Habitat Suitability Modelling of Economically Important Fish Species

ICES Journal of Marine Science, 63: 1590e1603 (2006) doi:10.1016/j.icesjms.2006.06.008

Habitat suitability modelling of economically important ﬁsh species with commercial ﬁsheries data

Liz Morris and David Ball

Morris, L., and Ball, D. 2006. Habitat suitability modelling of economically important ﬁsh species with commercial ﬁsheries data. e ICES Journal of Marine Science, 63: 1590e1603. Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021

In this study we used catch and effort data from a commercial fishery to generate habitat suitability models for Port Phillip Bay, Victoria, Australia. Species modelled were King George whiting (Sillaginodes punctata), greenback flounder (Rhombosolea tapirina), Aus- tralian salmon (Arripis trutta and A. truttaceus), and snapper (Pagrus auratus). Locations of commercial catches were reported through a grid system of fishing blocks. Spatial analyses in a Geographic Information System (GIS) were applied to describe each fishing block by its habitat area. A multivariate approach was adopted to group each fishing block by its dominant habitats. Standardized catch per unit effort values were overlaid on these groups to identify those that returned high or low catches for each species. A simple set of rules was then devised to predict the habitat suitability for each habitat combination in a fishing block. The spatial distribution of these habitats was presented in a GIS. These habitat suitability models were consistent with existing anecdotal information and expert opinion. While the models require testing, we have shown that in the absence of adequate fishery-independent data, commercial catch and effort data can be used to produce habitat suitability models at a bay-wide scale. Ó 2006 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved. Keywords: catch and effort, fisheries habitat, Geographic Information Systems, multivariate analysis, Port Phillip Bay. Received 25 May 2005; accepted 26 June 2006. L. Morris and D. Ball: Primary Industries Research Victoria e Marine and Freshwater Systems, PO Box 114, Queenscliff, Victoria 3225, Australia. Correspondence to L. Morris: tel: þ61 3 5258 0111; fax: þ61 3 5258 0270; e-mail: [email protected].

Introduction problems when used as a surrogate for fishery-independent data, relating to the scale of information, sampling bias, The protection of fishery habitat is a vital part of ecosystem- reporting issues, and confidentiality (Mace, 1997; Starr and based approaches to fisheries management, and acknowl- Fox, 1997; Zheng et al., 2001; Gallaway et al., 2003a, b). edges that fish populations should not be considered Most commercial fisheries data do not have precise geo- independently of their environment (Sharp, 1997; Parsons graphic coordinates to define spatial location; more often and Harrison, 2000). Despite this, we often lack detailed they use a system of coarse-scale grids for fishers to record information about the importance of different habitat types catch locations. Further, associated environmental data are to fish species, so may be failing to provide adequate pro- not typically recorded with the catch information (Rubec, tection for important habitats. Where fish-habitat associa- 1996). Despite these problems, fisheries scientists and man- tion data exist, it is possible to combine them with habitat agers are increasingly interested in accessing the large data in a Geographic Information System (GIS) to provide amount of information that exists within the fishing com- a spatially explicit model of habitat suitability (Gallaway munity (Bowen, 1997; Maurstad, 2002). et al., 1999; Rubec et al., 1999, 2001, 2003; Brown In areas where characteristics of the fishery are well et al., 2000; Guisan and Zimmermann, 2000; Stoner et al., known, one way of sourcing fisher knowledge is to use 2001). commercial catch and effort data. An implicit assumption Commercial fisheries’ catch and effort data from vessel in using these data is that the regions in which fishers are monitoring systems and logbooks are routinely collected operating have the highest densities of the targeted species. for stock assessment and fishery management purposes in Several recent studies have used logbook data and vessel Australia and internationally. Such data have intrinsic monitoring systems to investigate the spatial distributions

1054-3139/$32.00 Ó 2006 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved. Habitat suitability modelling of fish species with commercial fisheries data 1591 of fish, and have attempted to link this information with In this study we analysed catch and effort data for King environmental data (Denis and Robin, 2001; Zheng et al., George whiting (Sillaginodes punctata), greenback floun- 2001; Denis et al., 2002; Kemp and Meaden, 2002; Marrs der (Rhombosolea tapirina), Australian salmon (Arripis et al., 2002; Reynolds, 2003). In this study, we extend spp.), and snapper (Pagrus auratus) to produce habitat suit- this approach to predict the distribution of suitable habitats, ability models. These species are all major components of and by extension, fish distributions, based on commercial the Port Phillip Bay fishery and include demersal species catch and effort data in Port Phillip Bay, Victoria, Australia (King George whiting, greenback flounder, snapper) and (Figure 1). a pelagic predator (Australian salmon). We assumed that Port Phillip Bay is a large and relatively shallow marine models for the demersal species would be more reliable be- embayment. It is characterized by predominantly bare sand, cause of the closer association of these species with the silt, and clay sediments, with extensive shallow seagrass types of habitat parameters investigated in the study. Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021 beds bordering the southern and western shores (Figure 2). The fishery for King George whiting and Australian The bay is linked to the oceanic waters of Bass Strait salmon targets sub-adults, with minimum catch sizes of through a narrow entrance called The Rip, and the tidal 27 and 21 cm, respectively. The snapper fishery can be di- range in most of the bay is <1 m. Its fishery is managed vided into a longline fishery that targets primarily adult fish, through a licencing system with restrictions on fishing and a haul-seine/mesh-net fishery that primarily targets sub- gear types, minimum allowable fish sizes, fishing seasons, adults, colloquially known as ‘‘pinkies’’. Sub-adult snapper and spatial closures (e.g. marine national parks). There have a minimum legal catch size of 27 cm, whereas adults are 59 licenced commercial fishers operating in the Bay, may reach lengths >80 cm. The greenback flounder fishery who primarily fish from small vessels with seine-nets targets adults with a minimum catch size of 23 cm. (haul, purse, and beach), mesh-nets (also known as gill- Haul-seining is restricted to the shallower areas of the nets), and longlines (Anon., 2001). bay. Mesh-nets are used throughout the bay, but most of

Figure 1. Port Phillip Bay location maps, and ﬁshery catch and eﬀort block boundaries. 1592 L. Morris and D. Ball

Longlines consist of a monoﬁlament main line weighted at each end with a maximum of 200 hooks typically attached at 10-m intervals by 1-m snoods (Coutin, 2000). Fishers are only permitted to deploy one longline at a time, and these are typically used in the deeper areas of the bay away from possible seabed snags, interference from recreational ﬁshers and boating, and where the bycatch of low value species is likely to be minimized.

