Scaling the observed-sample estimate of BPUE to an estimate of total bycatch in a fishery requires knowing the total amount of effort in the fishery (this is the sampling frame within which a subset of effort has been observed). Critically, the effort metric used for estimating BPUE and for characterizing the whole fishery must be the same. For example, if observers collect data for a random sample of fishing trips with an estimate of how many marine mammals on average are caught per trip, then the total number of trips made by the fleet must be quantified to properly extrapolate to the whole fishery. Similarly, if BPUE is quantified for a gillnet fishery by observing a random number of gillnet deployments (sets), then the total number of gillnet sets made by the fishery must also be known or estimated. In some observer programs, observers monitor all fishing activity over the course of a particular period (e.g., 24 h) and BPUE is measured as the number of bycatch events per effort-period (e.g., per-day); in this case, the number of effort-days (# boats x the # days each boat operates) would need to be known for the fleet. If the sampling frame is incomplete because the size and extent of the fleet has not been accurately determined, then the total bycatch mortality will be underestimated (i.e., negatively biased). Therefore, diligence is needed to identify all of the vessels operating in a particular fishery throughout its range.

Ideally, the units of fishing effort measured should be those most directly related to the amount of bycatch that occurs. For example, for a longline fishery, one might quantify the number of longline sets, or more coarsely, the number or total duration of longline fishing trips. However, the number of hooks on the line and their soak time (e.g., “hook-hours”) more closely relates to the likelihood of an animal being bycaught. This distinction is relatively unimportant if large numbers of longline sets or trips (i.e., effort units) are randomly sampled. However, if the size of the observed sample is small or the sampling is biased, then bycatch mortality may be more accurately estimated by measuring the number of bycatch events per hook-hour and scaling this to hook-hours in the fleet (rather than quantifying bycatch per longline trip and the number of trips). Effort recorded in finer units can always be re-scaled into coarser units as needed, whereas data recorded in coarse units cannot be more finely resolved. Of course, there are trade-offs to how finely one measures effort. Obtaining fleet-wide information about the number of vessels and trips is easier and less costly than monitoring the number of hook-hours, for example. In addition, coarser units tend to be more statistically independent. For example, observations of bycatch-per-trip are more likely to be statistically independent than observations of bycatch-per-set, since set data will be correlated in time and space within the same trips. Observations at coarser scales thus tend to give more valid estimates of precision unless autocorrelations in hierarchical or nested datasets are properly taken into account.

Measures of fishing effort vary greatly, as do the methods for quantifying those measures. McCluskey and Lewison (2008) reviewed the types of effort measures available for different types of fishing fleets around the world, including artisanal or small-scale and industrial fleets (as well as recreational and IUU⁷ fleets, not discussed further here). Though not well-defined (Tietze, 2016; Smith and Basurto, 2019), our use of “small-scale” refers to fleets that tend toward having lower capital or technological investment, being operated at the household/family level (rather than by companies), and having smaller vessel sizes. In the extreme, these fleets can consist of thousands of such vessels dispersed across vast geographic areas. Effort in small-scale fleets is usually poorly documented and rarely quantified, due to factors such as lack of awareness, funds and infrastructure, and institutional capacity. Interviews with a large, representative sample of small-scale fishers may be the most practical way to get useful estimates of effort (e.g., Gómez-Muñoz, 1990; Moore et al., 2010), and often measures of fishing-effort will necessarily be crude. For example, Lewison and Moore (2012), working with Nigerian colleagues, identified the number of fishing villages in each of three Nigerian states. For each state, they randomly sampled the villages, counted the number of fishing vessels on the beach in these villages and interviewed fishers there (stratified by boat or gear type) to obtain information about fishing methods, gears, seasonality, fish catch, and bycatch of marine mammals and sea turtles. The fishing effort metric was the average number of boats per village, multiplied by the number of fishing villages along the entire coast to estimate the number of boats per state. BPUE, also obtained from the interview data, was quantified in terms of animals caught per vessel per year. Rough total bycatch estimates were derived as catch per vessel (per year) multiplied by the number of active vessels in the state.

