Prediction of riverine fish assemblages through ... - CSIRO Publishing

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Marine and Freshwater Research, 2006, 57, 601–609

Prediction of riverine fish assemblages through the concept of environmental filters Bruce C. Chessman Department of Natural Resources, PO Box 3720, Parramatta, NSW 2124, Australia. Email: [email protected] Abstract. Although the taxonomic composition and richness of fish assemblages are important properties to be considered in freshwater bioassessment, conservation and rehabilitation, it can be difficult to establish a natural benchmark for these properties because of widespread human impact and a lack of pristine reference sites or preimpact data. As an alternative to the reference site approach, the concept of multiple environmental filters was used to predict the assemblages of fish taxa expected in the absence of anthropogenic stress at 85 sites on rivers in northeastern New South Wales, Australia. The predicted native fish assemblages were compared with the assemblages recorded by backpack and boat electrofishing at each site. The number of native species predicted by the filters model at each site was highly correlated with the observed number of native species (R2 = 0.75; P < 0.001) but the observed number was generally lower. The model had an average sensitivity of 93% and specificity of 87%, but sensitivity and specificity were considerably less for a few species, including some that are known to have suffered historical declines or been translocated outside of their natural ranges. Comparisons between predicted and observed richness and composition can be used to identify areas of high conservation value and areas where native fish assemblages have been adversely affected by anthropogenic impacts. Extra keywords: bioassessment, distribution, predictive modelling, reference condition, stream.

Introduction A central challenge in freshwater bioassessment is to estimate the biota that could be expected in a particular place in the absence of anthropogenic stress. A prediction of the unstressed biota can be useful as a benchmark from which to evaluate human impact or as a target for restoration. However, such a prediction can be difficult to obtain. Often, impact has already occurred before a study begins, and pre-impact data for the study area are non-existent. A comparison with nearby ‘control’ sites may be impossible because of a lack of unstressed but otherwise similar water bodies, or else a risky proposition because of potential confounding by factors other than anthropogenic stress (Beyers 1998). A solution to this problem that is often advocated is to select a large set of ‘reference sites’, distributed across a broader region, and develop a numerical model associating spatial variation in biological assemblages among these sites with environmental variables (Bailey et al. 2004). These relationships are then used to generate a synthetic, sitespecific ‘reference condition’ for each site where assessment of stressor impact is required, by application of the model to measured environmental attributes of that site. This type of approach was pioneered in the United Kingdom, where it led to the River Invertebrate Prediction and Classification System (RIVPACS: Wright 2000), and has since been applied © CSIRO 2006

to other organisms, including freshwater fish (Joy and Death 2000, 2002, 2003, 2005; Oberdorff et al. 2001; Gevrey et al. 2005; Park et al. 2005; Pont et al. 2005; Kennard et al. 2006). Two problems, in particular, can arise in this approach (Chessman and Royal 2004; Walsh 2006). First, it relies on the existence of large numbers of suitable reference sites to represent the full range of natural environments in a region. This is necessary so that a reference biological assemblage can be modelled for any site that is to be assessed, regardless of its environmental characteristics. In many regions, the variety and ubiquity of human impacts make a reference set of pristine sites unattainable, and consequently researchers settle for ‘least disturbed’ or ‘best available’ sites. This can make reference condition a rather arbitrary and inconsistent construct, which may not correctly represent biological potential in the absence of anthropogenic stress. Second, the predictive models used in reference-site methods often incorporate environmental predictor variables that can be affected by human activities. If human-influenced values of such predictors are used, the reference assemblage for an assessment site can differ from the assemblage that would have been predicted if the natural values of the predictor variables had been used instead. A reference-site approach to riverine bioassessment may be appropriate in regions where human impacts are relatively 10.1071/MF06091