Methods Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021 Habitat data GIS polygon layers for depth, sediment type, and substratum type/biota provided the habitat information to characterize each fishing block (Figure 2). The depth layer was produced by digitizing depth contours from 1:25 000 hydro- graphic charts sourced from the Port of Melbourne Corpo- ration. The sediment type layer was digitized from a 1:100 000 seabed sediment map presented in a study of grain-size distribution throughout the bay (PMA, 1987). A substratum type/biota polygon layer at a scale of 1:25 000 was available from mapping of seagrass distribution, through interpretation of high-resolution colour aerial photography combined with extensive ground-truthing (Blake and Ball, 2001). Because Port Phillip Bay is predominantly a marine system, salinity and water temperature were not considered to be significant influences on the distribution of the fish species investigated in the study. All spatial analyses and development of habitat suitability maps were undertaken with the GIS software ARCINFO and ArcView. To determine the habitat characteristics of each fishing block, we combined all habitat layers into a single layer in the GIS. The Identity command in ARCINFO was used to overlay the fishing block layer with substratum type/biota, depth, and sediment polygon layers, and to cal- culate the geometric intersection of each layer (Figure 3). Two layers were intersected at a time with the Identity command, and the output of the process formed one of the input layers to intersect with the next layer (i.e. a geometric intersection was calculated on the fishing block and substratum type/biota layers first, then the output from this was intersected with the depth layer, and so on until all layers had been intersected). The final output of this process was a single combined layer that retained the spatial features and attributes for each of the input layers (Figure 3). A composite habitat code for each feature in the output layer was then calculated by combining the habitat codes Figure 2. Port Phillip Bay depths, substratum type/biota (after from each input layer. The attributes of the GIS habitat Blake and Ball, 2001), and sediments (after PMA, 1987). layers used in this analysis are summarized in Table 1. The attribute table for the combined fishing block/habitat the effort of this gear type is in the same fishing blocks as layer from the Identity process (Figure 3) included all the the haul-seines. Seine-nets exceeding 460 m in length are attribute values from the input layers as well as the area not permitted, and the most frequently used mesh sizes in m2 of each combined spatial feature. We added a further for haul-seines range from 30 to 100 mm. Seasonal restric- column to the table, consisting of a ‘‘composite’’ habitat tions apply to the size and length of mesh-nets permitted in code generated by combining the respective codes for sub- the bay, with mesh sizes mostly ranging from 60 to 130 mm. stratum type/biota, depth, and sediment into a single code Habitat suitability modelling of fish species with commercial fisheries data 1593 Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021

Figure 3. Illustration of spatial analysis applied to characterize each ﬁshing block by its habitat characteristics. Input GIS layers (AeD) were overlaid and a geometric intersection calculated in ARCINFO to produce a single output layer that retained the spatial features of each input layer (E). The output layer also retained the attribute table items of each input layer (see Table 1), and these were combined into a single composite code (E and F above). Each row in the output layer attribute table (F) corresponded to a polygon in the combined habitat layer (E).

(Table 1). This table was exported to Excel and a pivot the analyses in the present study. As a result, the analyses table created to summarize each fishing block by the total presented here only used catch and effort data for the three area of every possible combination of the habitat parame- years between autumn 1998 and summer 2001 (Table 2). ters. In all, 135 habitat combinations present in Port Phillip The Port Phillip Bay catch and effort data are stored in Bay were identified in the analysis (Table 1). a relational database by Primary Industries Research Victo- ria (PIRVic) on behalf of the state fishery management agency, Fisheries Victoria. To assist querying and display- Fishery catch and effort data ing this data, PIRVic Marine and Freshwater Systems de- Commercial fishers in Port Phillip Bay are required to sub- veloped a customized ArcView GIS application known as mit catch and effort data each month in the form of logbooks Catch and Effort Info (Ball and Coots, 2001). This system that include daily records of the time spent fishing, gear was used to extract the data required for this study. types used, species and weight of the catch, and the catch We used catch per unit effort (cpue) values, where effort location. Fishers record the location of their catch through was measured by metre-lifts for mesh-nets, number of shots a system of 41 fishing blocks, based on a 5-min grid (ap- for haul-seines, and number of hook-lifts for longlines. proximately 9 km 9 km; Figure 1). Fishers are required Catches were recorded in tonnes. One problem with this to record the block(s) where the majority of their catch type of fishery-dependent data is that cpue data tend to be was taken. The current system of fishing blocks was intro- at different scales across different gear types owing to the duced in 1998. Prior to 1998, catch and effort returns for various units of measurements and the differing gear Port Phillip Bay were based on only seven catch blocks, efficiencies (Hilborn and Walters, 1992). A recommended which did not provide an adequate spatial resolution for approach to this problem is to standardize cpue data to 1594 L. Morris and D. Ball

Table 1. Parameters used to classify habitat, depth, and sediment type in commercial ﬁshing blocks. Habitat composite code ¼ substratum type/biota code and depth code and sediment code (e.g. habitat composite code S43 ¼ seagrass at depth 5e10 m on sandeclay sediment).

Substratum Depth Depth Sediment Substratum type/biota class type/biota code class (m) code Sediment class code

Macroalgae (on sediment) M Intertidal 1 Clay 1 Amphibolis antarctica seagrass A0e2 2 Sandesilteclay 2 Subtidal rocky reef R2e5 3 Sandeclay 3 Intertidal rocky reef IR 5e10 4 Fine sand 4 Pyura stolonifera P10e15 5 Coarse sand 5 Bare intertidal sediment BI 15e20 6 Rocky reef 6 Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021 Bare subtidal sediment BS 20e30 7 Medium sand 7 Drift algae (over sediment) D >30 8 Seagrass (predominantly Zostera tasmanica S and Z. muelleri) Seagrass bare edge (bare sediment in a 15-m SE buﬀer from edge of seagrass)

provide a consistent index of a species’ abundance (Hilborn distributions and their relation to benthic habitats, and not and Walters, 1992). We chose a simple method to standard- in differences among years or seasons, data were pooled ize cpue values across the different gear types in which we across all seasons and years, so that each fishing block assumed that the average cpue of each gear type repre- had one value for each species. Because of low catches, sented a similar density of fish. We divided the cpue within we excluded winter data for adult snapper from the each fishing block for a specific gear type by the average analysis. cpue for that gear type over the whole bay. Once the To link fish distributions with habitat parameters, we as- cpue values were standardized so that data from all gear sumed that habitats with higher cpue would also have types were effectively unit-less and at the same scale, we greater habitat suitability for that particular species. Be- combined these relative values by calculating the mean rel- cause of problems associated with working with commer- ative cpue for each fishing block. cial catch data, and in particular the differences in spatial scale between catch and habitat data, a multivariate approach was used to link benthic habitat to fish distributions. Statistical analyses The first step was to create a data matrix of habitat com- In the present study we were primarily interested in spatial binations that was independent of the differences in the rather than temporal patterns, so we used interaction plots spatial size of fishing blocks (Figure 1). We took the (Quinn and Keough, 2002) to determine whether the pattern summary table of fishing blocks vs. total area of habitat of relative cpue was consistent across fishing blocks combinations from the spatial analyses described previ- between seasons. There was no evidence of an interaction ously (Figure 3), and calculated the proportion (percentage) between fishing block and season, except for adult snapper, by area of each habitat combination in a fishing block vs. of which there were low to non-existent catches in winter. the total area of that fishing block. The resulting data matrix As we were primarily interested in spatial patterns in fish consisted of an array of rows (habitat combinations) and

