Repetitive capture of marine fishes: implications for estimating number and mortality of releases

Abstract

In some fisheries, releases are a high percentage of total catch. Recent tagging data of marine fishes have revealed that recapture of the same individual multiple times occurs frequently. We investigated the magnitude of this phenomenon and its effect on survival using previously collected mark-recapture data of four reef-associated species. We used Cox proportional hazard regression models to examine whether survival varied with release number. For three of four species, survival was significantly higher after the second, third, and/or fourth release as compared to the first release, perhaps resulting from selection for robust individuals. Repetitive recapture implies that the estimated number of unique released fish is biased. Increased survival following later releases as compared to the initial release suggests that the number of dead discards may be similarly overestimated. We analysed the sensitivity of stock assessment results to reduced estimates of dead discards using two of our species that had recently been assessed. We found that reduced estimates of dead discards had a modest effect on assessment results but could nonetheless affect the perception of fishery status. Our findings highlight the need to revise current practices for estimating live and dead discards, either internal or external to stock assessment models.

discards, stock assessment, survival, tagging

Introduction

The practice of releasing part of the catch (discarding) has expanded in some regions in the last half-century. This increase is largely a result of changing angler behaviour (Quinn, 1996; Graefe and Ditton, 1997; Allen et al., 2008), and, particularly for marine fisheries, greater fishing effort and stricter regulations (Kelleher, 2005; NMFS, 2016). Cooke and Cowx (2004) used Canadian release statistics to estimate that global annual recreational catch is ≈47 billion fish, of which only 17 billion (36%) are retained. Furthermore, the United States has seen substantial rises in releases in recent decades (Zeller et al., 2018). In the European Union, the landing obligation has banned discarding in certain situations (Guillen et al., 2018), though discarding still occurs in many fisheries for reasons including exemptions and non-compliance (Villasante et al., 2019). Consequently, numbers of live and dead discards (as well as discard mortality estimates) are of growing importance as inputs for many stock assessments (Cooke and Schramm, 2007; Pollock and Pine, 2007; Viana et al., 2013). In general, the total number of dead discards for a fishery is calculated as the assumed rate of discard mortality (i.e. mortality that will occur on live discards) multiplied by the number of live discards, plus any dead discards (i.e. fish that are discarded dead). This methodology implies that each capture is independent; all live discards are treated as unique individuals and the possibility that fish could be caught and released more than once is not accounted for (National Marine Fisheries Service's Marine Recreational Information Program (MRIP), 2018).

Many tagging studies have found a high proportion of unique individuals being recaptured multiple times (e.g. Kipling and Le Cren, 1984; Fabrizio et al., 1999). McCormick (2016) described the phenomenon of repetitive recapture in fisheries where a high proportion of individuals are caught and released. In such fisheries, the estimated number of releases may not be equal to the number of unique released fish (or discards), as a single fish captured repeatedly may be incorrectly tabulated as several independent fish. For example, Schill et al. (1986) estimated that Yellowstone cutthroat trout (Salmo clarkii bouvieri) were caught an average of 9.7 times each during the 1981 fishing season. Moreover, Slavík et al. (2009) monitored fish ladder occupation by brown trout (S. trutta) and noted high rates of repeated capture and calculated that without individual fish identification (tags) the number of fish in the ladder would have been overestimated by 63.7%. This issue may be particularly important when the number of discards is used in a stock assessment or management context (Crowder and Murawski, 1998; Punt et al., 2006).

Despite the possibility of cumulative physiological effects of multiple catch-and-release events, as suggested by Wydoski (1977), studies of repetitive capture on finfish are scarce. Bartholomew and Bohnsack (2005) posited that multiple capture events would result in cumulative mortality and recognized the importance of this phenomenon to stocks that experience high fishing pressure. Arlinghaus et al. (2007) speculated that repeated catch-and-release of fishes via angling may result in increased stress and behavioral changes. Arlinghaus et al. (2007) speculated that repeated catch-and-release of fishes via angling may result in increased stress and behavioural changes. For example, this additional stress has been shown to increase nest abandonment rates in male smallmouth bass (Micropterus dolomieu) (Kieffer et al., 1995; Philipp et al., 1997). Various authors have also recognized that repeated escapement from trawls may result in increased mortality (Suuronen and Erickson, 2010; Caddy and Seijo, 2011; Gilman et al., 2013). Others have documented the occurrence of repeated recaptures for tagged fish and incorporated those data into models for parameters such as movement rates (e.g. Whitlock and McAllister, 2009). However, no studies to our knowledge have empirically investigated the impacts of repetitive hook-and-line capture on the disposition or survival of wild fishes. Estimates of discard survival from tagging data are most often based on the first recapture event only (Hueter et al., 2006; Rudershausen et al., 2014; Sauls, 2014; but see Shertzer et al., 2018). If discard survival varies as a function of how many times an individual is caught and released, stock assessments that use discard survival from only the first capture may be biased and resulting management strategies may be ineffective (Coggins et al., 2007).

