key: cord-0324187-cxthi5w5 authors: Dainys, Justas; Jakubavičiūtė, Eglė; Gorfine, Harry; Kirka, Mindaugas; Raklevičiūtė, Alina; Morkvėnas, Augustas; Pūtys, Žilvinas; Ložys, Linas; Audzijonyte, Asta title: Impacts of recreational angling on fish population recovery after a commercial fishing ban date: 2022-03-08 journal: bioRxiv DOI: 10.1101/2022.03.07.483248 sha: 068bf92d79514e5a9f1462b18e15e70df299225e doc_id: 324187 cord_uid: cxthi5w5 It is often assumed that recreational fishing has negligible impact on fish stocks compared to commercial fishing. Yet, for inland water bodies in densely populated areas, this is unlikely to be true. In this study we demonstrate remarkably variable stock recovery rates among different fish species with similar life histories in a large productive inland freshwater ecosystem (Kaunas Reservoir, Lithuania), where all commercial fishing has been banned since 2013. We conducted over 900 surveys of recreational anglers during a period of four years (2016 to 2021) to assess recreational fishing catches. These surveys are combined with drone and fishfinder device-based assessment of recreational fishing effort. Fish population recovery rates were assessed using standardised catch per unit effort time series. We show that recreational fishing is having a major impact in retarding the recovery of predatory species, such as pikeperch and perch. In contrast, recovery of roach, rarely caught by anglers, has been remarkably rapid and the species is now dominating the ecosystem. Our study demonstrates that recreational fishing can have strong impacts on some fish species, alter relative species composition and potentially change ecosystem state and dynamics. Lakes and rivers have been an essential source of fish and other ecosystem services for people all around 33 the world. Yet, these are also among the most vulnerable ecosystems, heavily impacted by fishing, 34 pollution, energy generation and other human activities. Despite freshwater fish populations being 35 among the most threatened in the world (Su et al., 2021) , the status of fish stocks in inland waters has 36 received far less consideration than marine stocks (FAO, 1999; Hilborn et al., 2003; Kura et al., 2004; 37 Funge-Smith & Bennett, 2019). This is partly due to limited funding because most commercially 38 valuable and therefore properly researched fisheries occur in marine ecosystems (Walters, 1998; Mangin 39 et al., 2018) . Another reason is that commercial fishing, which is often easier to monitor, has largely 40 diminished in inland waters (FAO, 2010) , while at the same time, the effort from more participants 41 engaging in freshwater recreational fishing has increased rapidly (Cowx, 2002; Coleman et al., 2004) . 42 The global recreational harvest is poorly documented but may be in the order of 2 million metric tons 43 (FAO, 1999 ), yet there are few studies which specifically address the influence of recreational fishing 44 on stock recovery after commercial fishing closures. 45 In developed countries, recreational angling often involves participation rates of 10-30% of the total Estimation of the total recreational catch and rate of fishing mortality is important both ecologically, and 58 from a regulatory perspective (Pope et al., 2017) , but it can also be politically contentious (Winstanley, Net lengths differed among sampling periods and this effect was accommodated in the statistical catch 112 per unit effort (CPUE) standardisation (below). Catches were typically weighed to the nearest gram, 113 separately for each mesh size, net and species. In aggregate, the study included 480 fishing events. Standardised annual CPUE was estimated for five main fish species commonly observed during 118 scientific surveysroach, bream, silver bream, perch, and pikeperch. Other fish species, such as 119 vimba, Prussian carp or carp are also commonly caught by anglers in Kaunas Reservoir, but their 120 abundances cannot be estimated using gillnetting surveys. This is either because these species mostly 121 stay in shallow vegetated areas which cannot be sampled with gillnets or, in the case of carp, because 122 even the largest mesh of 70 mm is too small. To account for systematic and random variation across 123 sampling events, we standardised CPUE for each of the five fish species using generalised linear 124 models (GLM), as implemented in the R package 'statmod' (v. values were scaled to ensure that the mean estimate in the entire time series is equal to 1, using the R 140 package 'r4cpue' (Haddon, 2020). Exact net lengths, mesh sizes or soak time were not available for 141 all scientific survey data. Therefore, in GLM analyses net lengths were assigned into five groups 142 (short to very long), mesh sizes into three groups (small, large, and full), and soak times into three values (short to long) and treated as ordered factors. Our sensitivity analyses showed that these 144 categories were sufficient to capture CPUE trends (Table A. Over the course of 2016, 2017, 2020 and 2021 a total of 910 angler surveys were conducted, with a 158 daily maximum of nearly 150 surveys. Surveys were conducted during all seasons, but the largest 159 number of surveys was completed during the ice fishing season when angler activity was highest ( Fig. 160 A.3.). The goal of the angling surveys was to assess recreational catches rather than to estimate 161 recreational effort, which instead was estimated using a different method (see below). We surveyed 162 anglers using slightly modified methods recommended by Malvestuto (1996) and Kaemingk et al. 163 (2018). Generally, surveys were distributed throughout the year, with more surveys conducted during 164 weekends and the ice fishing season, when fishing activity was high. During the open water fishing 165 season anglers were interviewed at boat ramps and access points around Kaunas Reservoir, whereas 166 during the ice fishing season anglers were interviewed at their fishing spots on the ice (Fig. 1) . 