Antarctic krill fishery effects over penguin populations under adverse climate conditions: Implications for the management of fishing practices

Abstract

Fast climate changes in the western Antarctic Peninsula are reducing krill density, which along with increased fishing activities in recent decades, may have had synergistic effects on penguin populations. We tested that assumption by crossing data on fishing activities and Southern Annular Mode (an indicator of climate change in Antarctica) with penguin population data. Increases in fishing catch during the nonbreeding period were likely to result in impacts on both chinstrap ( Pygoscelis antarcticus ) and gentoo ( P. papua ) populations. Catches and climate change together elevated the probability of negative population growth rates: very high fishing catch on years with warm winters and low sea ice (associated with negative Southern Annular Mode values) implied a decrease in population size in the following year. The current management of krill fishery in the Southern Ocean takes into account an arbitrary and fixed catch limit that does not reflect the variability of the krill population under effects of climate change, therefore affecting penguin populations when the environmental conditions were not favorable.

Full text

Journal: Ambio
Print ISSN: 0044-7447
Online ISSN: 1654-7209
DOI: 10.1007/s13280-020-01386-w
Antarctic krill fishery effects over penguin populations under adverse climate conditions: implications
for the management of fishing practices
Lucas Krüger
1
*, Magdalena F. Huerta
2
, Francisco Santa Cruz
1
, César A. Cárdenas
1
1
Departamento Científico, Instituto Antártico Chileno
Plaza Muñoz Gamero 1055, Punta Arenas, Chile
2
Centro de Humedales Rio Cruces, Universidad Austral de Chile, Valdivia, Chile.
*Corresponding author: lkruger@inach.cl
MFH magdalenahuerta@gmail.com ; FSC fsantacruz@inach.cl ; CC ccardenas@inach.cl
Abstract
Fast climate changes in the western Antarctic Peninsula are reducing krill density, which along
with increased fishing activities in recent decades, may have had synergistic effects on penguin
populations. We tested that assumption by crossing data on fishing activities and Southern Annular
Mode (an indicator of climate change in Antarctica) with penguin population data. Increases in fishing
catch during the non-breeding period were likely to result in impacts on both chinstrap (Pygoscelis
antarcticus) and gentoo (P. papua) populations. Catches and climate change together elevated the
probability of negative population growth rates: very high fishing catch on years with warm winters and
low sea ice (associated with negative Southern Annular Mode values) implied a decrease in population
size in the following year. The current management of krill fishery in the Southern Ocean takes into
account an arbitrary and fixed catch limit that does not reflect the variability of the krill population
under effects of climate change, therefore affecting penguin populations when the environmental
conditions were not favorable.
Keywords: Antarctic Peninsula, chinstrap penguin, gentoo penguin, population growth rate,
southern annular mode