Our objectives in this study were to examine the impacts of repetitive captures on estimates of (i) the number of independent fish released and (ii) survival rates for four reef fish species using previously collected mark-recapture data from the southeast United States. The latter objective used tagging data to examine the recapture rate of individual tagged fish that were caught on multiple occasions, which can be interpreted as relative survival. Information obtained from the two objectives was used to simulate the impact on stock assessments under several levels of non-unique releases.

Methods

Empirical analyses

We examined tagging datasets on four species of reef fish: black sea bass (Centropristis striata), grey triggerfish (Balistes capriscus), red grouper (Epinephelus morio), and Warsaw grouper (Hyporthodus nigritus). Each of these are demersal, physoclistous reef-associated species that inhabit continental shelf waters of the Atlantic Ocean. Black sea bass are restricted to the US Atlantic and Gulf of Mexico, red grouper and Warsaw grouper are distributed throughout North and South American waters, and grey triggerfish are ubiquitous on both sides of the Atlantic in temperate and tropical latitudes. All four species have current and/or historic value as commercial and recreational targets. The tagging datasets were collected over the last few decades by scientists (in the case of three species) or via a citizen science programme (for Warsaw grouper). These datasets have previously been used to estimate discard mortality, as each species is susceptible to barotrauma. Rudershausen et al. (2014) tagged black sea bass with internal anchor tags in a narrow depth range in Onslow Bay, North Carolina. Runde et al. (2019) detail tagging procedures for grey triggerfish with internal anchor tags, and grey triggerfish data included here were from a single site in Raleigh Bay, North Carolina. Sauls (2014) published data on gag (Mycteroperca microlepis) but tagged red grouper as part of the same tagging programme using identical methodology, involving a plastic dart tag inserted through the anterior dorsal musculature (BS, unpublished data). For our analysis, we truncated the large red grouper dataset to include only fish from a single region (Tampa Bay, Florida area) to eliminate the confounding effects of variable spatial effort on estimates of relative survival (Royle et al., 2013). Shertzer et al. (2018) and Wiggers (2010) described methods for tagging Warsaw grouper with plastic dart tags, which took place at a single site off the east coast of Florida. All fish in these studies were single-tagged, as estimating tag retention was not a priority for the original investigators.

For the purposes of their respective studies, past analyses performed with these tagging datasets only used information up to the first recapture event [with the exception of Shertzer et al. (2018)]. We reanalysed versions of each dataset that contained information about subsequent recaptures as well. Our goal was to determine how survival varied by release number. For each dataset, the number and proportion of individuals recaptured from each successive release were determined. A Cox proportional hazards regression model was used to determine whether release number had an effect on survival (Cox, 1972). The formulations associated with this model are detailed elsewhere (Fabrizio et al., 2008; Sauls, 2014), so we summarize them only briefly here. The model is used to estimate the probability that an individual experiences a recapture at a particular time. The hazard function (briefly, the probability of a particular fish experiencing recapture) is explained by a set of covariates in addition to the baseline value for a reference group.

For each species, we modelled release survival probability as a function of release number in addition to other covariates when information was available such as release condition, month, year, depth, capture gear, and fish size. Covariate values were changed for each observation of the same fish to reflect each specific release event. Variable selection was conducted using a stepwise selection procedure based on AIC, with iterations between “forward” and “backward” selection (α = 0.05). Full models from which variables were selected varied by species were: black sea bass, release number + condition; grey triggerfish, release number + condition + gear; red grouper, release number + condition + size + year + month + depth; and Warsaw grouper, release number + size. Condition categorizations (e.g. deep-hooked, severe barotrauma) were determined by the authors of each study (Rudershausen et al., 2014; Sauls, 2014; Runde et al., 2019). The resulting hazard ratio for each variable was interpreted as a factor of survival when all other variables were held constant. Reference variables were Release number 1 (black sea bass, red grouper, Warsaw grouper), Condition 1 (where higher number indicates worse condition; black sea bass, grey triggerfish, and Warsaw grouper), Year 2009 (red grouper), and Month Apr (red grouper). All analyses here and below were performed in R version 3.4.1 (R Core Team, 2019) using the R package “survival” (Therneau, 2015).

Potential impacts of tag shedding and tagging-induced mortality

For some of the species examined, we found increased recapture rate following releases two, three, and four as compared to the initial tag and release event; hereafter, we refer to these subsequent releases as “release numbers 2+”. One explanation for increased recapture rate is that acute (or immediate) tag shedding or tagging-induced mortality (combined rate denoted as Φ; Beverton and Holt, 1957) reduced the number of alive tagged individuals, thereby reducing the number of tagged fish at large and available for recapture; these rates are assumed to occur after the first release only. Given the information available from these four datasets examined, it was impossible to decompose Φ into its component rates; however, this decomposition was unnecessary, as both acute tag shedding and tagging-induced mortality can be treated as rates that only occur immediately after the first release; therefore, their effects on our analyses are identical. Had Φ occurred, the perceived recapture rate would be biased low for the first release only, and not for releases 2+, since Φ does not occur on releases 2+ fish because they have the original tag.

