Quantitative relationships of invertebrates to pH in Norwegian river systems

The invertebrate fauna has been surveyed for twenty one unlimed generally acidic river systems in Norway. The data consist of 180 samples and 127 invertebrate taxa and associated water chemistry data (pH, calcium, acid neutralizing capacity, total aluminium, and conductivity). Multivariate numerical methods are used to quantify the relationships between aquatic invertebrates and water chemistry. Detrended canonical correspondence analysis (DCCA) shows one dominant axis of variation with high correlations for pH and aluminium. DCCA axis 2 is significantly correlated with calcium. The predictive abilities of invertebrates to pH are explored by means of weighted averaging (WA) regression and calibration and weighted averaging partial-least-squares regression (WA-PLS). The performance of the methods is reported in terms of the root mean square error of prediction (RMSEP) of (observed pH-inferred pH). Bootstrapping and leave-one-out jackknifing are used as cross-validation procedures. The predictive abilities of invertebrates are good (RMSEP_boot for WA = 0.309 pH units). Comparison of the invertebrates with diatom studies shows that invertebrates are as good predictors of modern pH as diatoms are. RMSEP_jack shows that WA-PLS improves the predictive abilities. Indicator taxa for pH are found by Gaussian regression. Anisoptera, Agrypnia obsoleta, Leptophlebia marginata, Sialis lutaria, and Zygoptera have significant sigmoidal curves where abundances increase with decreasing pH. Cyrnus flavidus shows a significant unimodal response and has an estimated optimum in the acid part of the gradient. Isoperla spp. and Ostracoda show significant sigmoidal responses where abundances increase with increasing pH. Amphinemura borealis, Diura nanseni, Isoperla grammatica, I. obscura, and Siphonoperla burmeisteri show significant unimodal responses and have high pH optima. Many taxa do not have statistically significant unimodal or sigmoidal curves, but are found by WA to be characteristic of either high pH or low pH. These results suggest that a combined use of Gaussian regression and direct gradient analysis is needed to get a full overview of potential indicator taxa.     

FRESHWATER DIATOMS FROM THE CANADIAN ARCTIC TREELINE AND DEVELOPMENT OF PALEOLIMNOLOGICAL INFERENCE MODELS1

Relationships between surface sediment diatom assemblages and measured environmental variables from 77 lakes in the central Canadian arctic treeline region were examined using multivariate statistical methods. Lakes were distributed across the arctic treeline from boreal forest to arctic tundra ecozones, along steep climatic and environmental gradients. Forward selection in canonical correspondence analysis determined that dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), total nitrogen (TN), lake surface area, silica, lake‐water depth, and iron explained significant portions of diatom species variation. Weighted‐averaging (WA) regression and calibration techniques were used to develop inference models for DIC, DOC, and TN from the estimated optima of the diatom taxa to these environmental variables. Simple WA models with classical deshrinking produced models with the strongest predictive abilities for all three variables based on the bootstrapped root mean squared errors of prediction (RMSEP). WA partial least squares showed little improvement over the simpler WA models as judged by the jackknifed RMSEP. These models suggest that it is possible to infer trends in DIC, DOC, and TN from fossil diatom assemblages from suitably chosen lakes in the central Canadian arctic treeline region.     

SCALED CHRYSOPHYTES (CHRYSOPHYCEAE AND SYNUROPHYCEAE) FROM ADIRONDACK DRAINAGE LAKES AND THEIR RELATIONSHIP TO ENVIRONMENTAL VARIABLES1

