and white or shades of grey? Detectability of Adélie penguins during shipboard surveys in the Antarctic pack-ice

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1 Journal of Applied Ecology 2009, 46, doi: /j x and white or shades of grey? Detectability of Blackwell Publishing Ltd Adélie penguins during shipboard surveys in the Antarctic pack-ice Colin Southwell* and Matthew Low Department of the Environment, Water, Heritage and the Arts, Australian Antarctic Division, 203 Channel Highway, Kingston, Tasmania 7050, Australia Summary 1. Estimates of penguin abundance are important for developing marine ecosystem models and assessing potential competition between penguins and fisheries operations. 2. Most shipboard surveys of penguins use strip transect (ST) or conventional distance sampling (CDS) methods to estimate abundance, but the assumptions of these methods are largely untested. To test their validity for surveys of Adélie penguins in the Antarctic pack-ice, we recorded mark recapture line-transect data and estimated detectability using a point-independence (PI) analysis. 3. Contrary to ST assumptions, detectability declined markedly with distance from the transect line and varied with group size, substrate and observer position. Estimated detection probabilities across a 300-m strip width, which has frequently been used in shipboard surveys, ranged from 0 09 for single penguins in water to 0 91 for groups of > 5 penguins on ice floes. 4. Contrary to CDS assumptions, only two-thirds of detections close to the transect line by one observer team were detected by the second team. Estimated detection probabilities on the transect line ranged from 0 30 for single penguins in water to 0 92 for large groups of penguins on ice. 5. Synthesis and applications. Most shipboard surveys have not accounted for incomplete detection, potentially resulting in large negative biases that may vary between surveys. Recent theoretical improvements provide the potential for these biases to be addressed, but this requires application of more sophisticated and rigorous survey protocols. Application of PI analysis to mark recapture line transect data demonstrated that substantial improvement to abundance estimates could be achieved for penguins compared with previously used methods. The protocols required for PI estimation can be applied to shipboard surveys of slow-moving species such as penguins and seals, but may be difficult to apply to species moving faster than the survey platform, such as flying seabirds. The benefits of multiple observers are maximized only if they operate independently. For multi-species surveys, it would be beneficial to have multiple teams of observers, each focussing on a species group. Improved estimation of marine predator abundance will facilitate the development of more realistic ecosystem models and allow improved management of the impact of fisheries operations on non-target species. Key-words: Antarctica, detectability, distance sampling, double observers, line transect, mark recapture, point independence, population estimation, seabird, Southern Ocean Introduction Shipboard surveys of penguins (and other seabirds) are undertaken for a variety of purposes. Some studies have aimed to estimate carbon flux attributable to penguins as an *Correspondence author. colin.southwell@aad.gov.au Current address: Department of Ecology, Swedish University of Agricultural Sciences, PO Box 7044, 75007, Uppsala, Sweden input to ecosystem models or prey consumption by penguins to assess potential competition with fisheries operations (e.g. Woehler 1997). In these cases, estimates of absolute abundance are required. Other studies have focussed on the distributional relationships between species abundances and their biological and physical environments to understand their functional ecology (Ainley & Jacobs 1981; Obst 1985; Ainley et al. 1998). In these studies, estimates of absolute abundance are desirable, but indices of relative abundance may suffice The Authors. Journal compilation 2008 British Ecological Society

2 Shipboard detectability of penguins 137 Most shipboard surveys of penguin density or abundance have used strip transects (ST) of half-width w = 300 m (i.e. 300 m to one or either side of the ship, for example, Ainley & Jacobs 1981; Woehler 1997; Ainley et al. 1998; Reid et al. 1999). Using ST methods to estimate absolute density or abundance rests on the assumption that detection probability P within the strip [i.e. P(0 w)] is 1. This assumption can be relaxed to P(0 w) 1 if ST counts are used in a relative sense to make comparisons across space or time, but the reliability of such comparisons rests on the assumption that P(0 w) is constant, or nearly so, across the units of comparison. Thompson (2002) argues that reliance on the untested assumption of constant P(0 w) may lead to incorrect conclusions regarding spatial distributions, trends or habitat relationships of bird populations. Rosenstock et al. (2002) reported that 95% of sampled articles involving landbird surveys relied on unadjusted counts and the untested assumption of constant detectability. Research aimed at improving abundance estimation methods plays a crucial role in advancing applied ecology (Elphick 2008). To our knowledge, detectability assumptions for shipboard surveys of penguins remain untested despite some researchers expressing doubts about the likelihood of them being met (Ainley & Jacobs 1981; Obst 1985). One