62 Years of Population Dynamics of European Perch (Perca Fluviatilis) In

Fisheries Research 195 (2017) 71–79

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Full length article 62 years of population dynamics of European perch (Perca ﬂuviatilis)ina MARK mesotrophic lake tracked using angler diaries: The role of commercial ﬁshing, predation and temperature ⁎ Christian Skova, , Teunis Jansenb, Robert Arlinghausc,d a DTU AQUA – National Institute of Aquatic Resources, Technical University of Denmark, Vejlsøvej 39, 8600 Silkeborg, Denmark b DTU AQUA – National Institute of Aquatic Resources, Technical University of Denmark, Kemitorvet, Building 201, 2800 Kgs. Lyngby, Denmark c Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany d Division of Integrative Fisheries Management, Albrecht-Daniel-Thaer Institute of Agriculture and Horticulture, Faculty of Life Sciences, Humboldt-Universität zu Berlin, Philippstrasse 13, Haus 7, 10155 Berlin, Germany

ARTICLE INFO ABSTRACT

Handled by A.E. Punt Standardised angler diaries could produce useful proxy data for assessing fish population density and size dis- – Keywords: tribution, but few rigorous studies about their utility exist. We use 62 years of angling diary data (1949 2010), Landing rates from a large mesotrophic lake, to investigate population structure (abundance, mean size and record size) of Size and abundance European perch (Perca fluviatilis L.) in relation to the impact of three commercial fishers with different fishing Recreational fisheries strategies, pike (Esox lucius L.) predation and temperature. We found that anglers’ harvest rates of perch varied Northern pike by a factor of 10 over time, indicating large variation in population abundance over decadal time scales. Our statistical analysis revealed that the anglers’ harvest rates of perch were related to pike CPUE (proxy of pike predation), temperature and commercial fishing directly through the harvest of perch and indirectly through the harvest of pike, the top predator of the lake. The size distribution and growth rates of perch caught by anglers also changed substantially during the study period, most likely controlled by density-dependent mechanisms as well as size-selective commercial harvest. The effect of selective harvest on size-structure was stronger than ecological density dependence. We conclude that commercial harvesting may exert strong impacts on the quality of the angling experiences, at least in the studied case. Moreover, our work showcases the value of detailed angler diaries to study and monitor changes in freshwater fish populations, but it also underlines the need for supplementary data on biotic and abiotic factors to reach the full potential of angler diary data.

1. Introduction examination of how each sector affects the other’s landings or catches. Comparing absolute harvests of commercial and recreational sectors Recreational fishers constitute the dominant users of most fresh- has been used to study the relative biological impacts of both fishing water fisheries resources in the industrialized world (Arlinghaus et al., sectors (e.g., Arlinghaus and Mehner, 2003; Coleman et al., 2004; 2002, 2015). However, the larger inland water bodies are often still co- Cooke and Cowx 2006; Dorow and Arlinghaus, 2011), but such simple exploited by commercial and recreational fisheries. Because quotas or proportional analysis can never conclusively answer whether over- other means allocating landings among the two fisheries sectors are fishing is actually taking place or which sector is responsible for it rarely implemented in inland fisheries (Arlinghaus et al., 2002; Cooke (Arlinghaus and Cooke, 2005). Data on joint exploitation of freshwater et al., 2014), there may be a race for fish in which the tragedy of the stocks by commercial and recreational fisheries do however indicate commons (i.e., overfishing) may occur (Hardin, 1968). Consequently, that both sources of mortality may impact stock dynamics and that the conflicts among commercial and recreational fisheries are well docu- impact of commercial fisheries on recreational fisheries may depend on mented in inland fisheries when stocks are shared, with each sector the actual fishing style and gear use (i.e., selectivity and catchability) blaming the other one as being responsible for overfishing, crowding, by each of the fisher types (Kendall and Quinn, 2011; Jansen et al., destruction or stealing of fishing gear and other issues (Arlinghaus, 2013; Kendall and Kostow, 2016). 2005). However, few long-term data sets are available for rigorous Perch (Perca fluviatilis) is a common fish species in Eurasia (Craig,

⁎ Corresponding author. E-mail addresses: [email protected] (C. Skov), [email protected] (T. Jansen), [email protected] (R. Arlinghaus). http://dx.doi.org/10.1016/j.ﬁshres.2017.06.016 Received 23 June 2016; Received in revised form 22 June 2017; Accepted 23 June 2017 0165-7836/ © 2017 Elsevier B.V. All rights reserved. C. Skov et al. Fisheries Research 195 (2017) 71–79