The relationships between 23 scaled chrysophyte taxa (Chrysophyceae and Synurophyceae) and measured limnological variables in 62 Adirondack, New York, drainage lakes were examined by canonical correspondence analysis (CCA). The major proportion of variation in chrysophyte species distributions was strongly related to total monomeric Al (Al_m) and Mg concentrations, and their close correlates pH, Na, Ca, and acid-neutralizing capacity (ANC). Total monomeric Al concentrations explain a greater proportion of species variation than pH, suggesting that Al_m concentrations may be more important in governing the distribution of chrysophyte taxa in these lakes. Gaussian logit (GL) and linear logit (LL) regressions of the relative percentages of individual chrysophyte taxa to lakewater pH and Al_m concentrations and the examination of pH–Al_m response surfaces show that many chrysophyte taxa exhibit unique responses to these environmental gradients; taxa can be characterized as alkaline, circumneutral, acidic, and pH indifferent. Within each of these groups, taxa can be characterized further based upon their optima and tolerances to Al_m concentrations. Chrysophyte indicator species (i.e. a taxon with a strong statistical relationship to the environmental variable of interest, a well-defined optimum, and a narrow tolerance to the variable of interest) for pH include Mallomonas hindonii, M. crassisquama, M. pseudocoronata, and Synura uvella; M. hindonii, M. crassisquama, M. pseudocoronata, S. petersenii, and S. spinosa are good indicators of Al_m concentrations. Highly significant predictive models were developed to infer lakewater pH and Al_m concentrations from the relative percentages of chrysophyte scales in the study lakes. Model evaluation was based on their correlation coefficients and the root-mean-squared error of prediction (RMSE) derived from bootstrapping. Weighted averaging regression and calibration with tolerance down-weighting (i.e. weighting taxa inversely to their variance) produced superior results when compared to the computationally and data-demanding maximum likelihood methods and to simple weighted averaging regression and calibration.     

PERIPHYTIC DIATOM ASSEMBLAGES FROM HIGH ARCTIC PONDS1

Epiphytic, epilithic, and surface sediment diatom assemblages were identified and enumerated from 35 study ponds on CapeHerschel (78°37″N, 74°42″W), east-central Ellesmere Island, Canada. All the sites are shallow (maximum depth <2 m), clear, oligotrophic, and freshwater. The ponds freeze completely for 10 months of the year. Major ion concentrations are relatively similar among the 35 sites, although environmental gradients exist. Over 130 diatom taxa from 28 genera were identified in the periphyton samples. Marked differences in species composition were evident among the ponds. Moreover, many of the diatoms exhibited varying degrees of microhabitat specificity. Variance partitioning by canonical correspondence analysis showed that 26% of the total variance exhibited by diatom species composition could be accounted for by the measured environmental variables (i.e. 10.2% by habitat and 15.8% by water chemistry). Pondwater alkalinity best explained the distribution of taxa, and weighted averaging regression and calibration were used to develop a transfer function to infer pondwater alkalinity from the diatom assemblages. Other important environmental variables included [Na⁺] for the epilithic and [SiO₂] for the epiphytic assemblages.     

Maximum likelihood prediction of lake acidity based on sedimented diatoms

Abstract. As an example of ecological gradient analysis, Gaussian response functions, with Poisson or quasi-Poisson error distribution, were fitted for diatom taxa on a pH gradient. It is possible to predict or infer the pH of lake water from the fitted curves using the method of maximum likelihood, which is easily implemented in standard non-linear regressionprograms. Due to overdis-persion with respect to the Poisson distribution, moment estimates forthe negative binomial distribution were also applied, both in estimating the species response curves and in prediction. Simulations indicated that the theoretical maximum precision (measuredby standard deviation of prediction errors) in our data set was 0.17 pH units. The observed errors were much greater (SD 0.35 to 0.43). It seems that roughly equal proportions of the excess error were caused (1) by systematic differences between the training (estimation) data and the validation (prediction) data, and (2) from a misspecified model. It is suggested that the error due to model misspecification consists of inadequacy of the presumed error distribution and of inadequacy of the simple Gaussian response function.     

Correlation of surface sediment diatoms with the present lake water pH in 28 Algoma lakes,Ontario, Canada

The surface sediment diatom analysis of 28 Algoma lakes (pH 4.40–8.13) indicates that even though each lake has a widely different aquatic environment and characteristic diatom assemblage, a definite relationship exists between the lake water pH and their diatom assemblages. In the acidic lakes acidobiontic and acidophilous diatom species predominate whereas in circumneutral and alkaline lakes circumneutral and alkaliphilous diatoms were most common. Cluster analysis of the pH indicator diatom assemblages grouped the study lakes into three distinct cluster groups. These groups also closely corresponded to lake water pH. On the basis of published ecological information as well as their presence in our study lakes, the pH indicator status of a number of diatom taxa have been discussed. A detailed listing of the diatom taxa identified and their pH indicator status is provided in order to facilitate their use in future diatom-inferred pH studies.     