Table 2. Summary of Port Phillip Bay commercial catch and eﬀort data for autumn 1998 to summer 2001 (na ¼ not applicable).

Species Blocks Days Hours Shots Hook-lifts Hook-hours

Snapper (mesh-nets/haul-seines) 233 7 483 48 267 11 279 na na Snapper (longlines) 174 2 326 na na 732 779 2 738 995 King George whiting (mesh-nets/haul-seines) 387 9 767 62 085 14 243 na na King George whiting (longlines) 19 303 na na 31 755 62 403 Australian salmon (mesh-nets/haul-seines) 256 8 050 51 201 11 621 na na Australian salmon (longlines) 11 189 na na 79 2 153 Greenback flounder (mesh-nets/haul-seines) 265 7 843 51 388 11 774 na na Greenback flounder (longlines) 0 0 na na 0 0 Total 1 627 44 676 268 093 61 643 764 613 2 803 551 Habitat suitability modelling of fish species with commercial fisheries data 1595 columns (fishing blocks). Then we created a similarity ma- Table 3. Summary of steps taken in assigning habitat combinations trix in which the similarities between each fishing block to high, medium, or low suitability categories. were calculated using the BrayeCurtis coefficient (Bray and Curtis, 1957). Ruling Suitability To test the a priori hypothesis that fishing blocks where a species was caught differed in their habitat parameters Habitat combination is present in 100% of High from those where no fish were caught, an analysis of simi- fishing blocks in a cluster group assigned high density larities (ANOSIM) was carried out. This is a non-parametric Habitat combination is present in 100% of Low randomization procedure that provides an R-statistic that fishing blocks in a cluster group assigned ranges between 1 and þ1, and a probability of getting low density this R-statistic if the null hypothesis is true (Clarke, Habitat combination is present in 100% of Medium Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021 1993). Where there was a significant difference between fishing blocks in a cluster group assigned the habitat parameters of fishing blocks with fish and high density, and habitat combination is also blocks without ( p < 0.05), the relationship between the present in 100% of fishing blocks in a cluster habitat parameters of those fishing blocks was further group assigned low density explored with ordination and cluster analyses. For the Habitat combination is present in >50% of Medium ordination, non-metric multidimensional scaling (nMDS) fishing blocks in a cluster group assigned high density, but has not already been assigned a was used in an attempt to place the fishing blocks on suitability value a ‘‘map’’ in such a way that the rank order between Habitat combinations present in <50% of Undefined the fishing blocks represented the rank order of the sim- fishing blocks in a cluster group assigned high ilarities in the similarity matrix (Clarke and Warwick, density 2001). The cluster analysis progressively links the sam- Habitat combinations present in <100% of Undefined ples based on the calculated similarities among hierarchi- fishing blocks in a cluster group assigned cal groups, and the analysis is represented in the form of low density a dendrogram (Clarke and Warwick, 2001). Primer Ver- sion 5 (Ó PRIMER-E 2000) was used for all multivariate analyses. years. Port Phillip Bay also has a large recreational fishery, Fishing blocks were grouped arbitrarily according to the and there is considerable anecdotal information and expert cluster analysis, so fishing blocks that had at least 40% sim- opinion detailing fish distributions within the bay. This infor- ilarity were considered to have similar habitat combinations. mation was used to provide a qualitative test of model predic- These groupings were then overlaid on the ordination, with tions to assess whether use of commercial catch data for the relative cpue values of each species positively related to modelling habitat suitability was a valid approach. the size of a bubble plot. The groups of fishing blocks deter- mined from the cluster analysis were designated as either Results high or low density groups according to the size of the relative cpue values within each group for each species. This There were significant differences (ANOSIM) in habitat step was based on the assumption that the relative cpue characteristics between fishing blocks where fish had and values were positively related to the actual densities of a had not been caught, for all species examined (Table 4). species. A series of simple rulings were then used to deter- Ten cluster groups were defined from the 40% similarity mine whether each habitat combination was of ‘‘high’’, level (Figures 4a and 5), and there was a good correspon- ‘‘medium’’, or ‘‘low’’ suitability for the species in question dence between cluster groupings and the 2-dimensional (Table 3). These rulings were based on the assumption that the nMDS ordination (Figure 4b). Blocks A6 and F4 clustered consistent presence of a habitat parameter in a cluster group out singly (Figure 4a) and could not be used in the analysis would be important in determining the density of that species, based on its mean cpue. Primer Version 5 was used to produce the bubble plots and to extract the information on the presence Table 4. Results of ANOSIM comparing the proportion of habitats of habitat parameters in the cluster groups. in fishing blocks where a species was caught with the habitats in Once each habitat combination was defined as high, me- fishing blocks where that species was not caught. dium, low, or undefined, the polygons in the combined habitat layer (Figure 3) were reclassified in the GIS to their Species R-value p-value corresponding suitability value to create a predictive model in the form of a map of habitat suitability. Ideally the hab- King George whiting 0.385 0.001 Greenback flounder 0.248 0.002 itat suitability models would have been validated with Sub-adult snapper 0.225 0.002 fishery-independent data, but suitable data were not avail- Adult snapper 0.184 0.014 able. However, numerous studies have investigated differ- Australian salmon 0.331 0.001 ent aspects of the fishery in Port Phillip Bay over the 1596 L. Morris and D. Ball Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021

