Illegal Long-Line Fishing and Albatross Extinction Risk

Illegal long-line fishing and albatross extinction risk

G OHAR A. PETROSSIAN,ROLF A. DE B Y and R ONALD V. CLARKE

Abstract Birds are commonly entangled in long-line fisher- threatened birds. Of the  albatross species assessed by ies, and increases in long-line fishing activity have consist- IUCN,  are threatened with extinction (IUCN, )by ently caused declines in seabird populations. Environmental fishing, human disturbance, disease or predation by intro- criminology would posit that the risk of such declines is duced species such as rats and feral cats (Croxall, ; greater in the case of illegal long-line fisheries, which are Gales, ). The greatest of these threats is long-line fishing less likely to implement bycatch mitigation measures. To in- (Alexander et al., ; Gales, ), which increased in the vestigate this possibility we examined the overlap between s when distant water fleets significantly expanded their data on illegal fishing and albatross at-sea occurrence commercial operations for tuna Thunnus spp., Patagonian ranges. Moderate correlations were found between mean ex- toothfish Dissostichus eleginoides and related species (Tuck posure to illegal fishing and the Red List status of albatross et al., ). species, but none were found between Red List status and In long-line fishing, whether pelagic (when fishing lines total fishing pressure. A second analysis overlaid albatross are set close to the surface of the water) or demersal (when at-sea occurrence ranges with long-lining data for the mem- lines are set along the sea floor), several thousand baited ber countries of the Convention on Conservation of hooks are attached to the vessel’s main fishing line. This Southern Bluefin Tuna to compare the effect of exposure commonly results in the incidental catch, or bycatch, of to legal and illegal hooks on Red List status. Lacking a better non-target species (Alverson et al., ; Hall et al., ; measure, Country A’s hooks were used as a proxy for illegal Huang, ), including seabirds that strike at the baited hooks. Critically Endangered and Endangered species were hooks (Gales et al., ) and become entangled or hooked  and . times more exposed to illegal hooks, respectively, and then dragged underwater as the line sinks (Gilman, than Near Threatened species, whereas there was no rela- ). Twenty-six of the  species of seabirds that fall vic- tionship between Red List status and exposure to legal tim to long-line fishing are threatened with extinction hooks. Country-level analyses confirmed these findings, (Gilman, ), including albatrosses (Anderson et al., which provide evidence that illegal long-line fishing poses ; IUCN, ). a particular threat to the survival of albatrosses. The findings Previous studies have reported the impact of long-line suggest that the bird conservation lobby should work closely fishing on albatrosses and other seabird populations. One with fisheries authorities to tackle illegal fishing, and that re- group of studies evaluated this impact globally (Gales, search should identify the highest risk areas of overlap be- ; Nel & Taylor, ; Small, ; Anderson et al., tween illegal fishing and albatross at-sea ranges. ; Huang, ). Another focused on specific ocean areas, such as the Southern Ocean (Tuck et al., ) and Keywords Albatross conservation, bycatch, environmental Inter-American Tropical Tuna Commission management criminology, illegal fishing, long-line fishing, Red List, sea- area (Anderson, ), or specific islands and countries bird (Croxall & Prince, ; Brothers et al., ; Gales et al., To view supplementary material for this article, please visit ; Prince et al., ; Arnold et al., ; Gandini & https://doi.org/./S Frere, ; Petersen et al., ). A third group of studies examined the impact of long-line fishing on specific albatross species, such as the Amsterdam albatross Diomedea amsterdamensis (Weimerskirch et al., ), wandering Introduction albatross Diomedea exulans (Croxall & Prince, ), shy albatross Thalassarche cauta (Brothers et al., ) and espite being protected under various national and black-browed albatross Thalassarche melanophris (Arnold international laws, albatrosses are among the most D et al., ; Petersen et al., ). This body of research has established that long-line fish-

GOHAR A. PETROSSIAN (Corresponding author) Department of Criminal Justice, ing has a significant impact on albatrosses. Furthermore, it John Jay College of Criminal Justice, Haaren Hall-63107, 524 West 59th Street, has been concluded that long-line fishing constitutes a New York, NY 10019, USA. E-mail [email protected] threat greater than any other to the viability of albatrosses ROLF A. DE By Department of Geo-information Processing, Faculty of Geo-   information Science & Earth Observation, University of Twente, Enschede, (Alexander et al., ; Gales, ), and our preliminary The Netherlands work for this study using BirdLife International () alba-

