25
Journal of Paleolimnology 25: 25–42, 2001. © 2001 Kluwer Academic Publishers. Printed in the Netherlands.
Diatom-environmental relationships in 64 alkaline southeastern Ontario (Canada) lakes: a diatom-based model for water quality reconstructions Euan D. Reavie1,2 & John P. Smol1 1 Paleoecological Environmental Assessment and Research Laboratory (PEARL), Department of Biology, Queen’s University, Kingston, Ontario, Canada K7L 3N6 (E-mail:
[email protected];
[email protected]) 2 Address for correspondence: Paleoenvironmental Assessment Laboratory (PAL), Department of Geology, University of Toronto, 22 Russell St., Toronto, Ontario, Canada M5S 3B1 Received 19 July 1999, accepted 19 November 1999
Key words: eutrophication, diatoms, Ontario, training set, calibration, phosphorus
Abstract Lake eutrophication is a problem in many areas of Ontario, although the history of nutrient enrichment is poorly documented. The aim of this study was to construct a diatom-based transfer function to infer past phosphorus levels in Ontario lakes using paleolimnological analyses. The relationship between diatom assemblages and limnological conditions was explored from a survey of diatoms preserved in the surface sediments of 64 Southern Ontario lakes, spanning a total phosphorus gradient of 0.004 to 0.054 mg L -1. Over 420 diatom taxa were identified, 98 of which were sufficiently common to be considered in statistical analyses. Canonical correspondence analysis (CCA) determined that pH, ammonium, aluminium, spring total phosphorus (TP), strontium, total nitrogen (TN), maximum depth (MaxZ), chlorophyll a (Chla) and mean depth were significant variables in explaining the variance in the diatom species data. The environmental optima of common diatom taxa for the limnologically important variables (TP, pH, TN, MaxZ, Chla) were calculated using weighted averaging (WA) regression and calibration techniques, and transfer functions were generated. The diatom inference model for spring TP provided a robust reconstructive relationship (r2 = 0.637; RMSE = 0.007 mg L-1; r2boot = 0.466; RMSEboot = 0.010 mg L-1). Other variables, including pH (r2 = 0.702; RMSE = 0.208; r2boot = 0.485; RMSEboot = 0.234), TN (r2 = 0.574; RMSE = 0.0899 mg L-1; r2boot = 0.380; RMSEboot = 0.127 mg L-1) and MaxZ (r2 = 0.554; RMSE = 1.05 m; r2boot = 0.380; RMSEboot = 1.490 m), were also strong, indicating that they may also be reconstructed from fossil diatom communities. This study shows that it is possible to reliably infer lakewater TP and other limnological variables in alkaline Southern Ontario lakes using the WA technique. This method has the potential to aid rehabilitation programs, as it can provide water quality managers with the means to estimate pre-enrichment phosphorus concentrations and an indication of the onset and development of nutrient enrichment in a lake. Introduction The effects of human activities and natural processes on lakewater nutrient chemistry are often poorly understood due to a scarcity of historical data. This situation has stimulated the development of paleolimnological methods to reconstruct past limnological conditions, such as trophic status and other variables, from biological remains such as diatoms (e.g., Dixit et al., 1992), chrysophyte remains (e.g., Smol, 1995) and
chironomids (e.g., Walker, 1987) preserved in lake sediments. For lake water chemistry, calibration is based on data sets consisting of assemblages identified and enumerated from surface sediments and contemporary water chemistry from many lakes in a particular region (Charles et al., 1994; Birks, 1995). Such training sets have been developed for diatoms in several regions worldwide (Stoermer & Smol, 1999). Among the indicators preserved in lake sediments, diatoms have proven to be sensitive indicators of
26 trophic status, and have been used in the reconstruction of past nutrient trends (Hall & Smol, 1999). However, for Southeastern Ontario, Canada (Figure 1), only one training set, developed to infer total nitrogen concentrations, has been published (Christie & Smol, 1993). Although nitrogen concentrations are related to trophic state, phosphorus is considered a more direct and reliable indicator of trophic condition, as phosphorus is the key nutrient in stimulating algal and plant growth in temperate lakes (Schindler, 1977). Furthermore, since Christie and Smol’s report, diatom taxonomy has been refined, and statistical methods have become more advanced, so new models need to be developed and assessed. Southern Ontario is an important region for paleoecological studies because several lakes have been impacted by cultural eutrophication. The history of the eutrophication process, however, is still poorly understood. In this paper, we used canonical correspondence analysis (CCA) to explore the relationships between 44 environmental variables and diatom species distributions in the surface sediments of 64 lakes located in Southeastern Ontario. The resulting transfer function for inferring total phosphorus concentrations was then evaluated to determine its reconstructive potential.
formations of limestone (Trenton and Beekmantown formations), granitic (Precambrian shield) rock, or a mixture of the two (Chapman & Putnam, 1966). The lakes are circumneutral to alkaline (average annual pH = 6.99 – 8.65), although 4 of the shield lakes (Beaver, Anstruther, Mississagua and Skootamatta lakes) had slightly acidic pH measurements in late summer (Table 1). Vegetation surrounding the lakes is typically mixed deciduous and coniferous forest. Southern Ontario contains thousands of lakes. As the region has become more developed, lakes have become more accessible from urban centers, resulting in extensive development of shorelines with summer cottages, waterfront resorts and campgrounds. Selected lakes for study range from undeveloped (e.g., Lindsay and Round lakes) to heavily developed drainage basins (e.g., Balsam and Charleston lakes). The training set includes oligotrophic, mesotrophic and eutrophic lakes, as indicated by the ranges of measured trophic variables (e.g. spring total phosphorus = 0.004 – 0.054 mg L–1; average total nitrogen = 0.25 – 0.68 mg L–1; average Secchi depth = 7.2 – 1.8 m).
Materials and methods Sampling
Study area The study region (Figure 1) is a glaciated area of relatively low relief. The 64 study lakes lie on bedrock
Our training set (Figure 1; Tables 1, 2) was selected to represent a broad range of total phosphorus (TP) concentrations typical for the region. Most selections
Figure 1. Map indicating the locations of the training set of lakes in southeastern Ontario, Canada.