Figure 4. (a) Dendrogram resulting from cluster analysis of the relationship between habitat parameters and fishing blocks. The solid line indicates the 40% cut-off point used for determining cluster groups used in subsequent data analysis. See Figure 5 for spatial distribution of cluster groups. (b) Ordination following nMDS of fishing area environmental data. Cluster groups (top panel) are indicated on the ordination. Note that fishing areas not used in subsequent habitat classification (i.e. areas A6, E4, E9, F4) are not shown on this ordination e see text for further information. described above because more than one block was required Reclassifying the habitat composites with suitability cate- per cluster group. Fishing block E4 (Swan Bay) was gories following our simple ruling system (Table 3) resulted excluded from the cluster analysis because no fishing is in the predictive model of habitat suitability for King George permitted in the area. Fishing block E9 clustered out in whiting (>27 cm) shown in Figure 6b. The most notable fea- group 1 (Figure 4a), but this area was excluded from cluster ture of this model was the high suitability of all habitats that group 1 because it appeared to be a large outlier in several include seagrass or seagrass-edge, which are primarily in the cases; this is discussed further below. southern and western areas of the bay. The shallow bare areas on fine sediment in the northern part of the bay as well as the King George whiting (Sillaginodes punctata) reef areas along the northeastern shores of the bay were also predicted to provide suitable habitat for King George whit- The cluster groups designated as high and low density for ing. The areas classed as low suitability habitats were mainly King George whiting are shown in Figure 6a. The relative the deeper bare substratum in the centre of the bay, and the cpue values for King George whiting were greatest in block coarse sand habitat on the eastern side of the bay. G6 (Figure 6a), at the southern end of the bay (Figure 1). The correspondence between cluster groupings and the relative cpue values was reasonably good across all groups. Greenback flounder (Rhombosolea tapirina) The only exceptions were the moderate relative cpue values The catch of greenback flounder came from exactly the in areas B7, B9, and D6, all of which were in cluster groups same cells as King George whiting, and may well be a assigned low density, and the comparatively low cpue in bycatch of the more highly valued King George whiting. area C5, which was in a high density cluster group As a result, the habitat suitability model was identical to (Figure 6a). that of King George whiting (Figure 6b). Habitat suitability modelling of fish species with commercial fisheries data 1597 Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021

Figure 5. Spatial distribution of ﬁshing block cluster groups from Figure 4a.

Australian salmon (Arripis trutta and the relative cpue values. The main exceptions were blocks A. truttaceus) B9 and F5, both of which were in cluster groups assigned low density ratings even though they had moderate relative The relative cpue values of Australian salmon were domi- cpue values, and C5, which had a low relative cpue and was nated by the large value for fishing block E9 (Figure 7a), in a high density cluster group. The predictive map of hab- but we treated this block as an outlier (see below). There itat suitability for sub-adult snapper was similar to those of was good agreement with the cpue values and the classifi- the other species, but it had more high suitability habitat in cation of the cluster groups to high or low density. The the north of the bay and at the entrance to the Geelong Arm, only exceptions were fishing blocks C5 and F5, which and more low suitability habitat around the southern and had low relative cpue values and were in cluster groups as- eastern fringes of the bay (Figure 8b). signed to high density. The resultant suitability codes and habitat suitability model (Figure 7b) were similar to that of King George whiting, in that the majority of seagrass- Adult snapper (Pagrus auratus) associated habitat and shallow fine sediment were classified as high suitability, whereas the low suitability habitat was The catch of adult snapper was mainly in different fishing again the deeper central region of the bay. The main differ- blocks from those of the other species (Figure 9a), and ence in predicted habitat suitability from that of King the assignment of cluster groups to low or high density George whiting was the shallow strip of coarse sand along groups was reasonably consistent with the relative cpue the eastern edge of the bay, which was classed as medium values. Cluster group 5 was assigned a high density rating, suitability, and the shallow strip of sandy sediment along although blocks A8, A7, and C3 had comparatively low rel- the western shore, which was classed as high suitability. ative cpue values; this group could arguably have been assigned a low density rating, but all blocks within the group did return at least some adult snapper. The habitat suitabil- Sub-adult snapper (Pagrus auratus) ity map (Figure 9b) for adult snapper differs from those for The majority of the catch of sub-adult snapper came from the other species. Suitable habitat for adult snapper was cluster groups 4, 5, 8, and 9 (Figure 8a), with fishing block predicted to be in the deeper areas of the bay, while shallow A7 in group 5 dominating the relative cpue values. There seagrass habitat and the coarser sediment on the eastern was a reasonably good agreement between the assignment side of the bay were predicted to be of low habitat suitabil- of cluster groups to high or low density groupings and to ity for this life history stage (Figure 9b). 1598 L. Morris and D. Ball Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021

Figure 7. (a) MDS based on percentage data of habitat composites Figure 6. (a) MDS based on percentage data of habitat composites with standardized cpue of Australian salmon overlaid in the form with standardized cpue of King George whiting overlaid in the of bubbles, where the bigger the bubble the larger the relative form of bubbles, where the bigger the bubble the larger the relative cpue of Australian salmon. Groups from the cluster analysis are cpue of King George whiting. Groups from the cluster analysis are outlined on the plot with cluster groups assigned to a high density outlined on the plot with cluster groups assigned to a high density grouped by a solid line and cluster groups assigned to a low density grouped by a solid line and cluster groups assigned to a low density grouped by a dotted line. (b) Habitat suitability model for Austra- grouped by a dotted line. (b) Habitat suitability model for King lian salmon in Port Phillip Bay. George whiting and greenback ﬂounder in Port Phillip Bay.

information on habitat affinities and distributions for the Discussion species. King George whiting recruit into shallow, seagrass-dominated areas, and move into reef and bare shallow The habitat suitability models for King George whiting, areas as they become older (Fowler and McGarvey, 1995; greenback flounder, Australian salmon, and sub-adult snap- Smith and MacDonald, 1997; Jenkins and Wheatley, per emphasized the importance of shallow habitat, but high- 1998). Our model predicted that the sheltered, shallow, lighted subtle differences between species. Validating and and seagrass habitats were of high suitability, including testing the habitat suitability models presented here was Swan Bay (fishing block E4) and the seagrass areas in fish- hindered by a lack of fishery-independent data. Although ing block G6, and both are important nursery areas for the data exist for some species, the unequal spatial distribution species (Jenkins et al., 1993; Jenkins and Hamer, 2001). and the concentration of sampling over only some of the The strip of low suitability habitat along the eastern side habitat types (mostly bare sediment in depths >7 m) re- of the bay corresponds to results from fishery surveys stricted data utility in model validation. As a consequence, (Parry et al., 1995) and recreational angling returns (Coutin we provide a qualitative assessment of the overall patterns et al., 1995; Conron and Coutin, 1998), and appears consis- of suitable habitat distribution. tent with a lack of structural habitat and a coarse sandy The habitat suitability model for sub-adult King George sediment (Figure 2). While independent validation of the whiting (Figure 6b) was consistent with the existing model will be necessary, the consistency of the model for Habitat suitability modelling of fish species with commercial fisheries data 1599 Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021

