Cormorant (Phalacrocorax carbo) predation on a coastal perch (Perca fluviatilis) population: estimated effects based on PIT tag mark-recapture ...
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ICES Journal of Marine Science (2020), doi:10.1093/icesjms/fsaa124
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Cormorant (Phalacrocorax carbo) predation on a coastal perch
(Perca fluviatilis) population: estimated effects based on PIT tag
mark-recapture experiment
1
L. Veneranta *, O. Heikinheimo2, and T. J. Marjomäki3
1
Natural Resources Institute Finland (Luke), Wolffintie 35, Vaasa 65200, Finland
2
Natural Resources Institute Finland (Luke), Latokartanonkaari 9, Helsinki 00790, Finland
3
Department of Biological and Environmental Science, University of Jyväskylä, PO Box 35, Jyväskylä FIN-40014, Finland
*Corresponding author: tel: þ358 295307203; e-mail: lari.veneranta@luke.fi.
Veneranta, L., Heikinheimo, O., and Marjomäki, T. J. Cormorant (Phalacrocorax carbo) predation on a coastal perch (Perca fluviatilis)
population: estimated effects based on PIT tag mark-recapture experiment. – ICES Journal of Marine Science, doi:10.1093/icesjms/
fsaa124.
Received 17 January 2020; revised 15 June 2020; accepted 17 June 2020.
The number of cormorants has rapidly increased in the northernmost Baltic Sea. In 2018, 50 km 50 km ICES catch rectangle 55H1 had 3140
breeding pairs. To estimate the predation effect of cormorants on perch populations, we Passive Integrated Tags tagged 1977 perch and 9.9%
of tags were found. The median instantaneous cormorant-induced mortality during the breeding time, with consumption by non-breeding
individuals, was estimated at 0.23 and at 0.35 during the whole residing period. We estimated with a yeild-per-recruit model that the long-
term maximum loss of perch yield of tagged sub-population would be at 80% probability interval 32–67%, and when extended to the entire
55H1, 10–33%, respectively. The cormorants’ share of the >2-year-old perch biomass and production would be 8%, while that of other natu-
ral mortality would be 63% and that of fishing 29% in 55H1. The yield-per-recruit-results should be interpreted as an estimate of maximum
cormorant effect because the dependence of predation rate on prey density was not accounted for, and density-dependence of growth, mor-
tality, and reproduction of perch could partly compensate the loss. The results indicate that high density of cormorants can reduce the perch
stocks and catches locally.
Keywords: cormorant, fishery, perch, predator–prey interaction, yield
Introduction number of birds vary from year to year (Anonymous, 2018). The
The history of great cormorant (Phalacrocorax carbo) occurrence number of nesting pairs in the Quark, central Gulf of Bothnia, ex-
in Finnish coastal area is short. The first breeding was observed in ploded in spring 2016. After a long positive catch development of
1996 and increasing up to 27 600 breeding pairs in 2018. The in- perch (Perca fluviatilis) and peak year in 2014, the commercial
crease follows the European level stock development (Herrmann catches have decreased and the fishermen consider cormorants to
et al., 2019). In the northern Baltic Sea, a strong debate has been be the cause of the negative trend (Svels et al., 2019). Perch is a
going on about the role of cormorant in the coastal ecosystem, focal species of coastal fishery in the Quark and, thus, commercial
and especially about its potential effects on fish stocks (e.g. Salmi and recreational fisheries and cormorants are partly exploiting
et al., 2015; Lehikoinen et al., 2017; Hansson et al., 2018). the same resources. Many studies have identified percids (Perca
The cormorant population growth has levelled off in Finland spp.) as particularly important in cormorant diet (e.g. Emmrich
in recent years, but location of colonies and thus the local and Düttmann, 2011; Östman et al., 2012; Skov et al., 2014).
C International Council for the Exploration of the Sea 2020. All rights reserved.
V
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2 L. Veneranta et al.
The studies concerning the effects of cormorants on fish stocks stocks is demanded by stakeholders: fishermen, environmental
in the Baltic Sea area are mainly based on correlations between authorities and organizations, fisheries managers, and politicians
cormorant abundance and catches, not on direct causalities (e.g. (Anonymous, 2019). This study aims to estimate the local-scale
Vetemaa et al., 2010; Östman et al., 2012), and only few studies cormorant predation effect on a coastal perch population. A tag-
consider mortalities (Östman et al., 2013; Heikinheimo et al., ging approach on perch population was used to quantify
2016). Worldwide, some studies suggest that cormorants can reg- cormorant-induced mortality on the tagged sub-population and
ulate fish populations (e.g. Rudstam et al., 2004; Fielder 2008; to assess the effects on the perch populations and catches.