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localised, or where the aim is to assess the effect of a specific stressor of limited prevalence. However, such an approach is not appropriate to assess the total impact of human influences on riverine ecosystems across the State of New South Wales (NSW: 802 000 km2 ) in south-eastern Australia. The State’s river systems have been extensively modified by impoundment, flow regulation and water extraction, the invasion of alien fish, plants and pathogens, and the agricultural and urban development of river catchments (Faragher and Harris 1994; Harris and Silviera 1999; Norris et al. 2001). Consequently, it is impossible to find examples of all the types of rivers that occur in the State where one could be confident that the native fish assemblage and other aquatic biota have not been substantially altered as a result of human activities. For example, an expert panel has estimated that native fish populations in the Murray–Darling Drainage Division, which covers 75% of NSW, are currently at a level only ∼10% of that 200 years ago, and still declining (MDBMC 2003). An additional problem in the application of the reference-site approach to fish is the great mobility of many fish species. For example, many NSW species of freshwater fish disperse and migrate over great distances in inland rivers (Reynolds 1983) or between rivers and estuaries or the ocean (Harris 1984). Therefore even if sites can be found with little evident human disturbance in the immediate vicinity, the fish assemblages at those sites can be greatly affected by human activities farther afield. An alternative approach to bioassessment that is applicable to freshwater fish and other organisms arises from the concept of environmental filters or screens (Smith and Powell 1971; Tonn 1990). In this conceptual model, environmental factors operating at a wide range of scales successively exclude a proportion of a global or regional species pool, leaving a residual local assemblage to occupy a particular site. Each species either passes or fails to pass through a particular filter according to its evolutionary history, dispersal ability, physiological tolerances, habitat requirements and fate in interactions with other species. For example, biogeographic barriers exclude those species that have been unable to disperse across them, and high or low local temperatures exclude those species that cannot tolerate them. Because the concept incorporates filters at a variety of scales, it can embrace both regional and local controls on species distributions (Quist et al. 2005). In addition, unlike many predictive models that simply establish statistical associations between environmental variables and species distributions, filters models are framed around specific causal relationships. This can facilitate management decisions through a greater understanding of the mechanisms regulating assemblage composition, both under natural conditions and in the face of anthropogenic stress. Although the metaphor of environmental filters has a long history (see Simpson 1940) it has only recently been applied to practical bioassessment (e.g. Quist et al. 2005; Stranko et al. 2005). Chessman and Royal (2004) applied a filters

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approach to river macroinvertebrates in north-eastern NSW, and found that it produced a much more stressor-sensitive bioassessment than an alternative reference-site approach. Here, I apply the filters approach to estimate potential natural fish assemblages at river sites in NSW, where a site is defined as a river reach long enough to contain the range of local habitats such as riffles and pools, generally in the order of 200 m. This is the typical spatial extent of site sampling for fish in NSW rivers (e.g. Gehrke and Harris 2000), and is also a relevant scale for management activities such as physical rehabilitation. As a test of this application, assemblages collected by an electrofishing survey at 85 sites in the northeastern portion of the State were compared with predicted assemblages. Materials and methods Development of the filters model A model incorporating three environmental filters was developed to predict natural distributions of 52 native species (including two undescribed species), one genus and one species-complex (Table 1). These include most native species recorded from the State’s fresh waters, apart from various essentially marine or estuarine species that sometimes occur in coastal rivers. Lampreys were considered at genus level because the two species of Mordacia known from NSW are difficult to distinguish and usually identified only to genus in routine surveys. The western carp gudgeon (Hypseleotris klunzingeri) species-complex was considered as a unit because its members are seldom separated in routine surveys and have complex genetic relationships (Bertozzi et al. 2000). For simplicity, all 54 taxa are referred to as species hereafter. The model was based on locality records and associated environmental information from published sources, the collection database of the Australian Museum, Sydney, the freshwater fish database of the NSW Department of Primary Industries, the author’s unpublished records and various surveys undertaken by or for the NSW Department of Natural Resources. Collectively, the data dated from 1880 to 2005 and included over 400 000 individual fish from localities distributed widely across NSW. Where not available from the data sources, altitudes of localities that were defined with sufficient accuracy were determined from topographic maps or by the use of a digital elevation model. The first, broad-scale environmental filter comprised drainage basins, which represent the barriers to fish dispersal posed by drainage divides. Basins probably also serve as surrogates for the latitudinal temperature gradient, which can exclude cold-adapted species from northern basins and warm-adapted species from southern basins. This filter was applied by constructing lists of recorded native species for each of the State’s 43 drainage basins, excluding instances in which the presence of a species in a drainage basin was known or strongly suspected to be due to translocation. Assumed translocations included all records east of the Great Dividing Range for Bidyanus bidyanus, Craterocephalus amniculus, Leiopotherapon unicolor, Macquaria ambigua, Maccullochella maquariensis and M. peelii, records of Tandanus tandanus east of the Great Dividing Range and south of the Manning River basin, and records of Hypseleotris klunzingeri from the Shoalhaven River basin (for further information on translocation see McDowall (1996), Unmack (2001), Allen et al. (2002) and Gehrke et al. (2002)). In a few cases, species were included in the list for a drainage basin in the absence of records because the species had been recorded in surrounding basins and dispersal between basins was considered likely given the environmental tolerances of the species. For example, it was assumed that lowland species could disperse between basins in the