Figure 8. (a) MDS based on percentage data of habitat composites Figure 9. (a) MDS based on percentage data of habitat composites with standardized cpue of sub-adult snapper overlaid in the form of with standardized cpue of adult snapper caught with longlines over- bubbles, where the bigger the bubble the larger the relative cpue of laid in the form of bubbles, where the bigger the bubble the larger snapper sub-adult snappers. Groups from the cluster analysis are the relative cpue of adult snapper. Groups from the cluster analysis outlined on the plot with cluster groups assigned to a high density are outlined on the plot with cluster groups assigned to a high den- grouped by a solid line and cluster groups assigned to a low density sity grouped by a solid line and cluster groups assigned to a low grouped by a dotted line. (b) Habitat suitability model for sub-adult density grouped by a dotted line. (b) Habitat suitability model for snapper in Port Phillip Bay. adult snapper in Port Phillip Bay.

King George whiting with other sources of information and similarly the high suitability classification around the suggests reasonable confidence in the model predictions. mouth of the Yarra River at the northern end of the bay Greenback flounder are probably less restricted to the also corresponds with an organic-rich clay sediment. shallow areas than the model suggests (Figure 6b; Kuiter, Juvenile Australian salmon recruit to a wide range of 1993; Gomon et al., 1994). Data from trawl surveys in coastal habitats, from medium energy sandy areas to shel- Port Phillip Bay report flounder in the deeper, more central tered mangrove-lined tidal creeks (Jones, 1999). As they areas of the bay (Parry et al., 1995). However, most flounder grow older, schooling behaviour becomes more apparent. are caught with haul-seines, and fishing effort using this gear In Port Phillip Bay they have been described as transient type is concentrated in shallower water (<10 m). Mesh-nets and gregarious, and have been recorded from shallow water deployed in slightly deeper areas do not target flounder par- over mosaics of seagrass and rocky reef interspersed with ticularly well, creating a bias in the data towards the shal- patches of unvegetated sand (Hindell et al., 2000a, b). lower areas for this species. Flounder are also associated Dietary studies from Port Phillip Bay also showed that with bare organic-rich substratum and have been recorded Australian salmon consume juveniles of seagrass-associated in large numbers in the bare areas interspersed between fish (Hindell et al., 2000b). The habitat suitability model patches of seagrass in Swan Bay (Jenkins et al., 1993). (Figure 7b) for Australian salmon in Port Phillip Bay is The high suitability area predicted in Corio Bay is compat- fairly consistent with this information, with a wide range ible with the large areas of seagrass and organic-rich clays, of shallow habitats classed as of high suitability. 1600 L. Morris and D. Ball