We used simulations to estimate the level of Φ that would be necessary for the automated variable selection procedure to exclude “release number” from the best model. That is, what level of acute tag loss and tag-induced mortality would it take to increase the recapture rate after release 1 and make it statistically similar to recapture rates for releases 2+? For the three species for which increased recapture rate for release numbers 2+ was found, the Cox proportional hazards models were repeated using bootstrapped variations in the original datasets. We first identified the individuals in each dataset that were never recaptured (in other words, the individuals that may have experienced Φ). Then, we removed a randomized portion of these individuals (to represent Φ) and refit the model using the same stepwise variable selection procedure as above, but with the reduced dataset. We varied the proportion of the individuals removed (Φ), and levels of Φ were chosen after preliminary runs to determine the approximate value necessary for exclusion of the variable “release number” for each species. We replicated this bootstrapping procedure 1000 times for each value of Φ to account for differences in covariate values for never-seen individuals. We examined the percentage of the 1000 bootstrapped replicates that contained “release number” in the best model. Finally, we graphically determined the value of Φ for which release number was excluded in an appreciable portion (∼5%) of replicates. This was considered the threshold value of Φ for each species that would be necessary to explain our finding of increased tag return rate for releases 2+.

Furthermore, we considered the possibility that some fish lost their tag and were recaptured later, therefore being incorrectly treated as new individuals (i.e. release number = 1). This phenomenon would artificially increase the number of first releases and decrease the perceived percentage of recaptures of first releases while having the opposite effect for releases 2+ fish; thus, incorrect assignment of release number could lead to a bias towards higher relative survival of subsequent recaptures. To investigate this, we simulated a population of 1 million fish that were exposed to five periods of capture and tagging. We assumed a capture probability of 0.1 for each fish in each period, a probability of immediate (acute) tag loss of 0.15 (×1.5 the highest value published for either tag type examined here; see Discussion), and a probability of tagging-induced mortality of 0.05 (each time a fish is tagged). We did not include the possibility of discard mortality, as we were only interested in the influence of tag loss on relative recapture rates. Similarly, we did not include chronic tag shedding in the simulation, as it is assumed constant and likely very small in magnitude when compared to immediate tag shedding. We generated capture histories for each fish, such that we were able to retrospectively identify recaptured individuals that had lost their tag and were incorrectly considered first-time releases. We used these capture histories to tabulate proportions recaptured after the first, second, third, and fourth releases as we had done with empirical tagging data. These proportions were calculated under two scenarios: “Observed”, in which we counted any untagged fish as a first release, and “Reality”, in which we counted untagged recaptures correctly (i.e. if it was untagged but had previously been captured/tagged once before then it was assigned a release number of two). We compared differences in recapture rates between the two scenarios to determine the likely influence of this phenomenon on our empirical results.

Magnitude of releases and dead discards

We calculated the impacts of two theorized biases in estimates of total releases and dead discards. The first bias was the difference between total releases and total unique released fish. We performed a simulation with several capture and release events assuming an initial release of 1000 fish with a constant discard survival of 0.85 after each of three releases. In addition, we assumed all alive fish would be captured in each time bin or period; although this is unlikely in a natural setting, this assumption aids in the illustration of this concept without impacting the conclusions that can be drawn from the results. The total number of releases was calculated as the sum of the releases following each of the three capture events (Figure 1a). The second bias examined was the effect of increased survival for releases 2+ on the estimates of dead discards (Figure 1b). For this analysis, we created another theoretical population of 1000 fish for which we used 0.85 as the discard survival for release 1 but 0.95 as the discard survival for releases 2 and 3. For each of these two theoretical populations, we calculated the estimated number of dead discards as the product of the total number of live discards (all releases from three release periods combined) and the discard mortality of 0.15; this is similar to how the number of dead discards are estimated for use in some stock assessments (National Marine Fisheries Service's Marine Recreational Information Program (MRIP), 2018). Next, we calculated the “actual” number of dead discards as the sum of the number of dead fish that occurred as a result of releases 1, 2, and 3. We compared the estimated number of dead discards to the “actual” number of dead discards both within and between the two survival scenarios.

Figure 1.

Calculations of total unique released fish, total live releases, estimated (est.) dead discards, and actual dead discards under (a) constant survival and (b) increased survival. In both scenarios, we assume all live fish are captured in each period. “Total unique released fish” is held constant at 1000 and refers to the number of unique fish that are released in time step 1 (first black circle) with those same (and alive) individuals being caught and released in subsequent time steps (second to fourth black circles). “Total live releases” is calculated as the sum of the number of releases in each time step (black circles); this value emulates the number of releases that would be reported by programmes such as NOAA Marine Recreational Information Program. “Est. dead discards” represents the sum of dead discards from each release. Estimated dead discards from all releases are calculated as “Total live releases” multiplied by (currently assumed constant) discard mortality of 0.15 for scenarios (a) and (b). Note that although the same discard mortality rate is used, the number of “Est. dead discards” is slightly higher in scenario (b) because the number of live releases is higher. “Actual dead discards” is the sum of the number of discard mortalities after each of the three release events; in the “Increased Survival” scenario (b), survival increases from 0.85 for release 1 to 0.95 for releases 2 and 3. This increase corresponds to our findings of increased survival for releases 2+ for three species of reef fish in the southeast United States.

