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Whales maintained a high abundance of krill; both are ecosystem engineers in the Southern Ocean

October 2014
Marine Ecology Progress Series 513:51-69

Authors:

University of Oxford

Krill abundance was predicted to rise after the end of commercial whaling in the Southern Ocean due to the release of predatory pressure from 2 million whales that were killed between 1915 and 1970, but contrary to expectations, there has been a substantial decline in abundance of krill since the end of whaling. I presented a model 7 yr ago which explained how krill behaviour, in response to the threat of predation by whales, may provide an answer to this paradox. The original model contained a speculative link: a mechanism by which krill could detect the presence of whales over a wide area, and therefore could behave in response to a credible threat. Recently, iron has been implicated in a positive feedback cycle between whales, krill and primary production. The cycle depends on the buoyant faeces of whales fertilising surface layers. This is both a plausible way for krill to detect whales over a wide area and an explanation for enhanced feeding at the surface, but this was not incorporated in the original model. Thus, nutrient retention and behavioural control are probably an example of niche construction and ecosystem engineering by both krill and whales. In this paper I revisit and update the simple model of krill mentioned above. The model is calibrated against known system states and is used to imply the ecosystem level changes caused by commercial whaling. This improved model may explain the reduction in krill abundance after the end of commercial whaling. Untested hypotheses which can be falsified in designed experiments are listed.

Flowchart of steps (functions in programming terms) that occur during the running of the model in this study. Model krill (n = 1000) are introduced and every model week they feed, grow and allocate fecundity. Each week they are also subject to risk of predation, and some die. Those that do not die contribute to the population additions in the annual loop. All the fecundity in the population is added together and used to determine how many new recruits appear. When a model krill dies, it is recorded how much fecundity it actually used in procreation. This is its individual lifetime reproductive success, which is the driver for evolution through natural selection

…

. Model krill risk and reward values for surface and deep zones in calibration scenarios. Values in bold are those that impact the comparative results between the scenarios. Risk is expressed in annual survival proportion; thus a high value indicates a lower risk

…

Figures - uploaded by Jay Willis

Content may be subject to copyright.

Content uploaded by Jay Willis

Content may be subject to copyright.

MARINE ECOLOGY PROGRESS SERIES

Mar Ecol Prog Ser

Vol. 513: 51–69, 2014

doi: 10.3354/meps10922 Published October 22

INTRODUCTION

The model presented in this paper has been devel-

oped assuming the validity of the following basic

statements about the Antarctic ecosystem:

(1) Krill Euphausia superba abundance in the South-

ern Ocean used to be enough to support the whales

(and other krill predators) that existed before exploita-

tion and was close to its pristine ecosystem carrying

capacity.

(2) Krill abundance is now lower than it was before

the start of commercial whaling.

(3) Godlewska’s hypothesis is true: krill have

changed their habitat use because of the lack of

whales (more correctly, the lack of the perceived risk

of predation from whales).

(4) Whales enhance the surface feeding opportunity

for krill through nutrient recycling.

The aim of this study is to show how a simple model

that encompasses these assumptions leads to a pre-

diction about the current state of the ecosystem, and

likely future scenarios, which is more plausible than

other models that have been presented to date. The

first statement is hopefully uncontested given that

the ecosystem has existed long enough for the partic-

ipants to have evolved and that it is not contradicted

by any observation or theory; it also follows the eco-

logical theory around mature ecosystems (Odum

1969). Evidence to support the subsequent state-

ments is outlined below. The most important contri-

bution of this study is to provide evidence to support

these basic assumptions.

Whales maintained a high abundance of krill;

both are ecosystem engineers in the Southern Ocean

Jay Willis*

Turnpenny Horsfield Associates Ltd., Ashurst Lodge, Ashurst, Southampton, Hampshire SO40 7AA, UK

ABSTRACT: Krill abundance was predicted to rise after the end of commercial whaling in the

Southern Ocean due to the release of predatory pressure from 2 million whales that were killed

between 1915 and 1970, but contrary to expectations, there has been a substantial decline in

abundance of krill since the end of whaling. I presented a model 7 yr ago which explained how

krill behaviour, in response to the threat of predation by whales, may provide an answer to this

paradox. The original model contained a speculative link: a mechanism by which krill could detect

the presence of whales over a wide area, and therefore could behave in response to a credible

threat. Recently, iron has been implicated in a positive feedback cycle between whales, krill and

primary production. The cycle depends on the buoyant faeces of whales fertilising surface layers.

This is both a plausible way for krill to detect whales over a wide area and an explanation for

enhanced feeding at the surface, but this was not incorporated in the original model. Thus, nutri-

ent retention and behavioural control are probably an example of niche construction and ecosys-

tem engineering by both krill and whales. In this paper I revisit and update the simple model of

krill mentioned above. The model is calibrated against known system states and is used to imply

the ecosystem level changes caused by commercial whaling. This improved model may explain

the reduction in krill abundance after the end of commercial whaling. Untested hypotheses which

can be falsified in designed experiments are listed.

KEY WORDS: Individual-based model · Ecosystem model · Cetacean

Resale or republication not permitted without written consent of the publisher

Mar Ecol Prog Ser 513: 51– 69, 2014

Krill abundance is now lower than before

commercial whaling started

Commercial whaling removed 2 million large

baleen whales from the Antarctic marine ecosystem

in less than 70 yr, starting around 1900 (Clapham &

Baker 2001). These included blue whales Balaeno -

ptera musculus, fin whales B. physalus, sei whales B.

borealis, humpback whales Megaptera novaengliae,

and Antarctic minke whales B. bonaerensis, here-

inafter referred to collectively as ‘whales’. These

whales were almost entirely sustained by krill. It was

assumed that the krill population would increase

tremendously as a result of the release in predatory

pressure due to commercial whaling (Laws 1977, Bal-

lance et al. 2006). This effect of predatory release

was expected to have been exacerbated by the com-

mercial harvesting of fur seals Arctocephalus gazelle

and ground fish, both krill predators, which occurred

around the beginning and throughout the period of

whaling, respectively (Ballance et al. 2006, Kock et

al. 2007). The krill population has not increased, and

rather the opposite is true. It is difficult to be certain

about krill abundance in the Antarctic marine eco-

system due to its size and inaccessibility; neverthe-

less, after 10 yr of searching, with equipment specifi-

cally designed for the purpose (Everson 2000),

scientists were unable to find enough krill to feed the

whales that were known to have been killed. Nicol et

al. (2000) calculated krill population abundance

around the year 2000 to be between 60 and 155 mil-

lion t. This figure was later revised to 133 million t

global biomass (Atkinson et al. 2009). Stomach con-

tents of >500 whales were used to estimate that,

before 1900, whales consumed a total of 175 to 190

million t of krill per year (Mackintosh 1973, 1974,

Ross & Quetin 1988) and this was used to infer that

the difference between total whale consumption of

krill before and after whaling was 147 million t (Laws

1977). This estimate was made under the assumption

that there were 975 000 whales in the pristine state

(Laws 1977) whereas now, with the benefit of Russian

catch records which were not previously available,

the best estimate is 1.697 million (Christensen 2006).

So, using the Laws (1977) multipliers (his Table 2) on

Christensen’s (2006) population estimates (her Table

3), the difference in krill consumed by whales before

and after whaling is now estimated at 276 million t.

This observed feeding requirement for the whales

that are known to have been killed is more than

twice the observed global standing stock of krill (133

million t mentioned above). Whales, however, are not

the only predators of krill: in 1985 it was estimated

that 470 million t of krill were eaten each year by

whales, seabirds, seals, squid and fish (Ross & Quetin

1988). This estimate of 470 million t required to main-

tain the system after exploitation of whales should be

compared with the present best modeled estimate of

342 to 536 million t per year gross larval production

(in 2000) and the consequent estimate of 128 to 470

million t per year available to predators (Atkinson et

al. 2009). The entire additional requirement of the

whales, if they existed now in their pristine abun-

dance, for ca. 276 million t of krill, would be unavail-

able at the higher bound of present modelled pro-

duction estimates. Hence, the production of krill

must have fallen by at least this amount between the

start and finish of commercial whaling (a decrease of

50% at the lowest proportional estimate). A huge

surplus of krill, and a tremendous rise in the abun-

dance of other predators of krill (compensatory pre-

dation) was expected (Ballance et al. 2006), but the

opposite has happened. Krill abundance has fallen,

and continues to fall (Atkinson et al. 2004). There

have been moderate changes in the abundance of

other krill predators; the fur seal population recov-

ered very strongly until around 1985 and has since

declinded, crabeater seals (Lobodon carcinophaga or

carcinophagus) have shown no long-term changes,

and most avian predators have declined in abun-

dance (Ballance et al. 2006). These changes do not

provide an adequate compensatory predator-based

explanation for the changes in krill abundance (Bal-

lance et al. 2006). The logical hypothesis is that

presently observed krill abundance is a fraction of

what was required to maintain the ecosystem before

whaling.

