermediate levels of survival, or to characterize maximum travel time. Although the need for a predator filter will be apparent only after comparing the filtered and unfiltered data, it may be expected to be more important in some settings than in others. If the home range of the predators lies entirely within the extent of a single reach for which survival is estimated, then tag predation will have no effect on survival estimation because predators are not expected to pass a monitoring station. It is when a predator’s range includes one or more monitoring sites that tag predation may bias survival estimation and a predator filter is recommended. Additionally, formal methods may be more necessary in settings in which individuals of the focus species may legitimately move in the direction opposite their eventual target, such as a tidal region with reverse flows; in simpler settings, transitions in an unexpected direction may be easily interpreted as evidence of predation, whereas in more complex settings it is necessary to compare observations with prevailing environmental conditions. Thus, formal predator filters are often omitted from studies of juvenile salmon migratory survival through the 40- to 100-km riverine reaches in the Columbia River; this region has no reverse flows and the predator community is dominated by avian species and piscine species such as Northern pikeminnow Ptychocheilus oregonensis, which exhibit mostly small-scale movements near dams or movements upriver during the spring juvenile salmonid emigration (Martinelli and Shively 1997). In this setting, upstream-directed movement may be easily interpreted as evidence of predation. Formal predator filters are more common in studies of salmon migratory survival through the 1- to 10-km tidally influenced reaches in the Sacramento–San Joaquin River delta in the presence of reverse flows and striped bass that may move about the entire south Delta and between the Delta and the Pacific Ocean (Boughton 2020).
Even in settings in which less complicated filtering methods may be suitable, we recommend that researchers clearly document their assumptions and filtering procedures. Unrecognized conceptions of what constitutes realistic behavior can influence results and may have a cascading effect. Furthermore, ad hoc or undocumented procedures bear the risk of investigator drift during implementation, in which a rule for omitting detections may become more or less strict as it is implemented over the complete set of tags and detections. Thus, we recommend identifying and documenting a well-defined procedure to detect predated tags and determine when in the detection history the tag no longer represented the live study fish. The methods should be tailored to the study’s focal species, likely predator species, setting, season, and monitoring network. We also recommend that study results be examined for a range of filtering assumptions, at minimum comparing outcomes using the researcher’s best understanding of the true state of the detection data (presumably the filtered data) with outcomes from data that are either entirely or partially unfiltered.
More sophisticated modeling techniques and new tag technologies could provide avenues for more robustly addressing the tag predation problem. Modeling techniques designed to account for unobservable or partially observable states, such as hidden Markov and state-space models (Patterson et al. 2009; Alós et al. 2016; Hurme et al. 2019), could be used to propagate the uncertainty in predation status to the final study results. To our knowledge, neither of these approaches has yet been used to account for tag predation. Additionally, there now exist specialized microacoustic transmitters that change their output when they have been predated (predator tags; Seitz et al. 2019; Daniels et al. 2019). For example, manufacturers have designed an acoustic tag that transmits an alternative signal after a polymer coating has broken down in a predator’s gut (Weinz et al. 2020). Such predator tags remove the sensitivity of predation detection to behavioral differences. Even with these specialized tags, however, there remain challenges of correctly identifying where and when the predation event occurred, caused in part by variation in digestion rate and the time delay between the predation event and the onset of the predation signal (trigger time) (Halfyard et al. 2017).