27 Table1. Environmental variables used in the analyses. The ranges, means and transformations (for skewed variables) are also presented
Latitude Longitude Watershed area (km2) Surface area (km2) Maximum depth (m) Mean depth (m) Volume (× 106 m3) Elevation (m) Shoreline length (km) Water exchange rate (yr–1) Residences Development Chlorophyll a (mg L–1) Suspended solids (mg L–1) Beryllium (mg L–1) Aluminum (mg L–1) Titanium (mg L–1) Vanadium (mg L–1) Chromium (mg L–1) Manganese (mg L–1) Iron (mg L–1) Cobalt (mg L–1) Nickel (mg L–1) Copper (mg L–1) Zinc (mg L–1) Strontium (mg L–1) Molybdenum (mg L–1) Cadmium (mg L–1) Barium (mg L–1) Lead (mg L–1) Total phosphorus (mg L–1) pH Secchi depth (m) Temperature (°C) Conductivity (µS cm–1) Oxygen (mg L–1) Alkalinity (mg L–1) Ammonium (mg L–1) Nitrite (mg L–1) Nitrate/nitrite Total nitrogen (mg L–1) Dissolved organic carbon (mg L–1) Dissolved inorganic carbon (mg L–1) Silicate (mg L–1)
Minimum
Maximum
Mean
Transformation used
44.35 76 2.14 0.149 5.2 2 2 80 1.762 0.08 0 0 0.6 0.5 0 1.05 0.0761 0 0.687 1.96 0 0.0272 0.34 0.155 0 34.5 0.0414 0 13.7 0.0109 0.004 6.985 1.8 17.1 28.25 7.95 12.4 0.011 0.001 0.005 0.25 3.1 2.2 0.16
44.9 78.83 4763.77 876 95 21.88 799.97 320 172 45.46 3000 4.95 8.4 2.5 0.104 31.1 1.38 0.438 9.71 43.8 75.2 0.29 195 0.985 9.48 1420 0.385 0.262 89 1.297 0.054 8.645 7.2 22.45 319 10.2 154 0.086 0.004 0.05 0.68 6.85 35.6 1.99
44.63 76.848 255.39 30.543 26.5033 7.9502 69.795 177.753 30.969 2.96 275 1.51 2.24 0.91 0.007 5.87 0.5682 0.161 3.925 12.34 14.4 0.0607 4.99 0.483 1.12 231.5 0.1455 0.022 37.2 0.0881 0.0141 8.0933 4.2675 20.346 166.175 9.1198 81.638 0.035 0.002 0.010 0.41 4.68 18.5 1.11
– – log log square root square root log – log log log(χ +1) square root log log log(χ + 1) log – – – log square root – log – log(χ +1) square root square root log(χ +1) log log – – – – – – – square root – – – – – –
were based on measurements taken as part of the Ministry of Environment’s ‘Great Ontario Dip-in’ program (A. Gemza, unpub. data), which is an ongoing program to monitor nutrients and Secchi depths in Southern Ontario lakes. Measurements for our dataset were taken in spring (May) and late summer (late August/early September) in 1998. A smaller suite of chemical variables was collected in the late summer sampling period (Table 1). Conductivity, pH, temperature
and oxygen were taken on-site using Yellow Springs Instruments meters. Water samples were collected in 500 ml bottles and analyzed for other chemical variables by the Ontario Ministry of Environment, using methods described by Janhurst (1998). Most measurements represent spot samples taken in 1998, but for many lakes, TP and Secchi dip-in data from 1996 and 1997 were also available. In a preliminary test of the TP inference model, the model performed slightly
28 Table 2. Some physical and water chemistry characteristics of the 64 study lakes. Environmental variables are shown that exhibited a statistically significant influence on patterns in the diatom communities. Lake codes match those used in Figure 2. Christie & Smol’s (1993) lakes, and lakes removed after data screening, were plotted passively in Figure 2 (codes marked by ‘*’). Significant digits for depth measurements vary depending on data source. ‘nd’ = no data Code
Anstruther Balsam Beaver Big Clear Big Gull Big Rideau Bobs Burridge Charleston Christie Collins Crowe Crystal Dog Eagle Farren Gananoque Gilmour Bay (Chandos Lake) Gould Grippen Hambly Hart Howes Indian Inverary Kashwakamak Knowlton Leggat Limerick Lindsay Loon Loughborough Lower Beverley Mississagua Newboro Otty Pike Red Horse Round Salmon Sand Sharbot Skootamatta St. Andrew North Sturgeon Tallan Thirteen Island Troutling Bay (Tangamong Lake) Troy Upper Rideau Black
Lat.
Long.
MaxZ
MeanZ
Chla
TN
TP
DOC
(m)
(m)
(mg L-1)
(mg L-1)
(mg L-1)
(mg L-1)
pH
An Ba Be BC BG* BR Bo Bu Ch Cs Co Cr Cy* Do Ea Fa Ga Gi
44°45′ 44°35′ 44°44′ 44°43′ 44°49′ 44°44′ 44°30′ 44°40′ 44°32′ 44°48′ 44°21′ 44°29′ 44°45′ 44°25′ 44°41′ 44°46′ 44°26′ 44°47′
78°12′ 78°50′ 78°17′ 76°55′ 76°58′ 76°14′ 76°38′ 76°33′ 76°00′ 76°26′ 76°27′ 77°43′ 78°29′ 76°22′ 76°42′ 76°30′ 76°09′ 77°57′
39 15 20 18.3 26 95 23 16.2 91 18.3 10.1 15.8 33 49.7 31.1 21.3 23.77 25
12.6 4.8 7.0 6.6 3.9 15.3 11.4 4.5 17.4 8.5 4.2 5.6 11.2 6.2 10.1 8.3 6.95 6.8
1.0 1.6 1.8 1.0 2.6 1.0 3.0 0.8 1.8 1.2 3.4 0.8 1.0 2.4 1.0 0.6 2.2 1.2
0.26 0.25 0.32 0.37 0.37 0.37 0.47 0.41 0.42 0.35 0.68 0.35 0.31 0.60 0.33 0.33 0.48 0.33
0.010 0.006 0.014 0.012 0.016 0.008 0.012 0.006 0.030 0.008 0.024 0.008 0.004 0.018 0.008 0.008 0.016 0.012
4.15 3.15 4.95 5.05 5.45 3.85 4.90 5.75 4.50 4.80 5.30 5.05 5.20 4.80 4.05 3.75 5.05 4.50
6.99 7.77 7.14 8.44 7.36 8.19 8.05 8 °36 8.42 8.15 7.99 7.91 8.03 8.43 8.14 8.42 8.36 7.79
Go Gr* Ha Hr Ho In Iv Ka Kn Le Li Ln Lo Lg LB Mi Ne Ot Pi Re Ro Sa Sn Sh Sk SA St Ta Th TB*
44°28′ 44°30′ 44°28′ 44°31′ 44°30′ 44°36′ 44°23′ 44°51′ 44°27′ 44°43′ 44°54′ 44°32′ 44°37′ 44°22′ 44°36′ 44°43′ 44°37′ 44°50′ 44°46′ 44°32′ 44°32′ 44°49′ 44°34′ 44°46′ 44°50′ 44°37′ 44°30′ 44°50′ 44°32′ 44°43′
76°34′ 76°09′ 76°41′ 76°19′ 76°41′ 76°19′ 76°27′ 77°01′ 76°36′ 76°43′ 77°37′ 76°23′ 76°22′ 76°25′ 76°07′ 76°19′ 76°18′ 76°14′ 76°20′ 76°05′ 76°23′ 78°26′ 76°15′ 76°42′ 77°14′ 76°40′ 78°37′ 78°02′ 76°37′ 77°52′
61.57 16 14.6 5.5 12.8 26 6.5 21.9 34 12 29 13.725 8.2 38.4 26 37 23.8 27.4 32.6 37 32 30 14.3 31 25.3 25 10 26 25.9 22.9
21.88 11.5 4.0 4.0 4.2 10.1 2.0 8.4 9.8 4.2 8.4 3.0 2.7 14.5 9.2 17.7 3.2 9.0 8.4 10.2 10 11.3 5.2 8.07 8.38 8.2 3.8 3.88 6.3 4.3
1.2 8.4 8.2 2.6 2.4 1.2 7.8 0.6 1.4 0.6 0.8 2.0 1.4 2.8 7.4 1.2 3.8 1.2 1.8 4.0 1.0 0.8 1.6 1.4 2.4 2.0 3.8 0.8 1.4 1.4
0.31 0.63 0.52 0.61 0.51 0.42 0.61 0.26 0.34 0.33 0.27 0.49 0.36 0.41 0.55 0.27 0.48 0.48 0.44 0.50 0.27 0.27 0.40 0.40 0.33 0.46 0.54 0.30 0.39 0.31