Influence on stock assessment

We investigated effects of repetitive captures on stock assessments using two case studies: black sea bass and red grouper. We chose these two species because their stock assessments had recently been conducted in the southeast US Atlantic and because we examined tagging data for those species herein. The assessment methods are detailed elsewhere (SEDAR, 2017, 2018), and so we summarize them here only briefly. Both assessments applied an integrated, age-structured model and incorporated time-series data on landings, discards, indices of abundance, age and length compositions of these time series, and life-history information such as natural mortality and reproductive capacity (Williams and Shertzer, 2015). To evaluate the importance of repetitive captures on assessment results, we ran the assessment model for each species four times. The first iteration used the base-level estimates of total dead discards as provided in the original time series (100%). Subsequent runs of the assessments reduced the total dead discards to 75, 50, or 25% of the original values. These reductions reflect the direction that the estimates would tend if they corrected for repetitive recapture of individuals in the count of total releases or if release mortality was lower (increased survival). All other data inputs and aspects of model configuration remained the same.

To quantify the effect of repetitive releases on stock assessment results, we focused on two primary model outputs: estimated stock status and fishery status. Stock status was computed as spawning biomass (S) relative to that at maximum sustainable yield (S_MSY), and fishery status was computed as fishing mortality rate (F) relative to its value at maximum sustainable yield (F_MSY). For management of these stocks, S_MSY serves as a biomass target to be achieved and F_MSY as a fishing limit not to be exceeded.

Results

Empirical analyses

For three of four species (black sea bass, red grouper, and Warsaw grouper), the proportion of fish recaptured was higher for releases 2+ than for release 1 (Table 1). The “release number” variable was retained in the best Cox proportional hazards regression model for each of these three species and was positively and significantly correlated with survival (Table 2). For the fourth species, grey triggerfish, release number was not retained. Other retained covariates, which were not of primary interest here, are shown in Table 2.

Table 1.

Releases and recaptures from four tagging studies of reef fishes in the southeast United States.

Species	Methods reference	Liberty period (d)	Release number	No. released	No. recaptured	Proportion recaptured	Relative proportion
Black sea bass	Rudershausen et al. (2014)	0–976	1	3807	933	0.25	1.00
			2	289	130	0.45	1.80
			3	33	18	0.55	2.20
Grey triggerfish	Runde et al. (2019)	0–354	1	273	76	0.28	1.00
			2	59	22	0.37	1.34
			3	19	7	0.37	1.32
			4	6	2	0.33	1.20
Red grouper	Sauls (2014)	0–2010	1	9250	1232	0.13	1.00
			2	281	177	0.63	4.85
			3	16	9	0.56	4.31
Warsaw grouper	Shertzer et al. (2018)	0–542	1	195	80	0.41	1.00
			2	59	38	0.64	1.56
			3	33	16	0.48	1.17

Liberty period refers to the range of time (d) fish were at large prior to their terminal recapture. Relative proportions are the result of scaling the proportion recaptured relative to the first recapture percentage, which is fixed at 1.00 and italicized.

Table 2.

Results from Cox proportional hazards regression models for each of four reef fish species in the southeast United States.

Species	Best model	Variable	Hazard ratio	p
Black sea bass	∼Release number + condition	Release number 2	2.26	<0.01
		Release number 3	2.80	<0.01
		Condition 2	0.89	0.05
		Condition 3	0.14	<0.01
		Condition 4	0.14	<0.01
Grey triggerfish	∼Gear + condition	Gear: trap	2.14	<0.01
		Condition 2	0.58	0.03
		Condition 3	0.12	0.04
Red grouper	∼Release number + size + depth + year + month	Release number 2	8.56	<0.01
		Release number 3	6.06	<0.01
		Release number 4	3.64	0.01
		Size (mm)	1.01	<0.01
		Depth (m)	0.97	<0.01
		Year 2010	1.04	0.59
		Year 2011	1.03	0.74
		Year 2012	0.85	0.08
		Year 2013	0.88	0.19
		Year 2014	1.10	0.62
		Year 2015	0.68	<0.01
		Year 2016	0.56	<0.01
		Month January	0.61	<0.01
		Month February	0.95	0.64
		Month March	0.97	0.81
		Month May	1.07	0.52
		Month June	0.81	0.05
		Month July	0.59	<0.01
		Month August	0.55	<0.01
		Month September	0.56	<0.01
		Month October	0.82	0.05
		Month November	0.82	0.05
		Month December	0.69	<0.01
Warsaw grouper	∼Release number + size	Release number 2	1.97	<0.01
		Release number 3	1.36	0.26
		Size (mm)	0.99	<0.01

Potential impacts of tag shedding and tagging-induced mortality (Φ)

Our first simulation found that it is theoretically possible for Φ to have been the cause of “release number” being significant in our models for two of three species. The first discernable drop in the proportion of bootstrapped models that excluded the release number variable occurred at Φ of ∼0.396 for black sea bass and ∼0.125 for Warsaw grouper (Figure 2). For red grouper, “release number” remained significant regardless of the level of Φ (up to the theoretical limit of all never-recaptured fish having experienced Φ), indicating that the change in survival was too great to be a result of any possible value of Φ. Thus, a combined acute tag shedding and tagging mortality rate of ˃∼39.6 and ∼12.5% would be required to account for the increased survival in the release 2+ fish for black sea bass and Warsaw grouper, respectively.

