Determinants of Angling Catch of Northern Pike (Esox Lucius) As

Fisheries Research 186 (2017) 648–657

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Fisheries Research

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Full length article

Determinants of angling catch of northern pike (Esox lucius) as

revealed by a controlled whole-lake catch-and-release angling

experiment—The role of abiotic and biotic factors, spatial encounters and lure type

a,b,∗ a,c a a,d

Robert Arlinghaus , Josep Alós , Tonio Pieterek , Thomas Klefoth

Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany

Division of Integrative Fisheries Management, Faculty of Life Sciences & Integrative Research Institute for the Transformation of Human-Environment

Systems (IRI THESys), Humboldt-Universität zu Berlin, Invalidenstrasse 42, 10115 Berlin, Germany

Instituto Mediterráneo de Estudios Avanzados, IMEDEA (CSIC-UIB), C/Miquel Marqués 21, 07190, Esporles, Illes Balears, Spain

Angling Association of Lower Saxony (Anglerverband Niedersachsen e.V.), Bürgermeister-Stümpel-Weg 1, 30457 Hannover, Germany

a r a

t i b s

c l e i n f o t r a c t

Article history: Studies on catches of anglers usually rely on observational data and are thus uncontrolled with respect

Received 10 November 2015

to angler skill, bait/lure choice and site choice. We performed a controlled ﬁshing experiment targeting

Received in revised form 8 August 2016

northern pike (Esox lucius) in a small (25 ha), weakly eutrophic natural lake situated about 80 km northeast

Accepted 10 September 2016

of Berlin (Germany) to understand abiotic, biotic and gear-related factors that relate to catch rates and size

Handled by George A. Rose

of pike captured by angling. The experiment was conducted over two one-week long ﬁshing campaigns

Available online 20 September 2016

where boat-based anglers randomly sampled across 30 pre-determined sites. Sites were systematically

exposed to two standardized lure types (soft plastic shad or spoon). We found the catch rates of pike

Keywords:

Catchability per 15 min to be signiﬁcantly higher in shallow water and when soft plastic lures were used compared

to deeper water and when spoons were used. Catch rates signiﬁcantly dropped over the course of seven

Diel period

Vulnerability days, suggesting either learning or other reasons moving pike from vulnerable to invulnerable pools

Habitat choice (e.g., due to stress caused by capture, sampling and release). Catch rates also varied by season and across

Avoidance learning anglers and sites as random effects. The variation in size of pike captured exhibited greater stochasticity

than variation in catch rate. There was no lure effect on the size of the pike captured, but we found a

seasonal effect and a day effect, suggesting larger ﬁsh were captured ﬁrst. Pike captured in sublittoral

areas were signiﬁcantly smaller than those captured in other habitats. Overall, our study documented a

novel effect of lure type on the catch rates of pike, but the explanatory power of the predictors was only

moderate. Therefore, our results support the idea that the best ﬁshing ingredients are investing time and

maximizing encounter probabilities through habitat choice, with only moderate additional effects to be

expected from attention to abiotic conditions, day time and choice of type of artiﬁcial lure.

1. Introduction importance to anglers. Unsurprisingly, the angling media are ﬁlled

with discussions about the best (i.e., most effective) lure type for

Angler satisfaction is strongly determined by catch-related com- catching many and/or particularly large ﬁshes. The lure industry

ponents of the ﬁshing experience, in particular size of ﬁsh and strategically taps into the intrinsic desire of anglers to optimize

catch rates (Arlinghaus, 2006; Arlinghaus et al., 2014; Beardmore catches and constantly releases novel lure types, colors, shapes and

et al., 2015). Therefore, private knowledge of determinants of catch attractants. However, from a robust scientiﬁc perspective surprins-

rates and how to catch the largest individuals in a population is of ingly little is known whether the lure innovations actually have

intented effects by boosting catch rates (but see Lennox et al., 2016;

Rapp et al., 2008; Webster and Little, 1947 for notable exceptions).

∗ Northern pike (Esox lucius, hereafter referred to as pike) is a cir-

Corresponding author at: Department of Biology and Ecology of Fishes, Leibniz-

cumpolarly distributed aquatic top predator that lives in river and

Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587

Berlin. lakes and low-salinity coastal waters (Craig, 1996; Raat, 1988). It

E-mail address: [email protected] (R. Arlinghaus).

http://dx.doi.org/10.1016/j.ﬁshres.2016.09.009

R. Arlinghaus et al. / Fisheries Research 186 (2017) 648–657 649

constitutes the prime target species of inland and some coastal In recent years, ﬁshing for pike and other freshwater predators

ﬁshers in many countries in the northern hemisphere (Crane with variants of soft plastic lures has become fashionable, partic-

et al., 2015; Paukert et al., 2001; Arlinghaus and Mehner, 2004; ularly among specialized angler groups (Arlinghaus et al., 2008b;

Arlinghaus et al., 2008a). Pike is especially desired by recreational Raison et al., 2014; Stålhammar et al., 2014). Given the more natu-

anglers. Due to its aggressive foraging behaviour and close associ- ral shape and texture, many specialized anglers are convinced that

ation to underwater vegetation, pike habitats are easily identiﬁed soft plastics (lures made out of soft rubber baits, often mimicking

by anglers, leading to pike being highly vulnerable to overexploita- the form of ﬁshes) are more effective lures in pike angling com-

tion diagnosed by severe size truncation (Pierce, 2010; Arlinghaus pared to traditional lures such as spoons (lures made out of metal

et al., 2010; Pierce et al., 1995; Post et al., 2002). Pike popula- blades) and spinners (lures made out of rotating metal blades).

tions are known to be composed of different behavioural types There is indeed evidence that pike ingest soft plastic lures of an