2000) where it is of high commercial importance (e.g., Dieterich et al., corresponding to 4500 population equivalents (pe) per year polluted 2004; Eckmann and Schleuter-Hofmann, 2013) and a relevant target the lake, where after the sewage was diverted to another drainage basin species of the recreational sector (e.g., Beardmore et al., 2011; (Jonassen, 2003). The highest values of phosphorous in historical time, Heermann et al., 2013; Vainikka et al., 2012). Perch form size-struc- i.e. since 1600 CE, were present in 1975, and P levels have only de- tured populations, are characterized by size-dependent ontogenetic creased slightly since then (Brodersen et al., 2001). Based on the niche shifts (Persson and Greenberg, 1990), and have potential for fragmented data on water clarity that are available, it seems that Secchi strong size-dependent cannibalism, prey control and density-dependent depths deepened in recent years, although the median water clarity did growth (Beeck et al., 2002; Persson et al., 2003). not vary much between the four time periods of different commercial With few exceptions, both commercial and recreational fisheries fishing regimes covered by the present study (Jansen et al., 2013). operate in a size-selective fashion, i.e. larger fish are selectively cap- Likewise, the coverage by aquatic macrophytes has been relatively tured (e.g., Lewin et al., 2006; Kendall et al., 2009; Kuparinen et al., constant throughout the study period, and data from 1988, 1998, 2001 2009). In addition, perch angling has been found to preferentially target and 2009 showed that aquatic macrophytes were well established in the females (Heermann et al., 2013). Hence, intensive fishing of perch may littoral area (max. depth 6–7 m). In 2009, water plants covered 12% of deplete large cannibalistic perch to a degree that their control of po- the lake area (Müller et al., 2010). Despite this apparent uniformity in pulation growth rate is reduced and the system moves from canni- aquatic macrophyte cover and the potentially slight increase in water balism-control to competition-control characterized by small size and clarity, the patterns of oxygen depletion in the bottom layers of the lake stunting (Claessen et al., 2002, 2004; Persson et al., 2003; van Kooten indicates that the lake is still influenced by the eutrophication that et al., 2007). Moreover, age and size truncation of perch may increase occurred in the 1960s. In fact, the number of days from stratification the effects of environmental forcing, which may amplify population until oxygen drops below 0.5 mg/l in the hypolimnion has declined fluctuations (Ohlberger et al., 2014). Since large perch provide great from 75 days in late 1950 to 40–50 days in late 1990’ies (Jonasson, utility to anglers (Arlinghaus et al., 2014; Beardmore et al., 2015), size 2003). truncation due to exploitation is unlikely to be viewed positively by anglers. Similarly, stunting will negatively affect the value of a fishery 2.2. Population dynamics data obtained from recreational fisheries from a commercial perspective. The objective of this work was to use a 62-year data set on landing Lake Esrom has a long history of recreational fisheries. Throughout rates and sizes of perch captured by recreational perch anglers in a large the last century several angler clubs have been active on the lake. Here, lake to examine how angler catches were affected by a range of eco- data from the largest of these angler clubs “Lystfiskeriforeningen” (LF) logical and commercial fishing-related factors. In particular, we in- are used. LF had between 457 and 703 members (mean 569, SD = 69) vestigated whether there was a negative impact of commercial fishing during the study period. Angling from the shore of Lake Esrom was not on landings per unit effort in perch angling and whether there were allowed during the study period, so all angling in this study was done signals of pronounced regime shifts in terms of the size structure and from boats. From 1940 onwards standardized logbooks were used to abundance of the population that may have been caused by commercial record catches by anglers (Jansen et al., 2013). Catch registration was fishing. Because perch abundance will also be influenced by natural mandatory for members of LF, even when the catch was zero. A mortality, we included a time series of catch rates (harvested and re- warning was issued if the logbook was not filled, and repeated failure to leased) of northern pike, hereafter pike, as an index of abundance be- follow the rule resulted in exclusion from the club; compliance was cause this species preferentially preys upon perch in large mesotrophic therefore high. inland water bodies in Europe (Langangen et al., 2011) and may thus The following information was available from the angler logbooks also affect perch abundance. Additionally, the role of temperature was on a monthly basis: number of fishing trips (boat-trips), number of explored because time-series analyses of perch population dynamics landed perch (and landed + released pike), weights of landed perch have repeatedly shown that temperature exerts significant impacts on (and landed pike) and number of boat-trips. There is no information their recruitment and population abundance (Edeline et al., 2008; about the size and frequency of released perch. Likewise, no informa- Langangen et al., 2011). Moreover, increasing water temperatures may tion is available about the duration of the trips or the number of people have disproportionate effects on larger size classes of perch by in- or fishing rods per trip. However, based on the sizes of the boats it is creasing metabolic demands and affecting intercohort competitive safe to assume that a maximum of three people were actively fishing abilities (Ohlberger et al., 2014). during any given trip with any unknown number of rods. Furthermore, the weight of the largest trophy record perch caught each year was 2. Material and methods available from a trophy perch competition among members, except in years when no perch exceeded 1.2 kg, which was the threshold to be 2.1. Study site description over time eligible for competition. Analysis of the catch time series was conducted using data from Lake Esrom is among Denmark’s largest freshwater lakes (area 1949 to 2010. Data prior to 1949 were excluded because a substantial 17.3 km2, mean depth 13.3 m, max depth 22.3). It is mesotrophic, di- fraction of the data had been lost and because the angling effort was mictic and stratifies 3–5 months during summer at 10–15 m depth incomparable with subsequent years as no motor boats were present on (Jonasson, 2003), and the water retention time is 12.7 years. No regular the lake before 1949. Logbook data and information from local anglers standardised monitoring on Secchi depth, water plant colonisation and suggests that recreational perch fishing on Lake Esrom peaks in August nutrients, such as phosphorous and nitrogen has occurred throughout and September when the perch gather in large schools in deeper waters. the study period from 1949 to 2010. Hence, data on these variables are The fishery is more mixed from October through the rest of the year somewhat fragmented and were therefore not included in the models of whereas fishing in May, June and July mainly targets pike (Jansen perch population dynamics. The fish biomass is dominated by perch et al., 2013). Hence, for the analysis of landings per unit effort (LPUE), with roach (Rutillus rutillus (L.)) as a subdominant species. In addition, where the unit of effort is a fishing-trip, only monthly data from August- the lake hosts populations of bream (Abramis brama (L).), tench (Tinca September were considered. tinca L.), pike, eel (Anguilla anguilla L.), rudd (Scardinius ery- throphthalmus (L.)), ruffe(Gymnocephalus cernuus (L.)) and in very few 2.3. Data from commercial fishers numbers trout (Salmo trutta L.) (Frederiksborg County, 1991, 1998). During the study period, Lake Esrom has been under influence of an- There have been records of commercial fishing on Lake Esrom since thropogenic eutrophication. Between 1961 and 1971 untreated sewage 1730. Commercial fishing has always been in the hands of only one