means by which these assumptions could be tested is through the use of distance sampling methods (Tasker et al. 1984). Conventional distance sampling (CDS) and multiple covariate distance sampling (MCDS) methods relax the ST assumption of P(0 w) = 1 to complete detection on the transect line only [P(0) = 1] by using distance data to estimate detection probability, and provide a means of obtaining estimates of absolute density and abundance that are robust to varying detectability (Buckland et al. 2001). Although the CDS/MCDS assumption is weaker and therefore more likely to be met than the ST assumption, it may still be violated in practice and until recently has itself seldom been tested. Recent developments in mark recapture distance sampling (MRDS) methods provide a means of estimating detection probability on or at any distance away from the transect line under weaker assumptions (Borchers et al. 2006). Here we use MRDS methods to estimate detectability of Adélie penguins Pygoscelis adeliae (Hombron & Jacquinot 1841) during shipboard surveys in the pack-ice off east Antarctica. The results provide some insights into the anatomy of the sighting process and have implications for the application and interpretation of shipboard surveys of penguins and other surface-based seabirds. Materials and methods MARK RECAPTURE DISTANCE SAMPLING This method requires that two observers or observer teams travel along a randomly assigned set of transect lines, searching the same area independently of each other, and when an object is detected, record distance data and additional data to allow classification of detections as singles (seen by one observer only) or duplicates (seen by both observers). Within a mark recapture framework, a detection by one observer or observer team can be conceptualized as a capture or trial for possible re-capture or detection by the second primary observer. Data of this form can be analysed within a mark recapture framework using distance and other covariates to account for detection heterogeneity (Borchers et al. 1998). However, Borchers et al. (2006) showed this analysis can lead to a biased estimate of detection probability due to unmodelled heterogeneity, and they developed an alternative, more robust method of analysis called point independence (PI). Unbiased estimation of detection probability using the PI method rests on the standard distance sampling assumptions of: (i) the distribution of perpendicular distances between objects and the transect line is uniform, (ii) distances to detected objects are measured accurately, and (iii) objects do not move prior to detection; and two additional assumptions specific to the PI method: (iv) observers search independently on the transect line, and (v) classification of sightings as singles or duplicates is accurate. DATA COLLECTION Shipboard surveys comprised one component of work aimed at estimating the abundance of seal and penguin populations in the pack-ice off east Antarctica. The work reported here contributed to the development of survey methods that were later applied in a broad scale, multi-species survey over 1 5 million km 2 of pack-ice between longitudes E (Southwell et al. 2004). The icebreaker RSV Aurora Australis was used as a survey platform along 3100 km of transect through pack-ice varying in concentration from 0 to > 80% surface cover. As the work reported here was aimed at developing and evaluating methods, particularly with regard to estimating detectability, rather than estimating abundance (a focus of later survey work), only aspects of survey design relevant to estimating detectability were considered important. In particular, an effort was made to maintain a straight-line track through the ice to meet assumption (i) of the PI method. Continuous observations were made during daylight hours with good visibility while the ship was moving at speeds of 5 10 knots. Observations were made by two-person observer teams located on the bridge [altitude 17 m above sea level (a.s.l.)] and above the bridge (19 m a.s.l.) on both sides of the ship (i.e. four two-person teams). Each team searched on only one side of the ship. Teams could not communicate with each other and hence worked independently. Under these circumstances, either bridge or above-bridge teams can be considered as primary observers. Observers had little prior experience in shipboard surveys, but were extensively trained before commencing survey work and were closely supervised at all times by an experienced survey coordinator. In contrast to most previous shipboard surveys of penguins, where observers have searched in strips of 300 m (e.g. Ainley et al. 1998; Reid et al. 1999), our observers searched with no distance limit. This approach was adopted, because, in later survey work for abundance estimation, we wanted to maximize the number of sightings and therefore minimize the variance around abundance estimates given the vast survey region and limited survey effort. This may appear to require a much greater search effort from one truncated at 300 m; however because of the oblique view observers have of the sea surface, the two strategies are very similar in terms of their angular search effort. A 300-m strip requires an observer at 17-m elevation to scan from 90 (i.e. directly below) to 3 25 below the horizon. The extra 3 25 of scanning to the horizon, for a search with no distance limit, therefore only requires a 3 7% increase in angular search effort.