For industrial fisheries, a greater variety of methods for quantifying fishing effort data are generally available. In addition to interview approaches, industrial fleets are more amenable to implementing observer programs. Fleet-wide effort can be quantified through complete dockside monitoring when all vessels return to one central port or a few main ports, or using logbook data, whereby data are recorded on when, where, how and how much they fish (e.g., Roman et al., 2014). Collecting spatially and temporally explicit information about fishing effort (e.g., through logbook data) is extremely valuable, especially if total fishing effort (or BPUE) needs to be modeled rather than estimated using design-based approaches (McCluskey and Lewison, 2008). Inaccuracy is a potential pitfall of both interview-based and logbook data due to response bias (e.g., Cosgrove et al., 2016; Northridge et al., 2017; Luck et al., 2020).

The most accurate data on fishing effort are obtained from electronic logbooks that provide spatial and temporal fishing effort data, such as via a vessel monitoring system (VMS) in which data are uploaded via satellite on a regular schedule. A challenge to this approach is the resistance commonly shown by fishers to being monitored. Nevertheless, the availability of VMS and Automatic Identification Systems (AIS) data had led to recent advances in the ability to make inferences about fishing activity (effort levels and distribution) using computer science algorithms such as Global Fishing Watch⁸ (e.g., Kroodsma et al., 2018).

In the absence of data to estimate effort directly, effort can sometimes be predicted or inferred from other characteristics of the fishery using models (e.g., McCluskey and Lewison, 2008; Greenstreet et al., 2009; Soykan et al., 2014; Johnson et al., 2017; Adibi et al., 2020), although their accuracy may be difficult to validate and may rely on unrealistic or unsupported assumptions or inaccurate information. For example, fish catch (landings) has been used as a proxy for fishing effort, either directly or through models, but landings data themselves are often inaccurate (e.g., Batista et al., 2015; Pauly and Zeller, 2016).

Biased sampling (e.g., extreme over- or under-sampling the fishing fleet with respect to characteristics such as area, season, gear, or vessel type) should be avoided if possible, but if the total fishing effort is well characterized, then stratifying the sample of observer data can help address some biases. For example, if a fishery operates over a 3-month period, with most effort occurring in the second month, but most of the observed effort comes from the first month, then bycatch (and variance) can be estimated separately for each month (stratum) and the stratum estimates combined to obtain the total bycatch mortality. However, it is important in this scenario that sampling within each stratum is largely representative of the fishing occurring within the stratum. Ideally, stratification should be built into the study design, to ensure sufficient representativeness and to ensure the adequacy of within-stratum sample sizes. “Post hoc stratification” may not overcome severe design biases, such as when sample sizes are very small or absent within some strata, or when sampling biases exist across multiple attributes of the fishery (e.g., sampling in the third month under-represented an important fishing area).

