Applied Geography (1991), II, 227-243 Relating salmon catch trends to acid deposition and catchment characteristics David Waters Institute of Hydrology, Wallingford, Oxon., OX10 8BB, UK Abstract Statistically significant trends in Scottish salmon catches for the 54 mainland fishery districts are identified. Three measures of salmon catch trend are extracted from the linear regression analysis. Water quality is ultimately controlled by atmospheric inputs and the interaction with vegetation, soils and geology; hence, catch trends are related to environmental variables. External and internal confounding factors are considered and reduced, this process compacting the data set into 29 fishery districts. As a statistical precaution, the raw data were checked to see that they did not seriously violate the assumptions of the regression model. Models for prediction and management use are established using regression and discriminant function analysis. The annual percentage change of relative catch trend provided a measure of annual resource change and was, therefore, con- sidered a more practical measure of salmon catch trend for management applica- tion. There was a good agreement in the predictor variables entered into the models for both statistical techniques and variables consistently entered into the models are identified and proposed for use in future modelling exercises. Introduction The detrimental effects of acid deposition on fisheries was first recognized by Dahl (1927), who suggested the relationship between acidity of surface waters and the status of trout populations in Norway. Most of the investigations to follow were completed in North America and Scandinavia but as the extent of the acid deposition problem grew, Howells (1979) chaired the first workshop on the ecological effects of acid precipitation in the United Kingdom at Galloway, southwest Scotland. This initial workshop prompted the Department of Agriculture and Fisheries for Scotland (DAFS) to investigate the possible ecological effects of acidification. The resultant study in central Scotland concluded that streams draining forested catchments were more acidic, contained higher levels of aluminium than streams draining non-forested catchments and that fish populations were directly related to stream water chemistry (Harriman and Morrison 1981, 1982). In 1981 the Loch Dee Project began to investigate the ecological effects of acid precipitation and forest management on an upland catchhment in southwest Scotland and the preliminary results were published by Burns et al. (1984). The Loch Fleet Project was also instigated to investigate and explore the effects of manipulating land use and water treatments and their possible ameliorating effects on surface water acidification and, subsequently, to monitor changes in fish populations. Harriman and Wells (1984) studied the causes and effects of surface water acidifica- tion in Scotland and noted that many acid streams in the upper reaches of the River Forth and Galloway region were fishless. Wells et a/. (1986) suggested sensitive surface waters could be identified from areas with sensitive groundwaters as defined 0143.6228/91/03 0227-16 0 1991 ButterworthhHeinemann Ltd
Transcript
Applied Geography (1991), II, 227-243
Relating salmon catch trends to acid deposition and catchment characteristics
David Waters
Institute of Hydrology, Wallingford, Oxon., OX10 8BB, UK
Abstract
Statistically significant trends in Scottish salmon catches for the 54 mainland fishery districts are identified. Three measures of salmon catch trend are extracted from the linear regression analysis. Water quality is ultimately controlled by atmospheric inputs and the interaction with vegetation, soils and geology; hence, catch trends are related to environmental variables. External and internal confounding factors are considered and reduced, this process compacting the data set into 29 fishery districts. As a statistical precaution, the raw data were checked to see that they did not seriously violate the assumptions of the regression model. Models for prediction and management use are established using regression and discriminant function analysis. The annual percentage change of relative catch trend provided a measure of annual resource change and was, therefore, con- sidered a more practical measure of salmon catch trend for management applica- tion. There was a good agreement in the predictor variables entered into the models for both statistical techniques and variables consistently entered into the models are identified and proposed for use in future modelling exercises.
Introduction
The detrimental effects of acid deposition on fisheries was first recognized by Dahl (1927), who suggested the relationship between acidity of surface waters and the status of trout populations in Norway. Most of the investigations to follow were completed in North America and Scandinavia but as the extent of the acid deposition problem grew, Howells (1979) chaired the first workshop on the ecological effects of acid precipitation in the United Kingdom at Galloway, southwest Scotland.
This initial workshop prompted the Department of Agriculture and Fisheries for Scotland (DAFS) to investigate the possible ecological effects of acidification. The resultant study in central Scotland concluded that streams draining forested catchments were more acidic, contained higher levels of aluminium than streams draining non-forested catchments and that fish populations were directly related to stream water chemistry (Harriman and Morrison 1981, 1982). In 1981 the Loch Dee Project began to investigate the ecological effects of acid precipitation and forest management on an upland catchhment in southwest Scotland and the preliminary results were published by Burns et al. (1984). The Loch Fleet Project was also instigated to investigate and explore the effects of manipulating land use and water treatments and their possible ameliorating effects on surface water acidification and, subsequently, to monitor changes in fish populations.
