SCRS/2008/028 1

Modelling the impact of fishery bycatch on

wandering and black-browed albatrosses of South

Georgia: preliminary results

Robin B. ThomsonCSIRO Division of Marine and Atmospheric Research

GPO Box 1538, Hobart, Australia, TAS, 7001

Richard A. PhillipsBritish Antarctic Survey, Natural Environment Research Council

High Cross, Madingley Road, Cambridge CB3 0ET, U.K.

Geoff N. TuckCSIRO Division of Marine and Atmospheric Research

GPO Box 1538, Hobart, Australia, TAS, 7001

March 12, 2008

1 Introduction

Longline fisheries have expanded throughout the world’s oceans since majorcommercial distant-water pelagic fleets began fishing for tuna and tuna-likespecies in the early 1950s. Along with the more recent development and ex-pansion of demersal longline fleets for species such as Patagonian toothfish,these vessels are a major source of mortality to several species of seabird.Vessels can set many thousands of baited hooks in a day across many kilome-tres of water. These waters are often used as foraging areas by wide-rangingseabirds. Attracted by baits and offal, the birds can be caught on the baitedhooks and subsequently drown.

To provide a greater understanding of the potential impact of the majorlongline and trawl fleets on seabird populations, in Section 2.1 we describethe trends in effort of the major pelagic and demersal fisheries consideredin this paper. Tuck et al. (2003) showed that the total reported effort fromall longline fleets south of 30oS has been well over 250 million hooks peryear since the early 1990s. However, the spatial and temporal distributionof this effort has not been constant. While effort from the Japanese pelagicdistant-water longline fleet declined through the 1990s, the Taiwanese fleetexpanded dramatically. Likewise demersal fishing for toothfish increased

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markedly during the mid-1990s. These fisheries, along with substantial trawland illegal longline fisheries, may be placing the long-term viability of manyspecies of seabird in jeopardy.

Tuck et al. (2004) present a model for three populations of wandering alba-tross, that coupled information on longline fishing effort with demographicdata and known life history information. The model revealed the extent towhich observed trends in the albatross populations, up to the year 2000,could be ascribed to fishing activity. Projection of this model revealed thelikely impacts on the albatross populations of future changes in fishing effort.

This working paper extends the model of Tuck et al. (2004) and presentsan update to 2006, using fishing effort data and demographic data, for wan-dering albatross (and a preliminary consideration of black-browed albatrosspopulations) on Bird Island, South Georgia. Some text presented here hasbeen copied from Tuck et al. (2004) and the report in which it was published.

2 Methods

The seabird model presented here differs from that of Tuck et al. (2004)in several ways. Characteristics of the new model are listed below, wherethese differ from the model of Tuck et al. (2004), italicised text describesthe Tuck et al. (2004) model:

1. Seabirds are classified as chicks, juveniles, breeding adults (incubating,brooding and post-brooding), successful non-breeding adults or failednon-breeding adults (instead of a single breeding adult category)

(a) birds in each category are distributed according to that category’sspatial distribution pattern (Section 2.5),

(b) the non-breeding categories and juveniles use the same spatialdistribution pattern, based on an observed range; bird densitieswithin this range are calculated (see (Tuck et al. , 2004) for de-tails),

(c) the spatial distribution patterns for the breeding categories arebased on observed data (instead of calculating distribution fromrange data for both breeding and non-breeding birds).

2. Seabird catches are a function of the number of longline hooks deployedby 4 fisheries, each having a different bycatch rate:

(a) pelagic Japanese fisheries south of 30oS (mitigation begins in 1998(Anon, 1997)), (instead of 1990),

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(b) all pelagic longline fisheries excluding (a),

(c) legal demersal fisheries (instead of dividing these into ‘SouthAmerican’ and ‘other’),

(d) illegal demersal fisheries.

3. Mitigation in the Japanese pelagic fishery is assumed to halve theincidental mortality rate (this will be estimated in future modellingwork).

4. Juvenile survival is density dependent, with survival increasing as thepopulation size declines.

5. Chick survival is density dependent, being lower when the populationcontains more breeding pairs.

6. The dynamics of female birds are modelled - male mortality con-tributes only to breeding failure through chick mortality.

7. A 5×5o spatial resolution is used for fishing effort and bird distribution.

8. Fishing effort data (number of hooks) between 1960 and 2006 is used(instead of 1960-2000).

9. Seabird demography data for Bird Island, South Georgia, up to 2006is used (instead of 2000).

10. Population dynamics are modelled on a monthly time-step (instead ofan annual model):

(a) seabirds move between incubating, brooding, post-brooding andfailed stages each month,

(b) monthly estimation of catches allowing for changes in distributionof both fisheries and seabirds.

11. The model makes allowance for an annual breeding cycle (instead ofmodelling a bi-ennial breeder).

12. The model has been implemented using the R statistical package (RDevelopment Core Team, 2007), facilitating easier graphical presenta-tion and access to statistical routines. (instead of coding the model inC++).

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2.1 Major fishing operations

The ability to fly long distances in short periods of time, and thus cover alarge area, combined with an affinity for baits and offal, make albatrosseshighly vulnerable to major longline and trawl fisheries. Major commercialpelagic longline operations began in the early 1950s, targeting tunas andtuna-like species on the high seas of the Northern Hemisphere and equatorialregions (Caton & Ward, 1996; Tuck et al. , 2003). Competition for the tunacanning market from Taiwan and Korea led the Japanese to expand theiroperations southward in search of higher quality sashimi tunas during thelate 1950s and 1960s (Caton & Ward, 1996). Southern expansion was has-tened with the discovery of southern stocks of bluefin tuna Thunnus maccoyiiand the development of longline vessels with deep freezers. The Japanesedistant-water fleet began expanding rapidly into the Southern Ocean fromthe mid 1960s, while the Taiwanese, mainly targeting southern populationsof albacore T. alalunga, gradually increased effort south of 30oS through the1970s. The Japanese and Taiwanese fleets are currently the largest and mostwidespread pelagic longline fleets fishing the Southern Ocean (Polacheck &Tuck, 1995; Tuck et al. , 2003). Other nations such as Korea and Spain alsohave distant-water longlining fleets operating in southern waters, but cur-rently at a much smaller scale. Recent years have seen the expansion of localpelagic fisheries, largely within their exclusive economic zones, of Australia,New Zealand and South Africa. Japanese-style pelagic longline vessels setaround 3000 baited hooks per set, on mainlines that may be over 100kmsin length (Baron, 1996). Sets are usually made on a daily basis and cantake between 4 and 6 hours, with hauls sometimes over 10 hours (Caton &Ward, 1996; Baird, 2001). The pelagic fisheries’ southern extent is typicallyto 45oS and directed at temperate water species such as albacore, swordfishand southern bluefin tuna. Pelagic longline vessels generally operate on thehigh seas, and the highly migratory habit of many species of seabirds, inparticular albatrosses, often leads to interactions between these vessels andforaging birds.

Southern Ocean demersal longline fisheries for Patagonian toothfish, hakeand ling did not begin until the 1980s or later (Tuck et al. , 2003). The fleetsof Chile, Argentina, New Zealand and those fishing under the jurisdictionof CCAMLR are the principal operators in the Southern Ocean. Demersalvessels can deploy many thousands of hooks in a set (eg up to 20000 perset reported for the Chilean industrial toothfish fishery (Garcia, 2001)), ona mainline that is usually shorter than that of pelagic longlines, around 15kms in length. More than one set may occur in a day with setting takingbetween 1 and 4 hours, and hauling between 8 and 13 hours (Brothers et al., 1999; Baird, 2001). Trawl fisheries have also recently been shown to causesubstantial incidental mortality of seabirds (Sullivan et al. , 2006). Bycatch

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on trawlers can be caused by collisions with netsonde cables, warp cablesand paravanes, or by entanglements with the net. As the demersal longlineand trawl vessels target species that inhabit shelf and slope waters, theypose a serious threat due to their proximity to the breeding sites of manyspecies of seabirds.