Model-based approaches can be useful when sampling biases cannot be remedied by stratification, and in some other contexts. Model-based estimators use statistical relationships between potential explanatory variables (e.g., properties of a fishing deployment in a certain time and place) and a response variable (e.g., bycatch mortality) to make predictions about bycatch mortality in the unobserved component of the fishery. If the sample data capture the range of variation in the important explanatory variables, then these relationships can be described (modeled) and used to predict bycatch throughout the fishery provided that the covariate values are known for all the fishing effort (e.g., from fishery logbooks). For example, Carretta et al. (2019) used a random-forest machine learning approach to estimate marine mammal, sea turtle and seabird bycatch in the California drift-gillnet fishery based on quantified relationships between observed bycatch and a suite of fishing-set characteristics (location, diurnal and seasonal time variables, bathymetry, oceanography, gear characteristics, etc.) (also see Stock et al., 2019 for a random forest example). Authier et al. (2021, this issue) showed how regularized multilevel regression with post-stratification could be used to estimate bycatch from non-representative sampling. Another common framework for estimating bycatch mortality using covariate data is generalized linear or generalized additive modeling (GLMs or GAMs), which can be implemented in a frequentist (Orphanides, 2009; Cruz et al., 2018; Stock et al., 2019) or Bayesian estimation framework (e.g., Martin et al., 2015; Moore and Curtis, 2016). Models can be particularly useful when multiple years of data exist, allowing information-rich years to inform bycatch estimates in more data-limited years, to resolve the problem of unestimable CVs in years when no bycatch is observed, and to evaluate longitudinal relationships to bycatch mortality such as a change in management actions (e.g., Carretta et al., 2019). Bayesian methods in particular are useful for obtaining probabilistic inferences, such as the probability that the bycatch rate has changed in response to a management action or that bycatch mortality exceeds a limit or other threshold (Martin et al., 2015; Moore and Curtis, 2016).

Taking a model-based approach may be the only option for obtaining valid estimates of bycatch if a sampling design is non-representative. Importantly, however, a model-based design cannot always provide unbiased estimation if the survey design is poor. In particular, if important covariates are not adequately sampled across their range of variation, or if many observations are not statistically independent, then the covariate relationships can be incorrect. As described earlier, there is no good substitute for a well-designed survey and fishery observer program.

Bycatch mortality estimates can be biased due to inaccurate counts of observed bycatch events (typically undercounts). Undercounts occur when observers are unable to record every bycatch event that occurs during a watch period. Observers may be engaged in other data collection tasks and not detect bycaught individuals, particularly those not brought on deck. The number of bycaught animals recorded by an observer can be less than the number that were actually bycaught because marine mammals caught on hooks can “drop-off,” or those entangled in nets can “drop-out,” at any time during the fishing or retrieval of the gear (e.g., Hamer et al., 2011). These problems can be exacerbated if the crew inadvertently or deliberately fail to inform the observer of the presence of bycaught individuals, or surreptitiously release or shake an animal out, or off, the gear. This source of bias can be minimized by assuring the cooperation of crews, although in practice it cannot be eliminated because it is very difficult to estimate the frequency of drop-offs and drop-outs that occur out of sight from the vessel.

“Deployment effects” refer to factors that make it logistically infeasible to carry out the planned sampling design, forcing non-representative sampling of the fishery (Benoît and Allard, 2009; Faunce and Barbeaux, 2011; Cahalan and Faunce, 2020; Fernandes et al., 2021). These factors (Table 1) include unequal ability to observe different vessel types in the fishery, non-participation in the observer program by fishers, inability to observe the fleet operating in certain locations and periods, sub-optimal allocation of observer effort due to incomplete knowledge of the fleet, and other logistical restraints (Table 1).

“Observer effects” occur when fishers use different gear or fishing methods, target different fish species, fish in different areas or at different times, reduce effort per trip, or handle bycatch differently when observers are on board (Liggins et al., 1997; Benoît and Allard, 2009; Faunce and Barbeaux, 2011), presumably to reduce the chance of bycatch occurring (or being detected and reported). Observer effects result in observer data that are not representative of the entire fleet and may not accurately reflect the bycatch that occurred on the observed trips. Subsequently, bycatch mortality estimates are biased and likely more precise than is warranted (Cotter and Pilling, 2007). Observer effects and the resulting “observer biases” are difficult to confirm [National Marine Fisheries Service [NMFS], 2011]. In some studies, observer effects have been inferred based on catch statistics that differed significantly between observed and unobserved portions of a fleet (e.g., Wahlen and Smith, 1985; Walsh et al., 2002; Cotter and Pilling, 2007; Burns and Kerr, 2008; Faunce and Barbeaux, 2011; Kirkwood et al., 2020); some other studies failed to find such differences (e.g., Liggins et al., 1997). It is widely assumed that observer effects are common (e.g., Faunce and Barbeaux, 2011), especially when (i) captains and/or crew believe that observer data can be used against them (e.g., have enforcement consequences or lead to disadvantageous management changes) [National Marine Fisheries Service [NMFS], 2011], that an onboard observer requires additional effort on their part, or that having an observer on board constrains their behavior in some way (Cotter and Pilling, 2007), or (ii) fishers believe that greater profits can be made without observers on board (Furlong and Martin, 2000). One way to address this challenge is for managers to identify and provide incentives for fishers to cooperate, thereby helping to ensure the safety of observers and the integrity of their data. Potential incentives might include financial compensation, increased quota allocation, access to closed areas or seasons, permit fee relief, or access to restricted access fisheries.