(Kobler et al., 2009), and recent research by Pieterek (2014) has appropriate size more deeply than other artiﬁcial lures (Arlinghaus

documented variation in individual vulnerability to angling as a et al., 2008b; but see Stålhammar et al., 2014), and several pisciv-

function of behavioural and life-history traits. More active, explo- orous ﬁsh species have even been found with soft plastic lures in

rative and faster-growing individuals were found to be particuarly their stomach, suggesting they confused such bait with natural prey

vulnerable to angling, while slower growing individuals spending (Danner et al., 2009; Raison et al., 2014). Stålhammar et al. (2014)

most of their life in refuges unaccessible to anglers were less readily showed that the average sizes of Baltic Sea pike captured on var-

captured (Pieterek, 2014). Intensive angling might thus also con- ious lure types differed, and there was a trend for pike captured

stitute an evolutionary force altering life-history and behaviour of on soft-plastics to be among the largest ﬁshes captured. Unfor-

exploited pike populations (Arlinghaus et al., 2009a; Arlinghaus tunately, no spoons were used in that study, which according to

et al. in press). anectodal information are commonly used among less experienced

The fact that pike cluster into vulnerable and invulnerable indi- pike anglers and are among the oldest and most traditional pike

viduals agrees with foraging arena theory (Cox and Walters, 2002), lures. Arlinghaus et al. (2008b) reported that the average catch

which assumes that not the entire ﬁsh population is available to rates of freshwater pike exposed to artiﬁcial lures were twice as

anglers (or other predators) to capture in any moment in time high as those of natural bait, but the average size of pike captured

(Matthias et al., 2014). Indeed, for behavioural and partly also on soft plastics and spoons did not differ. However, Arlinghaus et al.

for underlying genetic reasons, some individuals might even be (2008b) did not control for the size of artiﬁcial lures, and neither

entirely invulnerable to angling (Philipp et al., 2009), in turn never Arlinghaus et al. (2008b) nor Stålhammar et al. (2014) fully con-

showing up in the vulnerable pool accessible to anglers. Moreoever, trolled site choices and gears of experimental anglers. Because of

Kuparinen et al. (2010) showed that high pike angling effort two the potential co-variance of angler site choice and angler skill with

days prior to a ﬁshing event signiﬁcantly reduced catch rates in a a particular lure type (Matthias et al., 2014), robust knowledge on

total catch-and-release pike ﬁshery, suggesting that the vulnerable the relative effectivness of lure types can only be generated in a fully

pool of ﬁshes can further change in response to previous exposure controlled setting where experimental anglers use exactly the same

to ﬁshing due to learning (Beukema, 1970). Klefoth et al. (2008, gear types at the same location in a random fashion. We completed

2011), Arlinghaus et al. (2008c) and Baktoft et al. (2013), however, such a study using two one-week long experimental angling ses-

reported that recovery of normal behavioural patterns after catch- sions targeting pike in a small freshwater lake in Germany for which

and-release was very rapid in pike and completed within one or we had previous knowledge about abiotic and biotic determinants

two days. Similarly, rapid physiological recovery within a couple of of pike catch rates (Kuparinen et al., 2010).

hours after catch-and-release has been reported in pike (Arlinghaus We hypothesized that catch rates of pike would be elevated in

et al., 2009b). Still, learning to avoid artiﬁcial lures may happen and shallow littoral zones (which is the preferred habitat of pike, Kobler

systematically reduce catch rates when individuals are repeatedly et al., 2009), at dusk or dawn (Kuparinen et al., 2010), and be greater

confronted with artiﬁcial lures, while no such learning has been for soft plastics than for spoon of a similar size. The argument in sup-

shown after exposure to natural bait in pike (Beukema, 1970). port of the latter portion of the hypothesis was derived from work

Research on determinants of catches (both catch rate and by Beukema (1970) showing greater learning of pike when exposed

size) in pike ﬁshing has revealed that abiotic, biotic, ecological to artiﬁcial lures compared to natural bait and recent work by Cole

and ﬁshing-related factors are all involved in affecting catches et al. (unpublished data) in largemouth bass (Micropterus salmoides)

(Casselman 1978; Kuparinen et al., 2010). Both larger (Arlinghaus who reported bass more rapidly learned to avoid crankbaits (hard

et al., 2009a,b; Pierce et al., 1995; Pieterek, 2014) and female pike baits mimicking a ﬁsh made of wood or plastic) that made noise

(Casselman, 1975) are more vulnerable than smaller individuals compared to learning to avoid less noisy soft plastic lures. Simi-

and males. Moreover, it was found that pike catch rates increased at larly to crankbaits, metal-based spoons are likely to be more readily

dusk and dawn periods, varied with lunar cycles peaking at full and identiﬁed by pike as non-natural lures compared to soft plastics.

new moon (see also Vinson and Angradi, 2014 for similar ﬁndings in Hence, we assumed greater long-term catch rates on soft plastics

the congeneric muskellunge, Esox masquinongy) and were elevated compared to spoons. In relation to ﬁsh sizes we followed previous

in cool water with strong winds (Kuparinen et al., 2010). Baromet- ﬁndings of Arlinghaus et al. (2008b) and hypothesized no relation-

ric pressure had no effects on pike catch rates (Kuparinen et al., ship of average pike size and the lure type used as long as the sizes

2010), despite frequent claims in the popular angler press. More- of the lures are comparable.

over, pike vulnerability has been found to systematically vary by

latitude (Mogensen et al., 2013). The reason likely is that pike feed-

ing is much more contracted over shorter periods in more northern 2. Material and methods

latitudes, leading to substantially greater catchabilities at the pop-

ulation level compared to pike populations in southern latitudes 2.1. Fishing experiment and environmental variables