The deliberate under-reporting and misreporting of effort, and illegal fish-eries operations provides other major concerns for both fishery managementand conservation. Restrictive quotas and the high price for tunas and tooth-fish have led to a marked increase in illegal, unreported and unregulated(IUU) fishing since the 1990s (Lack & Sant, 2001). This uncontrolled ef-fort places extreme pressure on target and incidentally caught species andundermines attempts to manage stocks in an ecologically sustainable man-ner. By its very nature, IUU fishing catch and effort levels are difficult toestimate (further complicating assessments of impacted species). As suchvery little information is available on the IUU fleets, their catch and theireffort distributions. Identifying illegal activity even by major oceanic areais problematic (Tuck et al. , 2003). As illegal vessels are unlikely to be im-plementing bycatch mitigation measures to the same level as those vesselsregulated by conservation measures, the potential impact of these vessels onseabirds may be substantial. The large estimates of seabird bycatch fromdemersal IUU vessels operating in the CCAMLR region has led that Com-mission to state that the greatest threat to albatrosses and petrels breedingin the Convention Area is the mortality likely to be associated with demer-sal IUU longline fishing within the Convention Area and longlining for otherfish species in adjacent areas (SC-CAMLR, 2001).

2.2 Fishing effort datasets

The effort statistics applied in the models are based on reported effort tothe various agencies responsible for the management of the fisheries - Table1, (Tuck et al. , 2003). In addition, as the quality of data reporting andmaintenance varied greatly across data collection agencies, effort for somefisheries where data were missing or not available was estimated. Theseestimates were generally based on auxiliary information, such as time seriesof vessel numbers, target species catch (eg from FAO statistics), catch ratesor by using effort records from similar time/space periods - Table 2, (Tucket al. , 2004). Problems with fishery records of effort included effort statis-tics not being collected from the initiation of the fishery or being recordedinconsistently (eg Chile demersal fisheries), records of effort not measuredusing hook counts (or not measured at all), records with little or no spa-tial resolution (eg several demersal fleets), or, more infrequently, effort databy fleet not being publicly available (eg Pacific pelagic fleets - see below).Long-term datasets for some fisheries were also not obtained (eg the dem-

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ersal longline fisheries of Argentina). However, if data-poor fisheries (Table2) overlapped with the hypothesised range of the albatrosses and was eitherknown, or could reasonably be assumed, to impact the population then effortdata were modelled using information from the scientific literature if avail-able and sufficient, or by making simple assumptions regarding the fleets’spatial and temporal distributions. For example, some datasets only haddata in annual or quarterly time-steps (eg ICCAT). In this case, hook datawere spread evenly from the course time-scale to the finer monthly time-scale. Other datasets finished prior to the year 2006 model time horizon.These datasets were completed by using supplementary information, such astarget species catch (from FAO) and catch rate data or vessel numbers, orif this was not available then the same magnitude and distribution of hooksthrough to 2006 was assumed. . The general methods used to estimatehooks by space and time are outlined in (Tuck et al. , 2004, Supplementto Chapter 4) and are not repeated here. As these estimates are likely tohave large uncertainties associated with them, the sensitivity of results tothe modelled data will be explored in future analyses.

The public data obtained from SPC contains monthly, 5 degree longlineeffort data aggregated across all fishing nations. This can prove problem-atic for the model if not all of these fleets have the same catchability. Forexample, if it is assumed that the introduction of mitigation devices (torilines) by the Japanese in the Southern Ocean during the mid-late 1990s hasreduced seabird bycatch (Klaer & Polacheck, 1998), then the catchabilityattributed to this fishery will not be the same as (i) that of other fleets and(ii) the Japanese fleet prior to mitigation. As such, Japanese effort datasouth of 30oS from the CCSBT was subtracted from the SPC dataset andthen used as a separate ’mitigated’ fleet (see section 2.6).

Estimates of IUU toothfish catch and effort by CCAMLR statistical areaare provided as part of the annual fish stock assessment process for theCCAMLR region. These data were included in the analyses but should betreated with due caution (SC-CAMLR, 2007). In order to include these datain this analysis, the spatio-temporal distribution of IUU effort was assumedto mimic that of the legal longline fishery (with the exception of Division58.5.2, where legal fishing is restricted to winter months. IUU effort wasspread evenly across all months of the year in this case). While the esti-mated magnitude of effort may not reflect the true level of IUU fishing inthe area, the model can vary this through changes in the fishery catchabilitycoefficient assigned to CCAMLR area demersal IUU fishing.

Very little information was available to estimate either the magnitude orspatio-temporal distributions of effort for pelagic IUU fisheries. However,effort data from the Indian, Atlantic and Pacific Oceans (IOTC, ICCAT and

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SPC) has been adjusted to account for unreported catch and effort recordsfrom logbook data (Campbell, 2003; Tuck et al. , 2004; Taylor, 2007). WhileICCAT and SPC provide raised effort statistics, as part of this study thecatch and effort data of IOTC was raised to match the nominal catch accord-ing to the factors given in the Appendix. Only Taiwan, Japan (as these arethe major longline fleets operating in the Indian Ocean) and South Africawere considered. The spatio-temporal resolution of Korean catch statisticsto IOTC is poor and little large scale fishing by Korean distant-water vesselsoccurs in southern Indian Ocean waters (Anon, 2001). Australian swordfisheffort data from the Indian Ocean was provided as a separate dataset fromAustralian fishery authorities.

2.3 Seabird biology

The model focuses initially on the wandering and black-browed albatrossesbreeding at South Georgia, of which there were 1,553 and 75,500 breedingpairs, respectively, in 2004 (Poncet et al. , 2006), representing the third andsecond largest global population respectively, of both species. Black-browedalbatrosses return from migration on average in mid-October, lay in lateOctober/early November, hatch chicks at end December and fledge chicksat end April. By comparison, wandering albatrosses return in December, layin early January, hatch chicks in late March/early April and fledge chicks inDecember the following year i.e. chicks are raised predominantly during theaustral winter. After hatching, chicks are attended continuously by one orother parent for about 3 weeks during the brood-guard phase when they aremost vulnerable to predation and their limited body reserves necessitate top-up feeds to prevent starvation. Thereafter, the chick is unattended as bothparents forage at sea, returning at mean intervals of 2-3 days (sometimesmuch longer) to deliver large, energy-rich meals. In general, black-browedalbatrosses breed annually and wandering albatrosses breed biennial if suc-cessful. Wandering albatrosses that fail early in the season are much morelikely to return the following year than those that fail late. A pictorial rep-resentation of the life history is shown in Figure 1.

2.4 Demographic data

British Antarctic Survey has monitored the population size, timing of ar-rival, laying, hatching and fledging, breeding success, adult and juvenile sur-vival and non-breeding rates of birds in study populations of black-browedand wandering albatrosses at Bird Island, South Georgia since 1976, withsome earlier data on population size for wandering albatross (Croxall et al., 1998, 1990). Birds of virtually all life-history stages and status have beentracked using satellite-transmitters, GPS or GLS (geolocator) loggers. The

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Table 1: The available reported data by source, nationality and years (maynot be inclusive; Southern Hemisphere data only). Countries operating inthe Pacific and Atlantic Oceans with total reported effort less then 20 millionhooks were not listed under SPC and ICCAT. IOTC, Indian Ocean TunaCommission; SPC, Secretariat of the Pacific Community; ICCAT, Interna-tional Commission for the Conservation of Atlantic Tunas; CCSBT, Com-mission for the Conservation of Southern Bluefin Tuna; AFMA, AustralianFisheries Management Authority; CCAMLR, Commission for the Conser-vation of Antarctic Marine Living Resources; NRIFSF, National ResearchInstitute of Far Seas Fisheries, Japan. * some of the data were estimated inorder to complete the data series (see Table 2)

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Table 2: Fisheries where substantial modelling of spatio-temporal effort datawas required for some or all years and the sources of information used.Also listed are fisheries where adequate data are yet to be obtained. Effort= effort estimated; Position = 5 degree square estimated; Time = monthestimated.