3 138 C. Southwell & M. Low Observers searched with the unaided eye (but used binoculars on sighting a group to identify species and group size) in a 90 arc from directly forward to abeam of the ship for groups of penguins and pack-ice seals on ice floes or in the water, but made no attempt to observe or record flying seabirds. Observers were instructed to search closer distances most intensively and larger distances using peripheral vision only in an attempt to meet the central assumption of CDS/MCDS sampling. Data on groups sighted forward of abeam were not recorded until they passed precisely abeam, whereupon observers recorded (i) the exact time, (ii) the angle of declination between the horizon and the line of sight to the group s centre (with a hand-held inclinometer for groups < 50 m or a sextant for groups > 50 m), (iii) group size and (iv) behaviour, including (a) whether group members were on the ice or in the water when first sighted, (b) whether group members dived into the water or jumped onto an ice floe between the time of first sighting and passing abeam, and (c) the direction and magnitude of the perpendicular component of movement relative to the ship s track between the time of first sighting and passing abeam (estimated by eye). Angle data for abeam sightings were converted to horizontal distances from the transect line using simple trigonometry. The perpendicular distance when first sighted was calculated by subtracting the perpendicular distance moved between the time of sighting and passing abeam from the estimated perpendicular distance when abeam. Groups that were first sighted after passing abeam were recorded but could not be included in the analysis because the lack of abeam time and angle data made duplicate identification too difficult. CLASSIFICATION OF SIGHTINGS Correct classification of sightings as singles and duplicates is a critical aspect of MRDS analysis. However, because observers recorded sightings independently and classification was undertaken postsurvey, it was impossible to verify conclusively whether classifications are in fact correct. Despite this, one would expect incorrect classification of actual duplicate sightings as singles to occur if the classification criteria are too fine relative to the operational error in the data recording system, and the opposite to occur if the criteria are too broad. Southwell et al. (2004) argued that classification criteria should be just outside the operational error to minimize both types of error, and on the basis of a sensitivity analysis recommended time and angle criteria of 6 s and 8, respectively for an electronic data recording system similar to the manual method used for this study. A similar sensitivity analysis for data collected manually in this study indicated classification was sensitive to the time criterion, with a break-point in classification at 4 6 s, but relatively insensitive to the angle criterion (Fig. 1). This confirmed that Southwell et al. s (2004) criteria for the electronic system were also appropriate to the manual system, and the following results are based on time and angle criteria of 6 s and 8 respectively. In the terminology that follows, we use the term sightings to refer to unclassified observations of groups, and detections to refer to classified groups. DISTANCE ANALYSIS After sightings were classified, we used Distance 5 0 software (Thomas et al. 2006) to model detection functions and estimate detection probability using the PI method. In a PI framework, the overall detection function has shape and intercept components that can be estimated from MCDS (multiple covariate distance sampling) and MRDS (mark recapture distance sampling) data, respectively (Borchers et al. 2006). Within the MRDS module in Distance, we Fig. 1. Sensitivity of sighting classification relative to time and angle difference criteria: number of single (circles) and duplicate (squares) detections; solid lines and symbols represent 4 angle criterion, dashed lines and open symbols 8 angle criterion. modelled MCDS and MRDS data as functions of perpendicular distance from the transect line using three covariates: (i) observer position, height of the deck has been shown to affect sea-bird visibility (Dixon 1977), (ii) group size (1, 2, 3 5, > 5), large groups are usually more likely to be detected than single birds (Powers 1982), and (iii) background substrate (i.e. in-water or on-ice), the posture and colour of the bird in relation to the background may affect a bird s detection (Tasker et al. 1984). For substrate, in-water included only groups that were first sighted in water and remained in the water until passing abeam; on-ice included any group that was recorded on ice at some time between first sighting and passing abeam. Based on the shape of the detection histogram, the largest 5% of distance observations were discarded to ensure robust estimation (Buckland et al. 2001), resulting in right-truncation of data at x = 800 m. For the MCDS component, we compared half-normal and hazardrate key functions, and for the MRDS component, we used the default logistic link function (these represent all available functions which can be modelled in the MRDS module in Distance). To fit the detection function to MCDS and MRDS data, we created a set of likely candidate models and compared these using Akaike s Information Criterion with a second-order correction for sample size (AIC c ), with AIC c weights (w i ) indicating the strength of support for a particular model (Burnham & Anderson 2002). This was first undertaken for MCDS models, with the best of these used as the basis for MRDS model selection. Because the MCDS component within the MRDS analysis cannot account for differences between observers (as it pools detections across matched observer teams), only group size and substrate covariates were included in the MCDS candidate model set. For MRDS data, 13 models were compared using various additive combinations and interactions of the four parameters of interest (Table 1). Results Bridge and above-bridge teams independently sighted 660 and 571 groups of Adélie penguins within 800 m from the transect line, respectively. The 1231 unclassified sightings reduced to 834 classified detections (263 singles by bridge teams, 174 singles by above-bridge teams, 397 duplicates by

4 Shipboard detectability of penguins 139 Table 1. Model specifications and model selection results. Each model is comprised of a mark recapture distance sampling (MRDS) and a multiple covariate distance sampling (MCDS) component with hazard-rate (HR) or half-normal (HN) key functions ( f ). (+) denotes additive effects and ( ) interactions. Covariates selected in the analyses were: D, distance; Sub, substrate (water or ice); GS, group size; and Obs, observer position. Other columns show the number of parameters in each model (K), the difference between the best model and other models relative to the AIC c value (ΔAIC c ) and the strength of support for each model as a function of AIC c weight (w i ) MRDS MCDS f K ΔAIC c w i D + Sub + GS + Obs + Sub GS Sub + GS HR D + Sub + GS + Obs Sub + GS HR D + Sub + GS + Sub GS Sub + GS HR D + Sub + GS Sub + GS HR D + GS + Obs Sub + GS HR D + Sub + Obs Sub + GS HR Sub + GS + Obs + Sub GS Sub + GS HR D + GS Sub + GS HR D + GS + D GS Sub + GS HR D + Sub Sub + GS HR D + Sub + D Sub Sub + GS HR D + Obs Sub + GS HR D Sub + GS HR Sub + GS HR Sub + GS HN Sub HR Sub HN GS HR GS HN Fig. 2. Conventional detection histograms for (a) on-ice and (b) in-water detections. both teams). Most (86%) detected groups were on ice floes when first sighted. Of these, 89% remained on the ice as the ship passed by. Of the 14% of detections that were in the water when first sighted, 64% remained in the water until passing abeam of the ship. Groups contained 1 45 penguins; most (80%) had 5 individuals. Penguins were sighted in areas ranging from open water to complete ice cover. Conventional detection histograms (Fig. 2) show the number of groups detected on the ice and in the water was constant out to approximately x = 125 m. Detections beyond this distance in the water declined rapidly, with none seen beyond 325 m, whereas groups on the ice were detected out to 800 m and beyond. Viewing the detections in a mark recapture context, around 75% of on-ice captures or trial detections close to the transect line by an above-bridge team were recaptured or detected by the matching primary bridge team (Fig. 3a), compared with only around 40% for in-water detections (Fig. 3c). Although approximately 28% of groups within x = 800 m were observed to move in response to the approaching ship, in general the magnitude of observed perpendicular responsive movement was small; for example, for on-ice groups