0.008 0.035 0.012 0.020 0.012 0.010 0.018 0.004 0.006 0.006 0.004 0.008 0.010 0.012 0.018 0.008 0.014 0.014 0.010 0.014 0.004 0.004 0.012 0.008 0.010 0.012 0.014 0.012 0.010 0.008
3.10 4.50 6.85 6.00 5.20 4.40 6.15 4.15 3.50 3.50 4.25 5.20 3.80 3.30 5.20 4.55 4.70 5.45 5.15 5.15 3.45 3.60 4.10 5.25 5.20 6.30 5.15 5.25 4.30 4.10
8.21 8.52 8.25 8.04 8.11 8.38 8.10 7.91 8.29 7.85 8.18 8.21 8.23 8.02 8.48 7.18 8.33 8.42 8.40 8.45 8.30 8.13 8.21 8.40 7.12 7.82 8.07 8.08 8.33 7.89
Tr UR* Bl*
44°31′ 44°41′ 44°46′
76°15′ 76°19′ 76°18′
5.2 22 23
2.3 8.05 7.3
2.0 4.2 1.6
0.50 0.51 nd
0.018 0.023 0.032
4.60 4.10 nd
8.22 8.65 nd
29 Table 2. Continued
Crosby Dog north East Lyndhurst Mississippi Muskrat Opinicon Singleton South Stoco West Westport Sand Whitefish
Code
Lat.
Long.
MaxZ (m)
MeanZ (m)
Chla (mg L–1)
TN (mg L–1)
TP (mg L–1)
DOC
pH
Cb* DN* Ea* Ly* Ms* Mu* Op* Si* So* St* We* WS* Wh*
44°45′ 44°27′ 43°55′ 44°33′ 45°14′ 54°40′ 44°34′ 44°31′ 44°27′ 44°28′ 43°56′ 44°41′ 44°32′
76°26′ 76°18′ 77°12′ 76°06′ 76°15′ 76°55′ 76°19′ 76°07′ 76°04′ 77°16′ 77°17′ 76°26′ 76°14′
19 49.7 23.8 9.2 9.2 64 9.2 13.4 14.63 9.76 4.6 12.8 7
8.2 nd 2.8 3.6 2.7 17.7 4.88 5.6 5.33 4.0 2.8 6.7 2.8
2.6 8.7 4.9 6.1 3.3 7.7 2.8 5.1 5.5 15.4 7.5 1.4 3.4
nd nd nd nd nd nd nd nd nd nd nd nd nd
0.020 0.033 0.023 0.027 0.026 0.028 0.029 0.024 0.024 0.035 0.049 0.054 0.026
nd nd nd nd nd nd nd nd nd nd nd nd nd
nd nd nd nd nd nd nd nd nd nd nd nd nd
better when the averages of 3 yrs’ measurements were used, so, where possible, 1996–1998 data were incorporated into averages. Lakes were sampled as close as possible to their maximum depth using a Glew (1989) gravity corer, and 1-cm thick surface sediment sections were extruded using an upright extruder (Glew, 1988). Surface sediments were stored in Whirlpak bags and refrigerated prior to subsampling and preparation. In order to increase the number of lakes and the nutrient gradient in the dataset, we added materials and water chemistry data from 14 additional lakes assessed in the late 1980s by Christie & Smol (1993) (Table 2). Diatom assemblages from Christie & Smol’s slides were re-counted to maintain taxonomic consistency. Christie & Smol (1993) used a much smaller set of measured environmental variables, so data from these 14 lakes were only used in reconstructions and model testing, and not in exploratory ordinations. Laboratory techniques Diatom slide preparation involved digesting approximately 0.5 g of homogenized, wet surface sediment from each lake. In 15-ml glass vials, subsamples were combined with strong acid (a 50:50 mixture of nitric and sulfuric acid) and heated at 80 °C for an hour. Digested slurries were repeatedly washed with distilled water until the samples were acid-free. The resulting siliceous material was dried on coverslips and mounted on slides using Naphrax , a permanent mounting medium. A preliminary rarefaction analysis (data not presented) determined that a count of 300 diatom valves should be sufficient to characterize the diatom assemblages in our
samples. For each slide, a minimum of 300 diatom valves was counted and identified along random transects using oil immersion objectives at a magnification of 1000×. Diatoms were identified using standard floras (e.g., Krammer & Lange-Bertalot, 1986–1991; Patrick & Reimer, 1966) and iconographs (Camburn et al., 1984–1986; Cumming et al., 1995; Reavie & Smol, 1998). The dataset of environmental variables The environmental data consisted of 44 physical and chemical variables measured for each lake in 1996 to 1998. Physical variables, including latitude (Lat), longitude (Long), watershed area (WSA), lake surface area (SA), maximum depth (MaxZ), mean depth (MeanZ), volume (Vol), elevation (Elev), shoreline length (SL), water exchange rate (WER) and number of shoreline residences (Res), were obtained from the Ontario Ministry of Environment Lake Survey database. Cottage densities for some lakes were determined from the most current topographic maps. A conservative measure of lakeshore development relative to lake size (Dev) was calculated by dividing the number of cottages surrounding a lake by its surface area, multiplied by maximum depth. Secchi depth (Secchi), pH, temperature (Temp), suspended solids (SS) and 28 chemical variables [conductivity (Cond), oxygen (Oxyg), chlorophyll a (Chla), alkalinity (Alk), ammonium (NH4), nitrate (NO3), nitrate/nitrite ratio (NO2/3), total phosphorus (TP), total nitrogen (TN), dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), silica, beryillium (Be), aluminum (Al), titanium (Ti), vanadium (Va), chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu),
30 zinc (Zn), strontium (Sr), molybdenum (Mo), cadmium (Cd), barium (Ba), lead (Pb)] were measured in surface waters during spring, and a smaller subset of variables, which excluded metals, was measured in late summer (Table 1). Values for chemical variables are averages of concentrations determined in spring (May) and late summer (August–September) 1998. However, additional data for TP, Secchi and Chla were obtained from 1996-1998 measurements taken during ice-free periods as part of the Ministry of Environment’s “Great Ontario Dip-in Program”. Variables with skewed distributions were transformed (Table 1) in order to make their distributions closer to normal (Zar, 1984). Total phosphorus measurements were relatively evenly distributed along the measured gradient, thus no transformation was required. In a preliminary statistical comparison (constrained CCAs, WA, data not presented) of spring TP (average measurements from April to June) and average annual TP, it was determined that spring TP better described variance in the diatom assemblage data, so henceforth the TP variable represents