Introduction
The western Antarctic peninsula (WAP) is one of the areas most affected by climate change. Fast
warming in the last decades (Cook et al. 2016; Moffat and Meredith 2018) and the southward input of
warmer waters, are decreasing the seasonal sea-ice extent and duration (Stammerjohn et al. 2008;
Moffat and Meredith 2018). Climate change effects have also been observed in different macro-scale
atmospheric phenomena, such as the Southern Oscillation Index (SOI) and the Southern Annular Mode
(SAM) (Stammerjohn et al. 2008; Moffat and Meredith 2018). Specifically, warming in the WAP has been
related to strengthening a positive trend in the SAM, which describes atmospheric circulation patterns
associated to the belt of westerly wind surrounding Antarctica (Clem et al. 2016). The SAM has a strong
influence in the inter-annual variability around the WAP, driving changes in sea-ice formation and
melting, and the injection of meteoric water (combination of glacial discharge and precipitation) to the
Southern Ocean (Moffat and Meredith 2018).
Current climate change has had significant effects in the Antarctic ecosystem, particularly for
sea-ice dependent species, such as the Antarctic krill Euphausia superba. Several studies have shown
dramatic changes in Antarctic krill populations, including distributional range contraction (Atkinson et al.
2019), size reduction (Tarling et al. 2016), decreased recruitment (Atkinson et al. 2019; Perry et al. 2019)
and decreased density (Atkinson et al. 2009; Flores et al. 2012). Variability in regional sea-ice has been
identified as an important limitation for krill abundance (Flores et al. 2012). Sea ice cover can affect the
survival of krill larvae, due to their reliance on sea ice to feed and for shelter during winter (Meyer
2012). Predicted future environmental changes are expected to produce further changes associated
with seawater warming and reduced sea-ice cover having an impact on krill distribution and biomass
(Piñones and Fedorov 2016; Atkinson et al. 2019).
Krill is a keystone species in the Antarctic marine food web (Hofmann et al. 2011; Ballerini et al.
2014) and hence, it is expected that any negative effects on krill will not only affect its direct predators
but also will produce a cascade effect on the entire ecosystem. Whether climate-based reductions in
krill density continue at the predicted rate (Atkinson et al. 2009), it is expected that krill predator
populations will follow a steep decline (Trathan and Reid 2009). There have been widespread decreases
of penguin populations over the Antarctic Peninsula with climate change recognized as the main driver
(Lynch et al. 2012; Casanovas et al. 2015). The population trends in these species seems to be related to
a reduction in sea-ice cover and krill abundance (Forcada et al. 2006; Trivelpiece et al. 2011). Some
authors have supported the hypothesis that there is a direct relationship between the sea ice variations
and the penguin abundance, including contrasting trends for “ice-loving” and “ice-avoiding” species
(Forcada et al. 2006). In this sense, it would be expected that sea-ice retreat and the resulting access to
ice-free foraging areas should benefit both chinstrap (Pygoscelis antarcticus) and gentoo (P. papua)
penguins, which have been identified as “ice-avoiding” species. However, although this is consistent
with the Gentoo penguin global population trends (yet local decreases of Gentoo Penguin abundance
exist in the South Shetland Islands; i.e. Petry et al. 2016; Petry et al. 2018) the evidence pointing to a
decline of chinstrap penguin populations throughout the WAP suggests that reduction in krill availability
could be playing a critical role to explain the population dynamics of this species (Trivelpiece et al. 2011;
Lima and Estay 2013).

The Antarctic krill is target of an important fishery in the Southern Ocean, occurring mainly in
the Atlantic sector (FAO statistical subareas 48.1, 48.2 and 48.3). The Commission for the Conservation
of Marine Living Resources (CCAMLR) is the international organization responsible for the management
of the krill fishery and has successfully managed fishery based on a precautionary approach since its
creation in the early 1980s (Constable 2011). In recent years, there has been increasing concerns on how
climate-based changes and the current concentrated behavior of the krill fishery (Santa Cruz et al. 2018;
Krüger 2019) can affect synergistically the penguin colonies over the WAP. Moreover, krill catches have
been reaching values like those recorded in the 1990s before the adoption of a fixed catch (CCAMLR
2018). Although catches are considered to be low compared to the krill abundance
(catch limit is < 1% of
the total estimated biomass ≈ 60 million tones on Antarctic Peninsula alone, CCAMLR 2019), catch is
expected to keep increasing in the future with the development of new technologies and changes in
both fishing technologies and Antarctic environment. Contrasting to what fisheries did back in the
1990s, now the whole krill catches are concentrated on relatively small spots on the WAP and the South
Orkney Islands (Santa Cruz et al. 2018; Krüger 2019). While CCAMLR performance has been considered a
successfully sustainable managed practice (Nicol et al. 2012), under the current climate changes it is
likely that this may change (Hill et al. 2016) as signs of krill fishery decreasing performance of penguins
are becoming evident (Watters et al. 2020). This study aimed to evaluate the risk of the krill fishery to
populations of Pygoscelid penguins, testing if the population changes are proportional to changes in the
distribution of catches in WAP, and their synergistic relation with climate variability. We used 38 years
of fishing data to evaluate risk for both chinstrap and gentoo penguins and used mixed models to test
how population growth could have responded to the fishing pressure under contrasting Southern
Annular Mode conditions.
Methods
Penguin population data
All data on populations of chinstrap (Pygoscelis antarcticus) and gentoo (P. papua) penguins
breeding in the WAP area available at the Mapping Application for Penguin Populations and Projected
Dynamics MAPPPD (penguinmap.com, Humphries et al. 2017) between 1980 and 2017 were
downloaded (Fig. 1, Chinstrap = 197 colonies, Gentoo = 78 colonies). MAPPPD is a penguin population
databank, which puts together all available information about population counts of penguins on their
breeding colonies. Counts include different type of data: breeding pairs, adults and chicks. Only pair
counts made in November and December, matching the early breeding season and providing a better
picture of actual population size, were used. Temporal variation in colony-level population growth rate
was expressed as:
λ
std
= ((n
b
/n
a
)/years
b-a
)-1
where n is the number of breeding pairs counted in November or December of a given year (b)
and the number of breeding pairs counted in a previous year (a), divided by the number of years in
between b and a. This procedure allowed us to deal with differences in sampling size by smoothing any
too steep value resulting from a too large temporal gap in data, at the same time, providing population-
level temporal variability of each penguin colony. From this value then was subtracted 1, so that the
result varied from -1 (population extinction) to ∞, with posiQve values represenQng populaQon increase.