RONALD V. CLARKE School of Criminal Justice, Rutgers University, Newark, USA tross fact-sheets supported this conclusion. According to  Received  May . Revision requested  June . the fact-sheets albatross species are significantly affected Accepted  July . First published online  November . by fishing, and  are affected by the next most common

Oryx, 2018, 52(2), 336–345 © 2016 Fauna & Flora International doi:10.1017/S0030605316000818 Downloaded from https://www.cambridge.org/core. IP address: 170.106.202.226, on 30 Sep 2021 at 08:53:21, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S0030605316000818 Albatross at risk from illegal fishing 337

threat, invasive species such as rats and feral cats, which eat Anderson et al., ) and have raised concerns about the the eggs and chicks of albatrosses on islands where they impact of these fisheries on albatrosses, the contribution breed. However, fisheries and invasive species are not of illegal fishing to the extinction risk of albatrosses had equivalent in their likely impact. It is not difficult to see not previously been empirically tested. We investigated () how all species of albatrosses could be affected by fisheries whether illegal fishing presents a significant threat to alba- encountered in their ranges (although these encounters trosses and () whether it is illegal long-line fishing in par- need to be specified in more detail) but it is less easy to ticular that presents the threat. see how all albatrosses could be threatened by invasive species. This is because the threat of invasive species is more complex: albatrosses breed widely over  islands (del Theoretical background: environmental Hoyo et al., ; BirdLife International, ; Avibase, criminology ; Gill & Donsker, ; Xeno-canto, ) but % This study draws on environmental criminology, which () of these islands host only one or two species of alba- seeks to explain the interaction between motivated offenders trosses, and where invasive species threaten albatrosses and the opportunity structures within their environments these are typically cases of one island, with one or two inva- that make specific crimes possible. The ultimate purpose sive species. Thus, some species could be decimated by of such analysis is to identify ways to reduce the opportun- invasive species on particular islands; however, the incap- ities for those kinds of crimes by increasing their difficulties ability of invasive species to reach other widely scattered and risks, reducing their rewards and minimizing associated and remote islands without human assistance makes it temptations, provocations and excuses. Environmental less likely that they could threaten the existence of multiple criminology has guided the study of other wildlife crimes, species of albatrosses. In the case of long-line fishing, alba- including poaching of African elephants Loxodonta afri- trosses are naturally exposed to this threat by ranging widely cana (Lemieux & Clarke, ), tigers Panthera tigris in at sea. Indian reserves (Clarke et al., ; Kim et al., ) and par- Whatever the merits of the argument, there is little rela- rots in the Neotropics (Pires & Clarke, , ; Clarke & tionship between the number of breeding islands and IUCN de By, ). These studies have employed a range of analyt- Red List status of albatrosses. For the three Critically ical techniques, including overlaying maps of data collected Endangered species the median number of breeding islands by biologists and conservation scientists, to examine the fa- is two, for the four Endangered species it is , for the  cilitating conditions that give rise to these crimes. A general Vulnerable species it is four, and for the five Near finding of environmental criminology (to which we return Threatened species it is two (H()=.,P. .). The when discussing future research) is that problems are more findings about the importance of long-line fishing in the ex- concentrated than generally thought, and that preventive tinction risk of albatrosses attracted our interest as crimin- efforts should therefore be similarly focused. ologists because much long-line fishing is known to be Environmental criminology would predict that illegal illegal. It has been estimated that illegal fishing for fishers will choose to fish where the most commercially Patagonian toothfish alone kills c. , seabirds annually valuable species are to be found (Petrossian & Clarke, (Thomas, ), and this impact is unknown but potentially ), as well as where monitoring and control of waters high in other fisheries around the globe (Nel et al., ; is weak or minimal (Petrossian, ). These facilitating Sumaila et al., ). Consistent with theories of environ- conditions increase the offenders’ perceptions of potential mental criminology that guide our work, we would expect rewards and reduce their fear of being caught. Having cho- that illegal long-line fishers have a greater impact, propor- sen where to fish, they must then make a sequence of further tionate to their fewer numbers, on albatross populations decisions, including when to fish, where and when to offload than their legal counterparts. This is because illegal fishers, their catches and where to fish next. The need to avoid de- who have already shown themselves to be prepared to flout tection and the resulting complications are important in all the law, are less likely to conform to advisory bycatch miti- these decisions. Perhaps the easiest decision to predict is that gation measures, such as night (instead of day) setting, illegal fishers would not implement bycatch mitigation mea- underwater settings, bird-scaring devices, thawed baits sures that would be of little help in evading detection. and line weighting. That these measures have generally been found to be effective (e.g. Gilman et al., ;    Løkkeborg, ; Jiménez et al., ; Melvin et al., ) Methods may be of little importance to illegal fishers, who are likely to avoid them because of the costs and inconvenience The first analysis examined whether a species’ IUCN Red involved. List status was related to its exposure to illegal fishing; the Although several studies have attempted to estimate the second analysis examined whether a species’ IUCN Red bycatch of seabirds in illegal fisheries (e.g. Tuck et al., ; List status was related to its exposure to illegal long-line