average spring measurements from 1996 to 1998. Data screening Diatom taxa were included in ordinations and model development if: (a) they were present in a minimum of five lakes and achieved ≥ 1% abundance in at least one lake, or (b) they were present at ≥ 5% abundance in at least one lake. The included taxa are referred to as ‘common’ in this paper. Prior to statistical analysis, the species and environmental data were screened to identify and eliminate redundant and superfluous environmental variables and samples (Birks et al., 1990). Detection of unusual (‘outlier’) samples was accomplished by, first, running a principal components analysis (PCA) of the environmental data. Second, a detrended correspondence analysis (DCA) of the species data was run. Lakes whose sample scores lay outside the 95% confidence intervals in both the PCA and DCA were considered outliers. In order to reduce the effects of multicollinearity in our final CCA analysis, redundant environmental variables were removed. This was accomplished by (a) identifying groups of significantly (P < 0.05) correlated environmental variables in a Pearson correlation matrix and, subsequently, (b) performing an exploratory CCA with forward selection to determine which environmental variables in each of these groups explained the greatest amount of variation in the diatom
data. Finally, (c) a series of partial, constrained CCAs (Ter Braak, 1995; Ter Braak & `milauer, 1998) were performed to test the independence and partial effect of variables on patterns in the diatom assemblages. In this third test, the first ordination axis is selected as the significant variable of interest and correlated variables are set as covariables. If any covariable did not exert an independent influence on diatom distributions in the constrained CCA, it was removed from subsequent analyses. Data analysis Intercorrelation among environmental variables was examined using a correlation matrix with Bonferroni adjusted probabilities. Ordinations were performed using CANOCO version 4 (Ter Braak & `milauer, 1998). Principal patterns in the distributions of surface-sediment diatom taxa were explored using DCA and CCA. DCA was used to determine the maximum amount of variation in the species data (Hill & Gauch, 1980), also called the ‘species gradient’. This gradient, if greater than 4 standard deviation units, reflects the unimodal distribution of species in the dataset, thereby warranting the use of unimodal methods (i.e. CCA) in further analyses. Following data screening, CCA was used to explore the relationships among distributions of diatom taxa and the remaining environmental variables. For all CCAs, downweighting of rare taxa was applied (Ter Braak & `milauer, 1998). The computer program WACALIB version 3.3 (Line et al., 1994) was used to perform weighted-averaging regression and calibration (WA) functions on the diatom data and selected environmental variables. WA models were assessed using correlations (r2) between observed and diatom-inferred (DI) environmental data. Errors associated with model inferences were estimated using the apparent root mean squared error of prediction (RMSE), and the bootstrapped RMSE (RMSEboot). Bootstrapped error estimates were calculated from 1000 bootstrap cycles (Birks et al., 1990). The species data were square-root transformed for WA analyses to reduce the effect of dominant taxa on model calculations.
Results and discussion The southeastern Ontario diatom flora was relatively diverse, with more than 420 species identified.
31 Following the removal of rare taxa, 98 ‘common’ diatom species remained. The number of species encountered in any one surface sediment sample ranged from 12 in Troutling Bay (Tangamong Lake) to 69 in Mississippi Lake. Screening of sites using PCA and DCA identified five outlier sites. Due to extreme environmental measurements and diatom assemblages, Big Gull Lake, Crystal Lake, Grippen Lake, Troutling Bay and Upper Rideau Lake were removed from further analyses. In Crystal Lake, for example, surface sediments contained a high relative abundance of an unknown Cyclotella species, and levels of development and metals were also high. After screening, a total of 45 [59 including Christie & Smol (1993) samples] sites and 96 species remained. The first and second DCA axes (λ1 = 0.49; λ2 = 0.36) accounted for 21.2% of the cumulative variance in the diatom data. Detrended correspondence analysis also determined the gradient length of the species assemblages to be 4.8 standard deviation units. This long gradient indicated that unimodal methods (CCA) should be used for ordinations.
Ordination Forward selection identified nine environmental variables that could effectively explain the maximum amount of variance in the diatom data (Figure 2). This subset of variables included, in order of selection, MaxZ, pH, Sr, NH4, Chla, MeanZ, TN, DOC and Al. The extraction of typically correlated variable pairs such as MaxZ/MeanZ is surprising. However, although maximum and mean depth are correlated (Table 2), their low variance inflation factors (VIFs; Table 3) indicate that these variables independently explain a unique proportion of variance in the diatom data. A similar relationship among commonly correlated variables occurs with the pairs NH4/TN and pH/Al. The eigenvalues for CCA axes 1–4 (0.34, 0.27, 0.17, 0.09) constrained to these nine environmental variables were significant (p < 0.05), and species-environment correlations (axis 1 = 85.9%; axis 2 = 84.6%; axis 3 = 80.1%; axis 4 = 78.1%) were high, suggesting that the subset of nine variables could account for a large proportion of the variance explained within the data. Although the eigenvalue for axis 4 is low, this axis still merits interpretation.