Diatom communities along pH and hydrological gradients in three montane mires,central China

The distribution patterns of epiphytic diatom assemblages in three montane mires in central China were investigated to examine their relationships with selected environmental variables (pH and depth to water table, DWT). Two of the mires are considered to be in good ecological condition (Dajiuhu and Qizimeishan Mires) while Erxianyan Mire is extensively affected by acid deposition and human activities. A total of 206 taxa belonging to 56 genera were found in 44 Sphagnum samples. Multivariate analysis revealed that pH and DWT were significantly correlated with diatom distribution. In Erxianyan Mire, the characteristic taxa (Eunotia minor and Eunotia intermedia) had lower pH optima and may therefore be useful indicators of highly-acidic conditions. In Dajiuhu Mire, the dominant species had higher pH optima, and abundant xerotolerant taxa (Hantzschia amphioxys, Pinnularia borealis, Luticola mutica and Diadesmis contenta) were observed. In the partial canonical correspondence analyses with mire location as a covariable, the correlation between diatom data and pH was insignificant, likely because pH differences between mires were greater than those within mires. In contrast, diatom data were significantly correlated with DWT, suggesting that diatoms are good sensors of hydrological variability along the hollow to hummock gradient. Together, these data can expand current autecological information for these potential diatom indicator species, which is critical for refining our interpretations of bio-monitoring and palaeolimnological studies in montane mires.     

A diatom-phosphorus transfer function for shallow,eutrophic ponds in southeast England

Shallow ponds in southeast England are often eutrophic with high phosphorus concentrations. The aim of this study was to develop a diatom-phosphorus transfer function to enable past phosphorus levels in such waters to be inferred from the sediment record. A water chemistry survey of 123 randomly chosen, shallow, artificial ponds in southeast England was carried out. Principal components analysis (PCA) revealed that phosphorus was an important environmental variable. A subset of 31 sites was selected along a total phosphorus (TP) gradient (winter TP range 7–1123 µg 1^-1), in order to explore the relationship between the surface-sediment diatom assemblages and the contemporary water chemistry using canonical correspondence analysis (CCA). Annual mean TP was the most significant variable in explaining the variance in the diatom species data.Weighted averaging (WA) regression and calibration techniques were used to generate a transfer function, enabling annual mean TP (range 25–646 µg 1^-1) to be inferred from the diatom species TP optima of 102 common taxa in the dataset (r ² = 0.79; RMSE = 0.161; RMSE_(boot) = 0.279; n = 30). The model was applied to fossil diatom assemblages in a sediment core from Marsworth Reservoir, Hertfordshire, a Site of Special Scientific Interest (SSSI), with currently high TP levels of 476 µg 1^-1, to reconstruct past epilimnetic annual mean TP concentrations.The study shows that artificial, shallow waters can be suitable for palaeolimnological research and that it is possible to reliably infer lake water TP using the WA technique, across a large range of phosphorus concentrations. This method has the potential to provide limnologists, conservationists and water quality managers with an estimate of pre-enrichment phosphorus concentrations and an indication of the onset and development of eutrophication at a site. This information is essential for lake management strategies and restoration programmes.     

Laurentian Great Lakes Phytoplankton and Their Water Quality Characteristics,Including a Diatom-Based Model for Paleoreconstruction of Phosphorus

Recent shifts in water quality and food web characteristics driven by anthropogenic impacts on the Laurentian Great Lakes warranted an examination of pelagic primary producers as tracers of environmental change. The distributions of the 263 common phytoplankton taxa were related to water quality variables to determine taxon-specific responses that may be useful in indicator models. A detailed checklist of taxa and their environmental optima are provided. Multivariate analyses indicated a strong relationship between total phosphorus (TP) and patterns in the diatom assemblages across the Great Lakes. Of the 118 common diatom taxa, 90 (76%) had a directional response along the TP gradient. We further evaluated a diatom-based transfer function for TP based on the weighted-average abundance of taxa, assuming unimodal distributions along the TP gradient. The r² between observed and inferred TP in the training dataset was 0.79. Substantial spatial and environmental autocorrelation within the training set of samples justified the need for further model validation. A randomization procedure indicated that the actual transfer function consistently performed better than functions based on reshuffled environmental data. Further, TP was minimally confounded by other environmental variables, as indicated by the relatively large amount of unique variance in the diatoms explained by TP. We demonstrated the effectiveness of the transfer function by hindcasting TP concentrations using fossil diatom assemblages in a Lake Superior sediment core. Passive, multivariate analysis of the fossil samples against the training set indicated that phosphorus was a strong determinant of historical diatom assemblages, verifying that the transfer function was suited to reconstruct past TP in Lake Superior. Collectively, these results showed that phytoplankton coefficients for water quality can be robust indicators of Great Lakes pelagic condition. The diatom-based transfer function can be used in lake management when retrospective data are needed for tracking long-term degradation, remediation and trajectories.     