Harriman and Wells (1984) studied the causes and effects of surface water acidifica- tion in Scotland and noted that many acid streams in the upper reaches of the River Forth and Galloway region were fishless. Wells et a/. (1986) suggested sensitive surface waters could be identified from areas with sensitive groundwaters as defined
228 Relating salmon catch trends to acid deposition and catchment characteristics
by Kinniburgh and Edmunds (1984). They summarized previous papers and noted the findings of Egglishaw et al. (1986) that salmon catch declines in Scottish salmon fishery districts were highly correlated with areas under more than 20 per cent upland forestry. The weight of evidence that acidification and coniferous afforestation had a detrimental effect on fisheries was growing and this encouraged two further ecological studies by Harriman et a/. (1987) and Maitland et al. (1987).
Most of these previous investigations of the effects of acid deposition on fisheries in Scotland were micro-scale studies often centred in Galloway. A larger regional- scale study was attempted by the UK Acid Waters Review Group (1986), who analysed salmon catch trends in Scottish fishery districts but found no relationship with volume rainfall weighted mean hydrogen ion concentration or hard rock types. However, the validity of the relative salmon catch trend measures chosen was questionable (Waters and Kay 1987).
The rationale for a ‘catchment’scale regional analysis has been demonstrated by Bull and Hall (1989), who related physical catchment characteristics to stream chemistry and biology. It is quite clear that stream water chemistry, particularly the biologically important parameters, aluminium speciation, H + and calcium con- centrations, will be governed by the concentration and amount of pollutant deposition, as well as interaction and modification by the vegetation, overlying soils and geology. The interaction of these water quality variables is complex. However, salmonid life stage sensitivity to aluminium and hydrogen ion toxicity and the ameliorating effects of calcium have been well documented.
Following the publication of the Scottish salmon catch data for the 54 mainland statistical fishery districts (Fig. 1) and the growing evidence for the impact of acidification and coniferous afforestation affecting fisheries, particularly in Europe and North America, the opportunity arose for a macro-scale assessment of the environmental impacts of acidification and land use characteristics in many of the Scottish salmon fishery districts.
Data and analytical methodology
Data
The variables collated for analysis are given in Table 1. Data source and quality were considered and summarized in Waters (1989). Total Scottish salmon catch trend from 1952 to 1984 was examined for 54 mainland fishery districts (Fig. 2). The paucity of data on the relationship between catch and stock means that stock size cannot be estimated from individual catch records. However, long-term trends can be extracted from the 33..year data set (Department of Agriulture and Fisheries for Scotland 1983). The inadequacy of the statistical interpretation of Egglishaw et al. (1986) has already been mentioned, particularly in view of the fact that they only highlighted declines. Regression analysis was used by Martin and Mitchell (1985) to study the periodicity of grilse and multi-sea-winter salmon catches from the River Dee and a similar approach was employed in this study to indicate relative declines and increases in total salmon catch. The catch data for each statistical fishery district were used to calibrate a regression model of the form
Y=A,+BkU
where Y = annual salmon catch x = year U= standard error of the residual term A = constant B = constant
David Waft-n 229
- Region boundary
Dristrict boundary
Figure 1. Fishery districts as defined by Department of Agriculture and Fisheries for Scotland (see Table 2 for district numbers).