(Mogensen et al., 2013). Vinson and Angradi (2014) also found that

the lunar effect on muskellunge angling vulnerability was much We conducted two one-week long experimental ﬁshing events

stronger in northern latitudes compared to southern latitudes and in Kleiner Döllnsee – a mesotrophic to slightly eutrophic small

that larger individuals generally reacted stronger to moon effects (25 ha) and shallow (mean depth 4.8 m, maximum depth about

than smaller-sized individuals. 8 m) natural lake located about 80 km northeast of Berlin, Germany

◦ ◦

(52 59 ı´ N, 13 34 ı´ E, see Klefoth et al., 2008, 2011; Baktoft et al.,

650 R. Arlinghaus et al. / Fisheries Research 186 (2017) 648–657

Fig. 1. Study lake Kleiner Döllnsee (northeast of Berlin in Germany) along with 30 assigned ﬁshing spots in relation to water depth (a) and macrophyte coverage and height

(b). Distances between ﬁshing spots are approximately 100 m.

Fig. 2. Standardized baits used during angling experiments: (a) salt Shaker in Arkansas Shiner type color with a length of 115 mm, (b) copper-colored spoon with a length

of 85 mm.

2015 for full lake description). The lake is situated within a Bio- In September 2010 angling was conducted from 06:30 a.m. until

sphere reserve, is not open to the public and is surrounded by reed. 12:30 p.m. and from 14:00 p.m. until 20:00 p.m., and in May 2011

It offers abundant underwater vegetation up to a water depth of anglers were ﬁshing from 04:45 a.m. until 10:45 a.m. and from

about 4.5 m. The pike population is natural and unexploited other 16:00 p.m. until 22:00 p.m. every day over the entire week. Each

than sampling for scientific reasons. We recruited varying num- time frame consisted of five hours fishing and one hour time for

bers of experienced pike anglers (e.g., members of the “German lifting anchors and moving the boat to the next ﬁshing spot. We

Pike Anglers Club”) to the experiment (6 anglers in ﬁshing experi- allocated each angler × site combination into ﬁshing sessions of

ment 1 from September 13th until September 19th of 2010 and 13 30 min duration during which 15 min were ﬁshed by either of two

anglers in fishing experiment 2 from May 23rd until May 29th of different standardized artificial lures. Two anglers fished from the

2011). Anglers in the two ﬁshing sessions only partly overlapped, same boat such that in any moment in time both lure types were

but they were all experienced pike ﬁshers. presented at the same site, rotating rods with a given lure among

We pre-deﬁned 30 ﬁshing sites covering the whole lake and the two anglers after 15 min to control for angler skill related to

all meso- and micro-habitats accessible to angling (i.e., excluding ﬁshing a particular lure type. Lures were either a 85 mm sized

dense reed, Fig. 1). The distance of the sites to each other were cho- copper-colored spoon with a weight of 22 g and a size 4 treble

sen so that casting anglers from a boat would ﬁsh all microhabitats hook or a Salt Shaker softbait of the brand “Arkansas Shiner” with a

of the lake during each day over the course of the entire week in length of 115 mm (Lunker City Fishing Specialities, Colorado, USA)

both ﬁshing experiments. Sites were ﬁshed on any given sampling with a 4/0 sized 10 g jighead and a size 6 stinger treble hook with

day with identical effort in both experiments (Fig. 1). Anglers were a steel wire (Fig. 2). After 30 min anglers moved to a different

allocated to ﬁsh at random at each site, while covering all four dial site randomly chosen in advance (i.e., predeﬁned to the anglers

periods during the day (sunrise, morning, afternoon/evening and by the research team) until all sites were equally ﬁshed; all sites

sunset). were marked by buoys. The daily experimental ﬁshing block was

R. Arlinghaus et al. / Fisheries Research 186 (2017) 648–657 651

repeated over seven consecutive days during two different periods

(ﬁshing event 1 in 2010 and ﬁshing event 2 in 2011) changing the

order of sites according to the random selection of the site after

each 30-min ﬁshing (i.e., sites were visited at different times each

day to control for the time of the day). We standardized gears in

terms of type of rod (Black Stream, 2.75 m, cast weight 51–70 g,

SPORTEX, Puchheim, Germany), reel (Blue Arc 9400, Spro Corpora-

tion, Georgia, USA) and line (PowerPro, 0.15 mm, yellow-colored,

Shimano Germany Fishing GmbH, Krefeld, Germany).

Lures were connected to the ﬁshing line via 30 cm of 9.0 kg

steel leader (Drennan, Oxford, England) with a fastlock snap (Proﬁ

Blinker, Cologne, Germany). Although the two lures selected were

conceptually different and not exactly of the same length, they

were small enough (for the pike sizes to be expected) such that

any possible effect of bait size on size of pike was likely negligible

(Arlinghaus et al., 2008b). Anglers continuously ﬁshed and docu-

mented in logbooks (i) the number of bites, (ii) the number of pike

captured, and (iii) and the size of pikes (in mm) captured. To quan-

tify pike sizes and to collect a series of other data for other ongoing

experiments not reported here, the ﬁsh were given to the research

Fig. 3. Bi-plot of the two ﬁrst components resulting from the Principal Compo-

team and transported onshore in life-wells. There, the ﬁsh were nent Analysis (PCA) ﬁtted to explore the co-linearity among explanatory variables

in the meteorological and abiotic variables: wind strength, air temperature, mean

anesthetized in clove oil (to minimize stress during handling) and

humidity, air pressure, precipitation and global radiation. The original variables are

measured for total length and mass and the identity checked for

displayed as vectors and the correlation between two variables is represented in

presence of passive integrated transponders (PIT) (Pieterek, 2014).

the bi-plot by the angle between two vectors (the correlation of two variables is −1,

◦

In the May ﬁshing campaign 240 pike were landed, of which 147 0, or 1, if the angle between the two in the bi-plot is 180, 90, or 0 , respectively).