Vetemaa et al., 2010; Hansson et al., 2018) while some studies Predation mortality was first estimated for the tagged sub-
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have not found any effect (Engström 2001; Diana et al., 2006; population and then expanded to the statistical rectangle 55H1. A
Lehikoinen et al., 2017) or the effects have been considered to be yield-per-recruit-model incorporating parameter uncertainty was
site dependent (Östman et al., 2012). The influence of cormorant applied to assess the potential maximum effect of cormorant on
predation on wild fish populations is difficult to estimate with perch yield.
high precision and accuracy. Several factors regulating the popu-
lations act simultaneously; and especially in percids, there is large Material and methods
inter-annual variation in year class strength and consequently in Study area, fishery, and cormorant population
fisheries catches (Kokkonen et al., 2019). Cormorants typically The study was conducted in the Northern Baltic Sea, Quark, in
consume prey smaller than those targeted by fishing (Lehikoinen 2018. The ICES statistical rectangle 55H1 covers the study area,
et al., 2011; Salmi et al., 2015), which makes the estimation of di- cormorant colonies, and perch tagging site (Figure 1).
rect effect on fishery catch even more difficult, as environmental In years 2015–2017, r55H1 produced 20% of commercially
factors, other natural mortality, and density-dependent compen- caught perch in the Finnish coastal area (OSF, 2018a). The perch
satory effects affect the level of stocks (Heikinheimo et al., 2016). catches in r55H1 have increased rapidly when the acidification
To evaluate the cormorant conflict in fisheries, local-level problems in nearby rivers and estuaries (Hudd, 2000) have di-
quantitative information on the effect of cormorants on fish minished and perch reproduction has been successful (Figure 2).
Figure 1. Location of cormorant colonies (A–D), tagging site, and surrounding ICES statistical rectangle 55H1. Brown colour indicates the
most favourable perch reproduction areas (Kallasvuo et al., 2017). There are also numerous small shallow bays, flads, in the islands and
mainland that are important for perch reproduction. DepthsCormorant predation on a coastal perch population 3
likely4 L. Veneranta et al.
tagging site. The deposition probability to colonies, i.e. the proba- Estimation of cormorant predation mortality of perch
bility that a tag ingested by a cormorant will end up at one of the The effective number of released marked perch that survived the
colonies (A–D) and not in the sea or other places in the area, was marking (TS) was estimated from the total number of released
not examined in this study. Therefore, the estimates by Hostetter marked perch (T)
et al. (2015) were used (mean 51%, 95% credible interval 34–
70%). The number of young and non-breeding birds was as- TS ¼ T ð1 –pDeadÞ:
sumed to be 30% of the whole population, which means a 21%
share of the food consumption. The proportion of perch in their For explanation of the variables that follow and their values see
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diet was assumed equal to that of birds breeding in colonies. Table 1.
Table 1. The most likely value and likelihood distribution of different input variables used in estimating the proportional cormorant
predation mortality and the effect of cormorant predation on fishing yield in statistical rectangle 55H1.
Expected
Parameter name, abbreviation value Likelihood distribution Notes
Handling and tagging mortality of perch, pDead 2.01% Triangle (0; 2.04; 4) 2/98 died
Tag detection probability, pD 92.7% N (92.7, 1.4), max. 100 178/192 found
Probability of tag deposition to colonies, pCD 51% N (51, 10), max. 100 Hostetter et al. (2015)
Proportion of catch by other non-nesting cormorants of total 21% Triangle (16; 21; 26) Supplementary S3
catch, pCtoc
Correction factor for uneven distribution of tagged perch in 1.45 Histogram (0.8; 3; 36/91/91/75/ Own expert opinion, referred
tagged sub-population, DistCorr 56/46/39/35/32/29/27/24/ literature on feeding flight
21/19/16/14/12/10/8/6/4/2) distances and migration
range of perch
The factor introducing the uncertainty of age-specific length 1 Triangle (0.97; 1; 1.03) Own sampled data
of slow-growing sub-population of perch, ucGs
The factor introducing the uncertainty of age-specific length 1 Triangle (0.97; 1; 1.03) Own sampled data
of fast-growing sub-population, ucGf
The proportion of slow-growing sub-population, pSPs 50% Triangle (40; 50; 60) Own sampled data
Length of open water season, owp 6 months Triangle (5.5; 6; 6.5) Haapala and Leppäranta
(1997) and Jevrejeva et al.