72 C. Skov et al. Fisheries Research 195 (2017) 71–79 person at the time who has rented the fishing rights from the govern- were obtained from Jansen et al. (2013). The density effect on growth ment. During the study period, there have been three different com- could have occurred during multiple years of the perch life, and because mercial fishers: A (1939–1971), B (1972–1987) and C (1988–1997). the anglers’ landings consisted of perch of multiple ages (sizes and Each of these had different fishing behaviours, and their primary fishing growth histories) that reached the catchable size (> 20 cm, 4–6 year- methods varied as did their primary focal species (Jansen et al., 2013). old, Frederiksborg County 1991, 1998), we used the average LPUE from Fisher B fished with more equipment, i.e. a higher number of fykenets the prior seven years (the focal year and the previous 6 years) as a and pound nets than Fisher A, and Fisher C specifically targeted perch proxy for density. because pike landings were prohibited from 1988 onwards (see Jansen The yearly estimates of CPUE for pike and LPUE for perch were et al., 2013 for more details). At the same time, the Danish Ministry of obtained from Jansen et al. (2013) who introduced state-space mod- Fisheries, as manager of the commercial fishery on Lake Esrom, in- elling as an appropriate method for modelling angler logbook data. creased the commercial quota for perch. Hence, Fisher C intensified the State-space modelling assumes that the estimate of the mean at time t is use of pelagic trawls to catch perch and, in accordance with this, had a related according to a random walk to the estimate at time t-1. This higher annual catch of perch compared to the other two fishers (i.e., the inter-annual feature can be assumed when modelling population annual landings of perch averaged 4.7, 4.5 and 6.0 t per year for Fisher abundance of species such as pike and perch that consist of multiple A, B and C respectively), and lower landings of other target species such year classes. This model approach combines several time-series of ob- as coarse fish (A, 19.3 t; B, 22.8 t; C; 4.5 t) and eel (A, 11.0 t; B, servations (here the LPUE by month: August and September for perch, 13.0 t; C; 4.5 t) was realized compared to perch. From 1998 onwards, and May-July CPUE for pike). In addition, the approach handles commercial fishing was prohibited on the lake. missing data and provides uncertainty estimates. All this is done in a statistically consistent model that is fitted using maximum likelihood 2.4. Modelling variation in angler landings and size distribution of perch (Jansen et al., 2013). The key model fit diagnostics are as follows: Observation variances for the perch CPUE time series were 2.51 for A suite of General Additive Models (GAMs) were fit to the data to August and 2.16 for September. The observation standard deviations for explore how perch LPUE and size structure (i.e., mean weight and re- the pike time series were 1.01 for May, 0.64 for June and 0.21 for July. cord weight) were affected by different aspects related to biotic, abiotic The observations, estimates and the associated uncertainties are plotted and fisheries related variables. for pike (Fig. 1) and perch (Fig. 2), although observations and uncertainty are omitted from Fig. 1 to provide a more reader friendly 2.4.1. Fishery variables figure – this information was provided in Fig. 2 in Jansen et al. (2013). The anglers’ landings of perch were modelled with time periods fished by commercial fishers (A, B and C) included as a categorical 2.4.3. Abiotic factors variable (commercial fisher ID) to test for individual commercial fisher’s It is well known that temperature can affect growth rate (Thorpe, effects on recreational landing rates. The commercial fishing for perch 1977), recruitment (Langangen et al., 2011) and activity (Huusko et al., was mainly conducted later in the year than August-September, which 1996, but see Jacobsen et al., 2002) of perch, which could all affect was the main perch fishing period for anglers, and therefore commer- catchability. Temperature variation was therefore used as a predictor cial fishing was likely to affect angler landing rates in the subsequent variable (temperature) when exploring both perch mean weight and year. The period of commercial fishing was therefore delayed by a year perch LPUE. Because water temperatures were not available, we used in the analysis (i.e., when Fisher C stopped fishing in 1987, 1988 was air temperature data (monthly average values) from Copenhagen used as threshold time in the model). (∼50 km from Lake Esrom) provided by Danish Meteorological In- stitute (Fig. 1). When modelling perch mean weight, temperature 2.4.2. Biotic variables averaged over April-October in the last seven years (the growth season The density of pike based on logbooks of pike catches by anglers in the focal year and the previous six years) was used as a predictor (Jansen et al., 2013) was included in the LPUE model as pike can be an variable (Table 1). As before, this selection was based on growth data important predator on perch (Langangen et al., 2011). Yearly estimates that showed that perch in Lake Esrom reached a catchable size – of pike CPUE based on anglers pike catches in May, June and July were (> 20 cm) within 4 6 years (Frederiksborg County, 1991, 1998). fi obtained from Jansen et al. (2013) and used as a proxy for pike The rst model for perch LPUE included the average temperature abundance (Fig. 1). from April to October in the year of the landing (temperature, to re- ff Perch populations can show density-dependent growth through present activity and related catchability e ects) as well as in the year intra- and inter-cohort competition during the juvenile life stage when the landed perch were recruited as 0-year olds (recruitment tem- ff (Persson et al., 2003; Thorpe, 1977; Ylikarjula et al., 1999). Conse- perature, to represent possible e ects of temperature on recruitment). quently, we included the average perch LPUE in the years previous to To calculate the time series of temperature in the recruitment year, we the focal year (Perch_LPUE_PrevY) as a predictor variable of perch mean accounted for changes in the mean age of the landed perch across years. weight. The time series of annual estimates included in this average This was done by calculating the mean age of landed perch from their mean weight (each year). The mean age was thus derived from i) the annual mean weight of the landed perch, ii) a general weight-length relation for perch (Froese and Pauly, 2015) and iii) period-specific length-age relations from the age estimates (the relation in the un- sampled period A was assumed to equal the average relations from periods B and C that were also characterized by commercial fishing) (see Section 2.5 for details about scale readings).