If significant observer effects have been documented or are suspected, the problem can be handled in several ways, although the best approach is to address potential biases in the initial design of the observer program (Benoît and Allard, 2009). Theoretically, bycatch mortality estimates could be corrected if there is an estimate of the bias introduced by the observer effects, although such an estimate is rarely if ever available (Punt, 1999). Bias can be reduced by increasing observer coverage or deploying electronic monitoring devices on the unobserved portion of the fleet, although the latter may introduce its own sources of error (see “Electronic vessel monitoring as an alternative to observer programs” below) and may not be economically feasible. The bias should decrease to zero as observer coverage increases to 100%, although there is still the potential for bias due to unrepresentative sampling within trips or to fishers influencing the ability of observers to conduct their duties as required by the observer program (Benoît and Allard, 2009). Finally, a fishery can be stratified such that unbiased bycatch estimates are obtained from the observable vessels, thus confining the problems and bias to just a portion of the fishery, which can be subject to targeted monitoring to account for the under-representation (Furlong and Martin, 2000; Benoît and Allard, 2009).

In general, “cryptic mortality” refers to human-caused mortality that is not, or cannot be, observed. Bycatch should be estimated across all fisheries for a given marine mammal population. However, it is relatively uncommon that all fisheries are observed, and IUU fisheries are, of course, unobserved. Cryptic deaths and injuries can (1) occur in observed fisheries when deaths and injuries are not detected by observers (e.g., drop-offs and drop-outs), (2) go undetected because some fisheries are not observed, or (3) result from “ghost-fishing” (Gilman et al., 2013). Several methods have been developed to estimate the magnitude of overall cryptic mortality (e.g., Williams et al., 2011; Peltier et al., 2012; Barbieri et al., 2013; Gilman et al., 2013; Prado et al., 2013; Wells et al., 2015; Carretta et al., 2016), from which it may be possible to estimate cryptic bycatch mortality. The most common approach estimates the recovery rate of carcasses as the ratio of the number of known deaths due to all causes (obtained, for example, from stranding data) to the estimated total number of deaths in the population (e.g., from a population model). The product of the inverse of the recovery rate and the number of known deaths due to fisheries interactions, excluding those documented by observers, provides an estimate of the undetected (i.e., cryptic) fisheries related mortality. This approach depends strongly on the assumption that the detection rate of deaths due to fisheries interactions is not different from the overall detection rate. Cryptic mortality from all sources, not just bycatch in fisheries, has been estimated to be one-half to two-thirds, and in extreme cases up to and exceeding 90%, of total mortality for marine mammal populations (see references above).