(2004)
Length of cormorant residing period, crp 3.5 months Triangle (3.25; 3.5; 3.75) Lehikoinen (2003); own
observations
The correction factor for cormorant mortality (M1) for non- 0.9 Uniform (0.8–1.0) Own expert opinion to include
tagged perch, M1Corr uncertainty
The correction factor for other natural mortality (M2) for 0.9 Uniform (0.8–1.0) Own expert opinion to include
non-tagged perch, M2Corr uncertainty
The proportion of cormorant predation mortality of age 2 15% Triangle (10; 15; 20) Salmi et al. (2015)
perch, M1a2 of M1a3, pM1
Length of perch when cormorant mortality drops from M1a3 220 mm Triangle (210; 220; 230) Salmi et al. (2015)
to M1l, LM1
Length of perch when cormorant predation stops, ULCorPre 250 mm Triangle (230; 250; 270) Salmi et al. (2015)
Inst. other natural mortality of small perch during open water 0.49 Histogram (0.16; 1.06; 10/30/ Heibo et al. (2005) and
period, M2s 54/55/25/11/8/6/1) Horppila et al. (2010)
Inst. natural mortality of large perch during the open water 0.15 Triangle (0.1; 0.15; 0.2) Own expert opinion
period, M2l
The length of perch when mortality drops to M2l, LM2 210 mm Triangle (200; 210; 220) Own expert opinion
Proportion of winter of the annual M2, wpM2 20% Triangle (10; 20; 30) Own expert opinion
Inst. fishing mortality (F) of slow-growing sub-population 0.053 Triangle(0.03; 0.05; 0.08) Estimated value
during the open water period, Fsowp
Winter proportion of F of slow-growing sub-population pFsw 0.5 Triangle (40; 50; 60) Estimated value
Proportion of F of fast-growing sub-population during 47% Triangle (30; 50; 60) Estimated value
cormorant residing period of the annual F, pFcrp
Proportion of F of fast-growing sub-population during the rest 20% Triangle (10; 20; 30) Estimated value
of the open water period of the annual F, pFow
Length of recruitment to gillnetting, LFlg 233 mm Triangle (220; 230; 250) Own sampled data
Annual F of fast-growing sub-population after LFlg, Flg 0.77 Triangle (0.5; 0.8; 1) Own data from the
Archipelago Sea
The proportion of target sub-population for cormorant 41.75% Histogram (20; 90; 26.5/25.0/ Own data, see text
predation of the whole perch pop. in r55H1 22.5/13.5/7.5/3.0/2.0)
Expected value ¼ weighted average, likelihood distributions: triangle (minimum; most likely; maximum), normal N (mean; SD), uniform (min.–max.), histogram
(min.–max.; class-specific frequencies relative to probabilities).Cormorant predation on a coastal perch population 5
Then, the total number of marked perch caught and eaten by To estimate the uncertainty of output variable estimates,
cormorants during the mark–recapture study period (CTc) was instead of using one value for each input variable, a range
estimated from the number of recovered tags (TR) by and distribution were used for all of them (Table 1). Estimates
of output variables were reiterated using random values
CTc ¼ TR= pD pCD ð1–pCtocÞ : from these distributions for all input variables simultaneously
and the distributions of the recalculated output variables illus-
To compensate the bias in tag retention probability due to pos- trate the uncertainty of their value (see the Uncertainty
sible uneven distribution of tags in predated sub-population, the section).
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estimated catch by cormorants was multiplied with a distribution
correction factor (DistCorr) to gain the predicted total number of The effect of cormorant predation on the fishing yield of
tagged fish caught if even distribution of tags was assumed perch
The long-term equilibrium effect of cormorant predation on
CTcE ¼ CTc DistCorr: the fishing yield (Yf ¼ catch in biomass units) was assessed
using Ricker’s Y/R model (Ricker, 1975), assuming constant
Finally, the proportional total cormorant mortality on the tar-
population parameters including constant number of cormor-
get sub-population during the study period was estimated by
ants and constant predation on perch from year to year. The
vTcE ¼ CTcE=TS: yield is
The instantaneous cormorant predation mortality during the Y ¼ sumir imax Fi Bi
study period (M1p) was estimated by solving approximately the
exp ðGi –Fi –M1i –M2i Þ–1 =ðGi –Fi –M1i –M2i Þ;
Baranov’s catch equation (Baranov, 1918)
CTcE ¼ M1p=ðM1p where ir ¼ age of recruitment, 2 years, and imax ¼ maximum age
þ M2p þ FpÞ
used in calculations, 14 years. In the Y/R model, the year was split
1– exp –ðM1p þ M2p þ FpÞ TS:
into three periods: cormorant residing season, later open water
season, and winter.