Each colony was classified based on CCAMLR Small-Scale Management Units: Elephant Island, Drake
East, Drake West, Bransfield East, Bransfield West and Antarctic Peninsula West, which we will refer as
Gerlache Strait because fishing in this area concentrated within the strait (Fig. 1). Small-Scale
Management Units are zones proposed in order to be a spatial tool for local monitoring of the krill
fishery and krill predators, and devised for spatially subdividing the krill catch limits (Constable and Nicol
2002). Most penguin colonies did not have data on the whole 38 years considered; majority of colonies
had less than 10 counts while 38 chinstrap and 45 gentoo colonies had at least two counts (enough for
calculating the λ
std
) (Fig. S1).
Krill fishery data
Haul-by-haul data of the fleet operations was obtained from the CCAMLR Secretariat database
for the period between 1980 and 2017 (38 years). The accumulated catch within a 30-km radius of each
colony was used to evaluate the risk of exposition of each colony to the changes in catch distribution.
During breeding season (when counts were made) foraging of pygoscelid penguins is more probable
within 30kms of the colonies (Warwick-Evans et al. 2018). We therefore assumed this to be the distance
where krill availability would be more important during key periods of the year cycle and competition
with fisheries would be more impacting. Each fishing event was classified based on important period of
the penguin intra-annual life cycle: chick-rearing (January to March), non-breeding (April to September)
and early breeding (October to December). Catch was accumulated within those periods for each year
(Fig. S2, Table S1) in order to better describe the periods when penguin are more at risk to experience
impacts from fishery, but catch was accumulated throughout the whole year to test statistically the
response of populations (below)
Climate data
The Southern Annular Mode SAM is the main large-scale pressure system driving climate in
Antarctica (Kwok and Comiso 2002; Doddridge and Marshall 2017). SAM is defined as the difference of
the normalized zonally mean sea level pressure of 40°S and 65°S (see Gong and Wang 1999 for details).
As SAM indicates differences, it can have negative and positive values: negative values mean air
pressure in Antarctic (65°S) is higher than in the subantarctic (40°S); positive values mean air pressure is
higher in subantarctic (40°S) than in Antarctic (65°S). Pressure differences reflects the large-scale
movements of air masses. By examining SAM values is possible to infer whether warmer currents from
the north intruded areas further south, therefore, SAM can accurately indicate trends of sea ice and
temperature anomalies in Antarctica (Marshall and Bracegirdle 2014; Doddridge and Marshall 2017).
Penguins (Forcada et al. 2006) and Antarctic krill (Flores et al. 2012; Meyer 2012) are knowingly
responsive to abrupt changes on temperature and ice conditions during winter. SAM monthly data was
downloaded from NOAA Earth System Research Laboratory ESRL (esrl.noaa.gov). Data on Fractional Sea
Ice Cover, Surface Level Temperature and Open Water Sensible Heat Flux were downloaded from NASA
Giovanni data browser (giovanni.gsfc.nasa.gov) per month.