TABLE 1 Data sources and types of data used in the analysis of illegal Analysis We used the object-relational database long-line fishing and albatross extinction risk. management system PostgreSQL v. .. (The PostgreSQL Applied to Global Development Group, University of California,    research Berkeley, USA) with the PostGIS v. . . spatial extension Data Data source questions to overlay several datasets. We used the FAO illegal – Albatross threatened IUCN (2014)1&2fishing estimates averaged for (Agnew et al., status ) and albatross at-sea occurrence range (BirdLife Albatross at-sea BirdLife International 1 International, ) datasets to examine the extent to occurrence ranges (2014) which albatrosses are exposed to illegal fishing. We Illegal fishing estimates Agnew et al. (2009)1 calculated the weighted percentage overlap with each of for FAO fishing regions these FAO areas, thus estimating the weighted exposure Reconstructed catches for Sea Around Us Project 1 percentage, WEP(s), for any species s. To weight the 2003 (Pauly & Zeller, 2015) Country scores on illegal Pitcher et al. (2006)2 exposure to illegal fishing against total fishing pressure in fishing & compliance these regions we also calculated the weighted percentage with bycatch mitigation overlap with each of these FAO areas. We used measures reconstructed catch data (in tons in ) by FAO fishing Long-line fishing catch & Commission for the 2 area (Pauly & Zeller, ) and standardized these by effort Conservation of dividing the catch data by the size of the FAO fishing Southern Bluefin Tuna area. We then overlaid this dataset with albatross at-sea occurrence ranges (BirdLife International, )tocalculate the weighted exposure percentage to all fishing in these hooks. The data sources, types of data and the research regions. We calculated the weighted exposure percentage questions these data were used to answer are presented in for an albatross species s as follows. Range(s)denotesthe Table . geographical range of species s.Ifregions is the set of high The dependent variable ‘extinction risk of albatrosses’ seas regions recognized by FAO, then for any of its was conceptualized in terms of the IUCN conservation members, r, range(r) denotes the geographical range of status of each species of albatross. The  albatross region r. IF(r)denotesr the recorded percentage of illegal species fell under four IUCN status rankings in : fishing in r during –. For any geography g, area Critically Endangered, Endangered, Vulnerable and Near  (g) denotes the geography’sareainkm. To determine how Threatened. These were coded –, with the highest score much a region contributes to the illegal fishing exposure assigned to Critically Endangered. suffered by a species, we use the ratio between the species’ at-sea range within the region and its at-sea range globally. This ratio is denoted by SZR(r, s) and is defined as: Research question 1: Is a species’ IUCN Red List area(intersection(range(r), range(s))) status related to its exposure to illegal fishing? SZR(r, s)= area(range(s))