Figure 2. Canonical correspondence biplot showing the nine significant (p < 0.05) environmental variables (arrows) and site scores. Abbreviations for site names correspond to Table 2. Lakes which were run passively [Christie & Smol’s (1993) lakes, and lakes removed after data screening] are marked by ‘*’].
Lat Long WSA SA MaxZ MeanZ Vol Elev SL WER Res Dev Chla SS Be Al Ti Va Cr Mn Fe Co Ni Cu Zn Sr Mo Cd Ba Pb TP pH Secchi Temp Cond Oxyg Alk NH4 NO3 NO2/3 TN DOC DIC Silica
1.00 0.36 0.00 –0.06 0.16 0.12 0.19 0.66** 0.06 –0.21 0.29 0.33 –0.55** –0.36 0.00 0.14 –0.13 –0.34 –0.36 –0.37 0.06 –0.28 –0.13 –0.16 0.22 –0.70** –0.23 0.03 –0.41 –0.02 –0.43 –0.29 0.53** 0.26 –0.53** –0.25 –0.46* –0.58** –0.08 0.16 –0.61** –0.07 –0.46* –0.30
Lat
1.00 0.01 0.07 –0.10 –0.11 0.09 0.82** –0.09 0.15 0.29 0.46* –0.30 –0.21 0.10 0.46* 0.11 –0.16 –0.28 –0.30 0.34 0.21 –0.02 –0.07 0.00 –0.36 –0.03 –0.14 –0.51** –0.17 –0.38 –0.51** 0.39 –0.03 –0.45* –0.47* –0.42 –0.51** 0.10 0.32 –0.50** –0.11 –0.43* 0.19
Long
1.00 0.68** 0.13 0.13 0.73** –0.06 0.79** 0.42* 0.69** 0.04 0.13 0.02 –0.12 0.22 –0.12 –0.08 –0.18 0.04 0.17 0.02 –0.16 0.28 0.19 –0.25 –0.09 0.09 –0.19 0.23 0.06 –0.05 –0.15 0.01 –0.09 –0.01 –0.10 0.25 0.12 0.32 0.02 –0.07 –0.10 0.05
WSA
1.00 0.13 0.17 0.63** –0.06 0.66** 0.12 0.72** –0.16 0.24 0.16 –0.09 –0.01 –0.08 –0.22 0.09 0.08 0.05 0.16 –0.15 0.23 0.20 –0.09 0.11 0.01 0.01 0.15 0.22 0.01 –0.13 –0.14 0.06 0.06 0.04 0.13 –0.03 0.23 0.08 0.00 –0.02 0.06
SA
1.00 0.79** 0.57** 0.00 0.37 –0.46* 0.34 –0.23 –0.23 –0.33 0.18 –0.08 –0.12 –0.36 –0.10 –0.51** –0.15 –0.20 –0.03 0.03 0.10 –0.26 –0.25 0.01 –0.19 –0.06 –0.10 0.04 0.33 –0.16 –0.10 0.20 –0.03 –0.12 –0.01 –0.09 –0.32 –0.33 –0.03 –0.30
MaxZ
1.00 0.56** 0.03 0.26 –0.52** 0.30 –0.19 –0.21 –0.17 0.08 –0.08 –0.17 –0.38 –0.14 –0.58** –0.12 –0.26 –0.16 0.07 0.24 –0.22 –0.23 0.04 –0.18 –0.02 –0.09 –0.03 0.31 –0.29 –0.13 0.32 –0.06 –0.15 –0.11 –0.03 –0.33 –0.41 –0.09 –0.19
MeanZ
1.00 0.09 0.81** –0.12 0.79** 0.00 –0.03 –0.14 0.03 0.23 –0.17 –0.25 –0.24 –0.34 0.05 0.03 –0.30 0.20 0.32 –0.31 –0.22 –0.08 –0.29 0.21 0.03 –0.16 0.07 –0.14 –0.23 0.03 –0.18 0.05 –0.07 0.27 –0.20 –0.29 –0.24 –0.19
Vol
1.00 –0.10 –0.05 0.24 0.40 –0.46* –0.32 0.04 0.49** –0.04 –0.32 –0.46* –0.49** 0.33 –0.10 –0.10 –0.12 0.11 –0.48** –0.29 –0.13 –0.62** –0.21 –0.48** –0.68** 0.52** –0.05 –0.65** –0.46* –0.59** –0.64** 0.04 0.32 –0.65** –0.11 –0.58** –0.04
Elev
1.00 0.04 0.76** 0.03 0.09 –0.01 –0.18 0.06 –0.13 –0.19 –0.18 –0.08 0.02 0.09 –0.16 0.15 0.18 –0.21 –0.10 0.00 –0.17 0.20 0.12 0.01 –0.10 –0.11 –0.09 0.12 –0.09 0.20 0.05 0.22 –0.01 –0.19 –0.11 –0.10
SL
1.00 –0.03 0.06 0.18 0.01 0.14 0.35 0.07 0.27 –0.03 0.36 0.23 0.31 0.36 0.15 –0.15 –0.01 0.12 0.23 0.03 0.12 –0.01 0.01 –0.25 0.31 0.00 –0.29 –0.08 0.22 0.12 0.17 0.16 0.12 –0.03 0.23
WER
1.00 0.42 –0.07 –0.10 –0.20 0.14 –0.06 –0.30 –0.24 –0.18 0.13 0.13 –0.25 0.23 0.27 –0.37 –0.09 0.02 –0.32 0.22 –0.02 –0.12 0.13 –0.09 –0.22 0.05 –0.20 0.01 –0.02 0.28 –0.18 –0.14 –0.24 –0.05
Res
1.00 –0.26 –0.16 –0.29 0.23 0.12 0.09 –0.34 –0.07 0.24 0.12 –0.16 0.07 0.07 –0.25 –0.02 0.00 –0.35 0.11 –0.18 –0.18 0.18 0.09 –0.28 –0.09 –0.31 –0.10 0.09 0.19 –0.16 0.02 –0.30 0.11
Dev
1.00 0.62** –0.16 –0.05 0.19 0.26 0.37 0.55** 0.08 0.29 0.14 0.14 –0.08 0.44 0.28 –0.03 0.40 0.02 0.70** 0.16 –0.82** –0.36 0.47* 0.27 0.45* 0.71** 0.17 –0.17 0.78** 0.45* 0.38 0.21
Chla
1.00 –0.23 –0.28 0.18 0.21 0.45* 0.54** –0.17 0.10 0.10 –0.03 –0.08 0.37 0.34 –0.04 0.41 0.09 0.37 0.27 –0.60** –0.18 0.48** 0.20 0.45* 0.38 –0.08 –0.18 0.61** 0.29 0.41 0.20
SS
1.00 0.20 0.02 –0.11 0.01 –0.19 0.07 0.09 0.21 –0.16 –0.19 –0.13 0.22 –0.05 0.01 0.06 –0.27 –0.10 0.20 0.07 0.21 –0.20 –0.21 –0.30 0.01 –0.02 –0.28 –0.22 –0.19 –0.15
Be
1.00 –0.04 0.08 –0.61** –0.23 0.70** –0.01 –0.17 0.22 0.26 –0.29 –0.13 –0.07 –0.57** 0.17 –0.03 –0.72** –0.05 0.10 0.66** –0.52** –0.67** –0.03 0.03 0.48** –0.15 0.11 –0.66** –0.09
A
1.00 0.56** 0.32 0.14 –0.07 0.11 0.11 –0.18 –0.39 0.06 0.45* –0.15 0.12 –0.05 0.24 0.31 –0.08 0.03 0.25 0.03 0.27 0.10 0.18 –0.07 0.19 0.12 0.23 0.35
Ti
1.00 0.24 0.35 –0.06 0.11 0.05 0.00 –0.19 0.32 0.22 –0.12 0.24 0.08 0.39 0.30 –0.34 0.21 0.34 –0.15 0.26 0.34 0.13 –0.09 0.50** 0.31 0.28 0.26
Va
1.00 0.43* –0.61** 0.25 0.25 –0.27 –0.32 0.50** 0.47* 0.03 0.75** –0.23 0.26 0.63** –0.21 –0.06 0.87** 0.23 0.83** 0.16 –0.01 –0.31 0.42 0.24 0.84** 0.41
Cr
1.00 –0.10 0.17 0.20 –0.02 –0.10 0.37 0.38 0.18 0.48** 0.12 0.41 0.46* –0.65** 0.23 0.51** 0.16 0.43* 0.60** –0.06 –0.33 0.73** 0.46* 0.43* 0.09
Mn
1.00 –0.03 –0.15 0.29 0.18 –0.32 –0.03 –0.08 –0.49** 0.15 0.02 –0.62** –0.14 –0.01 0.62** –0.41 –0.62** 0.00 0.15 0.36 –0.03 0.28 –0.63** 0.03
Fe
1.00 0.19 0.17 –0.08 0.16 0.22 0.01 0.23 0.09 0.08 0.12 –0.16 –0.09 0.27 –0.07 0.23 0.14 –0.06 –0.03 0.13 –0.01 0.24 0.36
Co