(61%) were new captures; these ﬁsh received a new PIT tag follow- Note how humidity is negatively correlated with air temperature and precipitation

◦

is negatively correlated with air pressure (angles among variables ∼180 ). For this

ing the methods described in Hühn et al. (2014). In the September

reason, humidity and air pressure were not considered in the model.

campaign, a total of 97 individuals were captured, 28 of these were

new captures (29%) who in turn were PIT tagged. After recovery, all

ﬁsh were released at the capture point. Hühn et al. (2014) showed ﬁshing sites to maps of the study lake, which were developed based

that narcosis and PIT tagging does not lead to elevated mortality in on narrow transects run using hydroacoustics, see Fig. 1 and Zajicek,

pike. 2012Baktoft et al., 2015 for details). Speciﬁcally, we recorded the

In terms of overall weather conditions there were differences depth at the ﬁshing site (m), the main habitat at the ﬁshing site in

between both ﬁshing events. Whereas mean air temperature of categories (pelagic without macrophytes, open water with many

◦

ﬁshing event 1 in September 2010 was 12.7 C, ﬁshing event 2 in macrophytes at reach of the lure and open water with few macro-

◦

May 2011 was warmer with a mean daily temperature of 15.1 C. phytes at reach of the lure), the weed cover (0%, 0–75%, 75–99% or

Mean wind strength was stronger in ﬁshing event 1 (1.96 m/s) than 100%) and the weed height (no weed, 0–30 cm, and >60 cm height).

in ﬁshing event 2 (1.52 m/s). The ﬁshing event 2 in May was domi- Weed cover and height were correlated, and we thus only consid-

nated by clear weather with a mean radiation of 168.8 W/m while ered weed cover in further analyses.

ﬁshing event 1 in September was mainly cloudy with a mean radia-

tion of 105.7 W/m . Relatedly, the total sum of precipitation during 2.2. Data analysis

ﬁshing event 1 was higher (15.5 mm) than during ﬁshing event 2

(0.8 mm). The data (both catch rates and ﬁsh sizes) were composed of

Multiple environmental variables were recorded in each exper- repeated measures generated by experimental anglers in ﬁxed

imental angling session. First, multiple weather-related variables ﬁshing sites ﬁshed at random with standardized lures and gears.

were collected from a nearby whether station (Deutscher Wetterdi- Therefore, a hierarchical structure of ﬁxed and random effects was

enst Angermünde) including: the mean wind strength (m/s), mean initially considered in a mixed modelling approach. We built two

◦

air temperature ( C), mean humidity (%), mean air pressure (hPa), generalized mixed effects models (GLMM), one to explain varia-

mean precipitation (mm) and mean global radiation (W/m ). We tion in catch rates and another to explain variation on pike size.

reduced the dimensions of the weather-related matrix by explor- Considering the distribution of both model residuals (see below)

ing patterns of co-variation. To that end, a principal components and the nature of the data, as count data the catch rate data

analysis (PCA) was applied to the weather matrix to explore cor- were modelled using a Poisson-distribution, and the ﬁsh size data

relation patterns using the function princomp of the library stats of were modelled following a Gaussian-distribution. Bites (bites per

the R package (R Development Core Team, 2011). The PCA results 15 min) and pike catches (ﬁsh per 15 min) per lure type were

suggested that there were two groups of highly correlated envi- highly correlated (Spearman’s coefﬁcient = 0.69, p-value < 0.001).

ronmental variables (Fig. 3). Humidity was negatively correlated Therefore, we only analysed catch rate data that related to pike

with air temperature and precipitation was negatively correlated because there was the potential for Eurasian perch (Perca ﬂuvi-

with air pressure (Fig. 3). Based on these results, for further anal- atilis) also attacking the lures. The two response variables – catch

yses the initial raw weather matrix was reduced to wind strength, rates and size of pike – were modelled against the matrix response

air temperature, precipitation and radiation. The selection of the variables (as ﬁxed factors) and random variance. The full model

various abiotic variables followed previous studies by Kuparinen initially included the ﬁxed effects of day period (categorical), ﬁsh-

et al. (2010) on the main abiotic determinants of pike catch by ing event 1 or 2 (categorical), lure type (categorical), wind strength

anglers. We omitted modelling the moon phase and water temper- (continuous), radiation (continuous), air temperature (continuous),

ature as it does not vary within a given day and shows low variation habitat (categorical), weed cover (categorical), depth (continu-

over a seven day ﬁshing period. Microhabitat-related characteris- ous) and ﬁshing day (continuous), as well as the random effects

tics were also considered by matching the GPS location of each of angler (categorical) and site (categorical). All two-order inter-

652 R. Arlinghaus et al. / Fisheries Research 186 (2017) 648–657

actions were tested and eventually (see below) included in the

model as ﬁxed effects. We reduced the full model to ﬁt a minimal

adequate model, i.e., identiﬁcation of the ﬁxed and random struc-

ture that produced the least unexplained variation subject to the

constraint that all the parameters in the model should be statisti-

cally signiﬁcant at p < 0.05 (Crawley, 2005). We applied a forward

step-by-step approach by comparing the model with and without

the ﬁxed or random parameter through ANOVA and checking the

Akaike information criterion (AIC) and Bayesian Information Crite-

rion (BIC) following the approach proposed by Zuur et al. (2009).

Model parameters and p-values were estimated through the func-

tion glmer of the library lme4 of the R package (Bates et al., 2015).