close to (x < 50 m) the transect line, for which movement could be expected to be greatest, the median and 75 percentile distances moved were 10 m and 20 m, respectively, away from the ship. The MCDS model with AIC c support included substrate and group size with a hazard-rate key function. For MRDS data, only two models had any support (Σ w i > 0 99), with both containing the covariates substrate, group size and observer position as a function of distance; the best supported of these models also included the first-order interaction between substrate and group size (Table 1). Bridge observers were slightly more efficient in detecting groups than above-bridge observers (mean P(0): 0 79 versus 0 72 respectively; Fig. 4a). The most pronounced differences in detectability related to the effects of substrate and group size (Fig. 4b), with groups in the water substantially more difficult to detect than groups on the ice, and small groups harder to detect than large groups. Taking the extremes for these factors, a single penguin in the water was estimated to be detected by bridge observers with probability of only 0 30 ± 0 11 if on the transect line and with an average probability across the entire analysed 800-m strip of just

5 140 C. Southwell & M. Low Fig. 3. Conditional detection histograms from mark recapture data for (a) on-ice groups and bridge primary teams, (b) on-ice groups and above-bridge primary teams, (c) in-water groups and bridge primary teams, and (d) in-water groups and above-bridge primary teams. Table 2. Estimated probability of detecting Adélie penguin groups on the transect line [P(0)], within 300 m of the transect line [P(0 300)], and within 800 m from the transect line [P(0 800)], by bridge teams for each level of substrate and group size. Estimates are given with standard errors derived from the multiple-covariate distance sampling component of the best model in Table 1 Substrate Group size P(0) P(0 300) P(0 800) In-water ± ± ± 0 02 In-water ± ± ± 0 02 In-water ± ± ± 0 03 In-water > ± ± ± 0 06 In-water All groups 0 57 ± ± ± 0 03 On-ice ± ± ± 0 03 On-ice ± ± ± 0 04 On-ice ± ± ± 0 04 On-ice > ± ± ± 0 06 On-ice All groups 0 82 ± ± ± ± 0 02, compared with equivalent probabilities of 0 92 ± 0 09 and 0 62 ± 0 06 for groups of > 5 penguins on the ice. Pooling detections across matched bridge and above-bridge teams (i.e. treating the two teams as one) resulted in a substantial improvement in detection probability compared with bridge teams only (Fig. 5). We considered the possibility that the low detection probability partly reflected the inexperience of the observers, despite their pre-survey training and on-survey support. If this was the case, we expected that observers abilities would improve over time, and thus, we added observer experience (number of days spent surveying prior to any detection; range 1 22) as a time-varying covariate to the two models with AIC c support in Table 1. In neither case did this improve the model (ΔAIC c = 1 92 and 4 79 for the best and second-best models, respectively). Noting the frequent use of 300-m strip transects in previous shipboard surveys of penguins and other surface-based seabirds, and the similarity in angular search effort required for unlimited vs. 300-m strip searching, we used the detection functions in Fig. 4b to approximately estimate detection probability in the event that we had also truncated our search effort at 300 m (Table 2). Estimates of P(0 300) were < 1 for all group sizes and substrates, with underestimation ranging from approximately 10 90%. When averaged across all groups, we estimate that roughly only one out of three groups in the water and two out of three groups on the ice within 300 m of the ships track were detected (Table 2). Discussion Because the PI method, as applied in our study, cannot estimate the probability of detecting those penguins below the ocean surface during the time the ship was approaching, our estimates of detectability are only relevant to those penguin groups that are available for detection at the ocean surface for some time during the ship s approach. Estimating the proportion of the population unavailable to the sighting survey would have been necessary if the study aimed to estimate absolute abundance, but the present study does not attempt to do this and instead focuses only on detectability of the available population.