The weighted exposure percentage is defined as follows: Approach and data Answering this question required us to undertake two analyses examining the correlations between WEP(s)= IF(r)·SZR(r, s) IUCN Red List status and illegal fishing, and IUCN Red List r[regions status and total fishing pressure. In the first analysis we used For weighted exposure percentage calculations for total global estimates of illegal fishing that were obtained by fishing pressure, we replaced IF(r) with catch in tons per   analysing data for exclusive economic zones and FAO fishing area in . high seas regions (Agnew et al., ). Agnew et al. () aggregated the data into the United Nations Food and Agriculture Organization (FAO) fishing areas, and reported -year estimates for  of these areas. For each Research question 2: Is a species’ IUCN Red List area they provided a percentage estimate of illegal fishing status related to its exposure to illegal long-line averaged in -year increments for –, and for hooks? –. We used the most recent estimates, for –  (e.g. % for the South-west Atlantic (Area ) and % for the North-east Pacific (Area ); Fig. ). The FAO Approach and data To answer this question we undertook Yearbook of Fishery Statistics () report was used to two analyses of hook data, the mean differences between gather data on catches by FAO fishing area. illegal hooks and total legal hooks, and those between all

FIG. 1 Regional estimates of illegal fishing by FAO fishing area, – (source: Agnew et al., ).

’ legal hooks separated by country. The first analysis followed Commission s member countries in this analysis we chose previous researchers (e.g. Klaer et al., ) who examined to designate them by letters, as to name them could divert seabird bycatch by using data on total fishing effort reported attention from our intended focus (i.e. the relationship to the International Commission for the Conservation of between illegal fishing and albatross extinction risk) to that Atlantic Tunas (at  × ° longitude/latitude cell resolution). of blaming the worst offending country (i.e. Country A). We obtained data on long-line fishing catch and effort The information on illegal long-lining came from a study   (measured as number of hooks) for – from the by Pitcher et al. ( ) that included country-specific   Commission for the Conservation of Southern Bluefin reports amounting to , pages of information, and which  Tuna, a regional fisheries management organization took a research team years to complete. Their report  ’ responsible for collecting and managing catch and effort assessed the compliance of countries with the FAO s  data for its member countries (Australia, Japan, New Code of Conduct for Responsible Fisheries (FAO, )  Zealand, South Africa, South Korea and Taiwan). We used using questions. The study and reports were based on   .   these data because () the Commission is the only , separate analyses and reviewed , reference organization whose convention area overlaps with  of  materials, including international treaties, country synopses, (considering the white-capped albatross Thalassarche steadi fisheries agency reports, national legislation, and published as a subspecies of Thalassarche cauta) albatross at-sea and grey literature. In addition to internal validation occurrence ranges (excluded are short-tailed Phoebastria procedures, the research team also consulted external   albatrus,LaysanPhoebastria immutabilis, black-footed experts for of the countries to verify their assessment  Phoebastria nigripes and waved albatrosses Phoebastria scores (Pitcher et al., ). irrorata), and () it is one of the few regional fisheries Three questions from these reports were particularly management organizations that has publicly available relevant to our research, of which one assessed a country’s long-line fishing catch and effort data. We converted these involvement in illegal fishing and two examined a country’s catch and effort data into a geographical information compliance with bycatch mitigation measures. The re- system (GIS) data file and overlaid it with at-sea occurrence searchers scored countries on a scale of − to indicate ranges of  albatross species that fall within the convention the degree to which each country was involved in illegal fish- area of the Commission for the Conservation of Southern ing: , not involved in illegal fishing; ., occasionally in- Bluefin Tuna (Fig. ). Rather than identify the volved; , often involved; ., involved half as much as in

FIG. 2 Long-line fishing ranges in  of member countries of the Commission for the Conservation of Southern Bluefin Tuna.