Vessel logbook data and data collected through “dock-side” interviews, in addition to providing information about fishing effort, can provide information about marine mammal bycatch. Bycatch data collected by these methods are generally incomplete and inaccurate, usually in the direction of under-reporting (e.g., Walsh et al., 2002; Emery et al., 2019; see Mangi et al., 2016 for discussion of the efficacy of self-reporting). It is widely assumed that logbook data are incomplete and inaccurate because fishers are not skilled at collecting fisheries data (e.g., Faunce, 2011; Faunce and Barbeaux, 2011; Sampson, 2011; Mangi et al., 2016), or that they withhold information they believe could have negative consequences for them. Gilman et al. (2019) suggested that fishers “may have an economic or regulatory disincentive to record accurate data.” This problem may be less severe where there are strict legal requirements to report bycatch in logbooks, with surveillance, enforcement and punishments in place. Indeed, the use of electronic monitoring (see below) has been shown to improve the quality of logbook data (Emery et al., 2019). If logbook reporting can be assumed to be consistent throughout the fishery, then such data can be useful for extrapolating/estimating from more reliable data (observer program) that are limited in time and/or space.

Many interview-based assessments have been conducted to obtain semi-quantitative or qualitative information for characterizing fisheries in terms of describing gears and vessel-types, spatial or temporal patterns of fishing effort, and interactions with target and bycatch species, and for doing risk mapping, spatial planning and understanding socio-economic drivers of fisheries management issues (e.g., Moore et al., 2010; Liu et al., 2016; Whitty, 2016; Pilcher et al., 2017; Braulik et al., 2018). An advantage of using interview-based approaches to quantify bycatch mortality is the relatively low cost and relative logistical ease of talking with fishers compared to implementing an observer program. However, as is the case for logbook data, interview data are likely to provide biased inference if fishers are not forthcoming and honest (e.g., for fear of regulations that will limit their fishing opportunities), and interview responses are prone to memory error and interviewer effects. Conducting interviews is itself an art that requires skill and training (e.g., Moore et al., 2010; Lewison and Moore, 2012).

Nevertheless, there can be circumstances where self-reporting from logbooks or interviews provides useful information, at the very least providing information on minimum bycatch levels and on when and where at least some bycatch is occurring, in which gear types, and for which species (although accurate species identification can also be a problem with logbook and interview data, as fishers are unlikely to have been trained in species identification). Information from self-reporting can be useful for determining whether an observer program is needed, and, if it is, for guiding initial planning (e.g., prioritizing which fisheries or areas to observe first).

An alternative to using fisheries observers is electronic monitoring using various technologies, such as GPS or AIS, video cameras, and gear sensors, that capture information on fishing location, catch, bycatch, and discards. Electronic monitoring systems can be used to monitor compliance with catch retention requirements or bycatch of protected species. Systems are now available that can monitor fishing activities on a vessel, and they are starting to supplement data collected by observers or to obtain data from previously unmonitored fisheries (Gilman et al., 2019). These systems integrate GPS units, hard disks, gear sensors and video cameras that provide a visual record of what was caught when and where, including bycatch (Mangi et al., 2015; van Helmond et al., 2020). Gear sensors can improve the efficiency of data collection and storage. For example, a reel sensor can determine when a longline is being retrieved and turn the system on only at those times. van Helmond et al. (2020) reviewed 100 pilot studies and 12 operational implementations, as of 2018, to monitor catch from around the world. As electronic monitoring systems are in the early stages of development and use, it is not yet clear how effective they will be at detecting and accurately recording data on marine mammal bycatch. Nonetheless, a number of systems deployed to monitor protected-species bycatch have reported marine mammal or seabird bycatch (McElderry et al., 2007; Evans and Molony, 2011; Kindt-Larsen et al., 2012; Bartholomew et al., 2018; Emery et al., 2019; Glemarec et al., 2020; van Helmond et al., 2020).

While these systems may collect data on numbers of species with relatively high precision, they cannot yet match observers in many tasks (e.g., species identification, measuring and weighing, sample collection) (Gilman et al., 2020). On the other hand, electronic monitoring systems can collect some data that observers cannot necessarily collect consistently (e.g., precise time and location of individual events, nature of handling and disposition of animals), and they can collect data on 100% of the effort during a fishing trip; observers miss some effort when they are off duty or ill, or weather prevents them from being on deck. Important costs associated with electronic monitoring systems include the often substantial time and funding needed to review and analyze the video streams, although advances in machine learning software hold promise for addressing this issue, and the need for video storage, which can be expensive (Margolis and Alger, 2020).