Figure 1. Distribution of chinstrap Pygoscelis antarcticus (a) and gentoo P. papua (b) penguins breeding
colonies (white crosses) along the western Antarctic Peninsula, overlapped with the Antarctic krill
accumulated fishing catch. Data on fishing catch represents all the catch in the area accumulated
between 1980 and 2017, it is, all the krill that was extracted from a given spatial cell in 38 years. Penguin
data from MAPPPD (penguinmap.org; Humphries et al. 2017).

Penguins, fishery, and climate
Considering the high correlation of the climate variables in WAP (Fig. S3), and correlation of
climate variables with SAM variability with a temporal lag from zero to three months (Fig 2), we used
SAM during the non-breeding period
together with accumulated catch within each year in a binomial
Generalized Linear Mixed Model using the ‘lmerTest’ R package (Kuznetsova et al. 2018) and the ‘sjPlot’
R package (Lüdecke et al. 2019) to plot models:
binλ
std
~
catch
y
* SAM + (1| colony id)
where binλ
std
is a binary estimate of the standardized growth rate λ
std
(positive values=0,
negative values =1) understood as the probability of population decreasing in a given year. Catch
y
is the
accumulated year krill catch, SAM is the southern annular mode during winter (non-breeding season).
We used mixed models which allows to control for the effects of lack of independence and sample size
differences within the structure of the data. We used the colony ID as a random term in the formula
accounting for the colony-level differences on the intercept of the response to the explanatory
variables. The effect of the random term was tested with a Likelihood Ratio Test using the function
‘ranova’. All data processing and analysis were done in R environment (R Development Core Team 2014)
using ‘raster’ (Hijmans 2013), ‘plyr’ (Wickham 2020) and ‘ggplot2’ (Wickham and Chang 2015) packages.
Maps were produced using ArcGis 10.4.
Figure 2. Lagged regression model (cross correlation function CCF) testing the temporal response of
Fractional Sea Ice Cover (FSIC), Open Water Sensible Heat Flux (HFLUX) and Sea Level Air Temperature
(TLML) at the Western Antarctic Peninsula to the variation of the Southern Annular Mode (SAM). Lag
interval is in months. Dashed blue line indicates where the correlation is significant at the P<0.05 level.
Analysis was done in the ‘astsa’ R package (Stoffer 2008).

Results
Changes of spatial catches distribution
Krill catches within the 30 km radius from colonies of both species occurred predominantly
during chick-rearing and non-breeding periods (Fig. 3). Although, catches after the mid-1990s decreased
or remained stable in Elephant Island and Drake Passage sectors, respectively (Fig. 2); catches in the
Bransfield Strait increased near colonies of both penguin species (Fig. 3). During the last decade, fleets
started to operate more intensively in the Gerlache Strait, increasing catches during both chick-rearing
and non-breeding periods near chinstrap colonies (Fig. 3).
Figure 3. Seasonal accumulated catch within 30-km radius around each breeding colony of chinstrap
(Pygoscelis antarcticus) and gentoo (P. papua) penguins during Chick Rearing CR (January-March), Non-
breeding NBR (April-September) and Early Breeding EBR (October-December) periods classified
according to Small Scale Management Unities SSMUs: Elephant Island, Drake Passage East and West,
Bransfield Strait East and West ; Gerlache Strait. See also Figure 1.

Trend of the penguin populations
Most of the λ
std
values for chinstrap penguins were negative (58.78% of cases) in the WAP (Fig.
S4). Gentoo penguins presented a mean growth trend bordering the stability (Fig. S4) with 50.72% of
negative cases of λ
std
.
Probability of chinstrap population decrease was related to catch
y
(F
155,3
=2.96, z=2.65, P=0.008)
and to the interaction catch
y
* SAM (F
155,3
=1.72, z=-1.63, P=0.055) , but not to SAM alone (F
155,3
=1.17,
z=1.50, P=0.133). Random factor was not significant for chinstrap penguins (LRT=0.15, P=0.910),
meaning population-level response was homogeneous throughout the WAP. For Gentoo populations,
the probability of decrease was marginally related to catch
y
(F
251,3
=0.76, z=1.47, P=0.090) and catch
y
*
SAM interaction (F
251,3
=1.70, z=-1.75, P=0.085), but not to SAM alone (F
251,3
=0.11, z=0.74, P=0.461), and
random effects were significant (LRT=5.95, P=0.014), therefore population-level variability was
important in the response of Gentoo penguins to fishing catches (Fig. S5). For both chinstrap (Fig. 4a)
and Gentoo (Fig. 4b) probability of decrease in a given year (λ
std
) was constant with increasing fishing
catch during years of positive SAM, but increased with increasing catch during years of negative SAM. In
extreme negative SAM, fishing catches above ≈5 thousand tonnes meant a mean estimated probability
of decrease above 75% for both species (Fig. 4).
Figure 4. Estimated probability (trend lines) ±
standard deviation (shaded area) of chinstrap
Pygoscelis antarcticus (a) and gentoo P. papua (b)
penguins having a negative standardized
population growth rate as a response to fishing
catch within 30-km radius around colonies during
years of contrasting Southern Annular Mode SAM
values: negative (solid red line) and positive
(dashed blue line). The ‘sjPlot’ R package through
the function ‘plot_model’ allows visualizing the
estimated mean response to the extreme values of
the interacting variable, in this case maximum and
minimum SAM values.