Godlewska’s hypothesis of krill behavioural

changes

Godlewska (1996) presented the results of sonar-

based krill surveys and suggested that krill had

changed their behaviour after the end of commercial

whaling. She suggested that krill had spent most

days at the surface before the end of whaling, but

after whaling spent the daylight hours at 100 m to

200 m depth and migrated each night to the surface

(Godlewska 1996). During the early period of whal-

ing, observers consistently reported a high abundan -

ce of krill and constant oc currence of surface schools

day after day (Marr 1964, Mackintosh 1973). Surface

schools of krill were described as being thick like

pea-soup, and extending continuously like immense

pastures over areas as large as 150 square miles

Willis: Whales and krill

(Marr 1964, Mackintosh 1973). Such was the impor-

tance, regularity and clear association with whaling,

that daylight surface schools of krill were mapped

across the entire area of commercial whaling by

observers from each of the main whaling countries

(Arsenev 1958, Marr 1964, Ozawa & Sato 1967). Early

scientists did not ignore the possibility of vertical

migrations; they searched for, and occasionally

recorded, this behaviour for adult and developmental

stages of krill (Mackintosh 1934, Hardy & Gunther

1936, Fraser 1937, Marr 1964). During the early

period of whaling, Mackintosh (1934) sampled the

vertical position of krill and concluded that for the

majority of day and night they stayed near the sur-

face, especially when in schools with only a minor

degree of diurnal variation. Marr (1964) reported that

krill only inhabited depths within 10 to 40 m of the

surface, day and night. This conclusion was derived

from a literature review and a comprehensive survey

based on net hauls. (Like more recent studies, this

study accounted for the fact that krill are adept at

avoiding nets at the surface in daylight; Marr 1964,

Nicol et al. 2010). After the end of commercial whal-

ing, there were no records of krill seen at the surface

in daylight despite continued observation. At this

time, a commercial krill fishery was developing and

it would have been convenient for the fishers to see

krill at the surface. However, sonar, echo sounders

and knowledge of previous abundance were the only

ways in which krill were found by the commercial

krill fishery during daylight (Ichii 2000). No large sur-

face schools were recorded as visible at the surface in

daylight in the scientific literature between 1970 and

2000. Vertical daily migration is now considered

standard behaviour for krill (Everson 2000). Typical

scientific samples during this period were taken

between the surface and 160 m in depth using sonar

for school location (Ross & Quetin 1983). Where super

swarms were reported in daylight in this period after

whaling (e.g. Higgin bottom & Hosie 1989, God lews -

ka 1996) they were detected with sonar; no krill were

seen at the surface. Thus there is strong evidence to

suggest Godlewska’s hypothesis is more a fact estab-

lished through observation rather than a hypothesis.

Whales enhance surface prey abundance for krill

through iron recycling and fertilisation

Krill fertilise the ocean in situ with their excrement

as they feed; a high krill biomass causes enhanced

primary production in Antarctic waters (Lehette et al.

2012). Krill bodies also contain high levels of iron rel-

ative to the surrounding waters, and thus the popula-

tion of krill represents a carrier for a significant pro-

portion of all the iron flux present in the surface

ocean of this region (Tovar-Sanchez et al. 2007).

Whale bodies and whale faecal material are similarly

rich in iron (Smetacek 2008). Whale faeces are buoy-

ant and fluid with a high water content, and whales

have been photographed defecating at the surface

(Smetacek 2008). This implies an iron and nutrient

storage and recycling mechanism that leads to a pos-

itive feedback in the food chain between the abun-

dance of primary production, krill and whales

(Smetacek 2008, Nicol et al. 2010). Krill may also

have engineered their environment and enhanced

primary production through turbulence (Kerr 2006,

Smetacek 2008, Willis 2013). Turbulence can be a

greater determinant of phytoplankton growth than

either salinity or temperature (Margalef 1978). Tur-

bulence as a determinant of phytoplankton growth

was largely overlooked until the energetic strength

of turbulence caused by fish and krill schools was

found to be on the same scale as that caused by wind

and waves (Huntley & Zhou 2004). It was found that

this turbulence is unlikely to contribute to overall

ocean mixing in the same way as wind, due to the

comparatively short mixing scales (Visser 2007).

However, turbulence with such energy and short

mixing characteristics is a very rare event in the

ocean outside of swimming animal schools (Jimenez

1997). Krill-induced turbulence may constitute eco-

system engineering through at least 3 mechanisms:

(1) by directly enhancing plankton productivity

(Margalef 1978), (2) by facilitating the sorting of prey

sizes and types by krill, which allows krill to engineer

the size and age composition of their prey (Willis

2013), and (3) by mixing the surface ocean to redis-

tribute physical substances (nutrients) and properties

such as salinity and temperature (Kerr 2006). The

impact of krill-induced turbulence on primary pro-

duction as a type of ecosystem engineering, or

energy fertilisation, process may well be significant.

Retention of nutrients and complex symbiotic inter-

actions are also consistent with the characteristics of

a mature ecosystem (Odum 1969). It is also interest-

ing to consider the higher density caused by com-

pression of the krill population into the top 10 m as

opposed to the deeper zone between 10 and 200 m.

Mackintosh (1934) suggested krill form denser

schools when at the surface. Compression and higher

density patches would potentially lead to more accu-

rate targeting of fertilisation of nutrients by whales

and would enhance the effects of turbulence caused

by the swimming action of krill (Willis 2013). In sum-

Mar Ecol Prog Ser 513: 51– 69, 2014

mary, there is a nutrient retention and recycling

mechanism which includes whales, krill and primary

production.

Ecosystem implication of the iron cycle

The existence of a nutrient and fertilisation mecha-

nism induced by whales and krill in the food chain

implies that the throughput of the entire Antarctic

ecosystem has been reduced due to decreased nutri-

ent retention caused by commercial whaling. This

means that the carrying capacity of krill is variable

and is dependent on the abundance of whales,

among other factors. In terms of the theory of krill

behaviour and abundance, outlined in this study, the

theory of an iron cycle provides 2 important explana-

tions: (1) the mechanism by which krill are aware of

whales and thus are able to react to the threat of pre-

dation could be odour in faeces, and (2) the iron cycle

linked through whales and krill defecation at the sur-

face coupled with high turbulence, temperature and

sunlight would provide a good explanation for higher

availability of food for krill at the surface in daylight.

In an earlier study (Willis 2007), and the original

model of krill on which it was based (Alonzo & Man-

gel 2001), the preferential conditions of the near-sur-

face habitat relating to higher prey availability for

krill were assumed to be present whether or not

whales were around.

Niche construction and ecosystem engineering

The basic questions of this study are (1) whether

whales and krill are mutual niche constructors which

engineer their environment and (2) whether this has

a significant impact on the abundances of both spe-

cies which that environment supports. The niche

construction theory explicitly recognises environ-

mental modifications (ecosystem engineering when

abiotic) by organisms and their legacy over time

(Odling-Smee et al. 2013). Ecosystem engineering is

not a new concept. Darwin (1881) showed that

worms were ecosystem engineers on geological

scales. Nevertheless it is often overlooked in stan-

dard behavioural ecological theory (for instance

Krebs & Davies 1993). Standard theory, although

recognising the physical environment as a source of

selection, tends to focus on the interactions of pheno-

types in different species, for example in food webs

(Odling-Smee et al. 2013). The model presented in

this paper may support niche construction theory by

giving a quantitative numerical example of the popu-

lation impact resulting from individual reproductive

success caused by niche construction. Ecosystem

engineering and niche construction as evolutionary

processes are possibly overlooked because they are

assumed to be of relatively minor impact and to hap-

pen over time scales much shorter than evolutionary

time scales (Odling-Smee et al. 2013). This is particu-

larly true of pelagic marine science where the eco-

system approach has tended to focus exclusively on

the food-web interactions of species, and concepts

such as mass-balance between species (Pauly et al.

2000). The theory around fisheries management has

given minimal regard to even the ecosystem level of

standard ecological theory and is generally based on

single species exploitation (for instance Hilborn &

Hilborn 2012). This lack of theoretical treatment is

challenged by the fact that pelagic marine species

can directly influence their physical environment,

especially through turbulence as mentioned above,

and through nutrient retention (Smetacek 2008). The

scale of niche constructor influences may be pro-

found (Laland et al. 1999), and it is certainly not out-

side the range of possibilities that marine animals

may impact their physical environment or even the

climate (Kerr 2006).

Mutualism and niche construction

Mutualism is important (Hay et al. 2004). Odum

(1969) summarised the importance of parasitism, pre-

dation, commensalism, mutualism and other forms of

symbiosis as a defining character of mature ecosys-

tems. It is not clear if the relationship between

whales and krill is simply mutualistic, in which both

cooperate to gain net survival or net reproductive

benefit, or is better described as manipulative (Krebs

& Davies 1993) or exploitative mutualism in a similar

way to which human farming exploits other species

(Smetacek 2008). For instance whales may influence

krill to a mode of behaviour which, on average,

increases the abundance of krill, whereas individual

krill may increase their reproductive success by

alternative strategies in the absence of whales (Willis

2007). Therefore niche construction is perhaps a bet-

ter fundamental concept with which to start than

mutualism because it encompasses both the wide

range of possibilities in which 2 species interact with

each other, as well as the range of ways they interact

with other species and their physical environment.