Proper ﬁtting of the two GLMMs was examined checking the

distribution of the residuals. In the case of the catch rate data, we

assessed the overdispersion of the residual to be ∼1 as required for

Poisson data. In the second case of the ﬁsh size data, the residuals

were checked and a log-transformation was applied to the response

variable to reach normality as required for Gaussian data. The pre-

dictive ability of the minimal adequate models was ﬁnally assessed

via the coefﬁcient of determination (R ). The effects and conﬁdence

intervals of each parameter included in the model were plotted

using the function allEffects of the library effects of the R package

(Fox, 2003). Moreover, the effect size of each parameter included in

the minimal adequate model was calculated using the z-values (for

Poisson data, i.e., catch rates) and t-values (for Gaussian data, i.e.,

ﬁsh size) following the procedures recommended by Nakagawa and

Cuthill (2007). In relation to effect sizes, d or r statistics were cal-

culated for categorical or continuous predictors respectively, and

we considered ‘small’, ‘medium’, and ‘large’ effect sizes using the

benchmarks proposed by Nakagawa and Cuthill (2007) (r = 0.1, 0.3,

0.5 and d = 0.2, 0.5, 0.8, respectively).

3. Results

3.1. Pike catch rates

A total number of 3232 experimental ﬁshing sessions (each of

15 min duration) were carried out, and a total of 313 individual pike Fig. 4. Frequency histograms of the raw data used to explore differences in (a) catch

rates (bites per 15 min and pike per 15 min) and (b) pike size (in mm, total length).

were captured (Fig. 4). The minimal adequate model ﬁtted to test

The sample size is given in each panel.

the effect of different variables on the catch rates (number of pike

per 15 min) retained the effect of ﬁshing event (autumn vs. spring),

lure type (spoon vs. soft plastic), water depth and ﬁshing day as well

as the random structure composed of variation attributed to the Table 1

angler and the site (Table 1). The latter suggested that catch rates Akaike information criterion (AIC), Bayesian information criterion (BIC) and

deviance of the full model and the minimal adequate model ﬁtted to test the effect

varied across sites and were affected by the angler (skill effect). The

of multiple variables in relation to the number of pike caught in 15 min (n = 3232

within-day variable diel period, weather characteristics, microhab- 2

experimental ﬁshing sessions). The table shows Nagelkerke pseudo-R and the

itat (e.g., weed cover) and any of the two-order interactions (e.g., overdispersion of the minimal adequate model. The variance and standard devia-

lure type × day) did not explain enough variance to become signif- tion (s.d.) of the random structure, and the estimates as well as their standard error

(s.e.), z-value, p-value and the effect size of the ﬁxed structure are shown. Variables

icant and were thus not retained in the ﬁnal model. Although the

are ranked according the main effect size.

predictive ability of the ﬁnal model was only moderate (Nagelkerke

pseudo-R = 0.16), all ﬁxed factors included in it were signiﬁcant AIC BIC deviance

(Table 1). In all cases, however, effect sizes were small, indicating a

Full model 1458 1574 1420

signiﬁcant but weak effect of the ﬁxed factors on the catch rates of Minimal adequate model 1440 1482 1426

pike (Table 1). The model-predicted effect on catch rate attributed Nagelkerke pseudo-R = 0.16

Overdispersion = 0.948

to each ﬁxed factor is plotted in Fig. 5. The main effect (as ranked

by its effect contribution shown in Table 1) was the ﬁshing event

Random effects Groups Variance s.d.

(a seasonality effect). Higher catch rates were achieved in autumn

site 0.06 0.25

compared to spring. The second most important variable was water angler 0.09 0.31

depth, indicating a decline in the catch rates with increasing depth Fixed effects Estimate s.e. z-value Pr( > |z|) Effect size

or distance from the shore (indicated by the negative coefﬁcient for

(Intercept) 0.787 0.319 2.465 <0.05*

− −

water depth in Table 1). The third most important variable was the Fishing event (Spring) 0.774 0.162 4.782 <0.001*** −0.16

Depth −0.005 0.001 −8.846 <0.001*** −0.14

type of lure. Catch rates achieved through the use of spoons were

Bait (Spoon) −0.279 0.108 −2.591 <0.01** −0.09

signiﬁcantly lower than those achieved by anglers using a soft plas-

Day −0.073 0.028 −2.568 <0.05** −0.04

tic shad (Table 1, Fig. 5) (mean and s.e. [CI] for soft bait: 0.08 ± 0.1

[0.06–0.1] pike per 15 min; spoons: 0.06 ± 0.1 [0.04–0.08] pike per

R. Arlinghaus et al. / Fisheries Research 186 (2017) 648–657 653

compared to spoons. Soft-plastics elevated catch rates by 33% com-

pared to spoons. In absolute terms, while one would catch 0.06

pike per 15 min with spoons, one can expect to catch, on aver-

age, 0.08 pike per 15 min with soft plastics in Kleiner Döllnsee.

Extrapolated to 8 h of angling for pike, the difference in catches

would be 1.5 more ﬁsh captured on soft-plastics (2.6 pike per

8 h) relative to the spoon (1.9 pike per 8 h). The largest effect size

determining pike catch rates over a period of 15 ﬁshing minutes

was, however, related to the season in which the ﬁshing experi-

ment was conducted. Seasonal variation in pike and the catch of

other top predators is a well-known phenomenon (e.g., Lux and

Smith, 1960; Heermann et al., 2013) related to swimming activ-

ity and bioenergetic demands driven by temperature (Casselman,

1978; Pieterek, 2014), habitat choice (Matthias et al., 2014), and

seasonal changes in food availability and distribution (Raat, 1991;

Lux and Smith, 1960; Van Densen and Vijverberg, 1982; Stoner,

2004; Heermann et al., 2013). We found generally lower catch rates

of pike in our one-week experiment conducted in May (spring)

relative to September (autumn). Although the May ﬁshing period

following spawning should have been associated with aggressive

foraging of pike to reﬁll lost energy stores, our experiment hap-

pened to coincide with a rather warm and calm (in terms of wind)