6 Shipboard detectability of penguins 141 Fig. 5. Estimated detection functions for on-ice (bold) and in-water (light) penguins groups, averaged over all covariates other than distance, for detections by bridge teams only (dashed lines), and for combined or pooled detections by both bridge and above-bridge teams (solid lines). Fig. 4. Detectability as a function of distance from the transect line, observer position, group size and substrate; (a) overall detection functions for bridge (bold line) and above-bridge (dotted line) teams averaged over all other covariates; (b) overall detection functions for bridge teams only, as a function of substrate (bold lines, on-ice; dotted lines, in-water) and group size, averaged across all other covariates. Detection functions for group size categories 1, 2, 3 5, > 5 can be identified by increasing values of P(0) within each substrate grouping. The accuracy of our detectability estimates for penguins at the surface depends on how well the assumptions of the PI method were met. Assumption (iv) cannot be tested. It was addressed in an operational sense by eliminating communication between matching teams. This does not necessarily ensure actual independence because observers may respond similarly to the same sighting cues despite not being able to communicate, but the assumption is much weaker than that required for ST, MCDS and earlier MRDS methods, and hence more likely to be met. Assumption (v) was addressed by using classification criteria that minimized classification errors. Southwell et al. (2004) found assumptions (i) and (ii) were satisfied in shipboard surveys of crabeater seals Lobodon carcinophaga where a similar survey design, survey protocol and distance measuring equipment to those described here were used; we expect those assumptions would also be satisfied for penguin data given the similarity of conditions in the two surveys. We attempted to meet assumption (iii) by using protocols recommended by Buckland et al (searching forward of abeam). This would minimize, but not necessarily eliminate, unobserved movement, which could occur prior to detection. Movement toward or away from the transect line prior to detection, if it occurs, can sometimes be indirectly inferred from spikes in the detection histogram (Buckland et al. 2001); no spikes are evident in Fig. 2; hence, there is no indirect evidence for significant unobserved movement. However, such indirect evidence is not conclusive and the potential for unobserved movement cannot be ruled out. If unobserved movement did occur, it was likely to be greater for penguins in the water than on the ice; we therefore recommend that our estimates of detection probability for penguin groups in the water are to be regarded with some caution. Despite our efforts to detect all penguin groups on or close to the transect line, the point-independence analysis clearly demonstrates that this effort was successful only in the case of larger groups on the ice. We attribute this failure of detection to several factors. Rafting of ice up to 3 m in height can completely or partially obscure penguin groups prior to their passing abeam, even when close to the transect line. Penguins swimming in cracks between ice floes can also be difficult to detect because the elevated edge of ice floes can obscure vision. Although Adélie penguins on the ice appear obvious with their contrasting colours of black and white, a single penguin facing the observer may be difficult to detect because its white ventral surface blends into the white background. The exposed dark dorsal surface of swimming penguins presents a smaller profile and little colour contrast to the dark surface of water. The apparent scarcity of penguin groups in

7 142 C. Southwell & M. Low the water compared with on the ice during our surveys is, to a substantial extent, an artefact of the different detectabilities of penguin groups using these two substrates; although only 11% of groups were in the water when first sighted and remained in the water as the ship passed by, we estimate, after correcting for detectability, that these groups actually comprised 30% of all groups present (and 34% of individuals) in the 800-m strip. Because observers have an oblique view of their search area, search effort is more realistically considered in terms of the vertical arc of searching than the horizontal width of strip searched: a 3 vertical arc of searching between below the horizontal by an observer 17 m a.s.l. covers a horizontal distance of only 2 m, compared with a 160-m horizontal distance for a 3 arc from 3 6 below the horizontal. In this context, the reward (number of detections) per unit of search effort was 1 2 orders of magnitude higher at middle distances compared with close distances (e.g. four detections within a vertical arc covering m from the transect line vs. 246 detections within a 3 6 arc covering m). A high detection reward at middle distances results from the opposing effects of increasing horizontal distance searched and decreasing detectability, as the eye scans from the transect line to the horizon. This may create, contrary to the instructed protocol, a subconscious incentive for observers to search the middle ground more intensively than areas close to the transect line. Our use of multiple observers in each team is consistent with the recommendation of Spear et al. (2004) as a strategy to improve detectability in shipboard surveys of seabirds. Nevertheless, having two observers in each team did not guarantee high or constant detection at any distance. Van der Meer & Camphuysen (1996) also reported