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Figure 1: A diagrammatic representation of the albatross population model.Juveniles (J) become breeding adults according to the rate parameter λ.There are three breeding adult stages: incubating (Inc), brooding (Br) andpost-brooding (P-Br). Breeding adults are either successful (S) or fail (F).At each of the breeding stages birds can fail. If chicks are successfullyreared, adults become breeders the next year according to the rate param-eter pr (pr = 0 for wandering albatross) or non-breeders (Nbs) with rate1-pr. If unsuccessful, adults breed the following year with rate pf or becomenon-breeders with rate 1 − pf . The rates of return from the non-breedingpopulations are given by α and β for successful and failed breeders respec-tively.

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published and unpublished data on population size, demography and dis-tribution forms the basis for the model described here. It should be notedthat few, if any, other data-sets exist, worldwide, of comparable longevityand quality for any long-lived species of bird. The wandering albatross dataused to condition the model are presented in later figures, which show themodel’s ability to fit to those data. The data on the number of breedingpairs for wandering albatross has been scaled upwards so that it representsthe whole South Georgia wandering albatross population.

Unless otherwise stated, the simulations presented in this paper use an adultnatural mortality rate of M = 0.04, giving an annual finite survival rate of0.961. This reflects observations of high survival rates for known individuals.

For the wandering albatross at Bird Island the proportion of non-breedingadults that join the breeding population after successfully breeding at theprevious attempt is α, while the proportion of non-breeding adults that jointhe breeding population after unsuccessfully breeding is β. These parame-ters were derived by collapsing the year-specific parameters of Croxall et al.(1990) into a single, annual return rate parameter for each of the successfuland unsuccessful non-breeding birds. These produce very similar dynam-ics to the original year-specific parameters and greatly simplify the modelstructure and programming (Tuck et al. , 2004).

Birds that are unsuccessful in their breeding attempt will return to thebreeding colony the following year at rate ρf , and those that are successfulat rate ρr. Wandering albatross are biennial breeders so that ρr = 0 whereasmost adult black-browed albatross attempt to breed every year.

Table 3: Demographic parameter values used in modelling wandering alba-tross.

Symbol Description WanderingAlbatross

M Adult natural mortality 0.04α Return rate for successful non-breeders 0.741β Return rate for unsuccessful non-breeders 0.8ρf Return rate for failed breeders 0.8ρr Return rate for successful breeders 0- Age at first breeding 10

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2.5 Seabird distribution data by bird category

The at-sea distributions of both wandering and black-browed albatrossesfrom South Georgia are extensive: birds are capable of movements of ¿800kmper day, engage on foraging trips during the breeding season in excess of 5-10,000km, and in the case of wandering albatrosses, may twice circumnavi-gate the Southern Ocean during a non-breeding period of 11-12 months. Thedistribution of both species shows dramatic seasonal changes depending oncentral-place foraging constraints associated with reproduction. Foragingtrips are much longer during incubation and post-brood chick-rearing thanduring brooding (Figure 2), (Phillips et al. , 2004; Xavier et al. , 2004).During brooding, one adult is always in attendance at the nest to protectthe young chick from predation/inclement weather and to provide top-upfeeds while the chicks stomach is too small to ingest large meals; hence,feeding trips are much shorter. In black-browed albatrosses, birds that havefailed or are deferring breeding (on sabbatical) exhibit an extended versionof the feeding range of active breeders, but depart much earlier (late Feb.)on migration. During the non-breeding period, the great majority of black-browed albatrosses migrate to the Benguela upwelling off south-west Africa,and a much smaller proportion (< 5%) to either the Patagonian Shelf orAustralia (Phillips et al. , 2005), Figure 4. In contrast, non-breeding wan-dering albatrosses show a much wider distribution throughout the SouthernOcean (BAS, unpublished data), Figure 3).

In black-browed albatrosses, sexual segregation in distribution is consid-erable during incubation, and slight or negligible at other times of year(Phillips et al. , 2004, 2005). By comparison, adult male wandering alba-trosses tend to show a more southerly distribution than females throughoutthe year (Weimerskirch & Jouventin, 1987; Prince et al. , 1992). This hasimportant implications for mortality as females are more likely to encountermajor tuna longline fisheries targeting southern bluefin tuna Thunnus mac-coyii and albacore Thunnus alalunga in low latitudes, whereas males show agreater overlap with those for Patagonian toothfish Dissostichus eleginoides(Nel et al. , 2002; Tuck et al. , 2003). This is reflected in higher mortality offemales (Weimerskirch & Jouventin, 1987; Croxall et al. , 1990; Prince et al., 1992). Juvenile wandering albatrosses are considered to be more suscep-tible to fishing than adults because of their more sub-tropical distribution,and potentially increased per capita likelihood of capture when behind avessel (Weimerskirch & Jouventin, 1987). Alternatively, their vulnerabil-ity to capture behind a vessel may be lower if there are large numbers ofadults in attendance, reducing the ability of the competitively inferior juve-niles to access baited hooks. Through similar mechanisms, the larger, maleblack-browed albatrosses tend to be caught in greater numbers than femalesin many fisheries, attributed to their competitive advantage in aggressive

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Figure 2: Observed spatial distribution of female wandering albatross dur-ing the (a) incubation (and when failed), (b) brood, and (c) post-broodingphases. Birds that attempt to breed but fail are assumed to have the samedistribution as incubating birds for the remainer of the breeding season.Cyan indicates low and purple indicates high bird density.

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Figure 3: Distribution of non-breeding adult wandering albatross. Observedpresence/absence were used and bird density within this range was calcu-lated (see (Tuck et al. , 2004) for details). Cyan indicates low and purpleindicates high bird density.

encounters over bait (Ryan & Boix-Hinzen, 1999). Presumably as a conse-quence, males at South Georgia show higher mortality (by 2%) than females(Croxall et al. , 1998), However, most of the black-browed albatrosses killedin South African waters are juveniles, which remain in the area and are po-tentially vulnerable to bycatch throughout the year (Ryan et al. , 2002).

2.6 Modelling incidental catch

The incidental catch of wandering albatrosses will depend on the space andtime overlap of the albatross population and fishing effort. This, in turn, willbe a function of the sex of the birds (females tend to feed in more northerlylatitudes (Weimerskirch & Jouventin, 1987), age (juveniles may be more sus-ceptible to capture due to their more sub-tropical distribution overlappingwith that of longline vessels), breeding status (breeding individuals foragein different areas than non-breeding birds), and the particular populationunder consideration. Juveniles are believed to be more susceptible to fishingthan adults due to their more sub-tropical at-sea distribution and possiblyto inexperience (Weimerskirch & Jouventin, 1987). In contrast however, ju-veniles may be competitively inferior to adults at longlines and when bothare present adults may be caught on baited hooks more often than juveniles(J. Croxall, pers. comm.). Tuck et al. (2004) found there were insufficientdata to estimate differential catchability between adults and juveniles, in

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(a)

(b)

(c)

Figure 4: Observed spatial distributions of black-browed albatross (sexescombined) during the (a) incubation, (b) brood, and (c) post-broodingphases. Yellow indicates low and red indicates high bird density.

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the present preliminary study the catchabilities are assumed to be equal.

To model total catch Ccy,m of female birds taken from each bird categoryc during month m of year y, the catch in each 5× 5 degree square s by eachfishery f is summed. The catch in a particular square s is a function of thenumber of female birds N c

y,m present in square s at that time. This is givenby the proportion P cs,y,m of the birds of category c that have been observed(or assumed) to occupy square s, multiplied by the number of female birdsN cy,m in the population at that time. This is, in turn, multiplied by the ef-

fective number of bird-catching hooks deployed in that square at that time,which is given by the product of the total number of hooks deployed by eachfishery f Efs,y,a multiplied by the model estimated ‘catchability’ of hooks forfishery f , qf . The total effective number of hooks is calculated by summingacross all fisheries f

Ccy,m =∑s,y,m

N cy,m P cs,y,m

∑f

qf Efs,y,a

. (1)

2.7 Super-fleets

The catchability of seabirds may differ between longline fleets, eg accord-ing to gear specifications, application of mitigation measures, time of linedeployment (Brothers et al. , 1999). For this reason, separate catchabilitycoefficients qf were estimated for each of the main fishery types f (referredto as super-fleets). The super-fleets are composed of multiple smaller fleetsthat are assumed to have similar physical characteristics, pursuing similarstrategies and have similar bycatch rates. These were:

i the Japanese pelagic longline fishery operating south of 30oS (f = J),

ii all pelagic longline fisheries operating north of 30oS and non-Japanesefleets south of 30oS (f = OP ),

iii all demersal longline fisheries with the exception of demersal IUU (f =OD),

iv the demersal IUU longline fishery operating within the CCAMLR Area(f = IUU) and,

v trawl fleets (f = TR).