Relative catch trend using Z values Relative catch trend, annual percentage change Relative catch trend, percentage change 19522 1984 Percentage area of coniferous afforestation Annual rainfall weighted hydrogen ion concentration Total bases in the A horizon Cation exchange capacity of the A horizon Percentage base saturation of the A horizon pH of the A horizon Total bases in the B horizon Cation exchange capacity of the B horizon Percentage base saturation of the B horizon pH of the B horizon Total bases in the C horizon Cation exchange capacity of the C horizon Percentage base saturatjon of the C horizon pH of the C horizon Annual rainfall weighted sulphate concentration Annual rainfall weighted nitrate concentration The most sensitive geological classification The second most sensitive geological class The third geological sensitivity classification Non-sensitive geological classification Percentage area of coniferous afforestation above 183 m Annual wet-deposited acidity Annual wet-deposited suiphate Annual wet-deposited nitrate Dry-deposited sulphur, 1983 Dry-deposited hydrogen, 1983 Total deposited sulphur Total deposited acidity Percentage area of susceptible soil in the A horizon for water acidification Percentage area of slightly susceptible soil in the A horizon for water
acidification ASOILNS Percentage area of non-sensitive soil in the A horizon BSOILS Percentage area of susceptible soil in the B horizon for water acidification BSOILSS Percentage area of slightly susceptible soil in the l3 horizon for water
acidification BSOILNS csoILs CSOILSS
Percentage area of non-sensitive soil in the B horizon Percentage area of susceptible soil in the C horizon for water acidification Percentage area of slightly susceptible soil in the C horizon for water
acidification CSOILNS Percentage area of non-sensitive soil in the C horizon
Thirty-two out of the 54 mainland fishery districts show a statistically significant trend at the 90 per cent significance level and were therefore retained for further analysis (Tables 2a and 2b). Seventeen districts show a significant decrease and fifteen a significant increase (Fig. 3). Three measures of relative catch trend were derived from the regression model. The first-‘Trend’-involved standardizing the catch and using the slope of the regression line. The second measure-‘Reslop l’-used the
David Waters 231
1
El z > +0.05
r,j,jjl::IIl:;jil 0 < z < +0.05
m -0.05 < z < 0
z < -0.05
Figure 2. Relative catch trends for all 54 mainland fishery districts (I 952- 1984).
232 Relating sahnon catch trends to acid deposition and cutchment characteristics
Table 2a. Summary table of regression statistics for districts which do not demonstrate a statistically significant trend in total salmon catch (1952- 1984)
slope of the regression line divided by the mean catch, which yields percentage annual increase. The third method-‘Reslop 2’-involved the following method
Y = ( Y&I - Ysz) x 100 Y 52
and indicates the percentage increase over the 33-year period. All three measures are used in the main data set and an assessment of the most appropriate trend measure is made.
The Atlantic salmon, S&no salar, is an anadromous species and, therefore, possibly vulnerable to a number of internal and external catchment factors. Each of these factors were evaluated by Waters (1989) and Waters and Kay (1987) and eliminated as possible reasons for salmon catch trends for 29 statistical fishery districts. Another three districts were excluded from further analysis because they were affected by significant confounding factors (the influence of the North East Drift Net Fishery in the Forth and Tay districts and pollution in the Clyde fishery district).
Acid deposition parameters including concentration of sulphate, nitrate and acidity were derived from the Review Group on Acid Rain ((1983, 1987) and the con- centration isolines overlaid on the digitized fishery district areas. Deposition data included wet deposited sulphate, acidity and nitrate and dry and total deposited sulphate and acidity, which were calculated in the same way. Although the data
resolution was fairly crude this was the best available and, considering the macro- scale resolution of this study, the available data were considered appropriate. The percentage area of coniferous afforestation was also digitized for each fishery district, with the areaof upland afforestation obtained from DAFS (Morrison 1988). Soil data including mean pH, mean base saturation, mean cation exchange capacity and a sensitivity index were derived for a surface (A horizon) and two mineral soil horizons (B and C horizons) for each fishery district. The percentage area of the four geological sensitivity classifications derived by Kinniburgh and Edmunds (1984) were also digitized for each fishery district and again the resolution of the data was suitable for this study (Hornung and Edmunds 1986).
Analytical methodology
The goals of the statistical analysis were twofold:
1. to formulate predictive equations for salmon catch trend using environmental variables;
2. to classify districts into salmon producing catchments using trend as the dis- criminating variable.
Hence, out of a number of statistical techniques investigated, multiple regression analysis and discriminant function analysis were chosen as the most appropriate.
234 Relating salmon catch trends to acid deposition and catchmwt characteristkr
Table 2b. Summary table of regression statistics for districts which demonstrate a statistically significant trend in total salmon catch (1952- 1984)
District Sig.