Several authors have identified strengths and weaknesses of electronic monitoring systems and compared the technology to traditional methods (e.g., Mangi et al., 2015; Suuronen and Gilman, 2020; Table 2). Because these systems can be “on” all the time, or started and stopped remotely or automatically based on sensor input, the fishers do not know when the system is collecting data. Further, the data are likely to be subsampled later, which also prevents fishers from knowing when they are being monitored. Therefore, the use of these systems could eliminate an observer effect, or discourage fishers from attempting to influence the data collected by an observer, when electronic monitoring is used to supplement observer data. Further, a sampling design applied to the recorded data could be completely representative and would not suffer from a deployment effect. Electronic monitoring can create a record that, for the duration that it is stored, can be revisited to verify information or resampled to address new questions, although most current applications retain raw data only for finite periods because of high data storage costs and infrastructure requirements.

Impediments to deploying and implementing electronic monitoring include resistance from fishers out of concern about the upfront cost, difficulties of installation, especially on small vessels, and privacy issues (McElderry et al., 2007; Mangi et al., 2015). Fishers may consider electronic monitoring an intrusion into their private workspace (Plet-Hansen et al., 2017) and may argue that camera surveillance reflects a governmental mistrust against them (Mangi et al., 2015). There are also concerns that some bycaught marine mammals may not be brought close enough to the vessel to be seen on camera, and regarding the capability of the video cameras to record sufficient detail to confirm the species identification of marine mammals in the water alongside the vessel and determine the extent of their injuries, particularly at night.

There is no substitute for bycatch monitoring, but in the complete absence of a bycatch data collection system, there are indirect ways to infer whether bycatch is occurring and whether the impacts are likely to be trivial or worse. For example, beach-stranded and at-sea carcasses can provide information on interactions with fisheries and be used to help determine the need for an observer program. In the United States for example, stranding-network volunteers document human-caused injuries and deaths (e.g., as evidenced by vessel strikes, gunshot wounds, hooks, line, or net, or knife marks), and the data from strandings are used in marine mammal stock assessments [National Marine Fisheries Service [NMFS], 2016]. Often, carcasses bear clear evidence of a fishery interaction, although it is often not possible to link each case to a specific fishery or type of fishing activity. Stranding data can rarely be used to estimate bycatch mortality directly, but in some cases, models applied to stranding data have been used to infer estimates of the proportion of carcasses likely to strand ashore or minimum bycatch levels (e.g., Moore and Read, 2008; Williams et al., 2011; Carretta et al., 2016; Peltier et al., 2016, 2020). For pinnipeds, animals at rookeries can show direct evidence of entanglement; Page et al., 2004 used such data to calculate minimum entanglement mortality estimates. If a minimum estimate itself approaches or exceeds a bycatch-limit reference point (e.g., PBR) that may be sufficient to conclude that a management problem exists that needs to be addressed through an active effort to collect bycatch data more directly to inform mitigation.

It is widely understood that certain gear types represent a predictable threat to particular groups of marine mammals (Wade et al., 2021). For example, vertical buoy lines used to mark and retrieve fixed gear such as crab, lobster or fish traps have the potential to entangle large whales, and to result in their serious injury and death, but may not be a threat to smaller species. In contrast, gill nets are a serious threat to most marine mammals, including porpoises, dolphins and pinnipeds, as well as whales and sirenians. Similar to buoy lines, trawls, seines and longlines can be significant threats to particular marine mammals. Careful comparison by experts of the characteristics of an unobserved fishery with those of similar fisheries with known bycatch rates, combined with consideration of the extent of spatial-temporal overlap between the fishery and the distribution of marine mammal populations, can be used to make qualitative inferences about the likelihood of a population-level problem. Inferences of any kind can be made stronger by drawing upon multiple lines of information.