While necessarily subjective, a brief justification for the choice of super-fleets is given below.

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Due to the introduction of catch mitigating devices by the southern Japaneselongline fleet in the mid-late 1990s, the catch rate of seabirds is likely to beless than that historically in this fishery (Klaer & Polacheck, 1998). Mitiga-tion measures that have been implemented, but not necessarily consistently,include setting lines at night, tori lines, bait-throwing devices, bait thaw-ing, and line weighting (Brothers, 1991; Klaer & Polacheck, 1997, 1998).The decrease in catch due to mitigation since 1998 from the Japanese fleetis modelled as a constant proportion of the rate prior to this period, qm,where 0.1 ≤ qm ≤ 1 for effort below 30oS. The lower limit, suggesting a 90%reduction in albatross bycatch, is indicative of the maximum potential ofmitigation measures to reduce bycatch, as observed in the Japanese longlinefishery (CCSBT, 1997; Takeuchi et al. , 1997; Klaer & Polacheck, 1998).For example, a reduction of between 69% and 87% has been achieved onJapanese vessels with appropriately designed and implemented tori lines,while night setting can reduce albatross bycatch by between 70% and 96%(CCSBT, 1997). Of the distant-water pelagic longline fleets considered, theJapanese southern bluefin tuna fleet is the best documented and has thegreatest application of mitigation devices, with tori lines mandatory withinthe Australian and New Zealand EEZs and on the high seas south of 30oS.With growing awareness of the impacts of incidental mortality and conse-quent use of mitigation devices, other fleets may warrant similar modelling- this will be explored in future analyses. As an example, a combinationof seasonal closures and mitigation measures for the legal longline fisheryfor toothfish under the jurisdiction of CCAMLR has clearly been successfulin almost eliminating bycatch since the late 1990s and early 2000s (SC-CAMLR, 2001; Tuck et al. , 2003; Delord et al. , 2005). In this analysis, qmis applied to effort data from only the southern Japanese fleet.

Due to the highly uncertain nature of the effort data and potentially largeseabird catch rates, the illegal fleet operating within the jurisdiction ofCCAMLR was assigned to a separate super-fleet from other demersal fish-eries within the model (SC-CAMLR, 2007; Tuck et al. , 2003). As men-tioned, while the magnitude of estimated IUU effort may be incorrect, theeffect of the catchability parameter allows this to be scaled accordingly. Itis the relative temporal trend in hooks that will play a more important rolein the model fits (see SC-CAMLR (2007) for annual IUU catch estimates byCCAMLR statistical area).

The demersal longline and trawl fishery sectors (the trawl fleet are not shownin this document because they do not catch significant numbers of wanderingalbatross) have been assigned their own super-fleet. An exploration of anysystematic, and well documented, change in catch rates due to mitigationin these fleets is ongoing. Dividing the fleets (eg the CCAMLR regulateddemersal longline fishery, pelagic longline swordfish) temporally into super-

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fleets prior, and subsequent, to substantial mitigation (in a similar mannerto the southern Japanese pelagic longline fleet) will be considered in futureanalyses.

Figures 5(a)-(d) show the spatial distribution of hooks for each of the super-fleets over all years. The southern Japanese super-fleet shows strong con-centrations of effort off southern Africa, southwest and east of Australiaand New Zealand (Figure 5(a)). These are known areas of high southernbluefin tuna catches. The pelagic longline super-fleet covers much of theworld’s oceans, with strong concentrations of effort in equatorial regionswhere tropical tunas are caught (eg yellowfin tuna), (Figure 5(b)). Demer-sal fisheries clearly show spatial effort concentrations associated with landmasses or sub-Antarctica islands, with waters off Argentina, Chile, SouthAfrica and New Zealand particularly prominent (Figure 5(c)). The spatialdistribution of the IUU demersal longline super-fleet highlights the largeestimated effort surrounding sub-Antarctic islands, which is clearly criticalhabitat for breeding seabirds (Figure 5(d)).

Figure 6(a) shows the gradual and continual increase in hooks deployed bypelagic and demersal longline fleets across the globe, and in waters south of20oS; a key region of seabird habitat (see Figures 2 and 3). The estimatedannual number of hooks set by pelagic and demersal fleets in 2006 is 1700million and 200 million respectively.

The temporal trend in effort of each of the super-fleets is illustrated in Fig-ures 7(a)-(h). Figure 7(a) shows the rapid increase in the number of hooksset by the southern Japanese pelagic longline super-fleet from the mid 1960safter the discovery of southern populations of the highly prized bluefin tuna.This effort peaked in the 1980s at roughly 100-120 million hooks per year andsubsequently halved to be roughly 40-50 million hooks per year in 2006. Theannual trend in hooks from all other pelagic longline fleets (i.e. the pelagiclongline super-fleet) is shown in Figures 7(b)-(d), ICCAT fleets shown inFigures 7(b) and (c).

The time-series of estimated demersal longline effort for Argentina, Chile,Falklands and CCAMLR (Figure 2(f)), South Africa, Namibia, Australia,and New Zealand (Figure 2(g)) and IUU demersal longline fishing (Figure2(h)) shows a rapid increase in effort from the 1980s and 1990s with the dis-covery and exploitation of hakes, ling and Patagonian toothfish. In 2006, theestimated number of demersal hooks deployed is approximately 200 millionhooks, with CCAMLR, New Zealand and Namibian fisheries dominating.

Specifically looking at ICCAT fisheries (Figures 7(b) and (c)), the top 10fleets have been separated and an ’others’ category created that includes

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(a)

(b)

Figure 5: Number of hooks deployed by 5×5o square by (a) Japanese pelagicvessels south of 30oS, (b) other pelagic vessels, (c) demersal vessels fishinglegally, (d) demersal vessels fishing illegally. Continued next page...

20

(c)

(d)

Figure 5 ...Continued from previous page. Number of hooks deployed by5× 5o square by (a) Japanese pelagic vessels south of 30oS, (b) otherpelagic vessels, (c) demersal vessels fishing legally, (d) demersal vesselsfishing illegally.

21

Figure 6: Total number of pelagic hooks deployed south of latitude 20oS (akey region of seabird habitat), and the total numbers of pelagic and demersalhooks deployed across the globe between 1950 and 2006.

(a)

Figure 7: Number of hooks deployed by 5 × 5o square by (a) Japanesepelagic longline fishing south of 30oS, (b) larger pelagic longline fisheries inthe Atlantic ocean, (c) remaining pelagic fisheries in the Atlantic ocean, (d)pelagic longline fisheries in the Indian Ocean, (e) pelagic longline fisheriesin the Pacific Ocean, (f) selected demersal vessels fishing legally, (g) re-maining demersal vessels fishing legally (h) demersal vessels fishing illegally.Continued next page...

22

(b)

(c)

Figure 7 ...continued from previous page. Number of hooks deployed by5× 5o square by (a) Japanese pelagic longline fishing south of 30oS, (b)larger pelagic longline fisheries in the Atlantic ocean, (c) remaining pelagicfisheries in the Atlantic ocean, (d) pelagic longline fisheries in the IndianOcean, (e) pelagic longline fisheries in the Pacific Ocean, (f) selecteddemersal vessels fishing legally, (g) remaining demersal vessels fishinglegally (h) demersal vessels fishing illegally.