Indeed, the way in which krill and their prey interact

is also likely to be just as complex (Kawaguchi &

Willis: Whales and krill

Takahashi 1996). While mutualisms are fundamental

to all ecosystems, the dynamic balances between

exploitation, cooperation, benefits and disadvan-

tages are often very challenging to understand from

an evolutionary perspective (reviewed in Herre et al.

1999).

The model in this study

I have employed a krill life-history model which

was developed to target the behavioural choices (i.e.

between deep and shallow habitats) made by krill

under different predation pressures (Alonzo & Man-

gel 2001). This model was previously used for a simi-

lar task (Willis 2007) but here is extended to target

ecosystem-level changes. The environmental cycles

are based on Laws (1977) depiction of the annual

cycles of krill, whales and seals. I have used Chris-

tensen’s (2006) whale population estimates to model

changes caused by human exploitation. The object of

this study was to capture the basic patterns involved

in the ecosystem throughout the period of whaling to

provide a rational explanation for the apparent

changes in krill abundance that is consistent with (or

at least not falsified by) contemporary reports, con-

temporary data, and generally accepted ecological

theory. I have chosen to use an individual-based

modelling (IBM) approach, because these models are

usually simpler to understand than population mod-

els based on differential equations (a comprehensive

introduction to, and review of, IBMs in ecology is in

DeAngelis & Gross 2009).

METHODS

Design of the model

The fundamental driver of the model is individual

lifetime reproductive success of krill in a simple

model based on the balance of risks and availability

of nutrients. The model is calibrated to 2 overall sce-

narios: (1) whales present, and (2) whales absent.

There are 2 krill habitat options: (1) surface all the

time, and (2) surface at night, deep in day (standard

diurnal vertical migration [DVM]). At each time step,

krill feed, grow and develop fecundity and are sub-

ject to predation risk, and may die (Fig. 1). The

fecundity and growth of each krill using various

zones (surface or deep) is calculated using a set of

physiological parameters derived from earlier scien-

tific studies (summarised in Willis 2007 and also pre-

sented in Alonzo & Mangel 2001). Variables such as

feeding rate, length of day, temperature at depth,

and energy expenditure when moving between sur-

face and deep, are used to calculate weekly growth

and allocation of fecundity (Fig. 1). The equations

used in this model for these calculations are sum-

marised in Table 1 of Willis (2007). The changes in

habitat use throughout the year are predefined based

on Laws’s cartoon of the annual cycles of the Antarc-

tic ecosystem (Laws 1977) and daylight proportion is

calculated on a weekly basis using a standard math-

ematical model of sun elevation (NOAA Sunrise/

Sunset calculator, www.esrl.noaa.gov/) (Fig. 2). Each

year, in the annual loop (Fig. 1), the total population

fecundity is used to determine the number of new

recruits to the model. As the model progresses, the

abundance of krill changes (determined by risks and

population fecundity) along with the overall biomass

of krill, and the ‘production’ which is the sum of bio-

mass of krill which die at each time step and are

assumed to be eaten be predators (Fig. 1). The sum of

all surviving individual fecundity in the population

each year determines the number of new entrants

recruited into the population. Fecundity used in this

spawning process is therefore a direct measure of

individual lifetime reproductive success. Thus the

Feed

Grow

Calculate

fecundity

Survive

Die

Record as production

Assign

habitat

Weekly

loop

Krill Yearly

loop

Calculate total

population

fecundity

Risk

Reward

Add more

krill objects

Record individual lifetime

reproductive success (used fecundity)

Depends on

time of year

and whales

Aggregate all survivors

Record

length

and mass

Record used

fecundity in

survivors

Fig. 1. Flowchart of steps (functions in programming terms)

that occur during the running of the model in this study.

Model krill (n = 1000) are introduced and every model week

they feed, grow and allocate fecundity. Each week they are

also subject to risk of predation, and some die. Those that do

not die contribute to the population additions in the annual

loop. All the fecundity in the population is added together

and used to determine how many new recruits appear.

When a model krill dies, it is recorded how much fecundity

it actually used in procreation. This is its individual lifetime

reproductive success, which is the driver for evolution

through natural selection

Mar Ecol Prog Ser 513: 51– 69, 2014

model aims to explore the balance between risk,

growth, and fecundity in various habitat selections

under various risk assumptions, all in the context of

what is known about krill physiology. The model is

written in a MATLAB ® (Mathworks) or GNU Octave

compatible script and is available by contacting the

corresponding author.

The model can be represented in a flowchart

(Fig. 1) and as pseudocode, as follows:

1. Initialise krill

2. Burn-in krill population using a calibrated steady-

state population (50 yr)

3. Initialise habitat parameters including presence of

whales

4. Loop through 5200 steps (52 wk in each of 100 yr)

a. Update environmental parameters based on time

of year

b. Update whale presence parameters based on year

c. Krill behave (choose habitat selections for week,

based on presence of whales)

d. Krill feed (dependent on habitat use)

e. Krill grow (dependent on existing size and food

gained)

f. If week is spawning week (Fig.2)

i. Calculate fecundity

ii. Record used fecundity for each individual

(lifetime reproductive success)

g. If week is recruitment week (Fig. 2)

i. Recruit new krill (based on total fecundity

the year before)

h. Krill die (of predation dependent on habitat

risks)—record details

5. End loop

6. Output results

Use of the model

The model is used here in 2 ways; (1) the risk bal-

ance of the habitats is calibrated to produce a result

02 Aug 21 Sep 10 Nov 30 Dec 18 Feb 09 Apr 29 May

0.2

0.4

0.6

0.8

Baleen whales

Crabeater s eals

Pack i ce

Krill zone

Krill uncovered

02 Au

21 Sep 10 Nov 30 Dec 18 Feb 09 Apr 29 May

0.2

0.4

0.6

0.8

1Recruit

Proportion of time Relative proportion

Spawn

Sunlight lat. –58° to –65°

Sunlight lat. –51°

Summer season

Fig. 2. Annual cycles in the Antarctic ecosystem used in the model. (A) lines are derived from Laws’ (1977) cartoon of the eco-

system and show (proportionally) when the pack ice recedes and when whales (red line) and krill (grey solid line) feeding

activity occurs. The pack ice cover as a proportion of maximum in any one year (grey dot-dash line) subtracted from the total

potential area for krill (dotted grey line) is used to estimate the changes to the area in which krill become available throughout

the year (solid grey line). Green line is crabeater seal feeding as a proportion of maximum feeding from Laws (1977). (B) The

proportional length of daylight is shown for various latitudes (the latitude indicated by the central blue dashed line was used

in this study). The summer season was defined in this model as when the krill activity was above 1.15, and weekly time for

recruitment and spawning (calculation of used fecundity) are shown as vertical dashed lines. Note that the summer season for

whales is characterised by an almost continual decline in daylight. This suggests that the starting time for whale feeding is

critical to their success, as they are thought to be visual predators

Willis: Whales and krill

that is consistent with what is known about the

behaviour of krill pre- and post-whaling, and (2) the

calibrated model is used to estimate the changes in

the abundance of krill before, during and after whal-

ing. Since risk is not associated with any physiologi-

cal characteristic it is a free variable in the model and

any value is equally plausible, in itself, in any habitat.

The same values as in Willis (2007) were used for rel-

ative food availability, but now the enhanced feeding

availability in the surface layers is only represented

in the scenario with whales present. All other values

are similar, representing no difference in food avail-

ability between scenarios and habitats and thus no

implied knowledge. Therefore the 4 risk variables

(summer surface and summer deep in each scenario

[whales and no whales]) are the tuning variables of

the model.

Initialisation and model running

The model time step is 1 wk and the usual run

length is 150 yr (Fig. 1). The model was initialised

with 1000 model krill. The physiological parameters

of krill and their life history, including the energy

assimilation from food, and the energetic require-

ments of growth and fecundity at various tempera-

tures, under various lighting conditions, and under

different travelling regimes are all held constant and

are all similar to earlier studies based on field and lab

observations (Alonzo & Mangel 2001). Reward is a

proportional instantaneous feeding rate between 0

and 1 which is used to define the comparative feed-

ing opportunity for krill between locations. Risk is a

survival probability between 0 and 1. Krill can die

each week; those that die are recorded as ‘produc-

tion’ and thus all are assumed to have died through

predation. New krill are added (as age 1 juvenile

recruits) at an annual interval and the number of new

recruits is based on a linear function of population

fecundity. So the number of krill in the model

changes throughout the model run. The summer sea-

son is defined as when Laws (1977) (normalised) car-

toon of krill activity is greater than 0.15 and spawn-

ing and recruitment weeks were likewise chosen to

be approximately at the appropriate time of year

(Fig. 2).