Fishing block E9 (Figure 1) had only a small amount of but anecdotal information and previous studies (e.g. Jenkins habitat classed as highly suitable for Australian salmon, de- et al., 1993) suggest that they are not particularly abundant spite the very large catch of the species there. Fishers utiliz- there. Good sub-adult catches are recorded in deeper areas ing the area tend to be based locally and be very immediately adjacent to shallow seagrass beds in other experienced, and they target transient schools of Australian parts of the bay, so at the scale of modelling undertaken salmon. The schooling behaviour of salmon combined with here, this may be influencing the classification of this hab- the targeted effort may make it possible to obtain very large itat type as highly suitable. catches from a small area of highly suitable habitat, or Snapper move into deeper water with age (Gomon et al., alternatively from a reasonably large area of medium 1994; Coutin, 2000), and the habitat suitability model for suitability habitat. The comparatively small area of low adult fish (Figure 9b) predicted that the deeper areas of suitability habitat for this species within the bay overall is Port Phillip Bay had the most suitable habitat for this life Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021 also consistent with the fact that Australian salmon are pe- stage of snapper. Most of Geelong Arm/Corio Bay were lagic predators and therefore less likely to be strongly tied classified as low suitability for adult snapper (Figure 9b), to a particular benthic habitat. Rather, they are more likely with the balance of the area being classified as medium to move relatively large distances through the water column suitability. Recreational fishers catch good numbers of in search of suitable prey (Hoedt and Dimmlich, 1994). adult snapper in the western Geelong Arm, and the area The habitat suitability models for sub-adult and adult is recognized as having a good winter recreational fishery snapper (Figures 8b and 9b) are considerably different. for the species. Commercial fishers do not longline in Sub-adult snapper were predicted to occur in the shallower this relatively shallow and enclosed area, but instead use areas in the northern and western parts of the bay; findings haul-seines that target sub-adult snapper. As a result, the which correspond with studies of the Port Phillip Bay presence of any adult snapper in the area would be under- recreational fishery that primarily targets sub-adult snapper estimated by this method of modelling. (Conron and Coutin, 1998). Although most of the shallow If the two habitat maps for snapper were to be combined, areas along the eastern edge of the bay were predicted to the majority of Port Phillip Bay would be predicted to be of be of low suitability for sub-adult snapper, there are small high or medium suitability. This is consistent with available areas of reef along this strip that were predicted to be of information about snapper and, in particular, dietary data. high suitability (Figure 2). This is consistent with informa- Snapper are demersal predators, but appear to eat a wide tion provided by recreational fishing guide books (Wilson, range of prey species from a variety of habitats (Parry 1986; Classon and Wilson, 2002) and anecdotal evidence et al., 1995; Coutin, 2000). They are also a highly aggrega- about this species. tive species that can move considerable distances and so are Although the shallow reefs along the northeastern shores likely to utilize a range of habitats, and the predictive maps were identified as highly suitable for sub-adult snapper, the of habitat suitability agree with this information. adjacent band of coarse sand up to depths of about 15 m (Figure 2) was predicted to be of low suitability (Figure 8b). However, the same area also features reefs, The modelling approach rubble, lace coral, and cunji beds (Pyura stolonifera), Although the available information suggests that, with the which are recognized as good fishing sites for sub-adult exception of greenback flounder, we can have confidence snapper by recreational fishers (Wilson, 1986; Classon in the broad patterns predicted by the habitat suitability and Wilson, 2002). Two studies (GHD, 1997; Hamer models, there were still some anomalies. A fishing block et al., 1997) also report the presence of P. stolonifera that had similar habitat parameters to blocks where catches beds in the area, and Hamer et al. (1997) suggested that ju- were high could have a consistently low catch and effort for venile snapper may be associated with the presence of ses- all species (e.g. C5). There may be a number of reasons for sile organisms such as P. stolonifera. These habitats are not this type of anomaly, and they may affect our original as- currently represented in the GIS substratum type/biota layer sumption that fishers will target areas with the highest den- owing to the limitations of mapping to these depths in Port sities of fish. For example, there is a considerable amount of Phillip Bay from aerial photography, and the area is primar- drifting macroalgae in block C5 (Blake and Ball, 2001), ily defined as bare coarse sand at present (Figure 2). As a which may make fishing difficult or less cost effective. Al- result, the presence of these habitats was not accounted for ternatively, fishers may not consider it cost effective to in either the analysis of fishing blocks vs. habitat variables, travel large distances from port, or if they do will have or in the production of the predictive habitat suitability less time available for fishing. As a result, fishing effort models. These habitats are probably also avoided by com- may not be equally distributed among highly suitable hab- mercial fishers because of the potential for snagging their itats, or certain gear types might be excluded from highly nets. suitable areas. Interestingly, Swan Bay (block E4) was predicted to be There may also be sources of variation in fishing effort of primarily high suitability for sub-adult snapper owing that we have not measured and that have the potential to af- to the intertidal seagrass coverage on muddy substratum, fect fishing efficiency and, in turn, cpue. These may include Habitat suitability modelling of fish species with commercial fisheries data 1601 intangibles such as the experience and skill of the particular type in areas that have similar environmental attributes fishers that work an area, as well as differences in technol- will be important in determining the yield of a fish species. ogy used by fishers targeting different areas. Conversely, In fact, we do not know over what scale habitat is impor- where there may be suitable habitat for a species, other en- tant, and this type of scale-dependent habitat information vironmental factors that have not been measured may also would improve models of habitat suitability. be important in determining distributions of a species; Modelling of habitat suitability was also influenced by examples are hydrodynamics, effects of pollution, and the the accuracy of the spatial data for substratum type/biota distribution of prey items or introduced marine pests. The and sediment type. The substratum type/biota mapping reverse situation is one where a fishing block had a high rel- used in this study (Figure 2) was based on interpretation ative cpue for several species, but had habitat more similar of aerial photography and ground-truthing, which only al- to blocks with low relative cpue values (e.g. E9). In that lowed accurate definition of habitats to depths of about Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021 case, fishers might apply their experience and concentrate 7m(Blake and Ball, 2001). While 7 m is about the depth their efforts within a small area and, as previously dis- limit of seagrass in the bay, other habitats such as rocky cussed, if schooling fish are successfully targeted, relative reef and Pyura beds that are known to provide important cpue values can be very high. habitat for sub-adult snapper at depths >7 m were not de- An advantage of the approach we used in this study is fined in the substratum type/biota layer. Consequently, that these anomalies in relative cpue did not affect the pre- the composite habitat suitability models could not identify dictions we made about suitable habitat. Fishing blocks the presence of highly suitable habitat at these locations. were classified into high or low density groups depending Some substratum type/biota categories, such as seagrass on the standardized cpue values for all blocks in a cluster and algal beds, may also display seasonal and annual vari- group, so effectively we were averaging across all blocks ations in their distribution. The seabed sediment layer also that had similar habitat. At the same time, obvious outliers represented a broad-scale pattern of sediment distribution in (such as block E9) were excluded from the analysis, al- the bay, but did not represent localized variations in sedi- though habitats within fishing blocks that were excluded ment type. from the analysis because no fishing took place in that The models we produced are likely to be ‘‘over-protec- block (e.g. E4) were still included in the predictive model tive’’, owing to the methods used to classify habitat suitabil- of habitat suitability. This was as a result of all habitat com- ity. Emphasis was placed mainly on the fishing blocks where binations being assigned a suitability rating, so that predic- fish were caught, rather than the blocks where they were not tions could be transferred to areas with no fisheries data as caught. This meant that the models probably had more high long as the same habitat combinations were present. and medium quality habitats than was actually the case. A There are several important assumptions that underlie the model that over-predicts these upper levels of suitable hab- method of determining suitable habitat presented in this pa- itat would, though, seem preferable if it is likely to be used per. The first is that the areas targeted by fishers corresponded in initially identifying areas of important habitat. to high densities of the species under investigation. For species that are not the primary target, this may not be the case. For example, greenback flounder are mainly caught with Conclusions haul-seines in the same areas as King George whiting (the primary target species). As haul-seines are not used in the The spatial habitat suitability models produced in this study deeper, unvegetated, soft-sediment habitats of Port Phillip present a simplified picture of habitat suitability and do not Bay, that are known to provide suitable habitat for greenback account for many complex relationships and interactions flounder (Parry et al., 1995), the assumption that fishers only both between species and between species and environmen- target areas of high density is unlikely to be true for this spe- tal variables. However, in the absence of a more complete cies. We also assumed that demersal species would provide knowledge of the nature of these relationships and the spa- more reliable habitat suitability models than pelagic species, tial scales at which they operate, the habitat suitability but it appears that the degree to which a species is actively modelling approach presents a relatively effective method targeted is at least, if not more, important than the life history for conducting a first-pass identification of likely distribu- characteristics of that species. Similarly, the operational lim- tions of important fishery habitats. itations of a gear type may also exert an influence on where In the absence of suitable fishery-independent data, catch species are caught. Longlines are not typically used in Corio and effort data from a commercial fishery can be used to Bay or most of the Geelong Arm because of the relatively create habitat suitability models. The method developed shallow and enclosed nature of the area. Consequently, allows prediction of suitable habitat and, by extension, species targeted by these gear types and potentially present fish distribution, at a smaller scale than the actual catch in the area, such as adult snapper, do not appear in the catch returns. Within this method, the best models will relate to and effort data. species that are much targeted by commercial fishers The second major assumption of the method relates to because of the implicit assumption that areas targeted by our prediction that the consistent presence of a habitat commercial fishers have the highest densities of the species 1602 L. Morris and D. Ball being modelled. Although there is evidence that the habitat Environment, Marine and Freshwater Resources Institute, suitability models we produced provide good predictive in- Queenscliff, Victoria, Australia. 43 pp. formation on fish habitat and fish distribution for some spe- Coutin, P., Conron, S., and MacDonald, M. 1995. The Daytime Recreational Fishery in Port Phillip Bay, 1989e94. Department cies, the models and the hypotheses generated from the of Conservation and Natural Resources, Victorian Fisheries modelling process require further testing. In the absence Research Institute, Queenscliff, Victoria, Australia. 43 pp. of suitable fishery-independent monitoring data, the ap- Denis, V., Lejeune, J., and Robin, J-P. 2002. Spatio-temporal anal- proach described here provides a valuable step in develop- ysis of commercial trawler data using general additive models: patterns of loliginid squid abundance in the north-east Atlantic. ing spatial models to define important fishery habitats at ICES Journal of Marine Science, 59: 633e648. a bay-wide scale. Denis, V., and Robin, J-P. 2001. Present status of the French Atlan- tic fishery for cuttlefish (Sepia officinalis). Fisheries Research,