Discussion
In the last two decades, krill catches have increased consistently near penguin colonies in the
Bransfield and Gerlache straits during chick-rearing and non-breeding periods, whereas in the Drake
Passage and Elephant Islands catches have remained stable or mostly decreasing. These patterns reflect
the southward expansion experienced by the fleet during the last decade, mentioned by previous works
(Nicol et al. 2012; Santa Cruz et al. 2018; Krüger 2019). Our findings also indicated that the relation
between the standardized penguin growth rate and cumulative fishing catch was contrasting depending
on SAM conditions. In this manner, in positive SAM values, the range of the probability of decreasing
varied largely, while in negative SAM values, there was a consistent rise in the probability of decreasing
for both chinstrap and gentoo penguins when fishing catches near colonies was very high (>≈5000 t).
Moreover, the additional effect over krill recruitment caused by the decrease in sea ice coverage, due to
the key role played by this factor for the development of krill larvae, coupled with the increase in krill
catches in the areas near penguin colonies could generate a much more vulnerable scenario for these
species during the breeding season (i.e. Trivelpiece et al. 2011). Recovery of baleen whale populations
also have been suggested out as a potential explanation for current observed trends in penguin
populations, as whaling in the last century would have allowed for an increase in krill availability, the
krill surplus hypothesis, but so far, studies dealing with that hypothesis did not find solid evidences and
suggested environmental variability as more important to changes in krill biomass (Fraser et al. 1992;
Surma et al. 2014). Previous studies mentioned potential impacts of krill fishery on penguin populations
(i.e. Trivelpiece et al. 2011), and a recent paper (Watters et al. 2020) reached conclusions similar to ours
by applying a different method and evaluating population data at two sites. Our study, to our best
knowledge, is the first to reveal the effect of climate change and krill fishery on penguin population
declines looking explicitly at multi-population trends on the scale of the whole Antarctic Peninsula. It is
worth mentioning that a previous work by Che-Castaldo et al. (2017) tested whether krill fishery could
have an effect on Adelie penguin population dynamics; however, the spatial scale of the fishing data
used was too coarse to allow detecting strong local effects.
Considering what it is mentioned above, the next step from now on would be to move towards
the implementation of a new krill fishery management strategy (see further below), which could
consider new elements that are not currently included. Elements such as regular biomass estimations
and identification of the spatial scales of the impact of krill fishery on penguins, thus, identifying where
higher catches would have higher impact on penguins and other predators, and distributing catches
accordingly.
Risk of competition with krill fishery
Recent changes of the spatial distribution of the krill fishing fleet in the WAP (Santa Cruz et al.
2018; Trathan et al. 2018; Krüger 2019) can be linked to the general trend of decreasing winter sea-ice
extent and duration that has been reported for the area (Parkinson 2019). Increased ice-free conditions
allowing trawlers to continue their activities after the end of the Austral summer (Nicol et al. 2012)
explains the increasing catches near penguin colonies during the non-breeding season. While sensibility
of penguins to climate change is well known (Casanovas et al. 2015; Che-Castaldo et al. 2017), the
interaction of climate change with increasing catches may have a synergistic detrimental effect on

