Fisheries Research 186 (2017) 648–657
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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
a
Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany
b
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
c
Instituto Mediterráneo de Estudios Avanzados, IMEDEA (CSIC-UIB), C/Miquel Marqués 21, 07190, Esporles, Illes Balears, Spain
d
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.
© 2016 Elsevier B.V. All rights reserved.
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
0165-7836/© 2016 Elsevier B.V. All rights reserved.
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-
2
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-
2
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-
2
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
2
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
2
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
2
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-
2
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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