legal fishing; , involved almost as much as or more than in effort. Instead of treating all catch and effort data of these legal fishing. The two questions on bycatch mitigation mea- countries as % legal or illegal we weighted these numbers sures were () Are fishing methods known to be harmful to using the illegal fishing scores assigned by Pitcher et al. habitats or to create bycatch problems, or whose high fish- (). Thus, for example, Country A’s illegal fishing score ing capacity is difficult to control, being phased out? and () was . out of , and therefore we treated % of the coun- Is fishing gear mandated by a management plan to avoid by- try’s hooks as illegal. Similarly, Country D’s illegal fishing catch of non-target species, and environmental and habitat score was . out of , and therefore we treated only % damage? The researchers scored countries on a scale of – of the country’s hooks as legal. The long-line fishing efforts ( being worst, and  best) to indicate their performance on of member countries are illustrated in Fig. . these measures. We used the information from these reports to group member countries of the Commission for the Conservation Analysis To compute albatross exposure to hooks we used of Southern Bluefin Tuna. Among these countries, Country GIS software in combination with two data sources: annual A had the highest illegal fishing score (. out of ), and effort data on long-line hooks, per country of vessel origin  the lowest mean score (. out of )onthetwobycatchmiti- and per at-sea location (CCSBT, ), and albatross at-sea  gation measures. Other member countries had low scores on occurrence ranges (BirdLife International, ). We used the degree of illegal fishing (mean score of . out of ), and these data to determine the total exposure, per species, to average scores on questions measuring bycatch mitigation ef- both legal and illegal hooks, calculated in terms of density fort (mean score of . out of ). Catch and effort data for of hook setting, for all albatross species. We developed Country A were therefore used to measure the degree to two notions of hook density for any species s and year y: which albatrosses are exposed to illegal long-line fishing, legal_hook_density (s, y) and illegal_hook_density (s, y). and the total catch and effort data from the remaining coun- The annual effort data consisted of country, year, loca- tries were used to conceptualize other (legal) long-line fishing tion, and records of hooks set. The number of hooks set

by any given country in a given year at a given location was however, these results were not statistically significant determined. We used data from  onwards, as we found (ρ = −.,P. .,n=). inconsistencies in the data for earlier years. The data thus comprised c. , records, involving c. . billion hooks aggregated over the years. The location of each record is Research question 2 given as a point on a regular longitude/latitude grid at ° dis- We found that the higher a species’ exposure to hooks, the tance; thus a record is interpreted as representing a region of higher its Red List status (Table ). Critically Endangered  × ° longitude/latitude with that point as midpoint. These and Endangered species were . and . times more ex- regions are called cregions. For any such region c and species posed to Country A (illegal) hooks, respectively, than Near s the above formula for SZR can be used to determine the Threatened species. We used an analysis of variance test to fraction of a species’ at-sea range within region c as SZR(c, examine the overall mean differences between these groups s). The function hooks_exposed (s, y) gives the total illegal in their exposure to both legal and illegal hooks. We number of illegal hooks to which species s was exposed in found no significant differences in exposure to legal hooks year y:  (F(,)=.,P. ., ω = .), whereas the difference ’ between the four groups and the species mean exposure to hooks exposed (s, y)= hooks(country, y, c)·SZR(c, s) illegal (Country A) hooks was statistically significant, with a illegal  c[cregions large effect (Kirk, ; F(,)=.,P, ., ω = .). = country Country A We conducted country-level analyses to complement these findings and to further explore the exposure of albatross Analogously, hooks_exposedlegal (s, y) defines the total species to legal hooks. The analyses revealed no significant number of legal hooks: differences between the species groups for four of the five  countries (Country B: F(,)=.,P. ., ω = .;     .   ω   hooks exposed (s, y)= hooks(country, y, c)·SZR(c, s) Country D: F( , )= . ,P . , = . ;CountryE:F legal     .   ω       c[cregions ( , )= . ,P . , = . ; Country F: F( , )= . ,  country=Country A P . ., ω = .). For Country C the differences were sig-  nificant (F(,)=.,P, ., ω = .); however, consid-  These formulas are expanded to provide illegal (or legal) ering the mean exposure to Country C hooks is . per km hook density per species per year, as follows: for Critically Endangered species, which is less than that for other groups, this difference is not substantively significant. Of the five countries, Country B sets significantly more hooks exposedillegal(s, y) hooks density (s, y)= hooks than the other countries (e.g. Critically Endangered illegal area(range(s)) species are exposed to . times more Country B hooks than the legal hooks of the other four countries combined, Results but the exposure of albatrosses to Country B hooks was not significantly different when albatrosses in different threat cat- Research question 1 egories were compared. Therefore, it is the exposure to Country A hooks (i.e. those we conceptualized as illegal Spearman’s ρ analysis indicated a positive and moderate cor- hooks) that significantly affects albatrosses. relation (Cohen, ) between a species’ IUCN Red List status and its weighted percentage exposure to illegal fishing during – ρ   ,    ( = . ,P . ,n= ). We found similar re- Discussion sults for other periods (e.g. for –, ρ = .,P, .). The weighted percentage exposure to illegal fishing has been Summary of findings relatively consistent for albatross species since  (Agnew et al., ), whereas the IUCN Red List status of all alba- This study is the first to examine the relationship between trosses except for the Chatham Thalassarche eremita and illegal fishing and albatross extinction risk. In doing so we Buller’salbatrossesThalassarche bulleri has continued to de- undertook two analyses to examine how illegal fishing af- cline. This suggests that the effect of illegal fishing has been fects albatross populations. The first analysis examined the cumulative over time (see IUCN Red List status and Illegal differences in species’ exposure to illegal fishing and their fishing % overlap columns in Supplementary Table S). IUCN Red List status. The results showed that a higher cat- When examining the exposure of albatrosses to overall egory of threat was significantly associated with exposure to fishing pressure, the negative value of the correlation coeffi- illegal fishing. Moreover, this effect has been cumulative cient indicated that species with a high threat status on the over time. We found no correlations between overall expos- IUCN Red List were exposed to lower fishing pressures; ure to fishing pressure and IUCN Red List status.