23

(d)

(e)

Figure 7 ...continued from previous page. Number of hooks deployed by5× 5o square by (a) Japanese pelagic longline fishing south of 30oS, (b)larger pelagic longline fisheries in the Atlantic ocean, (c) remaining pelagicfisheries in the Atlantic ocean, (d) pelagic longline fisheries in the IndianOcean, (e) pelagic longline fisheries in the Pacific Ocean, (f) selecteddemersal vessels fishing legally, (g) remaining demersal vessels fishinglegally (h) demersal vessels fishing illegally.

24

(f)

(g)

Figure 7 ...continued from previous page. Number of hooks deployed by5× 5o square by (a) Japanese pelagic longline fishing south of 30oS, (b)larger pelagic longline fisheries in the Atlantic ocean, (c) remaining pelagicfisheries in the Atlantic ocean, (d) pelagic longline fisheries in the IndianOcean, (e) pelagic longline fisheries in the Pacific Ocean, (f) selecteddemersal vessels fishing legally, (g) remaining demersal vessels fishinglegally (h) demersal vessels fishing illegally.

25

(h)

Figure 7 ...continued from previous page. Number of hooks deployed by5× 5o square by (a) Japanese pelagic longline fishing south of 30oS, (b)larger pelagic longline fisheries in the Atlantic ocean, (c) remaining pelagicfisheries in the Atlantic ocean, (d) pelagic longline fisheries in the IndianOcean, (e) pelagic longline fisheries in the Pacific Ocean, (f) selecteddemersal vessels fishing legally, (g) remaining demersal vessels fishinglegally (h) demersal vessels fishing illegally.

26

all other fleets (this is a substantial fleet and accounts for nearly 74 millionhooks of 316 million set in 2005). The dominant fleets within the ICCATConvention Area are those of Taiwan (recent peak of 159 million hooks in2003), Japan (around 200 million hooks through the 1990s and 71 millionhooks in 2005) and Spain (around 70 million hooks in the late 1990s and 33million reported hooks set in 2005).

Close examination of Figure 7 gives the impression that effort has, in somecases, been allocated across larger spatial areas, seemingly 15o× 15o (a con-venient size often used in conjunction with geographical data, Klaer perscomm.). This is indicated by the apparent effort over areas of land; see(Klaer et al. , 2008) who plots only ICCAT data, where the same indicationis seen. It is beyond the scope of this investigation to attempt to re-allocateeffort among areas, therefore these data have been used in the form in whichthey were supplied.

2.8 Overlap between fisheries and bird distributions

Figure 8 shows the annual total effort from the super-fleets within the rangeof each of the bird’s breeding status categories, namely the breeding distri-butions (incubating, brooding, post-brood) and non-breeding and juveniledistributions. Not surprisingly, the greater spatial coverage of non-breedingand juvenile birds leads to a much larger overlap with fisheries than otherbreeding stages (Figure 8(d)). Thus, the potential impact of pelagic longlinefisheries on these breeding stages extends back to the 1950s and there arecurrently around 600 million hooks set within the juvenile and non-breedingranges each year. The much smaller ranges of the breeding stages leads to adifferent mix of fleets and a reduced (but still critically important) numberof hooks potentially experienced by the birds (Figures 8 (a)-(c)). For ex-ample, the southern Japanese fleet has rarely moved into the distribution ofbreeding birds, whereas it is a potentially major fleet overlapping the non-breeding and juvenile distributions. The key overlapping fleets for breedingbirds are those of the demersal longline super-fleet, with around 40 to 50million hooks set per year within each of the breeding stages.

2.9 With-in year dynamics

Only female birds are modelled, although the death rates of male birdscontribute to breeding failure. During the first year of their life, albatrossesare designated as ‘chicks’ and the number of female chicks hatched at thestart of year y is given by N0

y . Between this first year and the age of firstbreeding (age 10 for wandering albatross and age 11 black-browed albatross)birds are ‘juveniles’. The number of female juveniles of age a during year

27

(a)

(b)

(c)

(d)

Figure 8: Number of hooks deployed each year within the distributionalrange of (a) incubating and failed, (b) brooding, (c) post-brooding, breedingbirds, and (d) juveniles and non-breeding birds.

28

y and month m is given by N jy,m,a. Adult female birds are divided into 6

categories, so that the number of female birds in any category c during yeary and month m is given by the vector N c

y,m

N cy,m =

[N jy,m, N

bincuby,m , N bbrood

y,m , N bpostby,m , N bfail

y,m , Nnbfaily,m , Nnbsucc

y,m

]. (2)

Of the adult categories, four are within-year stages for breeding birds: in-cubation N bincub

y,m , brooding N bbroody,m , post-brooding N bpostb

y,m and failed at abreeding attempt during year y, N bfail

y,m . Birds remain in each of these cate-gories for only a few months of each year. At the start of every year, birdsthat attempt to breed during year y begin in the incubation category, andprogress through the brooding and post-brooding phases. By the end of theyear, all birds that attempted to breed will either be in the post-broodingcategory N bpostb

y,m , the failed category N bfaily,m , or will have died.

The remaining two of the 6 adult categories are non-breeding birds: thosethat were successful in their most recent breeding attempt Nnbsucc

y,m and thosethat failed Nnbfail

y,m .

Wandering albatross are assumed to arrive at their breeding colony at thestart of January, and black-browed albatross at the start of October (Table4). Therefore, when modelling black-browed albatross, the model ‘year’ ybegins on 1 October (model month 1) and ends on 30 Sept (model month12) of the following calendar year. On arrival, these breeding birds are as-signed to the ‘incubation’ category N bincub

y,m and, unless they fail during anymonth, they progress through the ‘brooding’ and ‘post-brooding’ categoriesas shown in Table 4.

There is no ‘pre-laying’ category, those birds are assumed to have the sameforaging distribution as incubating birds. Note that during December, somewandering albatross are completing their breeding attempt (occupying the‘post-brood’ category and having the ‘post-brood’ foraging distribution)while others are arriving at the colony, preparing for a new breeding at-tempt. This overlap is currently ignored by the model - birds are not con-sidered to arrive at the breeding colony until 1 January.

Each month, some birds fail in their breeding attempt. This occurs whentheir (male) mate dies (at instantaneous monthly rate M/12) or is caughtby a fishery (at instantaneous monthly rate F cm for appropriate breedingcategory c) or their chick dies (at instantaneous annual rate M0

y ). The an-nual chick mortality is apportioned among months according to the meanobserved proportion of birds failing each month ρm at Bird Island (Table 4).

29

Table 4: Monthly category assignments for all successfully breeding birds.Each month, all breeding birds are allocated to the category shown below.For wandering albatross, the first month of the model ‘year’ is January,whereas for black-browed albatross the first month is October. The meanproportion of birds that have been observed to fail each month, at BirdIsland, South Georgia, is also shown.

Wandering (Model) Black-browed Proportion failing ρmAlbatross Month Albatross Wandering Black-browedIncubating (1) Jan (4) Brooding 7.9% 26.6%Incubating (2) Feb (5) Post-brood 13.5% 17.5%Incubating (3) Mar (6) Post-brood 32.5% 7.1%Brooding (4) Apr (7) Post-brood 24.1% 12.9%

Post-brood (5) May (8) Non-breeding 7.0% -Post-brood (6) Jun (9) Non-breeding 2.2% -Post-brood (7) Jul (10) Non-breeding 2.3% -Post-brood (8) Aug (11) Non-breeding 2.3% -Post-brood (9) Sep (12) Non-breeding 0.5% -Post-brood (10) Oct (1) Pre-laying 0.7% 3.7%

(incubation)Post-brood (11) Nov (2) Incubating 0.8% 15.7%Post-brood (12) Dec (3) Incubating 6.4% 16.5%(pre-laying)

30

Each month m, the number of failed birds that had attempted to breedat the start of year y is supplemented by birds from each of the incubat-ing, brooding and post-brooding categories that failed during the previousmonth. All birds suffer natural mortality, and fishing mortality themselves(hence the multiplication by 2 below to account for the deaths of both par-ents)

N bfaily,m+1 = N bfail

y,m e−(M/12+F bfaily,m )

+N bincuby,m e−(2M/12+2F bincub

y,m +M0y ρm)

+N bbroody,m e−(2M/1F )+2F bbrood

y,m +M0y ρm)

+N bpostby,m e−(2M/12+2F bpostb

y,m +M0y ρm).