2.4.4. Statistical model building and selection The perch LPUE data showed temporal autocorrelation as revealed by inspection of normalized residuals plotted against year and Auto Correlation Function (ACF) plots (not shown). GAMs have been recommended to be used to account for temporal autocorrelation, con- Fig. 1. Air temperature (grey line) and pike abundance based on state-space modelling of sequently this was done using the R-package “mgcv” (Wood, 2006)by angler logbooks (solid black line). modeling the correlation structure with an auto-regressive function of

73 C. Skov et al. Fisheries Research 195 (2017) 71–79

distribution of landings revealed significant over-dispersion, and a negative binomial distribution for landings in the GAM models was therefore applied. This implied log transformation of the response variable perch landings and the effort. Compared to a Poisson model, the negative binomial model relaxes the assumption that the variance equals the mean by estimating an extra parameter. A likelihood ratio test can be used to test the null hypothesis that the restriction implicit in the Poisson model is true. This was done using the Test function from the pscl package (Jackman, 2015). The null hypothesis was rejected (p < 0.001), and the negative binomial model was therefore applied. Perch mean weight was continuous and well above zero, so a normal distribution was used in the “mean weight model”. Record perch was treated as a binomial response variable in the “record size model” because exact information on the weight of record perch each year was only available in years where the largest perch exceeded the 1.2 kg threshold. Model selection was based on p-values and t-statistics, because the aim of the study was hypothesis testing of potential ex- planatory parameters (where AIC would have been used if the goal was prediction). Insignificant terms (p>0.05) were sequentially removed starting with interactions (“backward” modeling). After each removal, all of the previously removed terms were re-included one-by-one in all permutations to evaluate whether exclusion of other variables changed the significance any of the previously excluded variables. This proce- dure was continued until all remaining terms in the model remained significant.