167 Accordingly, in subsequent analyses, fishing trips were categorised as those conducted from the 168 shore, a boat or from ice. Boat angler interviews were mostly complete trips, whereas shoreline Catch, effort and harvest data were obtained by asking a series of structured questions. The following 173 data were collected during each interview: geographic coordinates of angling location; fishing method 174 (shore, boat, ice); duration of the angling trip; numbers (and weight when possible) and species of fish caught in total; number and species of fish released; numbers, size and species of fish retained; 176 quantity and type of gear used (number of rods per angler); and a few other questions related to 177 gender, age, angling importance and usage of fishfinder devices (not used in analyses presented here) 178 (see Table A .3.). Whenever anglers agreed, the harvested fish were identified, counted, and measured 179 by the interviewers. To estimate total annual recreational catch in Kaunas Reservoir we used two sources of information. CPUErec ~ year + season + gear_type + gear_number + duration + method + error 205 Separate CPUE models were built for each species and for all species combined (total catch per trip). Analyses were done using the Tweedie distribution to account for zero catches. For each species the 207 full model was progressively reduced based on AIC values. In these analyses we included all reported catch, regardless of whether the fish was retained or The R code and full details of the statistical analyses and datasets of recreational data are available as 231 a supplement to this manuscript and through https://github.com/astaaudzi/recreationalCatch. The standardised scientific CPUE values for bream, silver bream, roach and pikeperch were higher 240 during the 1990s and then decreased in the 2000s (Fig. 2) , whereas for perch the values remained 241 relatively stable throughout the 1990s to the 2000s. The full scientific monitoring dataset showed that 242 for roach and silver bream, standardised CPUE has nearly tripled over the last 8 years since the closure 243 of commercial fishery in 2013 (Fig. 2) , although there appears to be a decline in roach CPUE during 244 the last few years. In contrast, for bream, perch and pikeperch no such clear trends were evident. In GLMs applied to recreational catch, we found that the best model for the total catch included all 284 parameters (Table A. 4.). This model indicated that total recreational CPUE was highest during 285 summer, when fishing from a boat and targeting bottom feeding (demersal) species (Table A. (Table 302 1). In autumn, the highest recreational CPUE was for bream, carp and perch. In winter, perch 303 dominated catches and uncertainty of parameter estimates for other species was very high due to the 304 limited number of observations. To assess total catch per year, we combined the estimated annual number of angling trips with the 306 catch per trip. For these extrapolations we used season-only recreational catch CPUE models, because 307 estimates of total angling trips were available daily, but without information about gear types, trip 308 duration or other variables that might affect CPUE. To assess whether season-only CPUE models 309 gave similar magnitude and direction of coefficient estimates, we compared best and season-only 310 CPUE models. Generally, the season coefficient estimates were similar, although these were more 311 uncertain when simpler models were used (Fig. A.5.) . This was especially true for catch estimates 312 during winter (for pikeperch, Prussian carp, carp, asp). Season had no significant effect on the catch 313 of roach in both full and season-only models, hence for this species catch extrapolation was 314 undertaken using the intercept only model (Table A. (Table 2) , with 332 the highest catches observed in summer and spring (61 t and 56 t respectively). The annual catch estimates for the five fish species that were important commercially and had 334 scientific CPUE trend estimates were as follows: for pikeperch they were around 19 ton, but with 335 very high uncertainty ranges (7-55 t), for perch at ca 9 t (4-28 t), for bream at ca 32 t (12-90 t), for 336 silver bream at ca 4 t (2-14 t) and only ca 3 t for roach (1-8 t). For roach and silver bream these 337 recreational catches were negligible compared with past commercial catches, which in peak years 338 used to reach >100 t for roach and >30 t for silver bream (Fig. A.1.) . For the other species, however, 339 the picture was completely different. For example, for perch, recreational catches were 10-20 times 340 higher than the former commercial take (at least 4 times higher if we apply the minimum uncertainty 341 range of the recreational catch and the peak of the commercial catch). Similarly, for pikeperch the 342 recreational take was about 5 times higher than former commercial catches and for bream about 3 343 times higher (Fig. 2) . Non-commercial fish species like Prussian and common carp also comprised a (Table 2) . with * the total catches were estimated excluding seasons for which an infinite recreational catch was predicted (see Table 350 A.5. determination to explore a wide range of places, anglers' catching efficiency is likely to be very high. 405 Yet, some species may still be difficult