Then, the study period specific M1p was expanded to the The perch population was divided into two sub-populations,
whole cormorant residing season using lengths (l) of periods
slow growing (mostly male), and fast growing (mostly female)
(Heikinheimo and Lehtonen, 2016, Supplementary S2). The
M1rs ¼ M1p rsl=pl:
individuals of the growth sample of an angler’s ice fishing
The natural mortality of perch is highly uncertain, but based catch were divided into two sub-samples based on the median
on general literature, it is negatively size dependent (Sogard, back-calculated length of 175 mm at age 5 years. The average
1997; Gislason et al., 2010). In fish stock assessment, a commonly lengths at different ages were estimated for both sub-samples
used value for instantaneous natural mortality (M) is 0.2 per year (Supplementary S2). Based on available data, the most likely
even in size classes targeted by fishing. According to Heibo et al. proportion of slow-growing sub-populations was considered to
(2005), the M of juvenile perch at the latitudes of our study area be 50% (pSPs).
ranged from 1.5 to 2.5 per year, and adult M from 0.2 to 0.8 per The growth rate in winter was assumed to be zero. The instan-
year. Horppila et al. (2010) reported average annual M from 0.59 taneous growth rate Gi was split into cormorant residing period
to 0.96 in unfished perch populations of small lakes. For the (crp) and later open water period (lop) based on their proportions
tagged perch in this study, an M (without cormorants) at 0.3– of the duration of the whole open water season.
1.0 per year was assumed and scaled for open water season The uncertainty about the average growth of both slow- and
(Table 2). The size classes of tagged perch are not targeted by fast-growing sub-populations was introduced into the model by
commercial fishing but may experience minor fishing mortality. multiplying the average age-specific lengths with random varia-
Thus, the instantaneous fishing mortality (F) value 0.1 per year bles ucGs and ucGf for slow- and fast-growing sub-populations,
was used in the analyses. respectively.
Table 2. The percentile distribution of equilibrium yield, iterated with input variables with random variability (for distributions see Table 1),
of perch for cormorants, other natural mortality and fishing and equilibrium spring perch biomass (kg per 1 000 kg of 2-year-old recruits) in
rectangle 55H1 with and without cormorants.
Cormorants present No cormorants
Yield for 10% 25% Median 75% 90% 10% 25% Median 75% 90%
Cormorants 138 194 285 413 565 0 0 0 0 0
Other natural mortality 1 884 2 029 2 200 2 376 2 550 2 241 2 401 2 589 2 783 2 986
Fishing yield 492 716 991 1 318 1 715 631 916 1 255 1 661 2 157
Total yield – – 3 476 – – – – 3 845 – –
Biomass 3 191 4 252 5 543 7 108 9 102 3 774 5 068 6 654 8 674 11 233
Median ¼ 50% percentile.6 L. Veneranta et al.
The cormorant predation mortality was assumed to be con- The proportional loss of fishing yield due to cormorant preda-
stant from age 3 onwards (M1a3) until perch reach the length tion was then estimated by
when predation rate starts to decline. It was suspected that the
average cormorant predation mortality in the perch population pLossYfpred ¼ ½ðYfcp =RÞ=ðYfSPn =RÞ–1 100%:
may be somewhat smaller than that for the PIT-tagged individu-
als. Therefore, the cormorant predation mortality from PIT-
tagging experiment was multiplied with a correction factor Uncertainty
(M1Corr) to gain M1a3 The uncertainty of the value of output variables was assessed us-
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ing @RISK-software (Palisade). Instead of using a single estimate
M1a3 ¼ M1Corr M1rs: for each of the input parameters above, a distribution was defined
M1l ¼ pM1 M1a3: for each of them (Table 1). This distribution encapsulates the
authors’ view of the likelihood that the true value of the parame-
After certain even higher length (LM1h) the cormorant mortal- ter is at a certain level. The likelihood is either based on the stan-
ity was set to 0. Based on previous studies (Salmi et al., 2015), the dard error of estimates or on expert knowledge (notes in
cormorant mortality for age 2 was assumed to be proportion Table 1). The output variables were iterated 10 000 times using
pM1 of M1a3 random values from these distributions for every input variable
simultaneously applying Latin hypercube sampling. Thus, for
M1a2 ¼ pM1 M1a3: simplicity, the possible covariance between the random values of
different input variables was not taken into account. The 5000
Also, for other natural mortality (M2), it was taken into ac-
values of the output variables were recorded. Their distribution
count that it may be somewhat lower in the population than that
then illustrates the uncertainty about the true value of the output
in PIT-tagged individuals. Therefore, in the Y/R model, the M2
variable. For each result, in addition to estimates based on aver-
was multiplied with a correction factor (M2Corr).