large differences in the efficiencies of two-person teams for surveys of seabirds on the sea surface, and concluded that the CDS/MCDS assumption of P(0) = 1 was violated. Our study shows that detection efficiency was improved by combining detections for bridge and above-bridge observers (i.e. creating a single four-person team by pooling their detections), presumably because the different vantage points and the greater number of observers offered a better chance of detection. The possibility that the initial inexperience of our observers resulted in lower-than-usual detection probabilities is unlikely, considering there is no support from our analyses that observers improved their detection skills over the prolonged period of survey (up to 3 weeks). An alternative explanation is that observers were distracted by the need to search for and record information on seals as well as penguins. This may be true, but this would also apply to the many shipboard surveys which routinely record data on multiple species of both flying and swimming seabirds, thereby placing greater demands on observers by requiring them to apply different sighting processes and to search over three- rather than just two-dimensional space. We suggest that further research aimed at assessing the effect of searching for multiple species on detectability would be instructive. Most previous shipboard surveys of penguins have used strip transects extending 300 m from the ship s track. Our estimates of P(0 300) indicate that the assumption of P = 1 was strongly violated, and suggest that 300-m strip transects may underestimate density or abundance by a substantial amount. Further, variation in P(0 300) across different substrates and group sizes indicates that the assumption of constant detectability may also be violated if strip transects are used to compare relative density or abundance between times or places when these factors vary. Such comparisons may be of particular interest in the future; for example, studies of climate change may need to make comparisons between times when sea-ice cover differs. When shipboard surveys of seabirds were instigated in the Southern Ocean under the BIOMASS (Biological Investigations of Marine Antarctic Systems and Stocks) programme in the 1980s (BIOMASS 1982), it was recommended that sightings be recorded within the distance intervals of m, m, and > 1000 m. This method would in theory allow some accounting for detectability using CDS/MCDS methods, but the distance intervals are probably too broad for effective use given the sighting distances observed in this study. We consider the distance intervals recommended by Camphuysen et al. (2004) for shipboard surveys of flying seabirds (0 50, , , and > 300 m) would be more effective for shipboard penguin surveys than the BIOMASS intervals. While using CDS/MCDS methods should result in less bias than 300-m strip transects, this may not eliminate bias because detection on the transect line remains incomplete. This residual bias might be further reduced by recording groups sighted after passing abeam in addition to those sighted ahead, as demonstrated by Southwell et al. (2004) for crabeater seals. Using independent observers and the MRDS methods employed in this study is a step up in complexity in both logistic and analytic terms, but is more likely to address the problem. SYNTHESIS AND APPLICATION Much of the current effort in assessing penguin (and other seabird and mammal) populations in the Southern Ocean is focussed on the impacts of fisheries operations and climate on ecosystem structure and function (Croxall, Trathan & Murphy 2002; Reid 2007). Shipboard surveys are an important tool for assessing such impacts. Our results for penguin shipboard surveys support the general view of Thompson (2002) that reliance on untested detectability assumptions could lead to incorrect conclusions regarding abundance, spatial distributions, trends or habitat relationships. Recent theoretical improvements provide the potential for these assumptions to be more readily tested and addressed than was possible in the past, but achieving this in practice requires application of more sophisticated and rigorous survey protocols. We believe the protocols required for PI estimation can be applied without too much difficulty to shipboard surveys of relatively slow-moving species such as penguins (this study), seals (e.g. Southwell et al. 2004) and whales, but may be very difficult to apply to species moving faster than the survey platform, such as many flying seabird species. For these species, movement may be the greatest source of bias and methods of correcting

8 Shipboard detectability of penguins 143 for movement the highest priority. Other studies have demonstrated the advantage of using multiple observers to improve detectability in shipboard surveys of seabirds. This study indicates that the benefits will be maximized only if they operate independently to allow estimation of objects undetected by all observers. Searching for multiple target species with differing behaviours over three-dimensional space may compromise detectability even with multiple observers. For multi-species surveys, it would be beneficial, if funding allows, to have multiple teams of observers, each focussing on a major species group, to maximize the reliability of data derived from expensive shipboard surveys. Improved accounting for incomplete and variable detection will allow managers and policy-makers to more reliably interpret and respond to real changes between and within populations. 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