Fig. 5. Partial effects predicted by the minimally adequate model ﬁtted to catch

week. High water temperature and low wind speeds are known to

rate data (pikes per 15 min) in relation to ﬁshing event (ﬁshing event: autumn = 1

reduce pike catch rates in Kleiner Döllnsee (Kuparinen et al., 2010),

vs. spring = 2), lure type (soft plastic shad vs. spoon), water depth (in cm, shown

suggesting that overarching abiotic conditions were contributing

for the values present in the data) and ﬁshing days (days 1–7). Standard errors are

indicated with dashed lines and categories are shown as circles and the black bars to the ﬁshing period effect found in our study.

in the x axis show the observations. The second strongest effect size explaining catch rate of pike

was associated with the depth of the ﬁshing sites. Larger catch

15 min). Finally, ﬁshing day signiﬁcantly affected catch rates; they rates occurrred in shallow water. Telemetry work in the study

declined over the duration of each of the two ﬁshing experiments lake has repeatedly shown that despite the presence of consis-

(negative ﬁshing day effect, Table 1, Fig. 5). tent variation in behavioural types the entire adult pike population

as well as the juveniles are regularly using shallow vegetated lit-

3.2. Pike size toral areas offering refuge and shelter (Kobler et al., 2008a,b, 2009).

The heavy concentration of pike in structured habitat is a gen-

The minimal adequate model ﬁtted to test the determinants of eral feature of the species (Grimm and Klinge, 1996). Hence, we

pike size captured by angling retained the effects of ﬁshing event, can suggest that the most important determinant of catching a

habitat, and ﬁshing day (Table 2). It did not retain the random fac- pike and other predators may be maximizing the encounter prob-

tors angler and site, indicating that the pike sizes were neither ability through proper mesohabitat choices by anglers (Matthias

systematically related to the sites nor to the experimental anglers. et al., 2014). Hence, the negative relationship of water depth and

Diel period, lure type, weather, weed cover and all of the two-order pike catch rates documented in our work can be explained by

interactions also did not explain enough variation in pike sizes and a self-evident encounter-based mechanism. Alternative reasons

were thus not part of the best model. The predictive ability of the could involve that speciﬁc habitats are more often used as forag-

ﬁnal model to explain pike size was lower (R = 0.08, Table 2) than ing habitat (e.g., shallow vegetated water) than others, and hence

the one ﬁtted to explain catch rates. In all cases, the effect sizes were encountering a pike in that speciﬁc habitat might facilitate its cap-

again small, indicating a signiﬁcant but weak effect of the ﬁxed fac- ture (cf. Matthias et al., 2014).

tors on the size of pike captured in the experimental angling ﬁshery We also found a signiﬁcant effect of lure type on pike catch rates

(Table 2). The effect of each signiﬁcant ﬁxed factor is plotted in in terms of higher catch rates generated by the use of soft plastics

Fig. 6. Similar to the catch rate model, in the size model, the ﬁshing compared to spoons. It is possible that the higher catch rates offered

period again ranked ﬁrst in importance, indicating that the exper- by soft plastics are a function of an intrinsically greater attractive-

imental ﬁshing even in spring (May) had a positive effect on the ness of the more natural appearance of the soft shad, potentially

average pike size that was captured (Table 2). We also found that further reinforced by stronger lure avoidance induced by the less

pike captured in sublittoral areas offering only sparse macrophytes natural spoon. Pike are poor learners compared to other species

were signiﬁcantly smaller than those captured in other habitats such as carp (Cyprinus carpio) (Coble et al., 1985) and therefore

(Table 2, Fig. 6). As the third most important variable, similar to the do not show a sharp drop in catch rates over the course of a ﬁsh-

catch rate model we found pike size to decline with the duration of ing season compared to other species (compare the erratic CPUE

the one week experiment (Table 2, Fig. 6). data across ﬁshing days in pike in Kuparinen et al., 2010 with the

contrasting pattern of sharp drops in CPUE across days in rainbow

4. Discussion trout, Oncorhynchus mykiss, van Poorten and Post, 2005; Askey et al.,

2006, and carp, Cyprinus carpio, Klefoth et al., 2013). Nevertheless,

Our controlled experimental study exploiting all microhabitats pike still learn to avoid being hooked by artiﬁcial lures, but fail

accessible to artiﬁcial lures (excluding dense reed) at the scale of to show altered foraging behaviour when confronted with natural

an entire lake over two one-week ﬁshing sessions conﬁrmed the baits (Beukema, 1970). In our study there was no statistical sup-

study hypotheses that (i) pike catch rates as well as the size of port for inclusion of a lure type × ﬁshing day interaction, such that

pike captured were greatest in shallow, vegetated areas, and (ii) we have no evidence that there was differential learning with the

that pike catch rates, but not the size of pike, were related to lure lure types we used. We therefore suggest that the soft plastic shads

type, with greater catch rates offered by the use of soft plastics probably were intrinsically more attractive to the pike relative to

654 R. Arlinghaus et al. / Fisheries Research 186 (2017) 648–657

Table 2

Akaike information criterion (AIC), Bayesian information criterion (BIC) and deviance of the full model and the minimally adequate model ﬁtted to test the effect of multiple

variables in relation to log10-transformed ﬁsh size (mm) (n = 313 pike). The variance and standard deviation (s.d.) of the random structure, and the estimates and their

standard error (s.e.), the z-value, the p-value and the effect size of the ﬁxed structure of the minimally adequate model are shown. Variables are ranked according the main

effect size.