Figure 2.

Proportion of bootstrapped datasets for which the best model contained release number for (a) black sea bass and (b) Warsaw grouper from our examination of the effect of Φ (combined rate of tag shedding and tagging-induced mortality).

Our second simulation, which examined the influence of misidentifying recaptured fish that had lost their tag, found higher recapture rate for first releases in the Reality scenario as compared to the Observed; however, the recapture rates between the two scenarios were similar for 2+ releases (Table 3). The relative proportions recaptured for releases 2, 3, and 4 were in general slightly lower in the Reality (0.55, 0.29, and 0.12, respectively) scenario as compared to the Observed (0.65, 0.35, and 0.10). Thus, misidentifying recaptured fish would lead to biased high survival of 2+ fish, but the magnitude of this bias is lower than that which we observed in the empirical studies examined herein (Table 1).

Table 3.

Results from our simulation investigating the possibility that tag shedding can lead to incorrect identification of recaptured fish.

Release number	N tagged	N recaptured	Proportion recaptured	Relative proportion
Observed
1	421 735	66 719	0.16	1.00
2	66 719	6 847	0.10	0.65
3	6 847	384	0.06	0.35
4	384	6	0.02	0.10
Reality
1	409 772	77 490	0.19	1.00
2	77 490	7 982	0.10	0.55
3	7 982	437	0.06	0.29
4	437	10	0.02	0.12

In the “Observed” scenario, recaptured fish that lost their tag were not able to be identified as such, while the “Reality” scenario accounts for the phenomenon. Relative proportions are the result of scaling the proportion recaptured relative to the first recapture proportion, which is fixed at 1.00 and italicized.

Magnitude of releases and dead discards

Our investigation of two initial releases of 1000 theoretical fish yielded large differences in the metrics we examined. Under both the constant survival and increased survival scenarios, we found the number of total releases to be more than twice the number of unique released fish. We note that this disparity would hold even if not every fish were recaptured in each time period, (as we assumed here for ease of illustration) because such a reduction would impact both the number of total releases and the number of unique released fish. In the constant survival scenario (Figure 1a), we show that the estimate of dead discards produced by using typical stock assessment methods (i.e. number of total releases multiplied by the discard mortality rate) is equivalent to the “actual” number of dead discards. Thus, the estimate of dead discards is not influenced by repeatedly capturing the same fish as long as discard survival is constant across releases. However, for the increased discard survival scenario (Figure 1b), we show that the computed number of dead discards is biased ∼70% higher than the actual number of dead discards. Different simulation conditions would lead to different levels of bias [e.g. more releases or a larger difference in initial release survival and survival for releases 2+ would both lead to increased bias (e.g. a value >70%)].

Influence on stock assessment

The stock assessments of black sea bass and red grouper were affected similarly by reductions in the annual number of dead discards (Figure 3a and d). In general, fewer discards resulted in average selectivity curves that emphasize lower mortality of younger fish. The estimated time series of S relative to its target (S_MSY) were relatively insensitive to these reductions (Figure 3b and e), while the estimated time series of F relative to its target were quite sensitive (Figure 3c and f). In general, S/S_MSY were marginally higher for model runs with lower amounts of dead discards. This was driven by slight increases in S paired with slight decreases in S_MSY. S increased due to more fish being allowed to reach maturity and S_MSY decreased slightly as a result of the decline in F attributed to a lower level of dead discards (Supplementary Table S1). For black sea bass, the annual number of dead discards influenced the estimate of whether the stock was experiencing overfishing (F/F_MSY > 1) or not (F/F_MSY < 1). The change in F/F_MSY resulted from larger changes in F_MSY relative to changes in F (Supplementary Table S1). For red grouper, the stock status was consistent across scenarios (overfishing); however, the degree to which F exceeded F_MSY varied, particularly in the terminal assessment years.

Figure 3.

Stock assessment results for black sea bass (SEDAR, 2018) and red grouper (SEDAR, 2017), given the annual number of dead discards at the base level used in the most recent assessment (100%) or at reduced levels (75, 50, or 25% of base-level estimates), representing scenarios with fewer releases of unique fish or higher discard survival. Panels show the annual number of dead discards for each species (a, d), the estimated spawning biomass relative to that at maximum sustainable yield (S/S_MSY; b, e), and the fishing mortality rate relative to its value at maximum sustainable yield (F/F_MSY; c, f). Note different scales on the x- and y-axes among panels.