Prediction of riverine fish assemblages


Table 1. List of NSW native fish taxa included in the model The observed and modelled prevalence of each taxon are tabulated for 85 test sites in north-eastern NSW, together with the sensitivity and specificity of model predictions of presence and absence at these sites. Sensitivity cannot be calculated for species that were not observed at any site Taxon

Acanthopagrus australis Ambassis agassizii Anguilla australis Anguilla reinhardtii Arius graeffei Bidyanus bidyanus Bidyanus welchi Craterocephalus amniculus Craterocephalus fluviatilis Craterocephalus marjoriae Craterocephalus stercusmuscarum Gadopsis bispinosus Gadopsis marmoratus Galaxias brevipinnis Galaxias maculatus Galaxias olidus Galaxias rostratus Gobiomorphus australis Gobiomorphus coxii Hypseleotris compressa Hypseleotris galii Hypseleotris klunzingeri species-complex Leiopotherapon unicolor Liza argentea Maccullochella ikei Maccullochella macquariensis Maccullochella peelii Macquaria ambigua Macquaria australasica Macquaria colonorum Macquaria novemaculeata Melanotaenia duboulayi Melanotaenia fluviatilis Melanotaenia splendida Mogurnda adspersa Mordacia spp. Mugil cephalus Myxus petardi Nannoperca australis Nannoperca oxleyana Nematalosa erebi Neosilurus hyrtlii Notesthes robusta Philypnodon grandiceps Philypnodon sp. (undescribed) Porochilus argenteus Potamalosa richmondia Prototroctes maraena Pseudaphritis urvillii Pseudomugil signifer Retropinna semoni Rhadinocentrus ornatus Tandanus tandanus Tandanus sp. (undescribed)

Observed prevalence (% of sites)

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1 5 13 61 0 2 0 4 0 7 12 0 4 1 1 8 0 33 34 29 20 29 7 1 1 0 13 12 0 0 19 28 8 0 2 0 20 18 0 0 5 0 18 25 19 0 15 0 0 18 46 4 47 9

4 40 62 65 0 11 0 29 0 20 25 0 6 0 8 39 0 41 45 46 45 78 18 4 4 0 19 16 0 0 22 34 19 0 25 0 22 14 0 0 11 0 34 49 36 0 35 0 0 39 67 15 58 15

100 100 100 100 – 50 – 100 – 100 90 – 100 0 100 100 – 100 90 100 94 92 100 100 100 – 91 100 – – 81 100 100 – 100 – 82 73 – – 100 – 100 100 100 – 85 – – 100 97 100 90 100

98 63 43 91 100 90 100 73 100 86 84 100 98 100 93 67 100 88 79 77 68 28 89 98 98 100 92 95 100 100 91 92 88 100 77 100 93 90 100 100 94 100 80 81 78 100 74 100 100 74 59 88 71 94