Table 3. Pearson correlation matrix with Bonferroni-adjusted probabilities for measured environmental variables. The symbols ** and * denote significance at P < 0.01 and P < 0.05, respectively
32
Ni Cu Zn Sr Mo Cd Ba Pb TP pH Secchi Temp Cond Oxyg Alk NH4 NO3 NO2/3 TN DOC DIC Silica
1.00 0.01 –0.21 0.12 0.11 0.33 0.35 0.05 0.05 0.34 –0.07 0.12 0.31 0.10 0.31 0.07 –0.16 –0.23 0.15 0.01 0.32 0.23
Ni
1.00 0.50** 0.00 –0.19 0.23 –0.07 0.54** 0.11 –0.07 –0.09 –0.09 0.01 –0.04 –0.03 0.28 –0.03 0.18 0.16 0.15 –0.05 0.11
Cu
Table 3. Continued
1.00 –0.11 –0.36 0.28 –0.19 0.60** –0.02 –0.27 0.07 0.03 –0.24 –0.09 –0.25 0.01 –0.21 0.21 –0.07 0.12 –0.26 –0.09
Zn
1.00 0.06 0.00 0.58** 0.00 0.33 0.24 –0.44* –0.31 0.62** 0.20 0.54** 0.49** 0.16 –0.19 0.56** 0.16 0.54** 0.21
Sr
1.00 –0.11 0.40 –0.08 0.25 0.27 –0.25 0.11 0.33 0.01 0.29 0.07 –0.05 –0.08 0.32 0.26 0.26 0.35
Mo
1.00 0.04 0.35 –0.05 0.18 0.03 0.34 0.08 0.11 0.03 0.09 –0.03 –0.13 0.05 0.03 0.05 –0.08
Cd
1.00 0.00 0.34 0.63** –0.38 –0.04 0.84** 0.23 0.79** 0.33 –0.31 –0.18 0.55** 0.23 0.78** 0.30
Ba
1.00 0.08 0.05 –0.13 0.19 –0.05 –0.08 –0.10 0.23 0.00 0.05 0.16 0.05 –0.12 –0.15
Pb
1.00 0.22 –0.67** –0.24 0.38 0.27 0.36 0.67** –0.01 –0.11 0.75** 0.32 0.26 –0.03
TP
1.00 –0.16 0.23 0.76** 0.44* 0.72** 0.33 0.02 –0.38 0.42* –0.02 0.72** 0.15
pH
1.00 0.08 –0.35 –0.22 –0.30 –0.76** 0.03 0.12 –0.80** –0.35 –0.25 –0.01
Secchi
1.00 –0.12 –0.31 –0.18 –0.13 1.00 0.04 –0.09 0.10 –0.13 –0.10
Temp
1.00 0.42* 0.37 –0.42* 0.23 –0.23 0.36 –0.07
–0.33 0.61** 0.23 0.96** 0.40
Oxyg
1.00 0.37 0.97** 0.46*
Cond
–0.34 0.53** 0.17 0.96** 0.37
1.00 0.44*
Alk
NO3
–0.16 0.82** 0.31 0.38 0.02
0.15 0.03 0.11 0.06 0.25
1.00 NO3
NH4
1.00 –0.22 –0.02 –0.35 0.22
0.00
NO2/3
1.00 0.55** 0.50** 0.13
–0.07
TN
1.00 0.23 0.36
–0.32
DOC
1.00 0.42*
–0.04
DIC
1.00
0.02
Silica
33
34 Based on canonical coefficients of the environmental variables and associated t tests, (Table 3; Ter Braak, 1995; Ter Braak & `milauer, 1998), CCA axis 1 captured a gradient of MaxZ, Chla and pH, and axis 2 was highly correlated with Chla, TN, DOC and Al. Strong intercorrelation among the variables Chla, TN, TP, Secchi and NH4 (Table 2) confirm that axis 2 is a good proxy for nutrient concentrations (Figure 2), although some of the nutrient-related variance (i.e. associated with Chla) was also captured by axis 1. Together, axes 1 and 2 captured a pH gradient, with low-pH sites in the lower left quadrant, and more alkaline sites in the upper right quadrant. Axes 3 and 4 were also related to nutrient and pH variables, respectively (Table 3). In summary, CCA ordination suggests that there are statistically significant relationships between diatom distributions and the nine forward-selected environmental variables. Therefore, these variables can potentially be inferred from fossil diatom assemblages. The first four axes highlight the importance of depth, pH and nutrient concentrations in determining the structure of diatom communities in Southern Ontario lakes. A number of studies have shown the influence of total phosphorus on diatom distribution in North America (e.g. Reavie et al., 1995; Dixit et al., 1999). However, in this case, TP was not identified by forward selection. This is due to the strong correlations that exist between TP and other trophic variables (i.e. Chla, NH4, TN and Secchi). Nonetheless, in a CCA constrained to TP, TP captured a significant (p < 0.01) proportion of variance in the diatom data. Furthermore, because TP has a low variance inflation factor (Table 3; Ter Braak & `milauer, 1998), this variable can be reliably reconstructed. We believe that the TP model should be interpreted with the consideration that it is highly correlated with a suite of nutrient variables, which may also be influencing diatom responses to TP. Site ordination CCA clearly separated the samples according to their environmental site characteristics (Figure 2). Deep lakes (e.g. Gould, Big Rideau and Charleston lakes) plotted near the end of the MaxZ and MeanZ arrows, whereas shallow lakes (e.g. Sturgeon, Balsam, Inverary, Hart and Lindsay lakes) plotted on the opposite side of the diagram. The plot also clearly separates high pH sites (e.g. Knowlton and Loughborough lakes) from low pH sites (e.g. Beaver, Anstruther and Skootamatta lakes), and productive (e.g. Sand and Collins lakes)