Calibration

There are 2 scenarios on which the model can be

calibrated; (1) pre-whaling and (2) post-whaling.

The assumed characteristics of these states are as

follows:

Pre-whaling state

• Roughly stable populations of krill and whales

• Population abundances close to the system carrying

capacity

• Krill abundance adequate to support pristine whale

population and populations of seals, flying birds,

penguins, squid and fish (approx. 700 million t).

• Krill are selected to use the surface zone in day-

light, therefore individual lifetime reproductive

success (termed ‘used fecundity’ in the model) is

expected to be higher for surface users.

Post-whaling state

• Stability is not assumed

• Population biomass of krill was around 150 million t

at some point

• Krill are selected to use the deep zone in daylight,

therefore ‘used fecundity’ higher for deep users in

daylight.

Operation

In the operation phase the model is run for 150 yr.

The first 50 yr are a ‘burn in’ period designed to allow

the model to stabilise. Since the model employs a

feedback mechanism to maintain a population of krill

(based on fecundity as described above) it is ini-

tialised with an estimated value of total population

fecundity. As the model progresses past the first few

years the influence of the initial estimated fecundity

is eliminated and the model reaches an independent

steady state dependent only on the risk parameters.

The operation stage of the model then progresses

from Year 50 to Year 150 and is designed to model

the impact of commercial whaling between 1900 and

2000. The abundance of whales in each year is calcu-

lated as a proportion that ranges between 1 (pristine)

and 0 (complete extinction) and is based on the total

biomass of the 5 main species of whale in this study.

The proportion is applied to the krill population to

determine if a krill is in an environment with or with-

out whales for the summer season. For example, if

there are 1200 krill at the start of a year, and, that

year, whale population biomass is at 50% of pristine,

on average (actual allocation is determined using a

Mar Ecol Prog Ser 513: 51– 69, 2014

random number generator) 600 krill will be in a

whale-free environment and 600 in an environment

with whales. In this concept a single krill represents

an isolated group of krill that either encounters or

doesn’t encounter whales during the year in ques-

tion. Those that encounter whales use the surface

habitat, and those that do not use the deep habitat

(surface at night, deep in day; DVM).

Abundance of whales

The abundances of whales during the period of

1900 to 2000 in the Southern Ocean were taken from

Christensen’s (2006) graphs of population abundance

(Fig. 3). The average adult mass of each species was

used to calculate the population biomass of each spe-

cies. The sum of species biomasses was used as the

estimate of the total population biomass used to

determine the proportional abundance of whales

throughout the period in the model (Fig. 3).

RESULTS

Calibration

The results of the calibration are

summarised in Tables 1 & 2, and

Figs. 4 & 5.

The key result in the calibration is

in the ‘used cumulative fecundity’

(lifetime reproductive success)

(Fig. 5A). Table 1 shows how in the

scenario with whales present, the

used lifetime fecundity was signifi-

cantly higher in the surface habitat

choice, which would suggest that

krill would be evolved to use this

habitat when they can detect whales.

With no whales the position is

reversed; the used fecundity was sig-

nificantly higher for the deep habitat,

suggesting that krill are evolved to

use the deep habitat when whales

are not detected. Table 2, giving

instantaneous risk (survival rates),

also shows this same pattern and

reversal, which is logically consistent

but not necessary for a plausible cali-

bration. That is; it is plausible that a

krill would trade a higher risk for

greater lifetime reproductive success,

but in this parameterisation that is

unnecessary. It is logically consistent

that the calibrated risk levels are also higher in both

zones when whales were present.

The weekly production in the surface habitat

when whales were around was 4 to 8 times as

much as the production in the deep habitat when

whales were not present (Fig. 4C), and the total

population biomass shows a similar pattern (Fig.

4B). This pattern is dimensionally similar to the

recorded changes in the real ecosystem discussed

in the introduction, where 1 g of model production

is equivalent to 1 million t of real krill. Individual

model krill growth shows the periodic shrinkage

which is thought to happen during the winter

period (Alonzo & Mangel 2001) (Fig. 4D).

Operation

The results of the operation of the model are sum-

marised in Fig. 6. The model krill abundance in -

creased over the initial 50 yr as the whale abundance

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

8x 105

AFin

Blue

Sei

Minke

Humpback

1900

Total population biomass (t) Abundance

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

4x 107

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

8x 107

Fin

Blue

Sei

Minke

Humpback

Fig. 3. Abundance, biomass and total population biomass of the 5 species of

whales in this study in the Southern Ocean from 1900 to 2000 from Chris-

tensen (2006). The whales are: blue Balaenoptera musculus, fin B. physalus,

sei B. borealis, humpback Megaptera novaengliae, and Antarctic minke B.

bonaerensis. The abundance values from (A) Christensen (2006) have been

multiplied by the average whale mass from the same study to calculate (B)

species biomass, and these are summed to produce (C) the total whale bio-

mass. Total whale biomass as a proportion of the maximum is used in this

study to calculate the number of krill using each habitat selection in the model

Willis: Whales and krill 59

Whales No whales

Risks (annual survival rates)

Surface daylight 0.9534 0.96

Deep daylight 0.94 0.974

Night time 0.99 0.99

Under ice (winter) 0.995 0.995

Feeding opportunity (day and night time, proportion of

maximum)

Surface 1 0.7

Deep 0.7 0.7

Under ice (winter) 0.5 0.5

Table 2. Model krill risk and reward values for surface and

deep zones in calibration scenarios. Values in bold are those

that impact the comparative results between the scenarios.

Risk is expressed in annual survival proportion; thus a high

value indicates a lower risk

Whales No whales

Krill abundance (from an initial population of 1000)

Surface habitat 925 ± 164 1 ± 1 (collapse)

Deep habitat 1 ± 1 (collapse) 447 ± 126

Weekly production (g)

Surface habitat 7.1 ± 1.2 0

Deep habitat 0 1.2 ± 0.4

Lifetime used fecundity of a group of 300 model krill

tracked from 50 yr until death (arbitrary units)

Surface habitat 874 ± 271 389 ± 123

Deep habitat 400 ± 60 792 ± 54

Table 1. Model krill population parameters after 50 yr in the

calibration scenario, using the 2 habitat selections (surface

[all the time] and deep [surface at night, deep in day; i.e.

diurnal vertical migration] during the summer season) for

the 2 scenarios (whales and no whales), mean values ±1 SD

of 5 similar runs of 1000 model krill

10 20 30 40 50 60 70 80 90 100

500

1000

1500

2000

No. of yr

Total model krill abundance

Deep whales

Surf whales

Deep no whales

Surf no whales

010 20 30 40 50 60 70 80 90 100

100

200

300

400

500

600

Total krill population bioass (g)

010 20 30 40 50 60 70 80 90 100

Total krill production wk–1 (g)

1 2 3 4 5 6 7 8 9 10 11

e (yr )

Length-at-age (mm)

Fig. 4. Results from the model in the calibration phase, showing the various implications of the risk and reward balances in the

4 habitat scenarios through 100 yr on model run time. ‘Deep’ means krill are at the surface in the nighttime and deep in day;

‘surf’ means krill are at the surface day and night. (A) Mean (± SE) total krill abundance of model krill after 5 model runs, show-

ing how abundance decreases for all scenarios except surface when whales were around. Mean ± SE (B) krill population bio-

mass and (C) production showing similar patterns. (D) Average length of a batch of 300 model krill after 50 yr. The lines are

truncated at different points as no krill lived past these ages. Krill are thought to shrink during periods of low food, and this

effect is also shown

Mar Ecol Prog Ser 513: 51– 69, 2014

Deep w Surf w Deep noW Surf noW

200

400

600

800

1000

1200

Used cumulative fecundity (sub-group, n = 300)

Arbitrary fec undity units

Habitat choices

Deep w Surf w Deep noW Surf noW

2.25

2.5

2.75

Years

Habitat choices

Age at deat h of group of 300

Fig. 5. Differences in (A) used cumulative fecundity (a direct measure of individual lifetime reproductive success) and (B) age

at death of a sub-group that entered the krill population after 50 yr in the calibration phase, based on the presence/absence of

whales in the model. The vertical lines indicate ±1 SD of 5 replicates. (A) When whales were around (deep w and surf w), the

used fecundity was significantly higher for the surface habitat, and that the position was reversed when whales were not pres-

ent (deep noW, surf noW). (B) Krill live longer on average in the deep habitat when whales were not present. Hence, ‘deep

noW’ is a live-long grow-slow habitat in contrast to ‘surf w’, which was a grow-fast die-young environment. This is the key

result of the calibration of the model. Krill evolved to go deep when whales were not present but to stay at the surface when

they were present

1900 1920 1940 1960 1980 2000

400

600

800

1000

1200

1400

1600

1800

Total population abundance

1900 1920 1940 1960 1980 2000

100

200

300

400

500

600

Total population biomass

(g)

Krill

Whales

1900 1920 1940 1960 1980 2000

Weekly production mass (g)

1900 1920 1940 1960 1980 2000

Weekly production mass (g)

Surface

Deep

Ice

Fig. 6. The krill model in this study in relation to the proportion of whales present (in terms of biomass) throughout the period

from 1900 to 2000. The whale biomass, and therefore implied presence, is external information which drives the model results.