52: 11e22. Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021 Fowler, A. J., and McGarvey, R. 1995. Development of an Inte- Acknowledgements grated Fisheries Management Model for King George Whiting (Sillaginodes punctata) in South Australia. South Australian This project was funded by the Fisheries Research and De- Research and Development Institute, Henley Beach. 231 pp. velopment Corporation and Fisheries Victoria. We thank Gallaway, B. J., Cole, J. G., Martin, L. R., Nance, J. M., and Long- PIRVic personnel Allister Coots for data extraction, and necker, M. 2003a. Description of a simple electronic logbook Greg Jenkins, Anne Gason, and Jeremy Hindell for com- designed to measure effort in the Gulf of Mexico shrimp fishery. ments on the manuscript. We also gratefully acknowledge North-American Journal of Fisheries Management, 23: 581e589. the constructive comments of two anonymous reviewers Gallaway, B. J., Cole, J. G., Martin, L. R., Nance, J. M., and Long- for improving the manuscript. necker, M. 2003b. An evaluation of an electronic logbook (ELB) as a more accurate method of estimating spatial patterns of trawling effort and bycatch in the Gulf of Mexico shrimp fishery. North-American Journal of Fisheries Management, 23: References 787e809. Gallaway, B. J., Cole, J. G., Meyer, R., and Roscigno, P. 1999. De- Anon. 2001. Fisheries Victoria commercial fish production infor- lineation of essential fish habitat for juvenile red snapper in the mation bulletin. Marine and Freshwater Resources Institute, northwestern Gulf of Mexico. Transactions of the American Queenscliff, Victoria, Australia. 28 pp. Fisheries Society, 128: 713e726. Ball, D., and Coots, A. G. 2001. Catch and effort info: an ArcView GHD (Gutteridge, Haskins, and Davey). 1997. Port Phillip Bay GIS application for querying and displaying commercial fishery coastal values assessment. Physical characteristics and environ- catch data. In AURISA 2001, 29th Annual Conference of the mental values. Part A. Report to Parks Victoria. Gutteridge, Has- Australasian Urban and Regional Information Systems Associa- kins and Davey Pty Ltd, Melbourne. 45 pp. tion, 19e23 November, Melbourne, Victoria, Australia. Gomon, M. F., Glover, J. C. M., and Kuiter, R. H. 1994. The Fishes Blake, S., and Ball, D. 2001. Seagrass Mapping of Port Phillip Bay. of Australia’s South Coast. State Print, Adelaide. 992 pp. MAFRI Report, 39. Marine and Freshwater Resources Institute, Guisan, A., and Zimmermann, N. E. 2000. Predictive habitat distri- Queenscliff, Victoria, Australia. 79 pp. bution models in ecology. Ecological Modelling, 135: 147e186. Bowen, B. K. 1997. What are the roles of science, economics, so- Hamer, P., Jenkins, G., and Welsford, D. 1997. Sampling of Newly ciology and politics in fisheries management. In Developing and Settled Snapper, Pagrus auratus, and Identification of Preferred Sustaining World Fisheries Resources. The State of Science and Habitats in Port Phillip Bay e a Pilot Study. Marine and Fresh- Management, pp. 169e176. Ed. by D. A. Hancock, D. C. Smith, water Resources Institute, Queenscliff, Victoria, Australia. 53 pp. A. Grant, and J. P. Beumer. Proceedings of the 2nd World Fish- Hilborn, R., and Walters, C. 1992. Quantitative Fisheries Stock eries Congress. CSIRO Publishing, Melbourne. Assessment. Choice, Dynamics and Uncertainty. Chapman and Bray, J., and Curtis, J. 1957. An ordination of the upland forest Hall, London. 570 pp. communities of southern Wisconsin. Ecological Monographs, Hindell, J. S., Jenkins, G. P., and Keough, M. J. 2000a. Evaluating 27: 325e349. the impact of predation by fish on the assemblage structure of Brown, S. K., Buja, K. R., Jury, S. H., Monaco, M. E., and Banner, fishes associated with seagrass (Heterozostera tasmanica) (Mar- A. 2000. Habitat suitability index models for eight fish and in- tens ex Ascherson) den Hartog, and unvegetated sand habitats. vertebrate species in Casco and Sheepscot Bays, Maine. North- Journal of Experimental Marine Biology and Ecology, 255: American Journal of Fisheries Management, 20: 408e435. 153e174. Clarke, K. R. 1993. Non-parametric multivariate analyses of Hindell, J. S., Jenkins, G. P., and Keough, M. J. 2000b. Variability changes in community structure. Australian Journal of Ecology, in abundance of fishes associated with seagrass habitats in rela- 18: 117e143. tion to diets of predatory fishes. Marine Biology, 136: 725e737. Clarke, K. R., and Warwick, R. 2001. Change in Marine Commu- Hoedt, F. E., and Dimmlich, W. F. 1994. Diet of subadult Austra- nities: an Approach to Statistical Analysis and Interpretation, lian salmon, Arripis truttaceus, in Western Port, Victoria. 2nd edn. PRIMER-E, Plymouth. 169 pp. Australian Journal of Marine and Freshwater Research, 45: Classon, B., and Wilson, G. (Eds). 2002. Fishing Victoria’s Coast- 617e623. line. Australian Fishing Network, South Croydon, Victoria, Jenkins, G. P., and Hamer, P. A. 2001. Spatial variation in the use Australia. 112 pp. of seagrass and unvegetated habitats by post-settlement King Conron, S., and Coutin, P. 1998. The Recreational Snapper Catch George whiting (Percoidei: Sillaginidae) in relation to meiofau- from Port Phillip Bay: a Pilot Survey of the Boat Based Fishery nal distribution and macrophyte structure. Marine Ecology Prog- 1994/95. MAFRI Internal Report, 11. Marine and Freshwater ress Series, 224: 219e229. Resources Institute, Queenscliff, Victoria, Australia. 22 pp. Jenkins, G. P., Watson, G. F., and Hammond, L. S. 1993. Patterns Coutin, P. 2000. Snapper e 1998. In Fisheries Victoria Assessment of utilisation of seagrass (Heterozostera) dominated habitats as Report, 19. Department of Natural Resources and nursery areas by commercially important fish. Technical Report, Habitat suitability modelling of fish species with commercial fisheries data 1603