Adult birds that do not attempt to breed during year y remain in a non-breeding category (either failed or successful, dependant on their most recentbreeding attempt) for the duration of the year, and suffer natural M andfishing mortality (Fnbsuccm and Fnbfailm ) during each month

Nnbsuccy,m+1 = Nnbsucc

y,m e−(M/12+Fnbsuccy,m ) (3)

Nnbfaily,m+1 = Nnbfail

y,m e−(M/12+Fnbfaily,m ). (4)

Similarly, juvenile birds remain in the juvenile category throughout themonth and suffer both natural and fishing mortality. However, juvenilesdo not have the adult natural mortality rate M but have their own, age anddensity dependent mortality rate M j

y,a (see Section 2.10)

N jy,m+1,a = N j

y,m,a e−(Mj

y,a/12+F jy,m). (5)

2.10 Fishing mortality rate

The instantaneous fishing mortality rate for birds in any category c is cal-culated from the catch (described in Section 2.6) by assuming that all birdsare caught in a pulse at the middle of each month, after half the month’snatural mortality has occurred

F cy,m = − log

(1−

Ccy,m

N cy,m e−0.5M/12

). (6)

31

2.11 Annual dynamics

At the end of each year, a year is added to the age of all juveniles - thoseaged 10 are promoted to the incubating breeder category, and adult birdsmove between the breeding and non-breeding categories.

The numbers of birds in each of the adult categories at the end of yeary (month 12) N c

y,12 are redistributed using transition matrix G, to give thenumbers of birds in each category at the start of year y+1 (month 1) N c

y+1,1

N cy+1,1 = G N c

y,12. (7)

The transition matrix G has 6 rows and 6 columns, corresponding with the6 adult categories (see equation 2). The ith row of transition matrix G givesthe proportion of birds from category c = i that will be present in each ofthe 6 bird categories, corresponding with the columns of matrix G, at thestart of year y + 1. Note, therefore, that all rows sum to 1.

Table 5: The transition matrix G that redistributes birds among the adultcategories at the transition from the end of year y to the start of year y+ 1.Birds move from the row categories to the column categories. All blank cellsare zeros. Non-blank rows sum to 1.

From To (N cy+1,1)

(N cy,12) bincub bbrood bpostb bfail nbsucc nbfail

bincubbbroodbpostb ρr 1− ρrbfail ρf 1− ρf

nbsucc α 1− αnbfail β 1− β

At the end of each year, breeding birds occupy either the post-brood cate-gory (if their breeding was a success), or the failed category (if not). Noneoccupy the incubating or brooding categories therefore the correspondingrows of Table 5 are blank. At the start of a year, all breeding birds oc-cupy the incubating category, so that the brooding, post-brood, and failedcolumns are blank.

32

2.12 Chicks

Wandering albatross, if successful in their breeding attempt, will rear a sin-gle chick. Chick mortality through natural causes (eg environmental, orphysiological) is expressed in the model by M0

y . Indirect chick mortalitymay also occur due to the death of a parent by either natural causes M ordue to fishing mortality F cy,m during any of the months m of the year, whenthe parent occupies any of the breeding categories c.

It is assumed that the death of a parent leads to chick mortality due tothe intensive nature of feeding required for the chick to survive (Tickell,1968). This leads to the inclusion of both male and female mortality termsin the formulation of the number of fledglings (resulting in a factor of 2 inthe equation below). Assuming a 1:1 sex ratio at hatching, the number offemale fledglings at the beginning of year y+1 (i.e. juveniles of age 1 N j

y+1,1)is

N jy+1,1 =

12

exp

[−

(2M + 2

∑c

12∑m=1

F cy,m +M0y

)](8)

The number of eggs produced during year y (all in month 1) is N bincuby,1 =

NBy, the total number of adult females that attempt to breed during yeary (thus entering the incubating category at the start of month 1). This isequal to the number of breeding pairs present in the colony at the beginningof year y - each breeding pair producing a single egg.

2.13 Density dependence

Tuck et al. (2004) found that it was necessary to allow the juvenile natu-ral mortality rate for wandering albatross to decrease (juvenile survival toincrease) as the population size decreased in order to model an observedincrease in juvenile survival at Possession Island (Croxall et al. , 1990, 1998;Weimerskirch, 1992; Weimerskirch et al. , 1997). A less pronounced trend isseen at Bird Island, too, where juvenile survival is seen to increase between1970 and 1990, while the breeding population declines. Although Tuck et al.(2004) noted a one-year decrease in the age of first breeding for the BirdIsland population, changes in the age of maturity have only a small effecton the potential rates of increase. As such, density dependence is modelledby allowing juvenile natural mortality to age 5 for a cohort, to be a functionof the total female population size. The ecological basis for this would bea decrease in the intra-specific competition for resources (food for example)(Weimerskirch, 1992). Therefore, juvenile natural mortality M j

y for birdsbetween the ages of 2 and 5 during year y is a function of the total number

33

of female birds in the population during year y, By

By =∑c

12∑m=1

N cy . (9)

For a particular cohort, the juvenile natural mortality rate to age 5 is mod-elled using the following non-linear function,

MJ5y = 4M −

(4M −M

J5) (

By/B)γ (10)

where By is the current year’s total number of female birds. Equation 10provides a non-linear function joining the point (B,MJ5), i.e. the equilib-rium total number of female birds and equilibrium juvenile natural mortalityrate to age 5 MJ5

y , to the minimum juvenile mortality rate to age 5 at zerobirds. The minimum juvenile mortality rate to age 5 is set to that whichwould be attained if juveniles’ dynamics mimicked that of adults, 4M . Theγ term controls the level of compensation (eg γ = 0 implies density inde-pendent juvenile survival to age 5, Figure 9). The annual instantaneousjuvenile mortality rate for the cohort is MJ5

y /4, assuming 4 years as a juve-nile after one year as a chick. Total juvenile survival to age 5 for a cohort isexp(−MJ5

y ) less the cumulative effects of fishing on the juveniles over the 4years since fledging.

Tuck et al. (2004) found that density dependence in juvenile survival toage 5 alone could not account for the apparent concomitant decline in thenumber of breeding pairs and an increase in breeding success (combiningto produce a relatively stable number of fledglings per year) observed forthe Bird Island population. As such, chick natural mortality was allowed tovary as a function of the number of breeding pairs. The non-linear functionthat defines the annual chick mortality is given by

M0y = M exp

[(NBy/NB

)κln(M

0/M)]

(11)

where NBy is the number of breeding pairs in year y, NB is the equilibriumnumber of breeding pairs, M0 is the equilibrium chick natural mortality, andκ controls the level of compensation (eg κ = 0 implies density independentchick mortality). Equation 11 defines a non-linear function between thechick mortality at the equilibrium number of breeding pairs and low chickmortality at zero breeding pairs (set to the adult mortality of M). Examplesof equation 11 are provided in Figure 10.

The equilibrium chick natural mortality is set such that the breeding successin equilibrium (year 1950) is equal to that observed prior to the onset of

34

Figure 9: The non-linear function specifying the relationship between ju-venile survival to age 5 (excluding fishing mortality) and the total numberof birds in the population as a function of the control parameter γ. Theexamples shown assume M = 0.04, an equilibrium number of birds of 5000and equilibrium juvenile survival to age 5 of 0.2.

35

Figure 10: The non-linear function specifying the relationship between chicknatural mortality and the total number of breeding pairs as a function of thecontrol parameter κ. The examples shown assume M = 0.04, an equilibriumnumber of breeding pairs of 1600 and an equilibrium instantaneous chickmortality of 0.45.

36

extensive fishing operations in the Southern Ocean. From equation 8, theequilibrium instantaneous chick mortality is defined by,

M0 = −2M − ln (BS0) (12)

where BS0 is the assumed breeding success prior to major longline opera-tions. The value of BS0 has been taken as the earliest record of breedingsuccess available. For wandering albatross at South Georgia BS0 = 0.59,the 1963 breeding success (Croxall et al. , 1990, 1998).