Discussion

Increased discarding of live fish because of regulations or fishing behaviour can result in repetitive captures of individual fish. Given that live releases dominate total catch for many species (Kelleher, 2005; Zeller et al., 2018), recreational effort is high and has increased through time in many regions (Post et al., 2002; Cooke and Cowx, 2006; Ihde et al., 2011; Hyder et al., 2018; Shertzer et al., 2019), and our finding of repeated captures in the four species examined, it is likely that any fishery with even a low proportion of releases experiences repetitive captures. Our study explored the effects of repetitive captures on important variables that are often used in stock assessments. First, we showed how the estimated number of total live releases using current approaches is higher than the actual number of unique released fish when there are repetitive captures. Second, we found that recapture rate in three of four species increased after the first release and conclude that this represents increased survival for individuals after their second release; thus, estimates of dead discards for these species that use survival rates based on first release are likely biased. Lastly, we found that biased estimates of dead discards have an impact on stock assessment results. We discuss the implications for each of these findings below.

Typically, studies using conventional tagging to produce estimates of discard mortality (e.g. Rudershausen et al., 2014; Sauls, 2014) calculate their estimate by using data from the first recapture only and disregard data from subsequent recaptures of those individuals. Published information about the prevalence of repeated recaptures exists for a variety of taxa worldwide, although authors typically make note of this information only in a cursory fashion (e.g. Springer and McErlean, 1962; Kipling and Le Cren, 1984; Recksiek et al., 1991; Fabrizio et al., 1999; Miller et al., 2001). It is likely that most large-scale tagging datasets contain information about repeated recaptures, although many do not report it. We recommend that future studies explore and report the prevalence of repeated recaptures and their influence on discard survival where possible.

We found significantly higher recapture rates for release number 2+ for black sea bass, red grouper, and Warsaw grouper, but not for grey triggerfish. Though theoretically possible, it seems highly unlikely that Φ (acute tag shedding and tagging-induced mortality) caused increased recapture rates for releases 2+ for black sea bass and Warsaw grouper. We searched the literature for empirical estimates of Φ from studies using internal anchor tags and dart tags (the two tag types used in the four datasets we examined), via holding studies and/or double tagging experiments. One hundred percentage of acute retention and post-tagging survival has been demonstrated for internal anchor tags in striped bass (Morone saxatilis), meagre (Argyrosomus regius), and spotted seatrout (Cynoscion nebulosus) (Dunning et al., 1987; del Mar Gil et al., 2017; Ellis et al., 2018). Latour et al. (2001) directly modelled acute tag shedding in red drum (Sciaenops ocellatus) for internal anchor tags and stainless steel dart tags (as opposed to plastic, which were used in the Warsaw grouper and red grouper studies examined in the present document). They estimated immediate retention of internal anchor tags to be 0.99 and of stainless steel dart tags to be 1.0. Fonteneau and Hallier (2015) described estimates of acute dart tag shedding for three species of tunas and stated that, while estimates have ranged up to 0.10 (Bayliff and Mobrand, 1972), this rate is “widely dependent on the expertise of the tagger”. Other than the single reference of acute shedding of 0.10, all estimates for tunas were ˂0.05 (Fonteneau and Hallier, 2015). In addition, such rates may vary by taxon and estimates for tunas may not match values for reef fishes. Our literature search yielded no published estimates of Φ that were above 0.10 for either tag type, and most estimates were close to 0. Because no published values of Φ surpassed even our lowest estimated threshold value of 0.125 (for Warsaw grouper; Figure 2), it is unlikely that this is the explanation for our finding of higher recapture rate for releases 2+.

Our examination of the effect of recapturing fish that had lost their tag demonstrated that while absolute recapture rates of release 1 fish would be higher if all recaptures were correctly identified, the relative recapture rates between releases 1 and 2+ were only marginally impacted. While the directionality of the bias in the Observed scenario was towards our findings, this simulation was conducted with the crucial parameter (acute tag shedding rate) set to an extreme value (0.15). As described above, there are no published estimates of acute tag shedding rates higher than 0.1 for the tag types in the studies we used. Further ad hoc simulations revealed that the bias all-but disappeared when tag shedding was set to a more realistic rate, such as 0.05. Given the large magnitude of the difference in relative recapture proportion in our empirical datasets (Table 2) and the marginal influence of incorrect identification of recaptured fish (even in an extreme scenario), we conclude this phenomenon is possible but is unlikely to have caused our finding of significantly higher recapture rates for releases 2+. We note that using tags that cannot be shed, such as electronic transmitters or genetic “tags”, would eliminate this potentially confounding phenomenon and that double tagging would allow for empirical estimation of tag shedding.

Time-varying chronic (or continuous) tag shedding could have explained increases in recapture rate as well; however, chronic tag shedding is often assumed to be a constant or near-constant rate, particularly within a few years of tagging (Fabrizio et al., 1996; Latour et al., 2001), and we make that assumption here. A constant tag-shedding rate would not explain our findings of increases in recapture rate of 2+ fish because the same rate would apply after each release. While some authors have modelled chronic tag shedding as a time-varying function (e.g. Hampton and Kirkwood, 1990; Adam and Kirkwood, 2001), the durations of the tagging studies examined herein were of much shorter timescales than those for which non-constant chronic shedding has been postulated (10 years or more). For our datasets, some individuals were recaptured out to 2010 d (Table 1); however, the proportion of recaptures occurring within 2 years of initial release was 99% for black sea bass, 97% for red grouper, and 100% for grey triggerfish and Warsaw grouper.