Weighted averaging partial least squares regression (WA-PLS): an improved method for reconstructing environmental variables from species assemblages

Weighted averaging regression and calibration form a simple, yet powerful method for reconstructing environmental variables from species assemblages. Based on the concepts of niche-space partitioning and ecological optima of species (indicator values), it performs well with noisy, species-rich data that cover a long ecological gradient (>3 SD units). Partial least squares regression is a linear method for multivariate calibration that is popular in chemometrics as a robust alternative to principal component regression. It successively selects linear components so as to maximize predictive power. In this paper the ideas of the two methods are combined. It is shown that the weighted averaging method is a form of partial least squares regression applied to transformed data that uses the first PLS-component only. The new combined method, ast squares, consists of using further components, namely as many as are useful in terms of predictive power. The further components utilize the residual structure in the species data to improve the species parameters (optima) in the final weighted averaging predictor. Simulations show that the new method can give 70% reduction in prediction error in data sets with low noise, but only a small reduction in noisy data sets. In three real data sets of diatom assemblages collected for the reconstruction of acidity and salinity, the reduction in prediction error was zero, 19% and 32%.     

Using change-point analysis and weighted averaging approaches to explore the relationships between common benthic diatoms and in-stream environmental variables in mid-atlantic highlands streams,USA

Diatoms are increasingly being used in the bioassessment of aquatic systems. However, autecological information for many common taxa is incomplete. We explored the potential of classification (CT) and regression tree (RT) approaches to identify the hierarchical interaction among water quality variables in predicting the relative abundance of ten common stream diatom taxa in the Mid-Atlantic Highlands ecoregion. RT analysis was also used to identify environmental change points corresponding to major shifts in species abundances. We also used traditional weighted-averaging approaches (WA) to model taxon pH and total phosphorus (TP) optima. RT and WA approaches provided different, yet complementary, information on the complex relationships between common stream diatoms and environmental variables. Both RT and CT highlighted the interaction of stream acidity (pH, acid neutralizing capacity (ANC)), and TP in structuring the stream diatom assemblage. For the RT of taxa, where pH was an important predictor, higher pH predicted higher relative abundances. In contrast, higher TP predicted lower relative abundances for some diatom taxa (e.g., Achnanthidium minutissimum (Kütz.) Czarnecki), while predicting higher relative abundances for other taxa (e.g., Planothidium lanceolatum (Bréb.) Round & Bukht., Gomphonema parvulum (Kütz.) Kütz.). The environmental change point for pH derived from RT analysis was lower than WA optima for all species. We suggest that RT change point analysis can be used to complement traditional WA optima approaches, especially when diatom taxa’s abundances are affected by interactive environmental factors, to provide more refined information on stream diatom environmental preferences. Handling editor: L. Naselli-Flores     

Forest Succession in Tropical Hardwood Hammocks of the Florida Keys: Effects of Direct Mortality from Hurricane Andrew1

A tree species replacement sequence for dry broadleaved forests (tropical hardwood hammocks) in the upper Florida Keys was inferred from species abundances in stands abandoned from agriculture or other anthropogenic acitivities at different times in the past. Stands were sampled soon after Hurricane Andrew, with live and hurricane‐killed trees recorded separately; thus it was also possible to assess the immediate effect of Hurricane Andrew on stand successional status. We used weighted averaging regression to calculate successional age optima and tolerances for all species, based on the species composition of the pre‐hurricane stands. Then we used weighted averaging calibration to calculate and compare inferred successional ages for stands based on (1) the species composition of the pre‐hurricane stands and (2) the hurricane‐killed species assemblages. Species characteristic of the earliest stages of post‐agricultural stand development remains a significant component of the forest for many years, but are gradually replaced by taxa not present, even as seedlings, during the first few decades. This compositional sequence of a century or more is characterized by the replacement of deciduous by evergreen species, which is hypothesized to be driven by increasing moisture storage capacity in the young organic soils. Mortality from Hurricane Andrew was concentrated among early‐successional species, thus tending to amplify the long‐term trend in species composition.     