Means ±1 SD of 5 replicates are shown. (A) The total abundance of krill increases as the decline in whales begins. (B) The pop-

ulation biomass of krill remains reasonably stable during this period as more krill change habitat, which is similar to (C) pro-

duction. Average weekly production is shown (7 g would relate to ~371 g yr−1). By 1960, however, the abundance, biomass and

production of krill all diminish in the same pattern as the abundance of whales. (D) Krill production according to habitat, show-

ing how krill production at the surface follows a pattern very similar to the whale biomass, as krill are leaving the surface habi-

tat in that same proportion (red line). The krill in the deep habitat builds up but begins to decline after 1970. There is no evi-

dence from the real ecosystem which contradicts this pattern. Production during the winter when krill are under the pack ice

is shown in green

Willis: Whales and krill

fell. This is caused by the longer life of krill in the

deep habitat (Fig. 5B), which causes a very slightly

higher percentage of additional older krill to begin

each year, and thus increase the population fecun-

dity as spawning happens soon after the start of the

new season (Fig. 2). This is supported by the rela-

tively unchanged krill population biomass during

this period (1900–1950, Fig. 6B). This effect was bal-

anced and reversed after 50 yr as the whale popula-

tion became so low as to put more krill into the deep

habitat, and the pattern of population decrease in the

habitat became dominant. Overall production was

un changed during the initial period, which implies

that there was more food available during this period

for other surface predators of krill (as whales’ re -

quirements diminished). Both whales and non-whale

predators are assumed to make a numerical or be -

havioural response to the additional resource and so

maintain the risks as constant. The production switched

from availability in the surface zone to the deep zone

after ca. 50 yr and finally ends up predominantly in

the deep zone (Fig. 6C). Krill production in the ice

remained relatively constant throughout, only slightly

diminishing toward the end of the period (Fig. 6D). If

under-ice production of krill is the determinant for

the crabeater seal population (which are assumed to

be a large proportional predator on krill in the win-

ter) then it would explain why their population abun-

dance has remained relatively un changed, until a

slow decline started ca. 1960.

DISCUSSION

The calibrated model shows that there are plausi-

ble balances of risk that produce results consistent

with what is known about the ecosystem and krill

behaviour before and after whaling. The calibrated

model linked to the decrease in whales produces a

reasonably consistent pattern of decreased krill

abundance and biomass that has been observed in

the actual system. This includes the decrease in sur-

face production (and thus visibility of krill) matched

with the decrease in whale abundance (Fig. 6).

Therefore it is reasonable to assume that whales did

maintain a high abundance of krill, until any of the

main assumptions of this study are falsified.

The mutualism between whales and krill (ex -

pressed in terms of population abundance) is clearly

shown by the model as the reduction in whales leads

to a delayed response in the abundance of krill

(Fig. 6A,B). The individual lifetime reproductive suc-

cess of the model krill would be a plausible cause for

their likely habitat selection behaviour (Fig. 5). The

instantaneous risk (Table 2) would also drive this pat-

tern. This type of lagged response is typical of niche

construction theory (Laland et al. 1999). The lagged

response is an emergent property of the model and is

caused by multiple factors, including simple preda-

tory release, mixed life histories (krill developing

quickly in the surface layers and subsequent years

living in the less risky habitat) and the rate of decline

of population abundance in the deep habitat. The

decrease in population biomass of krill lags that of

whales by around 20 to 40 yr (Fig. 6), which is 5 to

10× the average lifespan of krill (Fig. 5) which is

indicative of the concept of niche construction pro-

viding legacy impacts to future populations beyond

the lifetime of a single animal (Odling-Smee et al.

2013).

It seems counter-intuitive that whales evolved to

maintain a high abundance of krill. The al ternative is

even more counter-intuitive. The broad implication

of the importance of niche construction for mature

ecosystems are often mirrored elsewhere (Laland et

al. 1999, Odling-Smee et al. 2013). Regularly remov-

ing large quantities of what is essentially 99.9%

grassy biomass from a domestic lawn to maintain a

grass-based ecosystem is a direct analogue to the

fundamental mechanism of this study. Grass and her-

bivores evolved in lowland ecosystems, and removal

of large grass-eating herbivores leads to less grass,

rather than more grass (Vera 2000). In exactly the

same way, niche construction theory suggests that

the removal of whales from the ecosystem to which

that had evolved was bound eventually to lead to less

krill, their only prey, rather than more, and any other

outcome would be illogical and unexpected.

The predictive potential of the theory presented

here, based on niche construction, may be no better

than the previous, least daring, theory. For example,

expectations of an experiment involving the reintro-

duction of wolves in Yellowstone National Park

(Beschta & Ripple 2013) were not met. The ecosystem

did not return to its previous state after the reversal of

the removal of wolves. Irreversibility such as this is

called hysteresis, a common ecosystem phenomenon

(Mangel 2006). Species that were thought unimpor-

tant may in fact have a pivotal role; others held

footholds (constructed niches) that they had previ-

ously been denied. For example, krill preferentially

eat salps, which leads to potentially complex link-

ages between krill and their prey (Kawaguchi &

Takahashi 1996). Salps and copepods both are filter

feeders, like krill, and both have a far higher overall

biomass in the Southern Ocean than krill (Voronina

Mar Ecol Prog Ser 513: 51– 69, 2014

1998). Salps appear to have increased in abundance

while krill have decreased (Atkinson et al. 2004).

Prey at one life stage may be a competitor at another

stage, potentially leading to a further level of niche

construction which is very common in aquatic eco-

systems (Hay et al. 2004). Furthermore, large mobile

species which have been either invisible or at appar-

ently low abundances may play a pivotal role; espe-

cially fish and have cephalopods which predate krill

using the deep habitat and which have the potential

for a rapid numerical response to environmental per-

turbation (Ainley et al. 2010).

Objection to Godlewska’s hypothesis

The most compelling argument against Godlew -

ska’s hypothesis (i.e. that krill have changed their

behaviour after the end of commercial whaling) is

that it is hard to explain in terms of evolution by

natural selection, particularly in light of how quickly

the change is supposed to have occurred (Smetacek

2008). In response to this argument it is worth exam-

ining behaviours of closely related species, because

if they have been shown to exhibit similar behaviour

to that proposed for krill, it suggests that such behav-

iour is plausible for krill. In fact, there are many

examples of Crustacea changing their daily vertical

migrations in response to predatory threat. A partic-

ularly convincing example of this behavior is the

variations in patterns of DVM in the water flea Daph-

nia magna when the concentration of fish odour

increases or decreases (Loose & Dawidowicz 1994).

High concentrations of fish odour in the environment

cause an instantaneous switch to more intense DVM

and less exposure to light (Loose & Dawidowicz 1994,

Giske et al. 1998). There are many other examples of

marine and freshwater Crustacea changing DVM in

response to the perception of threat from visual pred-

ators (e.g. Stich & Lampert 1981). These similar

examples have been derived from field and labora-

tory observations and are cued through olfaction,

mechanical stimulation or vision (Stich & Lampert

1981, Gliwicz 1986, Bollens & Frost 1991, Fiksen &

Giske 1995, Fiksen 1997, Fiksen & Carlotti 1998).

Mackintosh (1934), Marr (1964) and Hardy & Gun-

ther (1936) all independently concluded that most

krill stayed at the surface but a minority exhibited

DVM. So, an instant switch in DVM due to avoid-

ance of whales as visual predators, cued by odour,

mechanical stimulus or vision, is not only plausible; it

is a very strong candidate for an explanation of DVM

of krill. Arsenev (1958) explains how whales and krill

are most often observed together at the surface, but

that deviations from this regularly occur (whales

without krill and vice versa). Arsenev (1958) explains

that it appeared that such phenomena (deviations)

are only observed in the course of a single day; on the

following day whales appeared where there was only

krill before, and vice versa. Arsenev’s (1958) obser-

vations suggest that krill will switch DVM locally

within one daily migratory cycle in response to the

threat of predation by whales, but also that whales do

not have to be physically present to invoke the

response. A similar rapid switch of DVM has been

shown experimentally for the marine copepod Acar-

tia hudsonica, again only due to the credible percep-

tion of predatory pressure rather than the presence of

actual predators (Bollens & Frost 1991). Added to

these examples of closely related species, theoretical

considerations of krill physiology and habitat selec-

tion support the plausibility of Godlewska’s hypothe-

sis (Alonzo & Mangel 2001). Although Godlewska

(1996) considered the behavioural change to be

potentially an evolutionary change, that is not a nec-

essary requirement of the hypothesis. The change

was most likely due to behavioural plasticity of krill,

however, the developing theory around rapid niche

evolution would suggest either is a possibility (Kozak

& Wiens 2010). So, in conclusion, it is likely that all

krill regularly switched between both behavioural

states in the pristine ecosystem. When the abun-

dance of whales was high, the majority of krill

remained at the surface day and night, but all krill

retained the ability to switch daylight habitat based

on the immediate perception of predatory threat

from whales. This study examines what this conclu-

sion implies for the changes to abundance in krill as

a result of commercial whaling.