Another explanation of our findings is a difference in capture probability for previously captured individuals (i.e. heterogeneity of learned behaviour). Populations of fish frequently contain individuals with varying vulnerability to angling or aggressiveness (Philipp et al., 2009; Sutter et al., 2012; Villegas‐Ríos et al., 2018). Askey et al. (2006) experimentally demonstrated a similar phenomenon with a population of rainbow trout (Oncorhynchus mykiss), finding declines in catch per unit effort of tagged fish by dividing the population into two classes based on different intrinsic catchabilities and incorporating a “learned hook avoidance function”. Cox and Walters (2002) modelled catchability dynamics by assuming two pools of fish: available and unavailable to capture (with the possibility of moving from one state to the other due to factors such as learned hook avoidance). If the tagged populations examined in the present study were divided into such pools, it is theoretically possible that rates of learned hook avoidance could differ between groups. This scenario would result in repeated recapture of “bait-happy” individuals, while reducing the probability of encountering educated fish. While it is possible that such heterogeneity influenced our findings, this explanation would require an extremely wide disparity in the catchability between the behavioural phenotypes to account for the magnitude of increases we found in recapture rates (Table 1) and would imply immediate onset of learned behaviour following the initial encounter. Given the complexity of this explanation, we consider it less likely to explain increases in recapture rate on releases 2+.

The final explanation of the recapture results that we explored was the possibility of heterogeneous movement behaviour, i.e. transient and resident fish. After the first release, transient fish would not be available for recapture at the release site but the resident fish would, leading to increase in recapture rate for releases 2+. Given that the fishery returned tags from other locations than tagging sites for most of the species (in particular for black sea bass and red grouper where we saw the strongest effects), we consider this explanation highly unlikely.

Due to the lack of evidence for tag loss, tagging-induced mortality, or bait-happy/movement behaviours to fully explain higher recapture rates on releases 2+, we conclude that the higher recapture rates are most likely a result of higher survival. The finding of increased survival was unexpected; intuition suggests that the effects of repeated capture would be cumulative, therefore reducing relative survival with each successive release [as was posited for fishes by Arlinghaus et al. (2007)]. The three species for which we observed higher relative survival for releases 2+ are in the family Serranidae; the remaining species examined (grey triggerfish) is in Balistidae. The common finding for the three serranids may be associated with their close taxonomic relationship and thus their similar morphology and physiology. Furthermore, it is possible that increased survival after releases 2+ does indeed occur for grey triggerfish (as indicated by the direction of the relative recapture rates; Table 1), but that our methods and/or sample size were not sufficient to detect this phenomenon.

Higher survival for release number 2+ may be caused by variability in individual robustness to discard mortality. The species examined in the present study are each susceptible to barotrauma. There may exist phenotypic heterogeneity in these populations resulting in different levels of barotrauma susceptibility. If a subset of the population were substantially more resilient (had higher discard survival), they would be more likely to be recaptured multiple times because they would survive release at a higher rate than less-robust individuals would (assuming constant capture probability). Such a scenario could result in a rapid elimination (via discard mortality) of less-robust fish from the tagged population and persistence of more-robust individuals and potentially result in increases in tagged population-level relative survival with each successive release event. If we are correct in our assertion that there exists heterogeneity in reef fish stocks with respect to their robustness to discard mortality, there are potential evolutionary implications. The concept of rapid, human-driven evolution of fish populations has been well documented with respect to selection against (i.e. removal of) larger, more aggressive fish (Law, 2000; Stokes and Law, 2000; Conover et al., 2005; Williams and Shertzer, 2005; Heino et al., 2015). We suggest that high rates of discarding may inflict a secondary (yet still substantial) selection pressure on many populations: selection for individuals that are resistant to discard mortality.

In light of our findings, improved methodologies for estimating the number of dead discards would strengthen stock assessment results. Kerns et al. (2012) recommended estimating instantaneous rates of discard mortality (e.g. at the population level) through the use of combined telemetry and high-reward conventional tagging; captures are reported via the high-reward tag, and if the fish is released its movements (and survival) are monitored via telemetry. This methodology eliminates the biases discussed in the present document. Another approach is to apply different discard mortality values to the portion of releases that are assumed to be unique and the remaining portion of releases. In the absence of tagging data, it is impossible to determine the proportion of releases that are occurring for the first time so selecting a value by which to decrement current estimates is challenging. For fisheries where releases dominate total catch (e.g. black sea bass in the US southeast; Table 4), it may be appropriate to assume that the proportion of unique releases is low. We suggest that stock assessments test the sensitivity of their results under a range of values for dead discards (treating the estimate produced by the current methodology as the maximum). Alternatively, the capture history of tagged animals could be incorporated into the stock assessment model framework to address the dynamics of repetitive captures.

Table 4.

Proportion of live releases to total catch for a selection of common or popular US Atlantic recreational demersal or reef-associated species in 1997 and 2017.