The relationship between sedimentary chrysophyte scales (Chrysophyceae and Synurophyceae) and limnological characteristics in 25 Norwegian lakes

The relationship between 16 taxa of chrysophyte scales (Chrysophyceae and Synurophyceae) from the surface sediments of 25 soft-water Norwegian lakes and 16 limnological variables from these lakes was examined using canonical correspondence analysis (CCA). The Norwegian chrysophyte flora reported in this study is typical of assemblages described from acidic to circumneutral lakes. Lake-water pH was the environmental variable that explained the most variation in the weighted averages of the chrysophyte taxa, although total aluminium, colour, and specific conductivity were also important. Predictive models were developed to infer lake-water pH based on species composition of chrysophyte scales in the 25 lakes, using weighted averaging regression and calibration with and without tolerance down-weighting. Model performance was evaluated by a computer-intensive bootstrapping procedure to derive root mean squared errors of predictions. In our data set, tolerance corrected weighted average regression and calibration was shown to be a more reliable predictor of pH than simple weighted averaging. This study shows that the remains of chrysophyte scales in Norway can be used as palaeo-biological indicators of lake-water pH.     

Diatoms,lake acidification and the Surface Water Acidification Programme (SWAP): a review

The Surface Water Acidification Programme (SWAP) was set up as collaborative research project involving scientists from Norway, Sweden and the UK. Its aim was to evaluate the factors responsible for fish decline in acid streams and lakes. A substantial sub-project was concerned with the palaeolimnological evidence for acidification and its causes. The central technique used was diatom analysis. In order to harmonise methodology between the seven diatomists from four laboratories in three countries a programme of taxonomic quality control was organised, involving slide exchanges, ‘blind’ counting, and regular workshops. In addition a calibration data-set of surface sediment diatoms and water chemistry from 170 lakes was constructed and archived on DISCO, the UCL diatom database. This data-set was used to generate diatom-chemistry transfer functions for pH, DOC and total Al using a weighted averaging technique. Application of the pH transfer function to sediment cores from a range of lakes demonstrated a dose-response relationship between lake sensitivity to acidification (as represented by mean Ca²⁺ values) and acid deposition (g S m ^-2 yr ^-1), indicating the overwhelming importance of acid deposition as the cause of lake acidification.     

Quantifying species indicator values for trophic diatom indices: a comparison of approaches

This study compares two approaches for constructing diatom-based indices for monitoring river eutrophication. The first approach is based on weighted averaging of species indicator values with the underlying assumption that species have symmetrical unimodal distributions along the nutrient gradient, and their distributions are sufficiently described by a single indicator value per species. The second approach uses multiple indicator values for individual taxa and is based on the possibility that species have complex asymmetrical response curves. Multiple indicator values represent relative probabilities that a species would be found within certain ranges of nutrient concentration. We used 155 benthic diatom samples collected from rivers in the Northern Piedmont ecoregion (Northeastern U.S.A.) to construct two datasets: one used for developing models and indices, and another for testing them. To characterize the shape of species response curves we analyzed changes in the relative abundance of 118 diatom taxa common in this dataset along the total phosphorus (TP) gradient by fitting parametric and non-parametric regression models. We found that only 34 diatoms had symmetrical unimodal response to TP. Among several indices that use a single indicator value for each species, the best was the weighted averaging partial least square (WA-PLS) inference model. The correlation coefficient between observed and inferred TP in the test dataset was 0.67. The best index that employed multiple indicator values for each species had approximately the same predictive power as the WA-PLS based index, but in addition, this index provided a sample-specific measure of uncertainty for the TP estimation.     

RELATIONSHIPS BETWEEN LITTORAL DIATOMS AND THEIR CHEMICAL ENVIRONMENT IN NORTHEASTERN GERMAN LAKES AND RIVERS1

We explored statistical relationships between the composition of littoral diatom assemblages and 21 chemical and physical environmental variables in 69 lakes and 15 river sites in the lowland of northeastern Germany. Canonical correspondence analysis with single treatment and with forward selection of environmental variables was used to detect 11 important ecological variables (dissolved inorganic carbon [DIC], Na ⁺ , total phosphorus [TP], dissolved organic carbon [DOC], total nitrogen [TN], pH, oxygen saturation, dissolved iron, SO₄^{2 ?} , NH₄ ⁺ , soluble reactive silicium) and maximum water depth or Ca^{2 +} or soluble reactive phosphorus that most independently explain major proportions of the total diatom variance among the habitats. Monte Carlo permutation tests showed that each contributed a significant additional proportion (P < 0.05) of the variance in species composition. Together, these 11 most important environmental variables explained 34% of the total variance in species composition among the sites and captured 73% of the explained variance from the full 21 parameters model. Weighted‐averaging regression and calibration of 304 indicator taxa with tolerance down‐weighting and classic deshrinking was used to develop transfer functions between littoral diatoms and DIC, pH, TP, TN, and Cl ^? . The DOC:TP ratio was introduced and a weighted‐averaging model was developed to infer allochthonous DOC effects in freshwater ecosystems. This diatom‐DOC/TP model was significant (P < 0.001) and explained 7.6% of the total diatom variance among the sites, surpassing the inferential power of the diatom‐TP‐transfer function (7.3% explained variance). The root‐mean‐square errors of prediction of the models were estimated by jack‐knifing and were comparable with published data sets from surface sediment diatom samples. The data set of littoral diatoms and environmental variables allows use of the diatom‐environmental transfer functions in biomonitoring and paleolimnological approaches across a broad array of natural water resources (such as floodplains, flushed lakes, estuaries, shallow lakes) in the central European lowland ecoregion.     