2.4.5. Interaction terms An interaction term between Pike CPUE and commercial fisher ID was included in the “LPUE model” (Table 1) to investigate if commercial fisher behaviour could play a role in the potential effect of pike predation on perch because commercial fishers might have selectively targeted either perch or pike. Furthermore, the interaction term between Pike CPUE and temperature was included to explore if temperature could affect the predation on perch by pike. In the “mean weight model” and “record size model”, we explored how perch density (Perch_LPUE_PrevY)influenced perch growth and thereby the mean weight/record size of perch in combination with commercial fisher ID by including a Perch_LPUE_PrevY × commercial fisher ID interaction.

2.5. Growth analyses

Age specific lengths were back calculated from scale readings of perch sampled in the autumns of 1990, 1997 and 2011 to provide a better understanding of changes in size distribution during the study period and to study the role of density-dependent growth. Scales were mounted on a dia slide and viewed and measured on a projector at a magnification of 20x. Back-calculations were based upon a Fraser-Lee linear regression model, which assumes that fish length is directly proportional to scale radius (scale proportional hypothesis, Francis, 1990). Perch used for growth analyses were sampled by gillnet fishing Fig. 2. A) Perch landings per unit effort (LPUE) between 1949 and 2010 based on angler in August 1990 and 1997 and by angling in September 2011. logbooks (observations and state-space model estimates with 95% confidence intervals, The raw data from the scale readings from 1990 and 1997 were not black solid and stipled lines). B) Perch mean weight based on angler logbooks (ob- available. Consequently, differences in growth between the fishing servations and state-space model estimates with 95% confidence intervals). C) Weight of periods were tested using summary statistics (mean, standard deviation, annual record perch caught in the LF angling club. Years indicated by a mark at 1.2 kg are ff the years where no perch exceeded the limit at 1.2 kg to enter the fishing club compe- sample size) by age and period. The null hypothesis of no di erence tition. Vertical grey lines indicate the transitions between individual commercial fishers between means was tested using the Welch modified two-sample t-test as well as the ending of commercial fishing. that does not depend on equal variances. The test was conducted using the tsum.test function from the BSDA package. Only the mean back order 1 and including year as a cubic regression spline smoothed calculated lengths of age 1 and age 2 perch were included in this parameter (Zuur et al., 2009). Perch LPUE was modelled with landings analyses, as these were the only age classes that, at the time of sam- in numbers as the response variable and log (effort) as an offset variable pling, had growth that consequently had occurred inside the time (a variable with a fixed coefficient of 1) similar to Kuparinen et al. periods of the commercial fishers. Moreover, the first sampling done (2010) and as recommended by Maunder and Punt (2004). The was two years after Fisher B ended and older fish was very rare in this period.

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Table 1 Initial GAMs used to evaluate biotic, abiotic and ﬁshery-related variables in relation to perch abundance (perch LPUE) and size distribution in terms of mean weight and size of annual record perch.

Response variable Predictor variables Interactions

Perch LPUE Month, Pike CPUE, commercial fisher ID, temperature, recruitment temperature commercial fisher ID × Pike CPUE, temperature × Pike CPUE Perch mean weight Month, Perch LPUE PrevY, temperature, commercial fisher ID commercial fisher ID × Perch LPUE PrevY Perch record size Perch LPUE PrevY + commercial fisher ID commercial fisher ID × Perch LPUE PrevY

Table 2 Statistics from the final model of perch landings per unit effort (LPUE). The base line for the commercial fisher effect is the period without commercial fishing. Significant values are in bold. Proportion of variance explained (adjusted r-squared from gam summary method) was 51%.

Signiﬁcant terms Estimate SE P

Intercept 3.84 1.59 0.017 Pike CPUE −1.22 0.31 < 0.001 Commercial Fisher A −2.25 0.84 0.009 Commercial Fisher B −1.50 0.71 0.037 Commercial Fisher C −0.56 0.50 0.265 Temperature 0.28 0.09 0.004

3.2. Perch mean weight and record weight

The size composition of the perch population changed substantially in recent years, as indicated by the increase in both mean and record weight of the perch caught by anglers since the late 1990’ies (Fig. 2b and c). Models of perch mean weight and record weight both showed that the size composition of perch was significantly negatively related to commercial fisher ID (Fig. 3b, Tables 3 and 4) whereas perch mean weight was also negatively related to perch density (Perch LPUE_PrevY) (Table 3). However, the effect of density-dependence seems to have been of minor importance compared to the effects of commercial fisher ID as illustrated by its much lower parameter estimate (Table 3).