2.14 Initial conditions

For the population prior to exploitation, the initial numbers at age in deter-ministic equilibrium are found by iterating the resource dynamics equationsfrom a unit number of fledglings, and adjusting a free parameter, eg thejuvenile natural mortality rate to age 5, MJ5 , so as to equate the numberof fledglings produced by breeders to the initial number of fledglings.

2.15 Fitting procedure

The objective function minimises the sum of squared residuals across fourtime series of response variables. In doing this, it is necessary to weight eachseries. A sequential procedure was used to assign relative weights. This wasdone by fitting to just one of the response variables i, in turn, recording theresulting sum of squared residuals Si,

Si =ni∑j=1

(Yi,j − Yi,j

)2, (13)

for i = 1 . . . 4, indexing the 4 response variables. The number of observationsfor response variable i is ni. The jth observed record for response variablei is Yi,j , and Yi,j is the jth model predicted value from fitting to responsevariable i alone.

The reciprocal value of Si was used to weight response variable i. Thus,the relative weight given to the different response variables becomes ap-proximately equivalent. The following equation is minimised

4∑i=1

1Si

ni∑j=1

(Yi,j − Yi,j

)2, (14)

where Yi,j is the jth model predicted value when all response variables are

37

used in the minimisation.

The response variables considered are the annual: i) number of breedingpairs, ii) number of birds fledged, iii) estimated juvenile survival to age 5,and iv) estimated total adult survival. Parameter values are chosen thatminimise equation 14. These parameters are: i) the 4 fishery catchabilitycoefficients, qf , ii) the density dependence parameters, γ and κ, and iii) theequilibrium number of breeding pairs, NB.

3 Preliminary results for wandering albatross atSouth Georgia

This section documents results for the model when fit to data from the SouthGeorgia wandering albatross population. From the defined base-case set ofdemographic parameters (Table 3), sensitivities to changes to the base-caseparameters are considered. In addition, changes to the input effort datasets(the super-fleets) were explored in order to investigate the potential relativeimpact of certain fleets (eg to see if ICCAT fisheries alone could explain thepopulation declines). Parameter estimates and model predicted populationtrends for the base-case parameter set and alternative scenarios are shownin Table 6 and Figures 11 to 15.

The model fits and estimated catches for breeding, non-breeding and juvenilebirds, when using the base-case parameter set, are shown in Figure 11(a).Abundance trajectories for breeding, non-breeding and juveniles birds, andestimated annual fishing and natural mortality rates for juveniles are showin Figure 11(b). Results illustrated in Table 6 and Figure 11 suggest that,in order to explain the observed census data (including the marked declinein breeding pairs), only the southern Japanese pelagic longline fishery andthe illegal demersal fisheries within the CCAMLR Area are predicted to beimpacting the population. The model estimates no impact from the indus-trial demersal longline super-fleet or the pelagic longline super-fleet (whichexcludes the southern Japanese fleet). The considerable catches of breedingbirds predicted by the model since the mid-1990s can be linked to the rapiddevelopment of the demersal IUU fishery. This occurred because the modelis attempting to explain the observed declines in the numbers of breedingpairs and numbers fledged over the same period.

Figure 11(a) shows that model fits to the annual observed number of breed-ing pairs and numbers fledged are very good. Although illustrated in Figure11(a), the model does not fit to the breeding success data because this isderived by dividing the numbers fledged by the number of breeding pairs.

38

Table 6: Model estimated parameter values for the base case model(BC) and sensitivity tests that use a higher adult natural mortality rate(M = 0.06), that set the catchability for the ‘other pelagic’ super-fleet toqOP = 0.01 (QPEL), and that uses only ICCAT fleet data (ICCAT). Theparameters γ (see Figure 9), and κ (see Figure 10), concern density depen-dence. The equilibrium number of breeding pairs is NB and the catchabilityparameters q can be found in Section 2.7, except for qICCAT , which relatesto a super-fleet consisting only of effort reported to ICCAT.

Model run Parameter valueBC M = 0.06 M j indep QPEL ICCAT

γ 1.77 0 0 1.59 0κ 0.83 1.67 1.06 0.80 0.68NB 2723 2651 2629 2752 2700qJ 0.013 0.008 0.007 0.002 −qOP 0 0 0 0.01 −

qICCAT − − − − 0.009qOD 0 0 0 0 −qIUU 0.091 0.095 0.083 0.065 −

This model is unable to provide a similarly close explanation of the observedtrends in the adult and juvenile survival rates - tending to under- then over-estimate juvenile survival rates, and to over-estimate adult survival rates.

In order to explore the lack of fit to juvenile and adult survival rates, twofurther models were considered, namely (i) a greater adult natural mortalityrate of M = 0.06, and (ii) density-independent juvenile survival (note thatchick mortality rates remain density dependent).

Using a greater adult natural mortality rate of M = 0.06, while provid-ing a somewhat better fit to adult survival, provides a poorer fit to theobserved numbers of breeding pairs, and a marked over-estimation of juve-nile survival to age 5 (Figure 12a). Note that the model has estimated avalue of γ = 0, causing the juvenile mortality rate to be density independent(Figure 12b). This results in the juvenile survival (to age 5) being equal tothe adult survival rate regardless of population size (Figure 12b).

Removing density-dependence from the juvenile survival rate leads to asimilar lack of fit to juvenile survival data (Figure 13a). Due to densityindependence, the juvenile natural mortality rate does not change with time(Figure 13b).

39

Figure 11: a. Base case model results showing the estimated (line) andobserved (points) breeding pairs, numbers fledged, breeding success (notused by the model as this is the numbers fledged divided by the number ofpairs), average survival to age 5, and adult survival rate. Annual catches offemales juveniles, breeding birds, and non-breeders are also shown.

40

Figure 11b. Base case model results the number of female juveniles,successful non-breeders (nbsuccess), and failed non-breeders (nbfail). Thenatural (M), and fishing (F) instantaneous mortality rates for juveniles arealso shown.

41

Lack of fit to the survival rate data is discussed further in the Discussionsection.

The base case model does not assign any part of the overall fishing mortalityrate of birds to the pelagic longline super-fleet. It is known, however, thatthis fleet (which includes the Taiwanese, Korean, South African and Aus-tralian fleets) incidentally catches seabirds, including wandering albatross(Klaer et al. , 2008; Chang et al. , 2007). Future models will include catchor catch rate data from individual fleets, enabling (and forcing) a more ap-propriate fit to observed bycatch data. In the meantime, Figure 14 shows theresults of forcing the catchability for the pelagic super-fleet to be non-zero(= 0.01) The resultant fits are similar to those of the base case model (Table6). The additional catch from the pelagic super-fleet leads to a concomitantreduction in catch from the southern Japanese fleet.

In order to investigate the ability of the ICCAT fishing effort alone to ex-plain the observed trends in the wandering albatross population, the modelwas run attributing all fishing mortality effort to ICCAT pelagic longlinefisheries (Figure 15). In this model, the catchability of all fleets is set tozero, except those pelagic longline fleets reporting to ICCAT. The result-ing fits to the data are poor. Clearly, under the current model structurewhere a single catchability parameter is assigned to ICCAT fleets, effortfrom ICCAT fisheries alone cannot explain the population declines observedat South Georgia. In particular, the recent marked decline in breeding pairsdoes not correspond with the overall decline in hooks set by ICCAT fish-eries. However, certain fleets within the ICCAT Convention Area may havea greater impact on the population than others. The inclusion of observedbycatch data by fleet may help resolve this possibility.

3.1 Projections

The model can be used to project the wandering albatross population intothe future, assuming some future fishery distribution and some level of fish-ing effort, eg that the 2006 level and distribution of fishing effort are main-tained. However, the inability of the model to explain recent declines in thesurvival rates of both adult and juvenile birds casts doubt on the validityof any projections made using this model, therefore no such projections arepresented at present.

42

Figure 12: a. Model results for the sensitivity test that uses an adult natu-ral mortality rate of M = 0.06, showing the estimated (line) and observed(points) breeding pairs, numbers fledged, breeding success (not used by themodel as this is the numbers fledged divided by the number of pairs), av-erage survival to age 5, and adult survival rate. Annual catches of femalesjuveniles, breeding birds, and non-breeders are also shown.