ages of input variables, also certain percentiles are given. Median
The other natural mortality (M2) was divided into different peri-
is the 50% percentile of the iterated results, the 25–75% percentile
ods of year (cormorant residing season, later open water period,
interval is the range within which the value of the output variable
winter) based on period length. It was assumed that natural mortal-
is with 50% likelihood (LR ¼ likelihood range) and the 10–90%
ity is highest for small individuals (M2s) and when perch reach cer- interval describes the 80% LR.
tain critical length (LM2), the level of mortality drops to Msl.
Both sub-populations, slow and fast growing, were assumed to Results
be target of minor fishing in tagging year, and only the fast- Tag recovery and mortality expansions
growing sub-population was assumed to grow to reach the main
In total, 178 tags (TR) out of 1937 (9.2%) were recovered from
target size of fishing. The fishing mortality (F) was split into dif-
colonies. The detection probability (pD) of tags estimates for test
ferent seasons based on the knowledge of seasonal distribution of transects varied from 76 to 100% depending on habitat and the
annual fishing effort from commercial fishery catch reports. habitat-weighted average estimate was 92.7% (SD 1.4%). Thus,
Value (Fs) was used for all small individuals and at all ages for the estimated number of tags deposited into the colonies (DT)
slow-growing sub-population. It was assumed that the individuals was 192 (178/0.927), proportionally (pDT) 9.9% (192/1937)
of the fast-growing sub-population recruit to gillnetting at length (Supplementary S1). Recovery rate with detection correction
(LFlg) and, from that length onwards, higher fishing mortality yields a minimum estimate of the number of consumed tags, as
(Flg) was used for individuals of the fast-growing sub-population. part of the tags will be regurgitated outside the breeding colonies.
The total yield was estimated based on proportions of recruits to Taking the literature-derived deposition probability 0.51 into
these sub-populations (pSub). account, the estimated number of consumed tags (Ctnc)
The proportion of sub-population in the r55H1 that was tar- increases to 376 (192/0.51; 95% CRI ¼ 274–565; 50% LR ¼
geted by cormorant predation (pSPcp) was estimated based on the 333–434; 80% LR ¼ 300–504). The estimate of the proportional
estimate of the typical maximum area that was covered by feeding cormorant predation mortality on tagged individuals (vtnc)
flight distance of the nesting cormorants. The most likely propor- during the breeding period is therefore 19% (376/1937; 95%
tion of perch target sub-population for cormorant predation in the CRI 14–29%, 50% LR ¼ 17–22%; 80% LR ¼ 16–26%).The con-
statistical rectangle 55H1 was estimated to be 42% of the whole sumption proportion of tagged perch by other than nesting cor-
perch population based on water area (Table 2), when predation morants was estimated to be 21% (16–26%, assumed likelihood
range was assumed to be 20–40 km. Then, the total fishing yield distribution in Table 1, calculations in Supplementary S3), thus
per one tonne of recruits of age 2 perch was estimated by weighing increasing the estimate of total proportion of tagged perch
the yields with proportions of sub-populations caught (vTc) to 25% f[376/(1–0.22)]/1937; 50% LR ¼ 22–28%;
80% LR ¼ 20–33%g.