TABLE 2 Mean exposure to long-line hooks of  albatross species categorized as Critically Endangered, Endangered, Vulnerable or Near Threatened on the IUCN Red List, whose at-sea ranges overlap with the Commission for the Conservation of Southern Bluefin Tuna convention area.

− Exposure to legal hooks km 2 (mean ± SD) Exposure to Country A − Red List status (illegal) hooks* km 2 (no. of species) Country B Country C Country D Country E Country F Total (mean ± SD) Critically Endangered (2) 8.54 ± 4.76 0.42 ± 0.05 0.00 ± 0.00 0.00 ± 0.00 0.02 ± 0.03 8.98 ± 4.84 44.12 ± 30.87 Endangered (5) 7.92 ± 4.60 0.28 ± 0.14 0.49 ± 0.51 0.33 ± 0.35 0.01 ± 0.01 9.02 ± 5.28 12.14 ± 7.72 Vulnerable (7) 4.25 ± 2.22 0.13 ± 0.12 0.57 ± 0.52 0.79 ± 0.42 0.00 ± 0.00 5.73 ± 2.82 3.17 ± 3.94 Near Threatened (3) 3.56 ± 3.66 0.07 ± 0.12 0.54 ± 0.41 0.92 ± 0.96 0.00 ± 0.00 5.09 ± 4.30 3.63 ± 6.11

*Country A hooks were used as a proxy measure for illegal hooks.

The second analysis examined the impact of exposure to albatross’ exposure to illegal fishing for the same -year in- illegal long-line hooks. Using Country A hooks as a proxy crements (during –) that Agnew et al. () used measure of illegal hooks we found that Critically to estimate illegal fishing in FAO fishing areas. Endangered and Endangered species were  and . times Our use of albatross at-sea occurrence ranges may not more exposed to these hooks, respectively, than Near necessarily reflect at-sea densities of these species, which Threatened species. Species’ exposure to legal hooks was vary over time as annual migration patterns unfold not significantly related to their Red List status. (Weimerskirch et al., ). Data on at-sea densities are not readily available but could potentially be derived or modelled from telemetric data on albatrosses in the non- Limitations and future research breeding seasons. Dynamic population models can be constructed to show dispersal of birds at sea as the season This study suffered from some limitations. The most im- progresses (Žydelis et al., ). These patterns and how portant of these was that we could find no data on illegal they differ per population and per sex are only beginning long-line fishing within the albatross ranges, and found it to be understood (Weimerskirch et al., ). Once such necessary to use instead a proxy measure: data for data become available for all albatross species, this study Country A’s long-line fishing catch and effort. This assump- could be repeated using these dynamic models instead of tion was grounded on Pitcher et al. () and their detailed at-sea occurrence ranges. findings from separate country reports. Their scoring of Our study did not differentiate between species’ ranges Country A as one of the worst performing countries during breeding and non-breeding seasons, nor did it ac- among the  that they studied was not only based on count for temporal differences. Considering Commission these analyses but was also verified by external country ex- for the Conservation of Southern Bluefin Tuna long-lining perts. In addition, we found that among the Commission for catch and effort data are available at the  × ° grid cell level the Conservation of Southern Bluefin Tuna member coun- and by month, telemetric data could be combined with these tries, Country A obtained the lowest mean scores (. out of long-lining data to pinpoint not only the riskiest activity ) on the two bycatch mitigation measures of Pitcher et al. spaces (Brantingham & Brantingham, ) where alba- (). Furthermore, in a detailed analysis of the member trosses are most vulnerable to bycatch but also the seasons countries, we found that it was exposure to Country A when this risk of exposure is significantly intensified. hooks, and no others, that significantly affected albatrosses. Further work on these spatial and temporal concentrations We therefore remain confident that, in the context of this would require a considerable effort outside the scope of the study, our proxy measure of illegal fishing was appropriate. current research. Should a new source of data specifically on illegal long-line fishing become available it would be important to analyse albatross extinction risk in light of that new information. Policy implications We also found it necessary to use the IUCN Red List status of albatross species as a proxy measure of species’ extinc- Despite the limitations of the data, we believe that our study tion risk. Although albatross population estimates would be has provided evidence that illegal fishing is an important a better measure, complete estimates were only available for contributor to the extinction risk of albatrosses. Illegal fish- . The availability of reliable population estimates from ing has broader implications than its threat to albatrosses, the same time periods would have facilitated more accurate yet decades of international efforts have made little progress analyses. If complete data on albatross population trends in dealing with that larger problem. The reasons lie beyond were available it would be possible to conduct analyses of the reach of this paper but perhaps there is some scope for