AIC BIC Deviance

Full model 111.1 182.3 −35.52

Minimal adequate model 16.04 42.26 −26.56 2

R = 0.08

Fixed effects Estimate s.e. t-value Pr(>|z|) Effect size

(Intercept) 6.120 0.047 131.083 <0.001***

Fishing event (Spring) 0.082 0.029 2.843 <0.01** 0.32

−

Habitat (Open water with few macrophytes) −0.100 0.042 2.370 <0.05* −0.27

Day −0.021 0.007 −2.992 <0.01** −0.13

Habitat (Pelagic without macrophytes) −0.047 0.046 −1.005 0.31584 −0.11

Fig. 6. Partial effects predicted by the minimally adequate model fitted to log10-transformed pike size (mm, total length) in relation to fishing event (fishing event: autumn

vs. spring), habitat type (1 = littoral with many macrophytes, 2 = sublittoral with few macrophytes and 3 = pelagic without macrophytes) and ﬁshing days (days 1–7). Standard

errors are indicated with dashed lines and categories are shown as circles.

the metal-based spoon. This explanation is further reinforced by non-captured pike likely became more wary in response to the

the fact that the spoon was a bit smaller in size compared to the heavy angling pressure with artiﬁcial lures and learned to avoid

soft plastic bait (Fig. 2) and hence for gape-size reasons a larger being captured even if not captured themselves (Beukema, 1970).

fraction of pike should in principle have been able to ingest the Both mechanisms are likely sufﬁcient to explain our ﬁndings of a

spoon. Nevertheless, soft plastics captured more ﬁsh per unit time. decline in catch rate with increasing ﬁshing days over one week

The uncertainty remains that the absolute size dimensions may not sessions (see also Alós et al., 2015 for a case in marine systems).

align with the “apparent” size of the two lure types, which will also In addition to the factors mentioned, we also found angler

be a function of lure shape and action. There is no research avail- and site to be relevant random factors in explaining pike catch

able to indicate what the apparent size of the lure types might be rates. Although we selected specialized and hence experienced

for pike. pike anglers, our data nevertheless indicated a relevant angler skill

We also found a drop in catch rates in progression of the one effect, conﬁrming results of previous angling studies (Alós et al.,

week ﬁshing experiment in each of the two study periods. The 2009; Dorow et al., 2010; Herrmann et al., 2013; Van Poorten et al.,

decline in catch rates with increasing effort has been previously in press). Moreover, unaccounted variation in microhabitats, e.g.,

reported from the same lake in a study involving several months related to prey availability, is a likely explanation for the random

of continuous angling, but effects were most pronounced when effect of “site” in our study.

effort was high in the short-term accumulated over two ﬁshing Neither of the weather factors nor diel period explained varia-

days (Kuparinen et al., 2010). Two not mutually exclusive rea- tion in catch rates in our study, which on ﬁrst sight disagrees with

sons are likely involved in explaining this pattern. First, in any previous work from the same lake (Kuparinen et al., 2010). How-

moment in time pike populations will be clustered into vulnera- ever, one should realize the fundamental difference in time scales

ble and invulnerable ﬁshes due to mechanisms of habitat choice, – and hence scope for variation in abiotic conditions – present in

previous exposure to ﬁshing and time of the last meal (Cox and our work here and in Kuparinen et al. (2010). While Kuparinen et al.

Walters, 2002; Matthias et al., 2014). In our study all vulnerable ﬁsh (2010) modelled daily catch rates over the course of several months,

were very regularly exposed to some form of ﬁshing because on any our work looked at small variation in weather conditions within

given day our design ensured angler (and hence lure) presence at a given day. Many abiotic variables will not even show variation

any site of the lake. Some of the vulnerable pike were consequently within a day, while varing much across days and seasons. We mod-

captured. Telemetry studies showed that pike that are captured and elled catch rates per 15 min, and the most likely prediction was to

released alter their behaviour over the course of one to two days, catch a very small number of ﬁsh or nothing at all (Figs. 3 and 4). The

by becoming less active and moving increasingly closer to structure highly skewed catch rate distribution coupled with small variation

(Baktoft et al., 2013; Klefoth et al., 2008; Klefoth et al., 2011). More- in abiotic conditions over 15 min time blocks is very likely respon-

over, we anesthetized the pike during data collection and some ﬁsh sible for the different ﬁndings of Kuparinen et al. (2010) and our

were PIT tagged, which might have elevated stress and prolonged work. The message to anglers simply is that while the weather is

recovery to a vulnerable state (Cooke et al., 2011). Hence, the pool unlikely to contribute signiﬁcantly to catch rate on small temporal

of vulnerable and “reactive” ﬁshes likely declined over the course scales within a day, small effects may accumulate over time gen-

of the one week ﬁshing sessions due to personal experience aggra- erating signiﬁcant patterns among days as revealed in Kuparinen

vated by handling stress. Second, as previously mentioned, even et al. (2010). Put simply: while an angler cannot expect to catch a

R. Arlinghaus et al. / Fisheries Research 186 (2017) 648–657 655

pike on, say, each full moon day that is ﬁshed, on average across be interpreted with caution in terms of predictive power of the

the season, full and new moon and dawn and dusk periods will be independent variables that were tested. Pike offer low catch rate

more productive as observed in several top predators angled with ﬁsheries, such that ﬁshing experiments integrating longer periods

lures (Vinson and Angradi, 2014; Cabanellas-Reboredo et al., 2012; of time (multiple h or an entire day as in Kuparinen et al., 2010)

Kuparinen et al., 2010). The most likely prediction for catching a are likely to generate a more balanced distribution of catch, in turn

pike over 15 min is, however, zero, corresponding with the ran- improving the model ﬁtting. The best prediction in a 15 min ﬁsh-

dom nature of angling in general, where overall catches per day ing session is to catch a small number of ﬁsh or nothing, (largely)

are strongly dependent on the degree of investment of ﬁshing time irrespective of ﬁshing site, daytime and lure type. Hence, our study