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Murray–Darling Drainage Division that were connected at their downstream ends, and that species with marine stages in their life cycles could disperse between coastal rivers in adjacent basins. Occasional errant records were excluded from basin lists because they were suspected to represent misidentifications. The second environmental filter was altitude, which was taken as a surrogate for the effect of temperature in controlling the downstream limit of cold-water species and the upstream limit of warm-water species. It probably also served as a surrogate for the role of distance from estuaries or the ocean in limiting the inland penetration of diadromous species. Tolerable maximum and minimum altitudes were established for each species in each currently or historically inhabited region of NSW, with regions as defined by Gehrke and Harris (2000) (Darling, Murray, North Coast and South Coast). These limits were estimated by considering the range of recorded altitudes for each species in each region, with exclusion of a few outlying records. Altitudinal ranges were set for regions rather than the smaller basins because the available records were not considered sufficient to set meaningful ranges for the latter. Finally, a local-scale filter was applied as a preference for particular types of rivers, assigned to each species on the basis of the locality records, the author’s experience, and published information on habitat requirements (e.g. McDowall 1996; Allen et al. 2002; Pusey et al. 2004). These preferences were scored as expected presence or absence in each of five river types: (1) estuaries; (2) shallow, soft-bottom rivers, generally 1 m deep; (4) shallow, stony-bed rivers; and (5) deep stony-bed rivers. Test data Fish were surveyed in 2001–2004 by the NSW Department of Primary Industries at 85 river gauging stations in 12 river basins in north-eastern NSW (Table 2). These basins lie within two of the four geographic regions of NSW defined by Gehrke and Harris (2000) – the Darling and North Coast regions. North-eastern NSW is a diverse part of the State with elevations from sea level up to 1500 m, and a mix of land uses ranging from national parks and other protected areas to timber harvesting from native eucalypt forests and exotic pine plantations, grazing of sheep and cattle on native or exotic pastures, cropping and urbanisation. The sampling sites ranged in elevation from 5 to 1290 m above sea level and in catchment area from 8 to 7930 km2 (Table 2). Each was sampled twice: once in ‘summer’ (January–March) and once in ‘winter’ (May–August). Fish were captured with a backpack electrofisher, boat electrofisher or both, depending on the range of water depth. Electrofishing covered the range of fish habitats at a site and generally lasted for ∼30 min in each season. At some sites with limited water, less time (down to 14 min) was expended. Stunned fish were collected in a dip net, placed in containers of water to recover, examined, identified and released. Fish observed but not captured were also recorded if the species could be identified confidently. Calculation of predicted assemblages and comparison with observed assemblages The environmental filters were used to generate a potential natural fish assemblage for each test site as follows: each species was included in the potential natural assemblage for the site if: (1) the species was within the list for the basin containing the site; (2) the site’s altitude was within the altitudinal range of the species within the region containing the site; and (3) the river type was within the suite of types favoured by the species. Each site was assigned to one of the five river types after observation of bed materials and measurement of maximum depths on at least two occasions. In a few cases, sites with areas deeper than 1 m were classified as shallow because only backback electrofishing was used to sample the site (e.g. because of lack of access for boat launching),

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Table 2. Numbers of sites surveyed for fish within each river basin in north-eastern NSW, and the range of altitudes and catchment areas among those sites Regions of NSW are as defined by Gehrke and Harris (2000) Basin

Region

Bellinger Brunswick Clarence Gwydir Hastings Karuah Macintyre Macleay Manning Namoi Richmond Tweed

North Coast North Coast North Coast Darling North Coast North Coast Darling North Coast North Coast Darling North Coast North Coast

Number of sites

Range of altitude (m)

Range of catchment area (km2 )

3 1 20 9 5 4 8 11 5 13 4 2

5–35 15 5–1290 255–1265 5–155 5–75 340–860 125–1225 5–840 265–905 15–45 15

51–539 34 31–2670 14–1970 241–515 150–974 505–2020 8–7930 96–1790 150–4000 39–332 213–275

and hence the deep-water fauna was not considered to be adequately sampled. The predicted suite of potential native species was compared with the suite observed at each site, with data combined from both sampling occasions. For each site a ‘species deficit’ was calculated, expressing the number of ‘missing’ species (those predicted to occur but not observed) as a percentage of the number of predicted species. Relationships among the numbers of predicted and observed species, the species deficit and other variables were assessed by linear and non-linear regression. The sensitivity and specificity of the model for each species were calculated as described by Fielding and Bell (1997) and expressed as percentages.

Results Thirty-seven of the 54 modelled native species were predicted to occur naturally at one or more of the 85 sites. The number of predicted species was strongly correlated with site altitude but showed little evident relationship with the catchment area upstream of each site (Fig. 1). All of the predicted species were recorded in the survey, together with one additional native species (Galaxias brevipinnis) and five alien species (Carassius auratus, Cyprinus carpio, Gambusia holbrooki, Perca fluviatilis and Oncorhynchus mykiss). The relationships of site altitude and catchment area to the numbers of observed native species were similar to those for the number of predicted species (Fig. 2). The number of observed native species per site ranged from slightly more than the predicted number to considerably fewer (Fig. 3). The linear regression relationship between the numbers of predicted and observed native species was highly significant (R2 = 0.75; P < 0.001). The number of sites at which individual species were observed ranged from a little more than the predicted number to many fewer (Fig. 4). Species that were recorded at fewer than a quarter of predicted sites were Ambassis agassizii (observed at 12% of predicted sites), Anguilla australis (21%), Bidyanus bidyanus (22%), Craterocephalus amniculus (12%), Galaxias maculatus (14%), Galaxias olidus