penguins. According to Doddridge and Marshall (2017), negative SAM anomalies precede higher
temperatures, low sea-ice and low krill productivity in the Southern Ocean, particularly in Antarctic
Peninsula, with effects being cascaded throughout the whole food web (Dahood et al. 2019).
Carry-over effects of the potential competition of penguins with the krill fishery during the non-
breeding season are still unknown, but given our results, cumulative catches within 30 km from colonies
seemed to impact negatively both Pygoscelis species in years when sea-ice was low. Although chinstrap
penguins tend to disperse from breeding grounds, it is evident that there is a large variability and part of
the population may remain nearby the breeding area (Trivelpiece et al. 2007; Hinke et al. 2015; Hinke et
al. 2017). On the other hand, Gentoo penguins tend to remain closer to the breeding grounds during
winter (Wilson et al. 1998; Thiebot et al. 2011; Hinke et al. 2017).
Winter distribution of fledgling and
immature stages of both penguin species is still unknown for most populations, but evidence suggests
penguin recruitment is an important population parameter explaining penguin population decrease in
the WAP, which has been also linked to decreased recruitment of krill (Hinke et al. 2007; Trivelpiece et
al. 2011; Atkinson et al. 2019). Penguin populations have been potentially affected by the krill fishery
(this study, Watters et al. 2020) in zones where intense fishing occurred in recent years (i.e. Santa Cruz
et al. 2018) which overlapped with important krill nursery and krill recruitment areas (Perry et al. 2019)
in the Bransfield and Gerlache straits.
We propose two hypotheses to explain our findings. Firstly, krill densities are declining and their
distributions are contracting southward (Atkinson et al. 2019), therefore, increased fishing activities in
areas with reduced krill availability increase competition between penguins and fishery, particularly in
periods of low productivity. Secondly, increased catches on years with low krill productivity decrease
availability of krill to penguin populations. Krill population rises and falls from year to year, with
potential recruitment cycles lasting five to six years (Reiss et al. 2008), mostly driven by food
competition (Ryabov et al. 2017; Walsh et al. 2020). Summer melting of sea ice accumulated during
winter can boost local productivity in the WAP (Eveleth et al. 2017b; Eveleth et al. 2017a), therefore,
during years of negative SAM (when winter sea ice cover is lower) krill could experience population
limitation due to low availability of food, and consequently low recruitment (i.e. Flores et al. 2012;
Meyer 2012). Under this scenario, increased fishing catches could mean a krill shortage for penguins in
the next breeding season if the krill caught is not recovered. The fishery would be extracting biomass
cumulatively from the same population before new adults arrive, temporarily depleting resources for
penguins.
Management consequences
CCAMLR manages the krill fishery following the principle of rational use of the marine living
resources, which implies both the precautionary and ecosystem-based approach. Since 1991 CCAMLR
has established catch limits for area 48 (WAP and Southern Scotia Arc), oftentimes updated depending
on the availability of new biomass estimations. So far, the current catch limit for area 48 is 620,000
tonnes (known as the trigger level, a value adopted entirely based on the previous highest catches).
Thereafter, trying to avoid potential concentration of the catches in small areas, and based on the
biomass distribution, the trigger level was split proportionally among the subareas, setting 155,000
tones for area 48.1 and 279,000 tones for subarea 48.2 (further details of this process see Nicol and