(Seekell et al., 2011; Seekell, 2013). supports arguments expressed elsewhere that the best ingredient

Our models explaining variation in size of captured pike were of ﬁshing success is simply the investment of ﬁshing time (Seekell

less predictive compared to the catch rate models. Hence, the odds et al., 2011; Seekell, 2013) allocated to sites that maximize the

of catching a large ﬁsh over a 15 min time period carry substantially encounter of ﬁsh and anglers (Alós et al., 2012; Matthias et al.,

more randomness than the odds of catching anything. Such result 2014). All other variables (gear, skill, lure, weather, time of the day)

is not surprising given that large ﬁsh are usually rare and hence the exert small additional effects that over time may become statisti-

encounter probability is also rare. Therefore, our results of a lower cally signiﬁcant, but anglers may want to accept that ﬁshing for

predictive ability to catch a large fish compared to catching a fish low abundant predatory fishes is essentially a stochastic process

at all entirely agrees with probabilistic arguments expressed else- that often leads to one outcome − to catch very little. To maximize

where (Wilde and Pope, 2004) and reports for perch recreational catch rates of pike, anglers can be advised to follow self-evident

angling (Heermann et al., 2013). We also did not detect an effect strategies of choosing ﬁshing sites in areas most likely to host pike,

of lure type on the size of pike captured, suggesting that the two which usually are the shallow vegetated areas of lakes and rivers

similarly sized lure types were not constraining individual pike due (e.g., Craig, 1996; Grimm, 1981; Casselman and Lewis, 1996; Pierce

to gape constraints. Our results conﬁrm previous work in the same and Tomcko, 2003; Kobler et al., 2009). Additional effects may arise

lake by Arlinghaus et al. (2008b) and also agree with Stålhammar from systematically exploiting abiotic conditions and gear types

et al. (2014) in Baltic pike who only reported a trend for larger ﬁsh known to elevate catch rates, but effects will be small compared to

being hooked with soft plastics compared to other lures. It seems investment of time and choice of suitable habitat. In this context,

that it is not the lure type per se, but its size that differentiates anglers interested in catching trophy pike are also advised to use

among the catch of large and small ﬁshes (Arlinghaus et al., 2008b; pelagic areas because pike increasingly loose attachmet to vege-

Wilde et al., 2003). This is particularly true for pike because all size tation as they grow and become adult and then regularly strive in

classes share a common habitat, which are vegetated shallow areas pelagic zones in the search for food (Vøllestad et al., 1986). This goes

(Kobler et al., 2008a,b, 2009). This particular feature also explains along with a change in foraging strategy from sit-and-wait in struc-

our ﬁnding of larger, likely more competitive pike being captured ture to ambush hunting (Eklöv, 1997), which increases encounters

in heavily vegetated littoral zones, which is the preferred habitat of of actively moving boat anglers and more actively swimming large

this species, whereas smaller individuals seemed to be displayed to pike (Alós et al., 2012).

suboptimal, sparely vegetated areas similar to previous ﬁndings in Although our results offer implications mainly for anglers,

this species (Skov et al., 2011). However, it is also known that larger they also show that uncontrolled ﬁshery-dependent data used by

pike more readily explore the open-water zones compared to juve- researchers and managers to indicate trends in population abun-

niles or small conspeciﬁcs (Chapman and Mackay, 1984; Vøllestad dance (e.g., Lehtonen et al., 2009) need to be interpreted with

et al., 1986; Kobler et al., 2008a,b), which probably contributed to caution because catch rates of pike are affected by angling skill,

the similar sizes of pike captured in fully open water areas and the choice of habitats, lure choice and effort sorting (Van Poorten et al.

heavily vegetated zones. It was the sparsely vegetated intermedi- in press). Hence, whenever possible it is advisable to also stan-

ate zone between the littoral and the open pelagic that produced dardize gear, anglers and the time of sampling if angling catch rate

the smallest pike. When dealing with overexploitation, curtailing data are to be used to index population developments over time.

fishing effort on vegetated habitats might be considered a possi- In any case, for monitoring of fish stocks fishery independent data

ble way forward. However, Kobler et al. (2009) documented the are preferrable (Van Poorten et al. in press).

presence of three similarly sized behavioural types of pike in the

study lake, of which one type readily explored the unvegetated

Acknowledgements

open water. Therefore, closing off certain littoral areas of the lake

for ﬁshing might indeed reduce ﬁshing mortality, while at the same

JA was supported through a Marie Curie grant (grant No.

time fostering strong selection pressures on activity and space use

FP7–PEOPLE–2012–IEF, 327160) funded by the European Union

(Pieterek, 2014) with so far unknown consequences for population

and a Juan de la Cierva post-doc grant (grant No. FJCI-2014-21239)

dynamics (Arlinghaus et al. in press).

funded by the Spanish Ministry of Economy and Competiveness.

The size of pike captured exhibited a seasonal effect, with larger

The work received further support by the Adaptﬁsh (Gottfried-

pike, on average, being captured in the spring ﬁshing experiment

Wilhelm-Leibniz-Community) and Besatzﬁsch (German Federal

compared to the autumn experiment. It is possible that the largest

Ministry for Education and Research) grants received by RA. We

ﬁsh had the greatest energy deﬁcit after spawning and hence were

thank all participating anglers for great support in this study,

more readily captured in May compared to September. In addition

reviewers for excellent feedback and Bernard Chéret for editorial

we found a decline in the pike size over the course of the one week

help.

ﬁshing period. This could again be explained by vulnerable pool

dynamics as elaborated before: positively size selective vulnerabil-

ity is very well known in pike (Arlinghaus et al., 2009a,b; Pierce

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