F(%) F value A B 2 value
Tweed (1) 99 Forth (2) 99 South Esk (4) 99 North Esk and Bervie (5) 97 Dee (Aberdeenshire) (6) 99 Ythan (8) 99 Ugie (9) 92 Nairn (14) 99 Ness (15) 99 Beauly (16) 99 Conon and Ainess (17) 99 Lute (I 8) 99 Cree and Fleet (20) 91 Dee (Kirkcudbright) (21) 95 Kyle of Sutherland (25) 99 Helmsdale (27) 99 Berriedale to Wick (28) 97 Thurso and Forss (29) 91 Naver and Kinioch (3 1) 99 lnchard to Kirkaig (33) 98 Ewe (35) 93 Badachro to Applecross (36) 99 Kishorn and Carron (37) 99 Glenelg to Kilchoan (39) 99 Morar to Shiel (40) 93 Sunart and Aline (42) 99 Add and Ormsary (45) 99 Rue1 and Drummachloy (50) 99 Echaig (5 1) 98 Clyde (52) 96 Irvine (53) 99 Ayr (54) 99
Multiple regression analysis has been used in many acidification studies for the prediction of key water quality variables from other water quality determinands, catchment characteristics and atmospheric deposition parameters (Neary and Dillon 1988; Sullivan et a/. 1988; Driscoll et a/. 1989). Stepwise discriminant function analysis was used by Hunsaker et al. (1986) to study the relationships between lake chemistry and catchment characteristics in the Adirondack Mountains and so was also selected here. With this method, group membership of each case is known. Hence, the cases are classified into one of several mutually exclusive groups using a number of predictor variables, after which an evaluation of the accuracy of the classification can be made.
Multiple regression analysis has been used by many researchers for descriptive and inferential purposes as well as prediction, but many fail to check whether the raw data
satisfies the many assumptions required of the regression model (Poole and O‘Farrell 1971). A detailed investigation using the SPSSx statistical software (Norusis 1985) indicated that the data did not seriously violate any of the seven assumptions (Waters 1.989).
Results and discussion
Pearson product moment correlations
The main data set contains 37 independent variables and three measures of trend. A Pearson correlation matrix was calculated to identify the variables significantly related to the three catch trend measures (Table 3). Between the three dependent trend
236 Relating salmon catch trends to acid depositiorr and catchrent characteristics
1. z > +0.05
p7-J 0 < z < +0_05
I -0.05 < z < 0
z < -0.05
u Not significant
Figure 3. Relative catch trends for the statistically significant fishery districts uGng ‘Trend’.
David Waters 237
Table 3. Pearson product moment correlation coefficients for all variables in the main data set
measures ‘Trend’ and ‘Reslop 1’ had a high bivariate correlation of 0.910 (P= 0.000) and ‘Trend’ and ‘Reslop 2’ the lowest value of 0.684 (P= 0.000). For the first relative trend measure, ‘Trend’, only three of the 37 independent variables have unexpected signs for the bivariate correlation coefficients: dry-deposited sulphur and hydrogen (DR YDEPS and DRYDEPH) and a parameter reflecting sensitivity of geology (GEOLSSZ). The highest correlations are with slightly sensitive soils in the C horizon, total bases in each soil horizon, percentage base saturation of the C horizon and percentage area of upland afforestation.
238 Relating salmon catch trends to acid deposition and catchment characteristics
For ‘Reslop 1’ nine variables have unexpected signs for the Pearson product moment correlation coefficients: HION, CCEC, SO4CONC, GEOLSS2, DR Y- DEPS, DRYDEPH, TOTDEPS, TOTDEPH and ASOILSS (see Table 1 for definitions). The highest coefficients are with all the parameters in the C horizon and total bases in the B horizon. The results for ‘Reslop 2’ are similar to those for ‘Reslop 1’.
Regression equations
A number of stepwise and forced entry procedures were used in the construction of the regression models using the SPSSx statistical package (Norusis 1985). To increase the transferable potential of the calibrated models the degree of allowed multi- collinearity and linear association in the model were controlled using ‘PIN’ and ‘Tolerance’ values (Norusis 1985). Models were constructed using three different ‘PIN’ values of 0.5, 0.1 and 0.2 and four ‘Tolerance’ values, 0.01, 0.05, 0.1 and 0.2. Increasing the ‘PIN’ value and decreasing the ‘Tolerance’ value had the effect of decreasing the number of variables entered into the models. Two basic models can be selected, those which possess high predictive capabilities (high adjusted R*), and those where the optimum R* is retained at the expense of data collation costs, that is, ‘management models’.
Best predictive equations. Each one of the three dependent variables produced different regression equations. For ‘Trend’ the best predictive equation contained 13 variables and the adjusted R* value was 0.904 (Equation 1).