Foster 2016). Unfortunately, the catch limit is fixed and does not vary according to the variability of the
krill population, being particularly problematic in years of low productivity (i.e. environmentally-
impacted krill recruitment, Thorpe et al. 2019). Catch limits should be established based on seasonal krill
abundance estimates that also must include predator demands. For instance, CCAMLR is pursuing a
feedback management of krill fishery that would be achieved through an ecological risk assessment (i.e.
Trathan et al. 2018; Warwick-Evans et al. 2018. Lowther et al. in review) quantifying the amount of krill
required from top-predators on a spatial grid; fisheries would use that information plus continuously
updated information on krill density to guide when, where, and how much they should fish. Our results
support the need for implementing such kind of management approach, meaning that in years when
krill density is lower, catch limit should be lower than the currently being used. In addition, an increasing
concern is that precautionary catch limit was calculated for a regional scale, but our results as many
others (Hill et al. 2016; CCAMLR 2018; Santa Cruz et al. 2018; Krüger 2019) demonstrated that the
fishery is not a randomly distributed activity, rather catches occurs in a highly concentrated manner,
especially in Bransfield and Gerlache straits. This, coupled with the new evidences of the impacts
produced by climate change and krill fishery on penguin populations (Watters et al. 2020, this study),
creates concerns about whether the precautionary catch limit is still precautionary under the current
scenario.
CCAMLR is pursuing the implementation of a Marine Protected Area network as a tool to
protect Antarctic marine ecosystems and manage human activities, including fisheries (Brooks et al.
2016; Coetzee et al. 2017). In this regard, a large MPA in the Domain 1 (WAP) was proposed recently by
Argentina and Chile parties (
https://www.ccamlr.org/en/ccamlr-38/25-rev-1),
aiming to provide extra
protection for several conservation objectives, including krill; however, despite many countries have
strongly supported the proposal, a few have expressed their concerns voting against its adoption:
since
decision-making in the Commission is based on consensus, the proposal has not been adopted. The
current proposal includes general protection zones that precisely encompass the major locations of the
synergistically climate and fishery affected penguin colonies (and other krill-predators). Particularly,
around SOI, South Shetland Islands and the Gerlache strait, where evidences support that closures to
krill fishing would be beneficial for krill predators if fishing pressure increases (Klein and Watters 2020).
Examples of MPAs that allowed for increases in stocks of harvested species are abundant (Duffy et al.
2016; Chirico et al. 2017; Sala and Giakoumi 2018), even producing better fishing yields (Lynham et al.
2020). Therefore, it is a strategy with potential to not only protect top-predators and its resources, but
also to allow for a long-term fishery in the WAP.
CCAMLR has recognized the need for a more precautionary and dynamic approach taking into
account contemporary changes in the WAP, and its currently working on the development of a new
approach of the management of the krill fishery (CCAMLR 2019). Evidence such as the presented here
along with other new research and new monitoring plans will be crucial for implementation of a more
dynamic management strategy of the krill fishery that ensures the protection of krill dependent
predator under a changing environment in this unique ecosystem.

Acknowledgements
The authors would like to thank the CCAMLR Secretariat and co-originators/owners for
providing data access on krill fishery. The authors acknowledge the important contribution of the
MAPPPD resources towards the increasing knowledge of penguin species ecology. This study benefited
from the “Marine Protected Areas program” of the Instituto Antártico Chileno.
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Figure S1. Summary of number of breeding pair counts for chinstrap (Pygoscelis antarctius) and
gentoo (P. papua) penguins breeding along the western Antarctic Peninsula.

Figure S2. Example of variability of Antarctic Krill (
Euphausia superba
) fishing catch and sea
ice cover overlapped by the 30km radius around penguin breeding colonies in the Western
Antarctic Peninsula during the year 2010. Data accumulated by period of Pygoscelid penguins
breeding cycle, approximately as: Chick-Rearing (January to March), Non-breeding (April to
September) and Early Breeding (October to December).

Figure S3. Seasonal frequency distribution of Fractional Sea Ice Cover (FRSIC), Open Water
Sensible Heat Flux (HFLUX) and Sea Level Air Temperature (TLML) in the Western Antarctic
Peninsula, and Pearson Correlation (Cor) for each pair of variables.

Figure S4. Temporal variation of the standardized population growth rate for chinstrap
(
Pygoscelis antarctius
) and gentoo (
P. papua
) penguins breeding along the western Antarctic
Peninsula. Data is for several populations in time, therefore populations are repeated in time, and
this image does not reflect global trends of the species in the Western Antarctic Peninsula. The
black solid line is a linear trend and the dashed grey is a loess fit line.

Figure S5. Random effect of a Generalized Linear Mixed Model testing the probability of gentoo
penguin population decrease between years as a response to Antarctic Krill (
Euphausia superba
)
fishing and winter Southern Annular Mode SAM in the Western Antarctic Peninsula. Colony
level intercept ± standard deviation. Likelihood ratio test LRT=5.96, P=0.015. For colony names
check the MAPPPD application (http://www.penguinmap.com/), and colony geographical
positions on Supp. Table 1.