Common name	Scientific name	1997	2017	Difference
Atlantic cod	Gadus morhua	0.44	0.91	0.47
Atlantic croaker	Micropogonias undulatus	0.52	0.69	0.17
Black drum	Pogonias cromis	0.55	0.74	0.19
Black sea bass	Centropristis striata	0.60	0.88	0.28
Gag	Mycteroperca microlepis	0.73	0.57	−0.15
Grey triggerfish	Balistes capriscus	0.21	0.57	0.36
Greater amberjack	Seriola dumerili	0.37	0.64	0.28
Red grouper	Epinephelus morio	0.82	0.69	−0.13
Vermilion snapper	Rhomboplites aurorubens	0.32	0.59	0.27

Total catch was calculated as observed harvest + reported harvest + released alive. Data are from NOAA Marine Recreational Information Program for the US Atlantic coast.

The bias in number of live releases may also be relevant to some stock assessments. For example, any assessment that uses a temporally aggregated form of catch (that includes live releases) to build an index of abundance is at risk for bias. Many assessments use catch per unit effort to index abundance. If catches were aggregated on a per-year or per-season basis, as opposed to per-trip or per-angler, the value would probably include many repeatedly captured fish. If the proportion of live releases to total catch was similar across years in the catch time series, then the bias would not impact the assessment results and total catch would index abundance. However, this proportion has grown (substantially, in many cases) for many species in the last two decades as catch-and-release angling has become more prevalent in the US Atlantic (Table 4) and in many other regions of the world (Kelleher, 2005; Ferter et al., 2013; Zeller et al., 2018). For species where this proportion has increased, total catch would be a biased index of abundance because of the overestimation of the number of unique live releases in recent time periods. We urge assessment scientists to use care when generating time series indices of abundance that include live releases.

Our investigation of the sensitivity of fishery status to the number of dead discards demonstrated that F/F_MSY was more sensitive than S/S_MSY for both black sea bass and red grouper (Figure 3). Differences among F/F_MSY were driven primarily by differences in the denominator (F_MSY). This resulted from the fact that discards were comprised mostly of younger fish, and thus a reduction in discard mortality allowed more fish to reach maturity, supporting a higher maximum sustainable fishing rate. We investigated why the variability in F/F_MSY for red grouper increased substantially in terminal assessment years. This variation was driven by several years of low recruitment, which caused lower estimated abundance of younger ages near the end of the time series. Therefore, estimated abundance of younger age classes for these years is much lower than in prior years; this trend in abundance interacted nonlinearly with estimates of F to account for the number of removals that were observed in these years. For assessments that rely on aggregated catch to index abundance, our findings of inflated catch (as a result of many fish being recaptured multiple times) would likely result in more substantial changes to S/S_MSY. Future research could investigate the degree to which this biases such assessments.

It is usually unknown whether an untagged fish has been previously caught and released. Therefore, it is likely that a portion of the individuals tagged in the studies examined herein had been captured and released prior to tagging. As a result, for species where discard survival increased after releases 2+, estimates of discard mortality based on the first recapture event (including those used as baselines in this study) probably lie between the true survival after first release and the survival after later releases. Therefore, the difference between these release number-dependent survival rates is likely larger than we found in this study. Thus, it is probable that our results are conservative in this regard.

The degree to which discards (both live and dead) are important to fishery management varies regionally and by stock (Zeller et al., 2018). In fisheries where discards are increasing and/or a large component of catch, accurate estimation is critical to management. We have demonstrated that in such fisheries, repeated captures may lead to large biases in the number of unique live releases that are sometimes used as stock assessment inputs. Furthermore, our examination of tagging datasets yielded evidence that discard survival may increase as fish are repeatedly captured, therefore biasing calculated numbers of dead discards. We propose that phenotypic variation in robustness to the effects of discarding leads to this disparity. We urge researchers conducting tagging studies to report the occurrence and magnitude of repeatedly captured individuals, even as auxiliary data. Finally, we advise stock assessment scientists to consider repetitive captures when performing calculations involving the magnitude of live and dead discards, as determination of fishery status may be impacted.

Supplementary data

Supplementary material is available at the ICESJMS online version of the manuscript.

Acknowledgements

We thank Gary Shepherd from the NMFS Northeast Fisheries Science Center for his initial idea to examine the effects of repetitive capture on survival and for a review on an earlier version of this manuscript. None of the studies examined herein would have been possible without the work of numerous taggers, tag returners, and scientists (in particular, R. Rincones and R. Wiggers); we are grateful for their efforts. We thank Jie Cao and three anonymous reviewers for reviewing early drafts of this paper and improving it greatly. The scientific results and conclusions, as well as any views and opinions expressed herein, are those of the authors and do not necessarily reflect those of any government agency or institution.

Funding

Funding for the tagging studies was provided by NOAA grants NA14NMF4540061, NA09NMF4720265, NA09NMF4540140, and North Carolina Sea Grant Fishery Resource Grant projects 07-FEG-01 and 11-FEG-04.

Data availability statement

The data underlying this article will be shared on reasonable request to the corresponding author.