Behavioural trigger

The linkage between odour and behaviour has not

been shown in krill but there is some evidence that

this is a plausible hypothesis (which is worth testing).

Dogs (Canis canis) can be trained to pilot boats to lo-

cate whale faeces from several kilometres away using

smell (Rolland et al. 2006). Whale faeces are buoyant

and remain at the surface for days (D. Ainley pers.

comm.). In turn, krill are sensitive to olfactory cues.

The highest krill olfactory sensitivity was shown to

be to Newcastle Brown Ale, where experimental sub-

jects needed to be prised off the pipette used to intro-

duce the substances into the test apparatus (Hamner

& Hamner 2000). Perhaps not coincidently Newcastle

Willis: Whales and krill

Brown Ale, which is a dark bottled beer, has a rela-

tively high concentration of soluble iron (Sancho et al.

2011). Whales in the Arctic have been observed pref-

erentially attacking krill at depth in daylight (Laidre

et al. 2010) which supports the concept that deep in

the day was higher risk for krill when whales were

around, but which also suggests that whales are used

to finding krill at depth as well as at the surface and

adds credibility to the theory that krill may have

evolved this behaviour as a response to the presence

of whales.

Alternative explanation:

a fisheries management approach

Mori & Butterworth (2004, 2006) attempted to

explain the changes in krill abundance due to com-

mercial whaling by fitting a fisheries management

model to whale catch data. This is a prime example of

standard ecological theory which is has been con-

trasted unfavourably to niche construction theory in

other examples (Laland et al. 1999). Their model led

them to hypothesize that a krill surplus did happen

due to release of predatory pressure, and that krill

abundance peaked in 1950, well before the end of

whaling (which ended around 1970), and has de -

creased since then. Mori & Butterworth (2006) con-

cluded that krill biomass under unexploited coexis-

tence with whales was around 150 million t, which

then gradually increased to about 700 million t dur-

ing the first half of the 20th century, after which it

declined again to around 200 to 300 million t around

2006. The main challenge in the Mori & Butterworth

(2006) model was to explain how the high abundance

of krill caused by the predator release from whaling

was subsequently reduced through alternative pred-

ators. There are similarities in the suggested abun-

dance pattern of krill in the Mori & Butterworth

(2006) model and the model presented here (Fig. 4),

the principle difference between the studies lies in

the explanation for the decline following the peak

abundance. The Mori & Butterworth (2006) model

fails to provide an explanation for the observed

changes in krill abundance, for 2 principal reasons;

(1) the main hypothesis has been falsified, and (2) the

initialisation is implausible. These points are ex -

plained in more detail in the paragraphs following.

The Mori & Butterworth (2006) model was conven-

ient for those wanting to support a fishery for krill or

minke whales; it suggested that the abundance of

both are higher now than they were in the pristine

state.

Contrary evidence to the conventional model

Mori & Butterworth (2006) proposed that minke

whales and crabeater seals increased in abundance

as a result of increased krill availability between

1920 and 1950. This then is supposed to have

caused compensatory predatory pressure which led

to a decrease in krill abundance since 1950, even as

whale abundance continued to be reduced through

whaling until 1970. They confirm that their model

would not fit with whales alone and so crabeater

seals are employed as the additional factor which

drives the krill abundance down after 1950. In order

for this to be a partially plausible scenario, the pop-

ulation abundance of minke whales would have

needed to have risen very quickly before the 1950’s

from a previously low level (they suggest from

300 000 to 1 200 000) and crabeater seal abundance

would also have needed to grow from very low

(~1 000 000) to high abundance (~20 000 000) during

the last 20 yr of commercial whaling. In addition to

no other evidence to suggest minke whales in -

creased in abundance, these hypotheses have been

falsified (1) through genetic studies of the minke

whale population (Ruegg et al. 2010), and (2) by

contemporary reports. For instance, crabeater seals,

sometimes in high abundance, were reported by

every major expedition that passed through the

pack-ice in the southern summer before 1962 or

stayed over winter in the pack ice, as a Belgian

expedition did in 1898; estimated abundances were

consistently be tween 2 and 5 million (Marr 1964).

There was no mention of a 20-fold increase, and a

44 yr time series in the Antarctica Peninsula region

suggests no long-term trend in crabeater abundance

after 1950 (Ballance et al. 2006). In comparison, the

model presented in this study suggests that krill

abundance, available to predators in the deep habi-

tat, was relatively high during the 20–40 yr around

the end of commercial whaling (1940–1980) but

then decreased (Fig. 6D). This may correspond to an

increase in crabeater seals (which is difficult to

quantify), and the population patterns of fur seals

(Ballance et al. 2006) and gentoo penguins (Emslie

et al. 2013).

Initialisation of the conventional model

The other difficulty with Mori & Butterworth’s

(2006) model was that it was based on implausible

initial assumptions about the pristine state of the

system. In short, they assumed too few whales eat-

Mar Ecol Prog Ser 513: 51– 69, 2014

ing too small an amount of krill. This then allowed

for the model to fit a massive proportional increase

in krill abundance to levels consistent with con-

temporary reports in the 1970’s. Their estimate of

the krill eaten by the whales which were killed

during whaling was roughly 50 million t as op -

posed to the commonly quoted estimate of 147 mil-

lion t (Laws 1977). The Mori & Butterworth (2006)

estimate was an implausibly low number; whales

alone weighed roughly 60 to 70 million t (see ear-

lier analysis of Christensen 2006). Mori & Butter-

worth (2006) excluded 50% of fin whales and all

the sei whales (although, for instance, Harrison

Matthews (1938) suggested sei whales only ate

krill). There is no reason, or precedent, for a sepa-

rate fin whale population, rather the potential sep-

aration of feeding locations is more consistent with

the theory of mature ecosystems and niche spe-

cialisation (Odum 1969). Their fitted model esti-

mated the pristine abundance of blue and fin

whales (187 000 and 235 000 respectively) to be

much less than estimates in other studies; an alter-

native study based on a reasonable life history

model estimated 327 000 and 625 000 respectively

(Christensen 2006). Laws (1977) had suggested

200 000 and 400 000, without the benefit of Russian

catch reports that were then not available. It is

likely the pristine abundances of these species

were more than these highest estimates, as has

been suggested through genetic studies of other

similarly exploited whales (Ruegg et al. 2013). The

Mori & Butterworth (2006) model further relied on

whales in the pristine state being continually

starved through ‘overfishing’ krill. This scenario

would simply have led to smaller or fewer whales

over time in either a behavioural or function

response to the limitation of food (Holling 1959).

The concept of whales over-harvesting the krill

below the maximum sustainable yield at a fraction

of the carrying capacity (~30%) is an anthropo-

morphic concept contrary to ecological theory.

Ecological theory would suggest that the amount

of standing crop biomass supported by the avail-

able energy flow is expected to increase to a max-

imum in the mature or climax stages of an ecosys-

tem (Odum 1969). The idea that whales were

‘half-starved’ as a result of overfishing the krill

before the start of whaling is also falsified by con-

temporary reports based on stomach contents.

Whales examined during the early whaling period

usually had stomachs full of krill (Hardy &

Gunther 1936). Mackintosh & Wheeler (1929)

examined 519 blue and fin whales and all but 68

had full stomachs of krill. Ruud (1932) found that

of 300 whales examined in factory ships, only 2

immature whales had empty stomachs, and all oth-

ers had krill in large quantities. Mori & Butterworth

(2006) make several other assumptions which are

inconsistent with ecosystem ecological theory. As

mentioned above, they suggest that there was a

high level of interspecific competition which caused

a suppression of the abundance of minke whales

and crabeater seals in the pristine state; apart from

the fact there is no evidence for this, and no expla-

nation for how it might have happened (baleen

whales do not attack each other or seals and vice

versa), it is inconsistent with the characteristics of

mature ecosystems which have high levels of

niche specialisation and mutualism (Odum 1969).

The only example they give to suggest this might

be plausible involves 2 species of odontocetes

fighting over fish hooked on a longline, again an

unnatural situation, involving whales which are

very different from the mysticetes discussed else-

where in this study. On the other hand, ecosystem

theory is again borne out through detailed reports

of separation between minke, fin, sei and blue

whales feeding times and/or places (Kemp & Ben-

nett 1932, Marr 1956, Laws 1977, Santora et al.