3.3. Growth

Growth patterns were estimated from 83 (54–348 mm), 109 (64–326 mm)and 56 (123–323 mm) perch sampled in 1990, 1997 and 2011, respectively. In all age classes growth appeared highest among fish sampled in 2010 (Fig. 4a). These fish represent growth in the years from 2002 to 2009, i.e. the period when perch density was lowest (Fig. 1). In contrast growth appeared slowest among fish sampled in 1990 (Fig. 4a) (representing growth years from 1981 to 1990) – the Fig. 3. A) Perch catch rate vs. the significant predictor variables, commercial fishery and period with a high perch density (Fig. 1). Perch sampled in 1997, re- the proxy for pike abundance (pike CPUE). B) The effect of the four periods with different commercial fishers and no commercial fishing on perch mean weight. Boxes indicate the presenting growth years from 1988 to 1997, had intermediate growth following quartiles: 25%, 75% and 50% (median). Dots indicate observations that exceed (Fig. 4a). The faster growth in recent years is supported by the clear 0.67 times the quartiles. The whiskers indicate the most extreme observations, excluding non-overlapping SD estimates for the back calculated size-at-age be- the dots. tween perch sampled from 1990 and 2010 (Fig. 4a). In support of this pattern the mean lengths at age 1 and 2 were significantly different 3. Results between all three periods (Fisher B, Fisher C and no fishing). This was tested using tsum.test function from the BSDA package (p < 0.001 in 3.1. Perch LPUE all 6 pairwise tests) (Fig. 4b).

Perch LPUE varied substantially through time, i.e. the lowest Table 3 average landing rate was in 1957 (1.1 perch landed per angling trip) Statistics from the final model of perch mean weight. The base line for the commercial and the highest average landing rate was in 1989 (11.5 perch landed fisher effect is the period without commercial fishing. Significant values are in bold. per fishing trip) (Fig. 2a). Perch LPUE was significantly related to the Proportion of variance explained (adjusted r-squared from gam summary method) was 55%. additively combined negative effects of commercial fisher ID (relative to no commercial fishing) and pike CPUE as an index of pike abundance Significant terms Estimate SE P (Fig. 3a). Moreover, a significantly positive effect of temperature on perch LPUE was found. LPUE of perch was unrelated to month, re- Intercept 0.81 0.12 < 0.001 Perch LPUE_ PrevY −0.03 0.01 0.043 cruitment temperature or any interactions among the predictor variables Commercial Fisher A −0.48 0.12 < 0.001 (see Table 2 for estimated coefficients of the significant effects). Commercial Fisher B −0.41 0.09 < 0.001 Commercial Fisher C −0.40 0.06 < 0.001

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Table 4 Finnish lakes, Olin et al. (2010) found low perch densities when pike Statistics from the final model of perch record weight. The base line for the commercial density was high and suggested that pike predation played a structuring fi ff fi fi sher e ect is the period without commercial shing. Signi cant values are in bold. role for the densities and growth of perch. Likewise, Langangen et al. Proportion of variance explained (adjusted r-squared from gam summary method) was 65%. (2011) analysed 60 years of population dynamics of perch and pike in Windermere (UK) and concluded that the increase in pike abundance Significant terms Estimate SE P induced a change from compensation to depensation in the perch stock- recruitment relationship. These findings underline that pike is an apex Intercept 3.73 2.02 0.070 ff Commercial Fisher A −4.70 1.52 0.003 predator with strong potential for a ecting the dynamics of lower Commercial Fisher B −6.41 2.23 0.006 trophic levels, particularly of perch in large mesotrophic lakes. Simi- Commercial Fisher C −6.17 3.36 0.072 larly, the data illustrate that changes in pike abundance, i.e. as a result of environmental and/or anthropogenic influences such as fishing (Jansen et al., 2013), may cascade downwards in the food chain to affect perch populations dynamics and abundance. Other work has similarly shown that pike predation can have strong effects on their prey base, (Tonn and Magnuson 1982; Tonn et al., 1990; Spens and Ball, 2008).