from more oligotrophic (Mississagua and Sharbot lakes) sites. Species ordination Several diatom taxa [e.g. Nitzschia gracilis (80), Cocconeis neothumensis (20), Achnanthes conspicua (2), Gomphonema angustum (56)] are clustered near the origin of the CCA biplot (Figure 3) suggesting that some taxa are generalists in Southern Ontario. Probably the most obvious trend in the biplot is the separation of planktonic taxa and benthic taxa along the first axis. Apparently CCA was able to separate diatom assemblages according to lake depth along axis 1. Planktonic Cyclotella, Stephanodiscus and pelagic Fragilaria species primarily plot on the right side of the diagram, corresponding with deeper lakes, whereas small benthic species (e.g. Navicula, Amphora, Achnanthes and the benthic Fragilaria pinnata/construens complex) occur in the left quadrants. Several taxa plotted near the TN, NH4, Chla and TP arrows in the top quadrants of the CCA biplot, suggesting that they are more common in eutrophic waters. A number of taxa [e.g. N. pseudoventralis (67), N. vitabunda (75), F. brevistriata (37) and A. exigua (4)] appear to favour high concentrations of nitrogen variables, but it is noteworthy that these taxa also have affinities for shallower depths. Some high TP taxa are F. capucina var. mesolepta (40), N. cryptotenelloides (63) and S. hantzschii (88), whereas low TP taxa include N. minima (66), N. subatomoides (72) and A. clevei (1). Although the range of measured pH is not great (6.99 – 8.65), some taxa, such as Brachysira vitrea (19), N. minima (66), N. subatomoides (72), A. daonensis (3) and Cyclotella pseudostelligera (31) are more common in lower pH conditions, while a few taxa [e.g. Navicula cari (61) and Fragilaria capucina var. mesolepta (40)] favour higher pH. Inference models We decided to test the strengths of the relationships between seven variables and diatoms using weighted averaging and calibration methods (Table 5). Although NH4, Sr and Al occur as important variables in canonical correspondence analyses, they are rarely used in eutrophication monitoring programs, so inference models for these three variables have not been constructed. The seven selected variables exhibit significant (p < 0.01) correlations between observed
35
Figure 3. Canonical correspondence biplot showing the nine significant forward-selected environmental variables (arrows) and species scores. Species score numbers correspond to Table 6.
and inferred values, indicating that these variables could be inferred from fossil data. Bootstrapped calculations indicate that some of these models (Chla, MeanZ and DOC) have little reconstructive integrity. For example, while some information may be gained by reconstructing Chla downcore, its observed/inferred relationship (r2boot = 0.297) after critical investigation is rather low, and the reconstructive error estimate is quite large (for log-transformed data: RMSE = 0.168;
RMSEboot = 0.245). Investigation of the relationships between measured and diatom-inferred values of some important variables (Figure 4) indicates the scatter relative to the ideal 1:1 relationship. Variables such as pH have little scatter, reflecting more robust reconstruction, whereas the transfer function for TN resulted in a greater scatter. The diatom-inferred total phosphorus (DI-TP) model provided a fair fit to the 1:1 line, and residuals for this
Table 4. Canonical coefficients, approximate t-test values and variance inflation factors (VIF) of the forward-selected environmental variables for each of the first four CCA axes Canonical coefficients
t values
Variable
axis 1
axis 2
axis 3
axis 4
axis 1
axis 2
axis 3
axis 4
VIF
MaxZ MeanZ Chla Al Sr pH NH4 TN DOC TP
0.4373 0.3282 0.7570 0.0752 0.0750 0.5817 –0.3402 –0.5128 0.0166 0.1261
–0.2192 0.2282 –0.5785 –0.4117 0.2328 –0.1476 0.0509 0.9103 –0.4038 0.2949
–0.2821 0.0029 0.0756 –0.0324 –0.3493 0.5560 0.9239 –0.9549 0.3293 –0.0016
–0.1035 0.3259 0.2108 0.6610 0.3250 0.4105 –0.1211 0.0046 0.1655 0.2978
2.9077* 2.1681 4.9141* 0.4944 0.6182 3.4646* –1.9343 –2.0643 0.1324 0.7374
–1.5454 1.5986 –3.9819* –2.8715* 2.0366 –0.9324 0.3069 3.8854* –3.4182* 1.9417
–2.0105 0.0206 0.5263 –0.2287 –3.0881* 3.5493* 5.6310* –4.1201* 2.8175* –0.0112
–0.8426 2.6357* 1.6755 5.3233* 3.2819* 2.9930* –0.8434 0.0227 1.6178 2.7870*
2.88 2.92 3.02 2.95 1.87 3.59 3.94 7.86 2.00 1.21
36 Table 5. Relationships between measured and diatom-inferred limnological variables using weighted-averaging regression and calibration models with classical deshrinking, without and with bootstrapping. Significant (p < 0.01) correlations are marked by an asterisk. RMSE values for MaxZ, MeanZ and DOC represent transformed data as indicated in Table1 Variable
Lakes
r2
RMSE
r2boot
RMSEboot
pH TP (mg L–1) TN (mg L–1) MaxZ (m) Chla (mg L–1) MeanZ (m) DOC (mg L–1)
45 59 59 59 59 59 45
0.702* 0.637* 0.574* 0.554* 0.516* 0.512* 0.496*
0.208 0.00729 0.0899 1.05 0.168 0.534 0.600
0.485* 0.466* 0.380* 0.380* 0.297* 0.320* 0.059
0.234 0.010 0.127 1.490 0.245 0.764 1.053
Figure 4. Weighted-averaging calibration models for TP, pH, NH4, TN, MaxZ and Chla. Scatter plots are shown for simple (left column) and bootstrapped (right column) estimates. Correlation coefficients and root mean squared errors are presented in Table 5.