to catch. Notably, in our interviews many anglers commented 406 that roach would be a desirable catch, yet recreational catches for this species were very low. It is 407 possible that roach are not attracted to lures, due to ample quantities of Dreissena mussels, an mollusc 408 which appears to be its main food source, constituting up to 80 % of the roach diet (Yount, 1991) . The situation is different for silver bream, another species that has showed rapid recovery since 2013. for one year and therefore had to be extrapolated on the basis of recreational fishing licence sales. Such extrapolation is commonly used to estimate recreational effort, but is imprecise. It is possible angler effort and catch, as have been initiated during this study and will continue to evolve. Year + season + Location.gr + Gillnet_category + Net.mesh.gr + Net_length_Or +Soak_time_Or 42 Year + season + Location.gr + Gillnet_category + Net.mesh.gr + Net_length_Or +Soak_time_Or 34 Year + season + Location.gr + Gillnet_category + Net_length_Or +Soak_time_Or 35 Year + season + Location.gr + Gillnet_category + Net.mesh.gr + Net_length_Or +Soak_time_Or 59 Year + season + Location.gr + Gillnet_category + Net.mesh.gr + Net_length_Or +Soak_time_Or 29 Big mesh sizes (>38 mm) only Year + season + Location.gr + Gillnet_category + Soak_time_Or 38 Year + season + Location.gr + Gillnet_category + Net_length_Or +Soak_time_Or 33 Year + season + Location.gr + Gillnet_category + Net_length_Or +Soak_time_Or 33 Year + season + Location.gr + Gillnet_category + Net_length_Or +Soak_time_Or 51 Year + season + Location.gr + Gillnet_category + Net_length_Or +Soak_time_Or 25 Estimating the number 604 of recreational anglers for a given waterbody a given waterbody Canada's recreational fisheries: the invisible collapse? Confidence interval estimation of CPUE year trend in delta-type two-step 610 model The role of community 612 participation in the effectiveness of UNESCO Biosphere Reserve management: Evidence and 613 reflections from two parallel global surveys Adjustment trend of China's marine fishery policy 616 since 2011. Marine Policy Estimation of regional mortality rates for Lake Erie walleye Sander 618 vitreus using spatial tag-recovery modeling Influence 620 of party size and trip length on angler catch rates on Oneida Lake Anomalous Predator-Prey Role Exchange between Cyprinids and Perch The underestimated dynamics and impacts of water-based recreational activities on freshwater 627 ecosystems Fishing for Fun': The Politics of Recreational Fishing Designing fisheries management systems that do not depend upon accurate 631 stock assessment A wake-up call for recreational fishing in Australia Ecology and management of the Zebra Mussel and other introduced aquatic What about recreational catch?: 641 Potential impact on stock assessment for Hawaii's bottomfish fisheries? Fisheries Research Inland fisheries development versus aquatic biodiversity 645 conservation in China and its global implications Categories of net length, mesh sizes and soak time used to standardise 651 scientific catch per unit effort. Because only approximate values were available for some fishing trips, 652 we used 5 net length, 3 mesh size and 3 soak time categories. These categories were sufficient to 653 capture the trend in CPUE, assessed using a smaller number of surveys where exact numeric values 654 of these parameters were available <=20 m, 657 • "short" -20-60 m, 658 • "medium" -61-120 m, 659 • "long" -121-300 m, 660 • Mesh_size_ categories <=38 mm, 663 • "big" >38 mm • "full" -when catches were reported for the full range of mesh sizes only Soak_time categories <=5 h, 667 • "medium" 6-12 h, and 668 • "long" =>12 h Supplementary Table A.2. Questionary that was used for onsite angler surveys done by the scientific 671 personnel Age group: <30 y.o.; 30-60 y.o.; >60 y.o. 678 679 Start time: _____ h. _____ min. Interviewing time: _____ h. _____ min. 680 Fishing: ☐ on the ice; ☐ from the shore; ☐ from the boat 681 Coordinates: X: __________________________ Y: __________________________ 682 683 Gear type and number: Spin fishing _____pcs * If no fish have been caught during the entire fishing period, enter "no catch How important fishing is to you? ☐ not very important; ☐ important; ☐ very important 689 How many times a year do you fish? What is the average duration of your fishing trip? ☐ <3 h Do you use fishfinder? ☐ Yes; ☐ No 692 If yes, how often? ☐ Always ☐ <20% of all fishing trips If yes, how long do you use fishfinder during fishing trip? 694 ☐ only in the beginning (<20% of all fishing time) ☐ 20-50%, ☐ 50-100% of all fishing time Supplementary Figure A.1. Commercial catches in tonnes (t) since 1961 in Kaunas Reservoir Comparison of the standardized CPUE when treating net length and 724 soak time as numeric vs ordinal variables (Curonian Lagoon data Trends in standardized scientific CPUE since fishery closure across 733 all fish sizes (left) and those restricted to mesh sizes > 38 mm (right) Estimates of general linear model coefficients (± standard error) for 736 total and species-specific recreational catch. Coefficient estimates for the best model are shown in 737 red and those for the season-only model The coefficients for variables that are treated as factors in the GLM are 740 shown as differences from the first value of each parameter; they are autumn for 'season', Feeder 741 for 'gear_type' (compared to Spin and Float fishing), shore fishing for 'method', and 2016 for 742 'year'; 'gear_no' and 'fishing_duration' were treated as numeric variables; and 'fishing_duration' 743 was estimated in minutes. The coefficient values were very small