2010). Mori & Butterworth (2006) also suggest a

relatively high range of natural mortality for the

larger whales, allowing their model to fit to large

whales with a low average natural lifespan and

thus quicker population turnover. They modelled

large whales, some of which were the largest ani-

mals that have ever existed, as if they were

smaller animals (bigger animals live longer than

smaller ones; Speakman 2005). For instance, they

used a limit of 0.03 to 0.06 in annual mortality for

blue whales (Laws [1997] had estimated 0.027 to

0.033). A range of 0.01 to 0.03 or lower would have

been more consistent with the facts (Branch et al.

2004, Ramp et al. 2006), our uncertainty, and the

theory of longevity of apex predators in mature

ecosystems (Odum 1969).

Other explanations: climate change

Flores et al. (2012) suggested that the long-term

decline in krill abundance was the result of climate

change through the reduction of sea ice and other

environmental changes. Sea ice extent has been

shown to be a factor in krill recruitment (Kawaguchi

& Satake 1994). Later studies showed that sea ice had

not reduced as Flores et al. (2012) had thought, and

Willis: Whales and krill

rather that it had an inverse relationship to sea tem-

perature (Shu et al. 2012). Therefore sea ice reduc-

tion is not a plausible explanation for the long-term

decline in krill abundance. In general, climate change

cannot yet be implicated as the major cause of krill

abundance changes since the end of whaling (Trathan

et al. 2012); the present study does not falsify any

hypotheses derived from that theory.

Hypotheses generated by this study

The model presented here, or more correctly the

modelling process, helps develop hypotheses that

would be valuable to test (Table 3). The model sug-

gests that krill respond to the threat of whales. The

smell of faeces is a likely cue. Krill can change their

daily migrations within a single cycle. All these

Condition Predictions or hypothesis

1. Surplus of krill in the deep habitat from about 1950 to Predators which are able to exploit krill in these areas 2000

(see Fig. 6). are likely to have had higher abundance during this period,

for instance: crabeater seals, fur seals, fish, squid, some pen-

guins and other species (Ballance et al. 2006).

2. Krill population abundance has decreased Krill population abundance may continue to decrease or be

since ca. 1970. approaching a low asymptote (Atkinson et al. 2004).

3. Non-whale surface predators may have benefited from Previous abundances of surface krill predators may provide

a cooperative relationship with whales. indication of previous abundance patterns of whales.

4. Whales may have developed mutualistic relationships The abundance patterns and numbers of fin, blue, minke, sei,

between species. and similar species may have been mutually interrelated. For

instance, they may have inhabited the same areas in succes

sion throughout the summer season as day length reduced.

Successively smaller feeding requirements could have main-

tained the nutrient retention cycle for surface krill.

5. Whales may have developed mutualistic relationships The abundance patterns of whales are likely to have been

between individuals. dispersed or grouped dependent on the availability of

surface-inhabiting krill. Where krill are at the surface, whales

may develop mutually beneficial fertilisation and strong

aggregation behaviour, whereas whales may disperse to

search for deep inhabiting krill.

6. Krill will react strongly to whale faeces. Krill are attracted to substances rich in soluble iron (Hamner

& Hamner 2000). They should react by changing their

behaviour.

7. Krill on the surface in daylight indicates whales are Observations already confirm this (Arsenev 1958) and more

around, will be around or were around the day before. advanced instruments should further confirm.

8. Abundance patterns of krill will be dependent on the Krill may swim laterally to areas of high concentration of

deposition of whale faeces. whale faeces and thus congregate in patterns that are differ-

ent from passively advected particles. The retention of krill in

the Antarctic Peninsula Plume (Smetacek 2008) may be an

example involving a major ocean feature. It is otherwise un-

known how krill navigate.

9. Krill size will vary less through the annual cycle: they If winter feeding opportunity is lower than summer, then the

may now be smaller and live longer than when whales lack of additional nutrients due to the breakage of the

were abundant. nutrient retention cycle means that krill body size fluctuation

will be less (Fig. 5), however the lowered risk may mean krill

live longer on average.

10. Whales will defecate in patterns (spatially and temp- If an area is saturated (over-fertilised) or unproductive it is

orally) which will serve to preserve the patterns of max- likely a whale will travel to an alternative location to defe-

imum feeding opportunity, and maximum retention of cate. This depends on digestive rate and swimming speed

faeces in the surface layers. and on the whale’s cognitive and sensory appreciation of the

hydrodynamic environment. Top predators target Lagrangian

coherent structures (Tew Kai et al. 2009) which are eddies,

fronts, convergences or diverges which can serve to concen-

trate substances, small animals and plants at the ocean sur-

face. Whales may sense these directly or indirectly.

Table 3. Summary of predictions and hypotheses derived from the modelling and discussed in the text

Mar Ecol Prog Ser 513: 51– 69, 2014

behaviours have been the subject of extensive

research on other closely related species, as outlined

in the introduction, and much further research with

krill in the laboratory and field is warranted. The fer-

tilisation process derived from whale faeces must

take some time, perhaps a few days or weeks, and

there must be a process of dilution and transport

related to water movements and thus weather or

hydrodynamics. There is great opportunity to model

the implications of fertilization and turbulence in bio-

geochemical ecosystem models. Mackintosh (1934)

mentioned the hydrodynamic associations with

whales in convergences, vortices, etc. and assumed

that krill passively aggregated in these areas, but the

situation is potentially more to do with the aggrega-

tion of planktonic prey of krill. There may also be

local feedbacks where a group of whales fertilising

the same area leads to an overall increase in feeding

opportunity for krill, and whales and other predators.

Whales have the choice where to deposit the fertiliser

and this might lead to spatial patterns that are inde-

pendent of physical processes or enhanced by them,

i.e. whales may congregate or disperse to defecate,

or go to certain areas such as persistent eddies or

upwellings in order to make best use of the fertilisa-

tion process. There is much scope for observation

and modelling of whale activity in this respect. The

distribution of krill is associated with ice cover

(Kawaguchi & Satake 1994) and so there may be

other confounding factors.

Improvements to the model

The model is very simple and so there is an almost

endless number of improvements that could be made

if realism is the objective, although this may not be

the best objective for such an idealised model (Oden-

baugh 2005). Firstly, the model includes no limita-

tions on resources and no density-dependent release

of predation at low abundances. This does not impact

the calibrated states, but does limit the model as a

predictor outside these states. For instance, the

model calibration suggests it is plausible that the krill

population has diminished, but cannot be used in any

way to aid prediction about when the decrease will

be terminated through density-dependent effects,

i.e. through a lower risk which should happen at

some low abundance. As for other more minor poten-

tial improvements, there are a number of alternative

options for linking whale abundance to krill en -

counter and thus krill habitat use. Rather than an

annual allocation, the likelihood of presence of

whales for each krill could be re-determined on a

weekly, monthly or other temporal schedule. In this

case, however, it is unknown how long the period

between whale fertilisation and feeding advantage

takes. Presumably it takes some time (at least 1 wk)

and begins a positive feedback mechanism locally

and so may take several weeks or months to build up

to a threshold or maximum. Presumably once it

begins to build up it makes the area more attractive

to other whales and so may reinforce itself for

sequential groups of whales. Kemp & Bennett (1932)

suggested whales of different species (fin and blue)

frequented the same areas in succession. Once the

positive feedback mechanism has started it seems

therefore unlikely that a whale would move on from

a higher feeding opportunity to speculate about feed-

ing potential in other areas (Table 3). However, water

movement, ice, competition and weather patterns

may impact the effect. Or the whales may purposely

fertilise and return on a schedule based around a

number of locations. Since these factors are mostly

unknown it seemed most parsimonious to assume

linkage on an annual basis.

CONCLUSIONS

The mechanisms outlined in this study offer the

potential for development of a krill fishery based on

fertilisation and behavioural control of krill. It must

be possible to distribute an analogue of whale faeces

over small areas and bring krill to the surface,

thereby causing krill abundance and production to

increase in the same way humans farm, e.g. grain

and cows, through a mixture of protection and aggre-

gation with individuals compressed in artificially fer-

tile areas. This would be a more sustainable plan

than competing with recovering whale populations

for a diminishing residual resource and perhaps driv-

ing the krill and whales to extinction.

Krill abundance is less than one quarter of what it

used to be in the pristine ecosystem. Even in the face

of high-quality unequivocal scientific evidence, this

stark fact has been overlooked or obfuscated in the

scientific conversation around the exploitation of

krill. The precautionary approach would be to stop

all krill fishing until the krill and whale population

abundance pattern is clearly understood. Further-

more, the impact of whales on the ecosystem is likely

to be a lot more complex than the one feedback

mechanism in this study (Ainley et al. 2010). At very

least, a representative spatial and temporal protected

area should be implemented in order to avoid any

Willis: Whales and krill

further confusion about the state of the ecosystem in

the absence of exploitation. Maintenance of an eco-

logically relevant protected zone would be the mini-

mum indication that a precautionary approach exists

in action rather than solely as rhetoric. The failed

protection of the Ross Sea would have been such a

signal of intent (Blight et al. 2010).