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(21%), Mogurnda adspersa (10%) and Rhadinocentrus ornatus (23%). Three of these (A. agassizii, B. bidyanus and M. adspersa) are formally listed as either threatened species or threatened populations under NSW legislation. The sensitivity of the model (the proportion of sites where a species was observed at which it was also predicted) ranged from 0% to 100% (Table 1). However, sensitivity was below 70% for only two species (Galaxias brevipinnis and Bidyanus bidyanus), and both of these had a very low prevalence, being observed at only 1–2 sites. Excluding 16 species that were neither observed nor predicted at any of the 85 sites, and for which sensitivity cannot therefore be calculated, sensitivity averaged 93%. Model specificity (the proportion of sites at which a species was not observed where it was predicted

to be absent) ranged from 28% to 100% and averaged 87% for all species and 82% for those that were either predicted or observed at one or more test sites. Six species had a specificity below 70%: Ambassis agassizii, Anguilla australis, Galaxias olidus, Hypseleotris galii, the H. klunzingeri species-complex and Retropinna semoni. The species deficit (percentage of predicted native species that were not observed) ranged from 0% to 100%. The relative deficit was not significantly related to site altitude or catchment area (P > 0.05), but had a weak negative relationship with the predicted number of native species (linear R2 = 0.05; P = 0.040) and a stronger negative relationship with the observed number of native species (linear R2 = 0.30; P < 0.001) (Fig. 5).

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Fig. 3. Relationship between the number of native fish species predicted and the number of native fish species observed at 85 sites in north-eastern NSW. Some data points overlap. The line represents equality of predicted and observed richness.

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Discussion The environmental filters model achieved a high average sensitivity of 93% and specificity of 87%, although values of these measures were considerably lower for a few species. Since the aim of the model was to predict natural potential distributions, rather than actual current distributions, low specificity and sensitivity do not necessarily represent poor model performance. Sensitivity, which measures the ability of a model to predict observed occurrences, can be reduced by the translocation of native species outside of their natural ranges. This was the cause of the low sensitivity value of 50% for the silver perch, Bidyanus bidyanus, since one of the two recorded occurrences of this species was in the Clarence River system, outside of the natural distribution of this species

in the Murray–Darling Drainage Division. If this occurrence were excluded, model sensitivity for this species would be 100%. The other case of low sensitivity involved the climbing galaxias, Galaxias brevipinnis. Three specimens of this species were recorded from a single site in the Wollomombi River, on the New England Tableland. This is well outside of the normally accepted distribution of this species, which extends only as far north as the Hunter River system (Allen et al. 2002). However, it is now recognised that various forms of upland galaxiids occur in northern NSW, and the region may well contain several undescribed species (e.g. Raadik 2005). Hence the identity of the Wollomombi River galaxiid warrants further investigation. In a model of natural distributions, low values of specificity, which measures ability to predict observed absences, can result from human impacts that have eliminated the species from predicted localities. For example, specificity was only 63% for Agassiz’s glassfish, Ambassis agassizii, which was predicted at 34 sites at which it was not observed. This species is known to have undergone a severe historical decline within the Murray–Darling Drainage Division (Pusey et al. 2004), and is now formally listed as an endangered population in western NSW. Specificity was also low for the mountain galaxias, Galaxias olidus, which was predicted at 26 sites at which it was not observed. Although this species is still widespread and locally abundant, it is known to be highly susceptible to predation by introduced trout (Tilzey 1976; Fletcher 1979; Closs and Lake 1996). G. olidus was not recorded at any of three test sites where rainbow trout, Oncorhynchus mykiss, were found, or at any of five sites inhabited by alien redfin perch, Perca fluviatilis. All of these were sites where G. olidus was predicted to occur. Other alien species recorded at some of the test sites – Gambusia holbrooki (46 sites) and Cyprinus carpio (19 sites) – have also been implicated as threats to native species (Howe et al. 1997; Ivantsoff and Aarn 1999; Gilligan 2005). Many anthropogenic factors besides alien fishes are likely to account for absences of native species from test sites where they were predicted to be found. For example, artificial barriers to fish migration such as dams, weirs and causeways have caused widespread interference with the movement of diadromous fish species in eastern NSW, isolating such species from potential habitats (Harris 1984). Other anthropogenic stressors such as overfishing, alteration of flow and temperature regimes, removal of riparian vegetation and in-stream wood, catchment and bank erosion, and water pollution have also been widely implicated in declines of native fish populations in NSW (e.g. Cadwallader 1978; Faragher and Harris 1994). Specificity was relatively low for a few common species that are not known to have suffered major impact from alien fish or other anthropogenic factors: the short-finned eel (Anguilla australis), some carp gudgeons (Hypseleotris spp.) and Australian smelt (Retropinna semoni). In these cases, the model may have failed to incorporate important natural filters