reducing illegal fishing where it overlaps with albatross temporal behaviours of albatrosses to reduce their exposure at-sea ranges. This could be accomplished through a greater to fishing. Consistent with a core finding of environmental collaborative effort between the bird conservation lobby and criminology, crime is not randomly distributed but instead fisheries management authorities to raise awareness about the is highly concentrated in particular places and at particular effect of illegal fishing on albatross extinction risk, as well as to times. Given the considerable overlap between albatross devise possible solutions. For example, a partnership could be at-sea ranges, several albatross species could benefit simul- built between BirdLife International and the International taneously from the identification and monitoring of small Monitoring, Control and Surveillance Network, which facili- high-risk areas. Identifying these areas more precisely tates communication between various fisheries enforcement could be of considerable assistance in the implementation agencies worldwide. These two organizations could raise of effective and practical measures for protecting albatrosses awareness about areas where estimated levels of illegal fishing from illegal fishing and would be a valuable use of scarce re- activityarehighandwheretheseareasoverlapwithalbatross search resources. at-sea occurrence ranges. In areas where the exposure of albatrosses to illegal fishing is high, more rigorous mitigation measures should, Author contributions where possible, be put in place. This could be accomplished, GP contributed to the conception and writing of the article for example, by creating an international conservation or- and conducted the analyses. RdB conducted the spatial ganization with law enforcement powers afforded through computations and contributed to the writing of the article. the United Nations World Charter for Nature (such as RVC contributed to the conception and writing of the article. those of the Sea Shepherd Conservation Society, which has had many successes in intercepting, intimidating and report- ing illegal fishing vessels to relevant enforcement authorities), References which could monitor vulnerable areas. Meanwhile, the Commission for the Conservation of Southern Bluefin Tuna AGNEW, D.J., PEARCE, J., PRAMOD, G., PEATMAN, T., WATSON, R.,  should be ever more watchful of the operations of illegal fish- BEDDINGTON, J.R. & PITCHER, T.J. ( ) Estimating the worldwide extent of illegal fishing. PLoS ONE, (), e. ing vessels. 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ŽYDELIS, R., LEWISON, R.L., SHAFFER, S.A., MOORE, J.E., BOUSTANY, unreported and unregulated fishing and related crime problems. ROLF A.M., ROBERTS, J.J. et al. () Dynamic habitat models: using A. DE BY ’s research interests include developing large-scale applica- telemetry data to project fisheries bycatch. Proceedings of the Royal tions of spatial data in the context of societal problem solving in devel- Society B: Biological Sciences, , –. oping countries, especially in the domains of rural development, agriculture and conservation. RONALD V. CLARKE is an expert on situational crime prevention, the science of reducing opportunities for Biographical sketches crime. He has applied this approach to poaching of African elephants, GOHAR A. PETROSSIAN applies criminological theories, in particular tigers in Indian reserves, and parrots in the Neotropics, as well as to environmental criminology and opportunity theories, to study illegal, illegal fishing.