43

Figure 12b. Model results for the sensitivity test that uses an adult naturalmortality rate of M = 0.06, showing the number of female juveniles,successful non-breeders (nbsuccess), and failed non-breeders (nbfail). Thenatural (M), and fishing (F) instantaneous mortality rates for juveniles arealso shown.

44

Figure 13: a. Model results for the sensitivity test in which juvenile naturalmortality is density independent, showing the estimated (line) and observed(points) breeding pairs, numbers fledged, breeding success (not used by themodel as this is the numbers fledged divided by the number of pairs), av-erage survival to age 5, and adult survival rate. Annual catches of femalesjuveniles, breeding birds, and non-breeders are also shown.

45

Figure 13b. Model results for the sensitivity test in which juvenile naturalmortality is density independent, showing the number of female juveniles,successful non-breeders (nbsuccess), and failed non-breeders (nbfail). Thenatural (M), and fishing (F) instantaneous mortality rates for juveniles arealso shown.

46

Figure 14: Model results for the sensitivity test that has catchability for the‘other pelagic’ super-fishery set qOP = 0.01, showing the estimated (line)and observed (points) breeding pairs, numbers fledged, breeding success (notused by the model as this is the numbers fledged divided by the number ofpairs), average survival to age 5, and adult survival rate. Annual catches offemales juveniles, breeding birds, and non-breeders are also shown.

47

Figure 15: Model results for the sensitivity test that uses only ICCAT fleetdata, showing the number of female juveniles, successful non-breeders (nb-success), and failed non-breeders (nbfail). The natural (M), and fishing (F)instantaneous mortality rates for juveniles are also shown.

48

4 Discussion and future work

The results presented here are preliminary, consisting of updated outputsfrom the model developed by Tuck et al. (2004), incorporating the now muchlonger time series on population size and demography (adult survival, juve-nile survival, age at first breeding, non-breeding rate, and productivity) ofwandering albatrosses from South Georgia, and recent data on fishing effortand year-round bird distribution (the latter from deployment of satellite-transmitters, GPS and GLS loggers). The intention is to further refine themodel structure to better account for differences in distribution of birds ofdifferent sex and status (active breeder, failed breeder, deferring breeder,non-breeder), and seasonal (monthly) changes in the ratio of birds in eachof these categories such that the overall model will be ‘state-of-the-art’ interms of biological representation.

The seabird model presented in this paper has two means by which it can in-troduce a trend over time in the annual survival rates of juvenile birds. First,the juvenile natural survival rate is at its lowest when the population is atits maximum size (in the 1950s), as the population declines, juvenile survivalcan only increase (see Figure 9). Second, the model can decrease the overall(natural and fishing) survival rate when fishing effort increases within the ar-eas frequented by juvenile birds. The recent observed declines in the juvenilesurvival rate cannot be explained by density dependent mechanisms becausethe overall population size decreases during this time - thereby increasingthe juvenile survival rate. Neither can the observed decrease be explainedby incidental fishery bycatch because fishing effort for the Japanese pelagicand illegal demersal fleets (to which this model attributes all fishing mor-tality, Figure 11a) declines during this period - again serving to increase thesurvival rate of juvenile birds.

The model is similarly unable to explain the recent observed decrease inthe adult survival rate. Adult survival is not density dependant, and re-cent decreased fishing effort will result in increased rather than decreasedsurvival. For all of the variants of the model presented in this paper, thedensity-dependent formulation and fishing mortality over recent years arenot adequate to reduce juvenile or adult survival rates to the levels observedin the data without severely impacting the fits to the other data sources.Removing the density dependence of juvenile birds did not resolve the prob-lem. The inclusion of depensation in juvenile survival would need furtherexploration. Models with no density dependence are not able to increasejuvenile survival from their equilibrium value and survival can only declinefrom this point due to the impact of fishing (Figures 13a and 13b).

An external factor, not accounted for in the model, is required to explain

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the observed survival rate decline in juvenile, and possibly adult birds. Onepossibility is that an environmental factor such as a decrease in food popu-lations (krill), which might be attributed to global warming and consequentreduction in the maximum extent of the ice edge, has imposed additionalmortality. Others are an additional fishery mortality from a source thatis not included in the model, or increased juvenile catchability as adultabundance has declined (eg the competitively inferior juveniles may becomehooked at greater rates due to the reduction of adult numbers).

4.1 Future work

The results presented in this paper are highly preliminary. The model esti-mates of juvenile, and possibly adult, survival rates are clearly inadequate.Future work will the explore the effects of including explanatory variablesthat cause survival rates to decrease.

In addition, estimates of natural mortality rates (currently fixed at 0.04for adults) may be possible using observations from the extensive taggingstudies of South Georgia birds.

Some of the data that have been compiled for the wandering albatrosspopulation could not be included in this presentation due to lack of time.These are, a recruitment ogive (which will replace the knife-edged age-at-firstbreeding of 10); observed distribution patterns for breeding male albatross(which contribute to chick mortality rates); and time series of return rates for(successful and unsuccessful) breeding birds (currently using a fixed rate).The bycatch rates provided by (Klaer et al. , 2008) will also be incorporated.

It has been suggested that offal from fishing vessels has improved breed-ing success by providing supplementary feeding of breeding birds duringcrucial stages of the breeding season when they are unable to fly far fromtheir nests. Such a relationship will be explored in future modelling.

Data have been collected, and compiled, on the demographics of black-browed albatross at Bird Island, South Georgia. The model presented herehas been altered to allow for such annual breeders, and results from an ap-plication of the model to those data will be conducted shortly. This willinclude the trawl effort data already collected, but not shown in this paper.

Once a satisfactory base case model able to explain the observed survivalrates of all birds has been compiled, projections of the albatross populationsinto the future will be presented. These will examine the effect on future

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abundances of possible time-area closures surrounding the breeding colonies,and the effect on future bird population size of possible decreases in birdbycatch rates in selected times-areas.

Model sensitivity will be investigated to some of the assumptions madewhen compiling longline fishing effort data. Increased recent compliancewith mitigation measures will be modelled for (at least) the CCAMLR fleet.

Data on Cory’s shearwaters, Atlantic yellow-nosed albatross, and Tristanalbatross have been sought and appropriate (possibly much simpler) mod-els will be presented. The model presented here, suitable for well-studiedwandering and black-browed albatross populations, may not support thelife-histories or data availabilities for these seabird poplations.

5 Acknowledgements

The authors would like to thank David Ramm (CCAMLR), John Barton,Paul Brickle, Wetjens Dimmlich (Falkland Islands Government Fisheries De-pertment), Michael Hinton (IATTC), Carlos Palma, Papa Kebe (ICCAT),Rob Leslie, Craig Smith, Saasa Pheeha (Marine and Coastal Management,South Africa), Frans Tsheehama, Johannes Iitembu (Namibia, Ministryof Fisheries and Marine resources), Neville Smith, Craig Loveridge (NewZealand Ministry of Fisheries), Tatiana Neves (Projeto Albatroz, Brasil),Jaime Mejuto, The Tuna Team (Instituto Espanol of Oceanography, ACoruna, Spain), Colin Millar, Peter Williams (SPC), Carlos Moreno (Univer-sidad Austral de Chile), Marco Favero, Sofia Copello (Universidad Nacionalde Mar del Plata, Argentina), for providing the data upon which most ofthis paper relies. Thanks are also due to Barry Baker (Latitude 42 Consult-ing), Andrew Constable (Australian Antarctic Division), Robert Campbell,Scott Cooper, Mike Fuller, Neil Klaer, Chris Wilcox, Ann Preece (CSIRO,Australia) and Samantha Petersen (WWF) for assistance and helpful dis-cussions regarding many aspects of the paper.

6 References

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7 Appendix

The factors used to raise IOTC catch and effort data to equal the nominalreported catch for Japan, Taiwan and South Africa. East IO is east of 80E,West IO is west of 80E

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