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Fig. 5. Relationships of the species deficit (the percentage of predicted native fish species that were not observed) to site altitude, catchment area and the numbers of predicted and observed native species at 85 sites in north-eastern NSW. Some data points overlap. The fitted relationships are semi-logarithmic for catchment area and linear for the other independent variables.

that limit distribution. For example, it is possible that competitive or predatory interactions among indigenous fishes result in the exclusion of certain species from some sites that they might otherwise occupy. The recent review by Jackson et al. (2001) of factors controlling freshwater fish assemblages found little evidence for a strong role of competition in riverine environments, but the effect of piscivory appears to be substantial. However, in north-eastern NSW the relative species deficit (the proportion of predicted native species that were not observed at a site) was significantly negatively correlated with both the number of native fish species predicted and the number observed. Hence the proportion of ‘missing’ species was actually lower at species-rich sites, the opposite of the pattern expected if interactions among native fish species were a major factor in limiting distributions. For some fish species, distributions may be naturally stochastic, and hence inherently difficult to predict at the site scale. Nevertheless, further assessment of habitat requirements might

help to define additional filters for those species that seem to be predicted less accurately by the current model. In the construction of a model that aims to predict natural distributions, it is important that the predictive relationships are not affected by human influences (Chessman and Royal 2004; Walsh 2006). The filters model used here relies on a knowledge of the natural occurrence of fish species by drainage basin, the natural altitudinal range of each species in each region, and the natural habitat requirements of each species. Although the dataset used to derive the filters model was extensive, it almost certainly did not include records of all species from all basins of current or historical occurrence. Further fish surveys, and a wider search for historical records, would help to fill in distributional gaps. It is also important to be aware of translocations of fish species outside of their natural ranges, since these have occurred frequently, especially for species that are valued for angling and consumption. Although some translocations are obvious or have been well

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documented, others may be hard to differentiate from natural occurrences, and genetic studies can yield valuable evidence on this question (e.g. Waters et al. 2002). A scarcity of records can also result in an underestimation of natural altitudinal ranges, particularly for species that have suffered large contractions in their ranges. Altitudinal zonation can be altered as a result of human activities such as the release of cold hypolimnial water from dams, which can exclude warm-water fish from downstream reaches (e.g. Todd et al. 2005). It is necessary to be aware of such possibilities when estimating natural altitudinal ranges. It is also important to recognise that some environmental filters can be anthropogenically altered at test sites. Although this is obviously not possible for filters such as altitude, local habitat filters can be subject to human-mediated change. For example, water depth can be affected by upstream impoundment and water extraction, and one of the test sites in northeastern NSW was partly occupied by a large slug of mobile sand, which may not have been a natural phenomenon. In such cases, simulation modelling of human influences on water regimes and river geomorphology may enable predicted natural depth information to be used in place of observed depths. The capacity to predict natural assemblages can be used to identify areas that retain their native fish assemblages and hence warrant conservation, as well as areas that appear to have suffered a decline in native fish richness, and therefore may be candidates for restocking and rehabilitation to reinstate the missing species. In the latter case, stressor diagnosis is likely to be necessary, and this would be assisted by a greater understanding of the sensitivity of each species to particular stressors (Stranko et al. 2005). Although the species deficit was not significantly correlated with catchment area for the test sites, the filters model should probably not be used to make predictions for very small streams that naturally lack sufficient habitat to support a native fish assemblage. Further testing of the model is needed in other parts of NSW so that sensitivity and specificity can be evaluated for species that do not occur in the north-east. It should also be possible to refine the model through the acquisition and analysis of further data on species’ distributions and habitat preferences. Acknowledgments I thank the Australian Museum (per Mark McGrouther) and the NSW Department of Primary Industries (per Simon Hartley) for providing fish collection records. The NSW Department of Primary Industries is also thanked for undertaking the fish sampling. I am grateful to Don Stazic (NSW Department of Natural Resources) for the derivation of site altitudes from a digital elevation model, and to Dean Gilligan (NSW Department of Primary Industries) and Ivor Growns (NSW Department of Natural Resources) for comments on an early draft of this work. Dean Gilligan is also thanked for comments on the distributions of some species.

B. C. Chessman

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Manuscript received 24 May 2006; revised and accepted 22 June 2006.

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