19. Victorian Institute of Marine Sciences, East Melbourne, Vic- support of ecosystem management. GIS Applications for Fisher- toria, Australia. 100 pp. ies and Coastal Resources Management, Gulf States Marine Jenkins, G. P., and Wheatley, M. J. 1998. The influence of habitat Fisheries Commission, Biloxi, Mississippi. pp. 157e192. structure on nearshore fish assemblages in a southern Australian Rubec, P. J., Bexley, J. C. W., Norris, H., Coyne, M. S., Monaco, embayment: comparison of shallow seagrass, reef-algal and M. E., Smith, S. G., and Ault, J. S. 1999. Suitability modeling to unvegetated sand habitats, with emphasis on their importance delineate habitat essential to sustainable fisheries. American to recruitment. Journal of Experimental Marine Biology and Fisheries Society Symposium, 22: 108e113. Ecology, 221: 147e172. Rubec, P. J., Smith, S. G., Coyne, M. S., White, M., Sullivan, A., Jones, K. J. 1999. Australian Salmon (Arripis truttacea). South Wilder, D., MacDonald, T., McMichael, R. H., Monaco, M. E., Australian Research and Development Institute, Aquatic Sci- and Ault, J. S. 2001. Spatial modeling of fish habitat suitability ences, Henley Beach. 13 pp. in Florida estuaries. In Spatial Processes and Management Kemp, Z., and Meaden, G. 2002. Visualization for fisheries man- of Marine Populations, pp. 1e18. Ed. by G. H. Kruse, N. Bez,

agement from a spatiotemporal perspective. ICES Journal of A. Booth, M. W. Dorn, S. Hills, R. N. Lipcus, D. Pelletier, C. Downloaded from https://academic.oup.com/icesjms/article/63/9/1590/696978 by guest on 01 October 2021 Marine Science, 59: 190e202. Roy, S. J. Smith, and D. Witherell. Alaska Sea Grant (College Kuiter, R. H. 1993. Coastal Fishes of South-Eastern Australia. Program), Fairbanks, Alaska. AG-SG-01-02. Crawford House Press, Bathurst, New South Wales, Australia. Rubec, P. J., Whaley, S. D., Henderson, G. E., Lewis, J., White, M., 437 pp. Sullivan, A. M., Vadas, R. L., Ruiz-Carus, R., Wilder, D. T., Mace, P. M. 1997. Developing and sustaining world fisheries re- Westergren, C., McMichael, R. H., and MacDonald, T. 2003. sources: the state of the science and management. In Developing Development and Evaluation of Methods for Habitat Suitability and Sustaining World Fisheries Resources. The State of Science Modeling in Florida Estuaries. Report submitted to U.S. Fish and and Management, pp. 1e22. Ed. by D. A. Hancock, D. C. Smith, Wildlife Service, Atlanta, Georgia, associated with Sport Fish A. Grant, and J. P. Beumer. Proceedings of the 2nd World Fish- Restoration Grant No. F-96b, Florida Marine Research Institute. eries Congress. CSIRO Publishing, Melbourne. 69 pp. Marrs, S. J., Tuck, I. D., Atkinson, R. J. A., Stevenson, T. D. I., and Sharp, G. D. 1997. Its about time: rethinking fisheries management. Hall, C. 2002. Position data loggers and logbooks as tools in fish- In Developing and Sustaining World Fisheries Resources. The eries research: results of a pilot study and some recommenda- State of Science and Management, pp. 731e736. Ed. by D. A. tions. Fisheries Research, 58: 109e117. Hancock, D. C. Smith, A. Grant, and J. P. Beumer. Proceedings Maurstad, A. 2002. Fishing in murky waters e ethics and politics of the 2nd World Fisheries Congress. CSIRO Publishing, of research on fisher knowledge. Marine Policy, 26: 159e166. Melbourne. Parry, G. D., Hobday, D. K., Currie, D. R., Officer, R. A., and Smith, D. C., and MacDonald, C. M. 1997. King George Whiting Gason, A. S. 1995. The distribution, abundance and diets of 1996. Compiled by the Bay and Inlet Fisheries Stock Assessment demersal fish in Port Phillip Bay. CSIRO Port Phillip Bay Group. Fisheries Victoria Assessment Report, 15. Fisheries Vic- Environmental Study Technical Report, 21. Victorian Fisheries toria, East Melbourne, Victoria, Australia. 18 pp. Research Institute, Queenscliff, Victoria, Australia. 107 pp. Starr, R. M., and Fox, D. S. 1997. Can fishery catch data supple- Parsons, T. R., and Harrison, P. J. 2000. Introduction. In Fisheries ment research cruise data? A geographical comparison of Oceanography. An Integrative Approach to Fisheries Ecology research and commercial catch data. In Developing and and Management. Ed. by P. J. Harrison, and T. R. Parsons. Sustaining World Fisheries Resources. The State of Science Blackwell Science, Oxford. 347 pp. and Management, pp. 190e197. Ed. by D. A. Hancock, D. C. PMA (Port of Melbourne Authority). 1987. Sediments of Port Phil- Smith, A. Grant, and J. P. Beumer. Proceedings of the 2nd World lip Bay. Report No. 87-8-15, Marine Model Laboratory, Port of Fisheries Congress. CSIRO Publishing, Melbourne. Melbourne Authority, Melbourne, Victoria, Australia. 48 pp. Stoner, A. W., Manderson, J. P., and Pessutti, J. P. 2001. Spa- Quinn, G. P., and Keough, M. J. 2002. Experimental Design and tially explicit analysis of estuarine habitat for juvenile winter Data Analysis for Biologists. Cambridge University Press, Cam- flounder: combining generalized additive models and geo- bridge, UK. 537 pp. graphic information systems. Marine Ecology Progress Series, Reynolds, J. A. 2003. Quantifying habitat associations in marine 213: 253e271. fisheries: a generalization of the KolmogoroveSmirnov statistic Wilson, G. 1986. Snapper. When, where and how to catch them. A using commercial logbook records linked to archived environ- Guide to Port Phillip and Westernport Bay. Geoff Wilson, Gee- mental data. Canadian Journal of Fisheries and Aquatic Sci- long, Victoria, Australia. 104 pp. ences, 60: 370e378. Zheng, X., Pierce, G. J., and Reid, D. G. 2001. Spatial patterns of Rubec, P. J. 1996. GIS applications for fisheries: for data base man- whiting abundance in Scottish waters and relationships with agement, data sharing sampling, analysis, and visualization in environmental variables. Fisheries Research, 50: 259e270.