Yfcp =R ¼ pSPcp YfSPcp =R þ ð1–pSPcpÞ YfSPn =R; In the study area, instantaneous cormorant predation mortal-
ity (M1p) was estimated to be 0.21 (median ¼ 0.23; 50% LR ¼
where SPn ¼ sub-population with no cormorant predation. The 0.16–0.31; 80% LR ¼ 0.13–0.39) during the study period and
yield YfSPn/R was calculated with the other parameters being simi- 0.31 (median ¼ 0.35; 50% LR ¼ 0.25–0.47; 80% LR ¼ 0.19–0.59)
lar as for sub-population with cormorant predation but setting during the whole residing period (M1rs) and instantaneous total
the cormorant predation (M1) mortality to zero. The total fishing mortality was 0.41 (median ¼ 0.44; 50% LR ¼ 0.36–0.53; 80%
yield of rectangle 55H1 assuming no cormorants present (Yfn/R) LR ¼ 0.30–0.63) and 0.63 (median ¼ 0.67; 50% LR ¼ 0.55–0.81;
was then simply equal to YfSPn/R. 80% LR ¼ 0.46–0.96), respectively. The estimated differenceCormorant predation on a coastal perch population 7
M2 no cormorants
70 %
0.3
with cormorants
M2
no cormorants F
51 % with cormorants M1 breeding populaon
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M1 non-breeding
no cormorants
34 %
M1 late summer
with cormorants
cormorant effect
0 20 40 60 80 100
Mortality %
M2 no cormorants
70 %
0.6
with cormorants
M2
no cormorants F
51 %
with cormorants M1 breeding populaon
M1 non-breeding
no cormorants
34 %
M1 late summer
with cormorants cormorant effect
0 20 40 60 80 100
Mortality %
M2 no cormorants
70 %
1.0
with cormorants
M2
no cormorants F
51 %
with cormorants M1 breeding populaon
M1 non-breeding
no cormorants
34 %
M1 late summer
with cormorants cormorant effect
0 20 40 60 80 100
Mortality %
Figure 3. Proportional (%) annual mortalities of PIT-tagged perch, with and without cormorants, at different assumed levels of other natural
mortality and deposition probability (51% is the suggested value, 34 and 70% the limits of the 95% credible range). M2 ¼ instantaneous other
natural mortality, F ¼ instantaneous fishing mortality, M1 breeding population ¼ the instantaneous mortality caused by breeding
cormorants based on tagging; M1 non-breeding ¼ the estimated instantaneous mortality caused by non-breeding individuals during breeding
time; M1 late summer ¼ the estimated instantaneous mortality after breeding time (August–September); cormorant effect ¼ the difference
between total proportional mortality with and without cormorants. All other variables were kept at their most probable values.
between total annual proportional mortality in the presence of estimated consumption by non-breeding individuals and con-
cormorants compared to the situation without cormorants (cor- sumption after breeding time in August–September doubles the
morant effect in Figure 3) is 17 percentage units at the value 0.3, proportional mortality estimated for the breeding population
and 10 units at the value 1.0 of instantaneous other natural mor- during the breeding period from tagging results.
tality, when estimated using the literature-derived deposition
probability (51%). The range is from 7 to 26 percentage units Potential effect of cormorant predation on equilibrium
with the most extreme likely values (34 and 70%) of deposition fishing yield of perch
probability. The estimated difference is largest when low level of In the cormorant predation target sub-population, the pre-
both other mortality and deposition probability is assumed. The dicted maximum proportional reduction in annual equilibrium8 L. Veneranta et al.
100 assuming no density-dependent compensation in population
parameters, the mean biomass of 2-year-old perch is predicted
90 to decrease by 17%.
80
Discussion
Cumulave likelihood
70 Cormorant predation mortality of perch and loss of
60 fishing yield
Based on this PIT tag recovery study, the cormorants induced
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50
mortality for perch in r55H1 and tagged perch were vulnerable to
40 predation by cormorants from all colonies, despite varying dis-
tances (3–19 km) from the releasing site (Figure 1 and
30
Supplementary S1). According to the Y/R analysis, the share of
20 cormorants of the total perch production and biomass of r55H1
would be 8%, if equilibrium state was assumed with no density-
10
dependent compensation, and the share of fishing 29%. The larg-
0 est part (63%) of the yield would be allocated to other natural
0 5 10 15 20 25 30 35 40 mortality. The level of other natural mortality steers the potential
Maximum loss of equilibrium yield, at least % impact of cormorants on perch stock. When compared to the sit-
uation without cormorants, assuming no density-dependent
Figure 4. The cumulative distribution function of proportional compensatory processes, the loss of fishing yield calculated for
(%) maximal non-compensated fishing yield loss due to the entire r55H1 was 10–33% at 80% confidence range.