Multicollinearity within the data set has caused some incorrect signs for the partial regression coefficients. It is difficult to judge whether the coefficients for the variables BCEC and CCEC are correct because different results would occur if the cation exchange capacity was attributed to bases or exchangeable hydrogen (Reuss and Johnson 1986). The other four soil variables reflect the ability to neutralize incoming acidity. Three atmospheric variables are included; the concentration terms might be considered surrogate measures for the frequency of polluted rainfall events (Cape and Fowler 1986). The latter deposition term is important in evaluating the maximum chemical potential for biological and chemical reactions. Wet deposited acidity was one variable Hunsaker et al. (1986) found important in the prediction of acid neutral- izing capacity. The percentage area of coniferous afforestation has been identified as an important variable in other studies and was consistently entered when ‘PIN’ and ‘Tolerance’ values were tightly controlled.
The best predictive equation for ‘Reslop 1’ contained 14 variables explaining 94 per cent of the variation in the dependent variable:
Again the proportion of the highest geological sensitivity class is important. Adamson and Bennefield (1987), studying solute concentrations of streams draining different geologies in upland Britain, also concluded that rock type had the greatest influence on stream water chemistry. Parameters reflecting the base status of the soil also appear in the equations and control the amount of aluminium and hydrogen leached from the soil. Brown (1987) confirmed that these are important variables mediating fishery status. Wet deposited acidity and total deposited sulphate are also important. The source of mobile anions is important for the leaching of acidic cations from the soil and is therefore negatively correlated with ‘Reslop 1’. The percentage of upland coniferous afforestation also appears in the equation.
Models developed for the final trend measure, ‘Reslop 2’, account for less of the total variance in the dependent variable. Controlling the degree of permitted multi- collinearity had no effect on variable entry. With a ‘PIN’ and ‘Tolerance’ value of O-2 the following model resulted in an adjusted R2 of O-77:
Y = - 1278.828 + 117.259 C-7-B + 8.237 GEGLSl - 31.615 NO3CONC - I5 * 53 UPFOR + 903 = 299 WETDEPS f I 1.527 SO4CONC
- 19.108 GEOLNS4 + 191.984 (3)
(R2 = 0.77, F = 14.42, Sig. = O*OOOO)
Both parameters reflecting the sensitivity of geology have incorrect partial regression coefficient signs. Trend is negatively correlated with the non-sensitive classification and positively correlated with the most sensitive classification. Two of the three atmospheric variables also have incorrect correlation directions.
Management mode/s. Management models require three main attributes:
1. minimization of variables with misclassifications; 2. minimization of variables in the model which reduces data acquisition costs; 3. retention of a high adjusted R2, once the first two criteria have been satisfied.
Decreasing the ‘PIN’ and increasing the ‘Tolerance’ values reduces the number of variables entered into the model but also reduces the R?. Hence the number of incorrect partial regression coefficient signs in each model is one measure of the management utility of the resultant equation.
The most applicable model for the first dependent trend measure, ‘Trend’, was constructed with a ‘PIN’ value of 0.12 and ‘Tolerance’ value of O-2, resulting in an adjusted RL of O-708:
Two models can be selected for management purposes when ‘Reslop 1’ is the dependent variable. Although equation (5) only has an adjusted R2 of 0.631, it only requires three independent variables:
Again base status of the soil and geological sensitivity are important variables.
Discriminant function equation (classification into discrete groups)
When a two-group classification was used all three trend measures produced the same equation, the best discriminant model being formed when the tolerance value was O-06. This contained eight variables and the Wilk’s lambda was 0.169:
The variables entered are similar to those in the regression equations. All 29 cases were classified correctly.
A four-group classification can be established for ‘Trend’ and ‘Reslop 2’ as the trend directions can be further broken down to classify cases into ‘moderate’ and ‘rapid’ trends. However, with ‘Reslop 1’ only a three-group classification is possible because originally there were only three discrete trend categories; hence, an enhance- ment in the accuracy of the classification of cases would be expected.
Three discriminant function scores can be calculated for the four-group discrimina- tion of ‘Trend’. The first discriminant function (equation 9) was developed with a ‘Tolerance’ value of 0.07 and produced a Wilk’s lambda of O-019.