Using species‐environmental amplitudes to predict pH values from vegetation

Question: Species optima or indicator values are frequently used to predict environmental variables from species composition. The present study focuses on the question whether predictions can be improved by using species environmental amplitudes instead of single values representing species optima. Location: Semi‐natural, deciduous hardwood forests of northwestern Germany. Methods: Based on a data set of 558 relevés, species responses (presence/absence) to pH were modelled with Huisman‐Olff‐Fresco (HOF) regression models. Species amplitudes were derived from response curves using three different methods. To predict the pH from vegetation, a maximum amplitude overlap method was applied. For comparison, predictions resulting from several established methods, i. e. maximum likelihood/present and absent species, maximum likelihood/present species only, mean weighted averages and mean Ellenberg indicator values were calculated. The predictive success (squared Pearson's r and root mean square error of prediction) was evaluated using an independent data set of 151 relevés. Results: Predictions based upon amplitudes defined by maximum Cohen's x probability threshold yield the best results of all amplitude definitions (R²= 0.75, RMSEP = 0.52). Provided there is an even distribution of the environmental variable, amplitudes defined by predicted probability exceeding prevalence are also suitable (R²= 0.76, RMSEP = 0.55). The prediction success is comparable to maximum likelihood (present species only) and – after rescaling – to mean weighted averages. Predicted values show a good linearity to observed pH values as opposed to a curvilinear relationship of mean Ellenberg indicator values. Transformation or rescaling of the predicted values is not required. Conclusions: Species amplitudes given by a minimum and maximum boundary for each species can be used to efficiently predict environmental variables from species composition. The predictive success is superior to mean Ellenberg indicator values and comparable to mean indicator values based on species weighted averages.     

Lake depth rather than fish planktivory determines cladoceran community structure in Faroese lakes – evidence from contemporary data and sediments

1. This study describes the environmental conditions and cladoceran community structure of 29 Faroese lakes with special focus on elucidating the impact of fish planktivory. In addition, long‐term changes in biological structure of the Faroese Lake Heygsvatn are investigated. 2. Present‐day species richness and community structure of cladocerans were identified from pelagial snapshot samples and from samples of surface sediment (0–1 cm). Multivariate statistical methods were applied to explore cladoceran species distribution relative to measured environmental variables. For Lake Heygsvatn, lake development was inferred by cladoceran‐based paleolimnological investigations of a ¹⁴C‐dated sediment core covering the last ca 5700 years. 3. The 29 study lakes were overall shallow, small‐sized, oligotrophic and dominated by brown trout (Salmo trutta). Cladoceran species richness was overall higher in the surface sediment samples than in the snapshot samples. 4. Fish abundance was found to be of only minor importance in shaping cladoceran community and body size structure, presumably because of predominance of the less efficient zooplanktivore brown trout. 5. Canonical correspondence analysis showed maximum lake depth (Z_max) to be the only significant variable in explaining the sedimentary cladoceran species (18 cladoceran taxa, two pelagic, 16 benthic) distribution. Multivariate regression trees revealed benthic taxa to dominate in lakes with Z_max < 4.8 m and pelagic taxa to dominate when Z_max was > 4.8 m. 6. Predictive models to infer Z_max were developed using variance weighted‐averaging procedures. These were subsequently applied to subfossil cladoceran assemblages identified from a ¹⁴C‐dated sediment core from Lake Heygsvatn and showed inferred Z_max to correspond well to the present‐day lake depth. A recent increase in inferred Z_max may, however, be an artefact induced by, for instance, eutrophication.