cormorants based on Y/R model iterated with input variables For the sub-population residing within the effective predation
with random variability (for distributions see Table 1).
range in the feeding area of the tagged perch population, the high
Interpretation: with 90% probability (y-axis), the proportional loss
of yield is at least 10.2% (x-axis) but with only 10% (y-axis) non-compensated loss (32–67% at 80% confidence range) and in-
>33.5% (80% LR), etc. stantaneous mortality (0.31) indicate that cormorants at high
densities can have a significant negative impact on perch fishery,
yield for slow-growing perch is 39% and for fast-growing indi- when no immigration from surrounding areas was assumed to
viduals is 44%. Combined, the predicted maximum reduction take place. The r55H1 is particularly favourable for perch repro-
in yield in the whole target sub-population is 43% (median duction and cormorant predation, as it consists of numerous
49%, 50% LR ¼ 39–59; 80% LR ¼ 32–67%). Extension to the islets for nesting and the proportion of shallow water is high.
scale of the total perch population in r55H1 sets the estimated Cormorants favour shallow areas (Cormorant predation on a coastal perch population 9
The cormorants are generalist predators targeting the prey that Tagging, recoveries, detection, and deposition
are most abundant and easily available (Lehikoinen et al., 2011; Perch catches of wire trap or fyke net were not sampled at tagging
Salmi et al., 2015). Due to this positive dependence of predation and, thus, the sex and age distribution is not known. Wire trap is
rate on the prey density, the perch mortality caused by cormor- considered to catch more males than females during spawning
ants and other predators is expected to decline with declining period (Olin et al., 2017), probably due to differing size structure
prey density. All these density-dependent feedbacks are compen- and activity levels between the two sexes. The aggregation of fish
satory, thereby mitigating the effect of cormorant predation on at spawning time might increase the vulnerability to predators, as
population density and consequently on equilibrium yield. Thus, in general the natural mortality is density dependent (Gislason
Downloaded from https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsaa124/5890397 by guest on 13 October 2020
the results of the Y/R analysis must be interpreted as the maximal et al., 2010). According to diet studies in the Archipelago Sea, the
impact. share of perch in the cormorant diet was highest in spring and in
According to the sensitivity analysis, the Y/R-modelling results autumn, although variation between colonies and years was re-
were mostly affected by uncertainty of the estimated proportion markable (Salmi et al., 2013). In our study, the tags were not
of the target sub-population of the total perch population in screened during the breeding period, and thus, the temporal ac-
r55H1. The uncertainty covers the value distribution of potential cumulation of tags in colonies and most intensive cormorant pre-
target area, which is based on local cormorant observations, dation period is not known.
knowledge on perch reproduction areas, and typical feeding range The small PIT tag was not expected to negatively affect the
of perch from spawning site. A wide likelihood distribution for condition of perch (ZakeR s et al., 2017; see also Ro_zy nski et al.,
the distribution of tagged perch in tagged sub-population was 2017). The perch used for tagging were visually selected from
used to cover the uncertainty of the parameter. Based on litera- catch to minimize capture-induced mortality. Yet, some post-
ture and local observations, the cormorants could consume tagging mortality (2%) was observed in the smallest tagged perch
tagged perch in the target population either in lower than average and, to cover this, slightly higher cormorant and other natural
proportion (DistCorr < 1) or on the contrary, with higher pro- mortality for tagged fish was assumed for tagged fish in compari-
portion than average (DistCorr > 1, aggregation of predation on son to population in Y/R modelling. The effect of tag loss due to
marked individuals). Consequently the results were quite sensi- expulsion was considered minor, as in a separate 7-month test in
tive to this input variable. The tag data covered only 1 year and tanks 0.1% of tags (n ¼ 737) were expelled from perch in the size
repeating the experiment over more years would enable estimat- range of this study (Natural Resources Institute Finland, L.
ing the average cormorant effect, taking into account the varying Härkönen, pers. comment).