Similar variables are selected to the two-group classification model. The number of misclassified correlations will vary because the unstandardized discriminant function coefficients vary for each function. For ‘Reslop 1’ and ‘Reslop 2’, soil and geology variables are the most frequently entered. The performance of the discriminant function to classify cases is similar to the model developed for ‘Trend’. Errors in the classification of cases can only be justified if the cost of collecting the predictor variables is greatly reduced.
The frequency with which certain variables are entered into the regression and discriminant function models serves to indicate the importance of the independent variables that need to be collected in future studies of the effects of acid deposition on
David Waters 241
salmonid fisheries. The variables that were consistently entered into the regression models were:
Annual rainfall weighted sulphate concentration Dry-deposited hydrogen, 1983 Annual wet-deposited sulphate Total bases in the C horizon Percentage base saturation of the C horizon Cation exchange capacity of the B horizon Percentage area of coniferous afforestation above 183 m. Most sensitive geological classification
Additional variables entered in the discriminant function models were:
Annual rainfall weighted nitrate concentration Percentage area of susceptible soil in the B horizon for water acidification Percentage area of slightly susceptible soil in the C horizon for water acidification
Wet deposited sulphate and acidity consistently emerged from the regression models as important variables. Hunsaker et a/. (1986) reported wet deposited acidity as the first variable entered into their model when predicting pH and acid neutralizing capacity, which are two important variables controlling fishery status. Loucks et al.
(1986), observing the role of precipitation chemistry and catchment characteristics in lake acidification, defined wet deposited sulphate as an important factor. Nitrate concentration was identified as an important variable in the discriminant function analysis. If thedeposition of nitrate is large, or at least greater than biological require- ments, it can contribute significantly to the total soil solution strength and, therefore, to the leaching of cations from the soil, which may have deleterious effects on the fishery. Especially in Scotland, where the soils are very acid, the amount of anion in solution will be much more important than the concentration of hydrogen ions.
Only soil variables relating to the mineral horizons were included in the regression models. It is these horizons that contain the largest amounts of aluminium and, therefore, offer the greatest potential threat to fishery status. The soils in Scotland are freely drained and this explains why mineral soil variables consistently emerge as more important than surface soil parameters.
Land use and percentage area of upland coniferous afforestation are also important, rather than the total forested area, as expected. Upland afforestation has a greater effect on water quality in the spawning grounds and, therefore, an indirect influence on recruitment failure. In most cases forestry in lower parts of the catch- ment surrounding higher-order streams which are buffered by greater amounts of groundwater has a negligible impact on water quality.
The percentage area of the most sensitive geological classification was also entered in all equations. This agrees with other work, such as that of Briker and Rice (1989) and Loucks et al. (1986), who showed that bedrock was an important factor controlling the acid neutralizing capacity of a catchment and an ultimate influence on stream water chemistry. However, the Pearson product moment correlation co- efficient with catch trend was not significant. This might be due to the fact that many districts in the Caithness and Sutherland region have large proportions of sensitive geology but showed increases in catch because of the ameliorating effect of the deep overlying peat. Peat has this effect because aluminium supply is limited and aluminium speciation in the leachate is usually in the biologically unavailable non- labile form or organically complexed.
242 Relating salmon catch trends to acid deposition and catchment characteristics
Conclusion
Salmon catch trends for Scottish salmon fishery districts were established with a measure of statistical significance. Twenty-nine districts were retained for further analysis after complicating factors were eliminated. Of the three trend measures used, ‘Trend’ and ‘Reslop 1’ yielded the equations with the highest adjusted R2. However, ‘Reslop l’, which indicates the annual percentage increase, is an easier trend measure to interpret and, therefore, the most suitable indicator for management purposes.
The change in the total Scottish salmon resource from 1952 to 1984, calculated from the 29 statistically significant districts, is a decline of 1 per cent. Hence, acid deposition has had little noticeable effect on the total Scottish salmon resource. However, this is not the case at the district level. The interaction of atmospheric, land use, soil and geological variables will determine the resultant stream water quality. Over the 33-year period soils and geology have remained relatively constant. Hence changes in acid deposition and land use are the most likely factors in explaining the district-level declines. Further studies investigating the effects of environmental variables on salmon catch trend should consider linear regression as a suitable technique for evaluating catch trends. ‘Reslop 1’ is the most suitable measure of trend which incorporates a degree of resource change. Multiple regression and discriminant function analysis are suitable techniques for relating environmental variables to salmon catch trends and provide models of management significance. Important variables identified above should be incorporated into future modelling exercises.
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