4.2. The role of commercial ﬁshing on perch abundance

Commercial landings may also influence perch dynamics and densities, although perch are more resilient to effects on spawning stock biomass compared to higher trophic predatory species such as pike or pike-perch (Sander lucioperca (L.)) (Johnston et al., 2013). Reasons for this include the particular combination of life-history traits, the ten- dency to mature also at small invulnerable sizes and a different catchability compared to the larger top predators (Johnston et al., 2013). Despite this general resiliency to collapse (Olin et al., 2017), we found a significant effect of commercial fishing on angler landing of perch, suggesting a substantial impact of commercial harvesting on perch populations that affected angler catches negatively. This corresponds to a study from the Finnish Lake Höytiäinen where perch biomasses were 30% lower in areas with high fishing pressure than in areas without commercial trawling (Haakana and Huuskonen, 2008). Because our analysis focused on the relation between temporal variation in angler catch rates and commercial fisher types (the individual commercial fishers), rather than modelling the absolute commercial fishing mortality rate in any given period (which would have demanded data that Fig. 4. A) Total length at age (incl. SD) back-calculated from perch scales sampled in we did not have), our work suggests that the impact of commercial 1990 (long dash), 1997 (dotted line) and 2010 (solid line). B) The average length and SD fishing will vary by the type of fisher that exploit the fishery. The of an age1 (white bars) and age 2 (grey bars) perch in 1981–1987 (Fisher B), 1988–1997 commercial fishers in Lake Esrom used different gear types, fished for (Fisher C) and 2002–2009 (no commercial fishing). different target species and in different habitats and times of the year (Jansen et al., 2013). Hence, different fishers had different effects on 4. Discussion the target species either because they fished for perch differently or because they affected other species such as pike or small perch prey We found that landing rates of perch by anglers varied by a factor of which in turns affected perch abundance and catchability. Our results 10 over decadal time scales. Under the assumption that the angler’s cannot exactly discern which mechanisms were at play, it is simply landing per fishing trip (LPUE) reflects perch abundance, the data in- clear that different commercial fishers had substantially different ef- dicated large variation in population abundance over time. This var- fects on the perch stock, unless there was some unaccounted co-variant iation was statistically significantly correlated with a linear combina- unknown to the authors. tion of pike CPUE (our proxy for pike abundance and hence predation), commercial fisher ID (our proxy for commercial fishing pressure and 4.3. Temperature variation and its impact on perch catchability size-selectivity) and temperature in the focal year. We argue that these results likely reflected causal relationships of the main drivers, sug- Perch recruitment and year-class strength has been shown to cor- gesting that biotic, abiotic and fisheries-related factors jointly influ- relate with temperature (i.e., Langangen et al., 2011; Linl & kken, 2003; enced the density and/or catchability of perch during the study period. Tolonen et al., 2003). We had no data on actual year class strength and We acknowledge that additional factors might have played a role and therefore could not directly model the recruitment process. Because that our analysis cannot be considered a causal proof given its corre- angler LPUE data normally consist of perch from several year classes, lational nature. We discuss below why we think that the mentioned we therefore explored the recruitment hypothesis by relating perch factors and their mechanisms are most likely, but we also present re- LPUE to summer temperature (April-October) in the year where the flections on unaccounted factors that might also have influenced our average aged landed perch where most likely to have recruited. How- results. ever, we saw no pattern that suggested a relationship between recruitment and temperature, which may have been because our methods 4.1. The role of pike predation on perch abundance were too crude to detect effects. An alternative explanation is that early recruitment and the year classes that later form the fishery may be In line with other studies, our results demonstrated that pike can decoupled, e.g., due to biotic and/or abiotic regulating factors oper- have a strong structuring role on perch populations. In an analysis of 18 ating during the first years of life unrelated to the abundance of adults