Southern Ocean whale population biomass de -

creased during the 20th century in a strangely

smooth curve (Fig. 3) only interrupted by the Second

World War, demonstrating the complete futility of the

size-based rules (Marr 1956) and any other manage-

ment intervention. Experiences of whalers from the

Arctic moving on to work in the Antarctic meant that

serial depletion of whale populations on a global

scale was clearly common knowledge well before the

peak of whaling in the Southern Ocean (Villiers

1925). Likewise, we should accept our own limita-

tions in the face of very strong evidence. We should

admit that history suggests that a profitable krill fish-

ery, however small it is now, is a warning of the likely

expansion of the fishery until krill are commercially

extinct.

Acknowledgements. I thank the editor, anonymous peer

reviewers and Marc Mangel for suggesting many improve-

ments to this study.

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➤

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Full-text available

Jun 2023

Whales have been titled climate savers in the media with their recovery welcomed as a potential carbon solution. However, only a few studies were performed to date providing data or model outputs to support the hypothesis. Following an outline of the primary mechanisms by which baleen whales remove carbon from the atmosphere for eventual sequestration at regional and global scales, we conclude that the amount of carbon whales are potentially sequestering might be too little to meaningfully alter the course of climate change. This is in contrast to media perpetuating whales as climate engineers. Creating false hope in the ability of charismatic species to be climate engineers may act to further delay the urgent behavioral change needed to avert catastrophic climate change impacts, which can in turn have indirect consequences for the recovery of whale populations. Nevertheless, whales are important components of marine ecosystems, and any further investigation on existing gaps in their ecology will contribute to clarifying their contribution to the ocean carbon cycle, a major driver of the world’s climate. While whales are vital to the healthy functioning of marine ecosystems, overstating their ability to prevent or counterbalance anthropogenically induced changes in global carbon budget may unintentionally redirect attention from known, well-established methods of reducing greenhouse gases. Large scale protection of marine environments including the habitats of whales will build resilience and assist with natural carbon capture.

On the rise: Climate change in New Zealand will cause sperm and blue whales to seek higher latitudes

Article

Full-text available

Sep 2022
ECOL INDIC

Climate impacts affect marine ecosystems worldwide with island nations such as New Zealand being extremely vulnerable because of their socio-economic and cultural dependence on the marine and costal environment. Cetaceans are ideal indicator species of ecosystem change and ocean health given their extended life span and cosmopolitan distribution, but limited data availability prevents anticipating change in distribution under future climate changes. We projected the range shifts of a key odontocete and mysticete species (Physeter macrocephalus and Balaenoptera musculus) in 2100 relative to present day in New Zealand waters, using an ensemble modelling approach, under three climate change scenarios of different severity. The results show a latitudinal shift in suitable habitat for both whale species, increasing in magnitude with severity of sea surface temperature warming. The most severe climate change scenario tested generated 61% and 42% loss and decrease of currently suitable habitat for sperm and blue whales, respectively, mostly in New Zealand’s northern waters. These predicted changes will have a strong impact on the ecosystem functioning and services in New Zealand’s northern waters but also in coastal areas (critical for the species’ foraging and survival). Not only do these simulated range shifts help to identify future potential climate refugia to mitigate a global warming, they also generate a range of socioeconomic consequences for island nations relying on wildlife tourism, industry, and environmental protection.

Climate change impacts on Antarctic krill behaviour and population dynamics

Article

Full-text available

Dec 2023

The Ecosystem Concept

Chapter

Jan 2023

Baleen whale prey consumption based on high-resolution foraging measurements

Article

Full-text available

Nov 2021
NATURE

Baleen whales influence their ecosystems through immense prey consumption and nutrient recycling1–3. It is difficult to accurately gauge the magnitude of their current or historic ecosystem role without measuring feeding rates and prey consumed. To date, prey consumption of the largest species has been estimated using metabolic models3–9 based on extrapolations that lack empirical validation. Here, we used tags deployed on seven baleen whale (Mysticeti) species (n = 321 tag deployments) in conjunction with acoustic measurements of prey density to calculate prey consumption at daily to annual scales from the Atlantic, Pacific, and Southern Oceans. Our results suggest that previous studies3–9 have underestimated baleen whale prey consumption by threefold or more in some ecosystems. In the Southern Ocean alone, we calculate that pre-whaling populations of mysticetes annually consumed 430 million tonnes of Antarctic krill (Euphausia superba), twice the current estimated total biomass of E. superba10, and more than twice the global catch of marine fisheries today11. Larger whale populations may have supported higher productivity in large marine regions through enhanced nutrient recycling: our findings suggest mysticetes recycled 1.2 × 104 tonnes iron yr−1 in the Southern Ocean before whaling compared to 1.2 × 103 tonnes iron yr−1 recycled by whales today. The recovery of baleen whales and their nutrient recycling services2,3,7 could augment productivity and restore ecosystem function lost during 20th century whaling12,13. A combination of 3D whale locations and acoustic measurements of prey density is used here to show that whales’ consumption of krill is several times larger than often thought.

Faecal sampling using detection dogs to study reproduction and health in North Atlantic right whales (Eubalaena glacialis)

Article

Full-text available

Jan 2006

Conservation and management of many cetaceans is hindered by the difficulty of acquiring samples from free-swimming individuals to obtain essential data on health, diet, reproduction and physiological impacts of environmental and anthropogenic stressors. This is particularly true for large whales, which are logistically difficult to live-capture for sampling. In North Atlantic right whales (Eubalaena glacialis), a significant decline in reproduction and health in the 1990s led to the application of faecal-based analyses to study stress and reproductive endocrinology, marine biotoxin exposure and prevalence of disease-causing protozoa. However, this approach was limited by low sample acquisition rates with opportunistic faecal (scat) collection methods. The work presented here evaluates the relative sampling efficiency of scent detection dogs trained to locate North Atlantic right whale scat versus opportunistic scat collection during photoidentification surveys. Three years of sample collection using both detection dogs and opportunistic methods are summarised. Faecal sample collection rates using detection dogs were over four times higher than opportunistic methods. The use of detection dogs for scat collection from free-swimming right whales has for the first time provided adequate numbers of samples for statistical analyses. The endocrine, disease, genetic and biotoxin studies currently being performed on these samples markedly improve the ability to address fundamental questions vital to effective conservation and management of highly endangered right whales.

Allocation patterns and diel vertical migration: Modeling the optimal Daphnia

Article

Full-text available

Jan 1997

Øyvind Fiksen

I present a model which at the same time evaluates optimal diel vertical migration (DVM) and optimal allocation patterns to growth or reproduction in the cladoceran Daphnia magna. The combined policies that maximize the intrinsic rate of increase r are found using the Perron Frobenius Theorem and dynamic programming. The model attempts to incorporate two traditions in aquatic behavioral ecology within the same model framework: variations in vertical migrations (short time behavior) and age or size at maturity (life history models). The predictions from the model are compared with observations from an experiment in thermally stratified flow-through tubes containing various concentrations of fish exudates. The model is able to reproduce many of the observations of both DVM and allocation, though there are some interesting deviations. I look at theoretical behavior and life history consequences of various size dependencies in mortality. Finally, I do a comparative analysis of two fitness measures, the intrinsic rate of increase r, and the net lifetime reproduction, R0, where it turns out that the use of r makes predictions in better agreement with the observations.

Overfishing: What Everyone Needs to Know®What Everyone Needs to Know®

Book

Jul 2012

Over the past twenty years considerable public attention has been focused on the decline of marine fisheries, the sustainability of world fish production, and the impacts of fishing on marine ecosystems. Many have voiced their concerns about marine conservation, as well as the sustainable and ethical consumption of fish. But are fisheries in danger of collapse? Will we soon need to find ways to replace this food system? Should we be worried that we could be fishing certain species to extinction? Can commercial fishing be carried out in a sustainable way? While overblown prognoses concerning the dire state of fisheries are plentiful, clear scientific explanations of the basic issues surrounding overfishing are less so - and there remains great confusion about the actual amount of overfishing and its ecological impact. Overfishing: What Everyone Needs to Know will provide a balanced explanation of the broad issues associated with overfishing. Guiding readers through the scientific, political, economic, and ethical issues associated with harvesting fish from the ocean, it will provide answers to questions about which fisheries are sustainably managed and which are not. Ray and Ulrike Hilborn address topics including historical overfishing, high seas fisheries, recreational fisheries, illegal fishing, climate and fisheries, trawling, economic and biological overfishing, and marine protected areas. In order to illustrate the effects of each of these issues, they will incorporate case studies of different species of fish. Overall, the authors present a hopeful view of the future of fisheries. Most of the world's fisheries are not overfished, and many once overfished stocks are now rebuilding. In fact, we can learn from the management failures and successes to ensure that fisheries are sustainable and contribute to national wealth and food security. Concise and clear, this book presents a compelling "big picture " of the state of oceans and the solutions to ending overfishing.

Modelling spatial dynamics of fish (vol 8, pg 57, 1998)

Article