population sizes and structures of perch stocks. The probability of recovering a fish tag on a bird colony
The Y/R-model-based estimate of cormorant effect on perch depends on (i) the probability that a tagged fish is consumed, (ii)
stock is affected by other sources of natural mortality. Data from the probability that the tag is deposited in the breeding colonies
prior to scanning, and (iii) the probability that the tag is detected
unfished perch populations (Heibo et al.,, 2005; Horppila et al.,
by researchers. Evans et al. (2012, 2016), Osterback et al. (2013),
2010) and size distributions of perch suggest that most likely val-
and Hostetter et al. (2015) have discussed the factors affecting de-
ues for annual instantaneous natural mortality without cormor-
tection and deposition thoroughly. The detection rate of tags in
ants are at level 0.5–0.6 for the perch size group typical in
this study was higher than in comparable PIT-tagging studies
cormorant diet (median size 150 mm in Salmi et al., 2015), but
(Skov et al., 2014; Hostetter et al., 2015; Ovegård et al., 2017), al-
due to the large uncertainty of natural mortality in small fish, the
though this study was carried out using 12-mm tags that have
much wider range was used here.
lower detection distance than the often used 23-mm tags. Lowest
In the slow-growing perch sub-population, the majority of
detection probabilities were observed in locations where the solid
cormorant predation occurs at ages 2–7 years, but the fast-
ground was difficult to reach with the antenna. The higher tree
growing population grows beyond the cormorant prey size spec-
nests in colonies B and C were not scanned, but likely the propor-
trum after age 5 years. The sizes of perch favoured by cormorants
tion of tags in these was low as only a minor proportion of tags
(Salmi et al., 2015) are smaller than sizes of those targeted by fish- was found inside nests on the ground level. Random tag distribu-
ing. The diet and prey size of cormorants varies during the breed- tion in colonies also indicates that tagged perch were preyed
ing season (Salmi et al., 2013; Salmi et al., 2015), but as in this upon by entire cormorant population, although in two-part col-
study, diet data were not available, it was assumed that perch is ony A (Supplementary S1) the tags were concentrated in the
consumed as prey at constant rate during the whole residing sea- larger part of the colony area.
son of cormorants. The slow-growing part of perch population, The deposition probability used in this study was estimated for
consisting mainly of males may not recruit to fishing at all double crested cormorant (Hostetter et al., 2015), but it is near to
(threshold value 230 mm, 220–250 mm) and may not grow over cormorant deposition and tag detection probabilities estimated
the preferred prey size of cormorants (threshold value 220 mm, by Boel (2012) (ref. in Jepsen et al., 2018). The sensitivity analysis
210–230 mm) during their life span. Slow-growing individuals indicated that the uncertainty of the deposition probability
have a higher probability to be preyed upon because of staying a strongly affected the values of the output variables. The roosting
longer period in the suitable size for predators (Craig et al., places vary during summer and were not screened for tags in this
2006). Predation from piscivorous fishes, such as pike, pikeperch study. The deposition values were assumed not to include the
or larger perch, likely forms a major part of other natural mortal- proportion of tags eaten by young or non-breeding cormorants
ity also in r55H1, but tags taken by predator fish were not that mainly regurgitate pellets on roosting locations outside colo-
screened. As indicated by natural mortality estimates (Rose et al., nies. The recovered tags in this study therefore represent the pe-
2001; Heibo et al., 2005), the highest predation risk is for youn- riod from tagging to the start of migration, when cormorants
gest and small-sized fish. leave the colonies. The tagged perch consumption of migrating10 L. Veneranta et al.
cormorants from northern colonies in the Bothnian Bay area was Smeds, and Jaakko Miettinen for perch tagging, and Ari Leskelä
not accounted for as there were no data on their presence and for project management. Pirkko Söderkultalahti was helpful with
numbers in r55H1. The number of cormorants and fish con- fishery datasets. Ornithologists Antti J. Lind, Jouni Kannonlahti,
sumption is on its highest when the autumn migration period and Aleksi Lehikoinen had valuable bird observations. Thanks for
starts (Gremillet et al., 1995) in the beginning of August. Laura Härkönen for providing the dataset on tag loss test con-
ducted in Kainuu Fisheries Research Station.
Cormorant effect on local fishery
The vast areas suitable for perch reproduction probably set the Funding
Downloaded from https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsaa124/5890397 by guest on 13 October 2020
share of perch in cormorant diet to a high level in the study area. The research was financed by European Maritime and Fisheries
The commercial perch catches in r55H1 have increased until the Fund Operational Programme for Finland 2014–2020 and
major establishment of cormorants but decreased in recent years, University of Jyväskylä.
and simultaneously, the commercial gillnet fishing effort has de-
creased. Inter-annual variability in perch catches in coastal waters
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