76 C. Skov et al. Fisheries Research 195 (2017) 71–79 as it typical for stock-recruitment relationships of many fish species. Nakayama et al., 2016). The already complex pattern of temperature-dependent interactions were further complicated by the well-established pike-perch interaction 4.5. Landing data from recreational fisheries as a proxy for abundance (Langangen et al., 2011) and the fact that pike recruitment and growth are also influenced by temperature-dependent mechanisms (Edeline Angler reports have been used as proxies for population densities et al., 2008; Langangen et al., 2011; Vindenes et al., 2014). This indirect (e.g., Jansen et al., 2013) but they have to be interpreted with great effect of temperature on pike could have “washed” down the tem- care (Cooke et al., 2000; Dorow and Arlinghaus, 2011). Temporal perature signal in our data. In addition, we used air temperature data changes in factors such as gear effectiveness, general angler behavior rather than in-lake temperature data, which might have further in- and type of angler (Heermann et al., 2013), consumptive orientation troduced uncertainty to our analysis. For example, inter-year variations (Anderson et al., 2007), fishing motivations (Fedler and Ditton, 1994; in Secchi depth could influence solar penetration and thereby water Beardmore et al., 2011) and the composition of the angler population temperature independently of air temperature. (Johnston et al., 2010) can all have potentially strong impacts on catch In addition to exploring relationships between lagged temperature rates and inferences from landings data. Moreover, temporally varying data (recruitment temperature) and LPUE, we also looked at relation- bait use changes could substantially affect catches in perch angling ships between the mean summer temperature in the present year and (Garner et al., 2016) and we cannot rule out that the catchability may LPUE’s (to test catchability effects). We found temperature dependent have changed in a systematic fashion either because anglers and gears differences in landing rates of perch with higher landing rates in got more skilled (van Poorten et al., 2016) or because the highly vul- warmer years. The within-year temperature dependence of catchability nerable fishes were evolutionary selected against over time (Arlinghaus (i.e., seasonality effects) has been shown for several species, e.g. pike et al., 2017). Moreover, and in contrast to the angler diaries from Lake (Kuparinen et al., 2010), and has been documented in perch as well Esrom on pike (Jansen et al., 2013), we have no data on the share of (Heermann et al., 2013). However, to our knowledge this is the first released perch in the anglers catch, which potentially could bias the documentation of the finding that, on average, warmer years increase results as landings rates might go down with anglers engaging in vo- perch landing rates in recreational fisheries. The relationship we de- luntary catch-and-release. However, based on anecdotal evidence, now tected could reflect higher feeding activity of perch, due to higher and in the past, perch are mainly fished for consumption on Lake metabolism in warmer water or that in warming waters large in- Esrum. Hence, perch are most likely only released in substantial num- dividuals might be competitively inferior to smaller individuals, which bers, if catch rates are high, which would result in a conservative bias of might foster hunger levels (Ohlberger et al., 2014) and increase the our landing estimates. To circumvent problems with temporal changes likelihood to be captured by anglers (Heermann et al., 2013). in angler behaviour and skills, a future focus could be on designing logbooks that take angler behaviour and angler skill/characteristics 4.4. Mean weight, record weight and growth into account by allowing questions about the angler typology to be included. Likewise, more knowledge on the individual fish, e.g. the fate Substantial variation in mean weight and record weight were ob- (harvested or released) and length of each fish in the catch, would in- served during the study period. This was mainly attributed to com- crease the quality of the logbooks (Jansen et al., 2013). mercial fishing as reflected by a distinct rise in the occurrence of record perch (> 1.2 kg) and an increase in mean weight shortly after the cease 5. Conclusion in commercial fishing. However, density-dependent mechanisms were also at play as indicated by the growth analyses. When looking at both Anglers’ landing rates of perch varied by a factor of ten over time, effects, it is likely that size-selective commercial fishing played a major indicating large variation in population abundance during the study role for the size distribution of perch in Lake Esrom, and that the stop in period. Modelling indicated that pike predation and commercial fishing commercial fishing made larger perch available for the recreational (directly through perch harvest and indirectly through pike harvest) fishing sector that would otherwise have been harvested by commercial appeared to be the main drivers of anglers’ perch landings. In addition, fishers. However, because the density of perch has been declining also temperature in the year of the landings played a role, potentially (compared to the 1970s-90s) even after commercial fishing stopped, related to temperature-dependent variability in catchability. The size most likely due to pike predation, a release in density-dependent me- distribution of perch also changed substantially during the study chanisms may also have played a role to explain the observed increase period. This was related in part to density-dependent mechanisms, but in perch size (i.e. improved growth in recent years). This is supported mainly to size selective harvest by commercial fishing. Despite the not only by the scale analysis showing the fastest growth in the years uncertainty that catchability of perch to angling gear might be changing with lowest density, but also the significant negative relationship be- over time, we conclude that commercial harvesting exerts strong im- tween mean weight and perch density in previous years. Similar, den- pacts on the quality of the angling experience, at least in perch, but that sity-dependent patterns are reported by Ohlberger et al. (2014) who other factors also shape the catches by anglers, in particular tempera- showed density dependence mechanisms among similar aged and older ture and the abundance of perch predators. perch in Windermere, and Arranz et al. (2016) who suggested density dependence mechanisms to be a key driver of fish size structure in Acknowledgements freshwater lakes for six fish species including perch. Perch size (mean length) has also been found to be correlated with We thank LF for letting us use their angler diary data. CS was funded temperature, both negatively (Ohlberger et al., 2014) as well as posi- by the Danish National Angling License Funds. We thank André E. Punt tively (Rask et al., 2014), though we found no such relationship. This and two anonymous reviewers for detailed feedback that improved our could be because size selective commercial fishing and density depen- paper. dent effects masked growth effect of temperature, as previously suggested (Arranz et al., 2016), or because our temperature measurements References were inaccurate, i.e. based on air temperatures measured 50 km away from our study lake. However, this latter uncertainty may be of less Anderson, D.K., Ditton, R.B., Hunt, K.M., 2007. Measuring angler attitudes toward catch- importance as air temperatures have frequently been used as a proxy for related aspects of fishing. Hum. Dimens. Wildl. 12, 181–191. 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