37 correlation (data not shown) are not significantly skewed. Although our TP model is fairly robust, there are many possible error sources that reduce its predictive ability. These include spatial variability in diatom assemblages and the effect of unmeasured variables. The limitations of diatom-based TP models have been outlined in other studies (e.g. Anderson, 1995) and, as recorded in previous work, many factors control diatom assemblage composition in Southern Ontario (Christie & Smol, 1993). Hence, it will likely be difficult to reduce errors of the model substantially because of the complexity of diatom-environment interactions, but also due to temporal variability in lakewater nutrient levels (Bennion, 1993), which cannot be fully accounted for in standard environmental monitoring programs. This is particularly relevant to total phosphorus, which fluctuates seasonally in concentration. The DI total phosphorus reconstructions appear to be most accurate within the oligotrophic to mesoeutrophic range. Because highly eutrophic lakes are uncommon in Southern Ontario, these lakes are underrepresented in our dataset. As a result, reconstructions for highly eutrophic lakes, such as West and Westport Sand (Figure 4; at observed TP 0.49 and 0.54 respectively), are somewhat underestimated. This appears to be typical of phosphorus models in North America (e.g., Reavie et al., 1995), as there is a lack of highly eutrophic freshwater lakes to include in training sets. Removal of these high TP lakes improves the model’s performance (r2 = 0.70, r2boot = 0.53) but, because they did not occur as outliers in the species/environmental ordination analyses, we have decided to present them here. If downcore reconstructions are to be performed on a known oligotrophic or mesotrophic lake, one might consider removing these two high-TP lakes. The WA procedure produces environmental optima for the common diatom taxa, and here we present these optima for variables that may be of use in lake management (TP, TN, Chla, pH, MaxZ; Table 6). The species optima provide several expected results, such as the high TP optima for the eutrophic diatoms Stephanodiscus hantzschii and S. parvus. Twenty-one taxa occurred with a Hill’s N2 value greater than 25 effective occurrences, and their distributions along the measured TP gradient are presented in Figure 5. Logit regressions to the diatom relative abundance data indicate that many taxa (e.g. F. pinnata and A. minutissima) have broad tolerances to TP, whereas others (e.g. Tabellaria flocculosa str. 3P) occur within a more specific range.
Despite the narrow range of pH in our study lakes, taxa still ordinate according to their pH preferences. For example, Brachysira vitrea occurs at a relatively low pH optimum (7.50), given the high pH range of the calibration lakes. The only taxon with a lower pH optimum (7.41) is Achnanthes daonensis, suggesting that it may also be a suitable indicator of lower pH. As also reflected by the species scores (Figure 3), most planktonic taxa (e.g. Stephanodiscus, Cyclotella and Tabellaria) have MaxZ optima greater than 20 m, whereas most of the raphid, benthic taxa (e.g. Achnanthes and Navicula) tended to have MaxZ optima less than 20 m. Compared to some studies (Bennion, 1994; Anderson et al., 1993), the TP gradient in Southern Ontario is relatively short, so it is likely that the TP optima for some taxa have been underestimated. For example, the TP optimum for Stephanodiscus hantzschii was calculated to be 0.2884 mg L–1 in southeast England, where hypereutrophic lakes are common (Bennion, 1994). In contrast, this optimum in Southern Ontario is only 0.024 mg L–1, undoubtedly due to the narrower measured gradient (0.004 – 0.054 mg L–1). Despite these considerations, the TP gradient used in this training set is the appropriate range to undertake paleolimnological work in Southern Ontario. The correlation coefficients (r 2 = 0.637, r 2boot = 0.466) derived for the TP model compare favourably with results from similar studies from North America. Following the CCA and WA analyses, it was clear that pH was the most significant of the 44 environmental variables in explaining the diatom species distributions in this dataset. This is consistent with other calibration studies (Reavie et al., 1995; Dixit et al., 1999), even when the pH gradient is not large, as in this training set. Other variables (i.e. depth, chlorophyll a and total nitrogen) can also be inferred from the diatoms, although less strongly than TP and pH. Total nitrogen and the nutrient-driven chlorophyll a concentrations, in association with total phosphorus, can serve as indicators of trophic status. Maximum depth is a strong determinant of diatom assemblages, primarily because shallower lakes archive greater relative numbers of benthic taxa in sediments, and conversely, the sediments of deeper lakes are often dominated by planktonic diatoms. Reconstruction of depth can be important in areas where flooding has been historically important, such as in Sand Lake (Southern Ontario), which was substantially deepened following construction of the Rideau Canal (Conservation Authorities Branch, 1970). Diatom investigation of a sediment core from Sand
Species number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
Species
Achnanthes clevei Grunow Achnanthes conspicua Mayer Achnanthes daonensis Lange-Bertalot Achnanthes exigua Grunow Achnanthes lanceolata ssp. frequentissima Lange-Bertalot Achnanthes lanceolata var. rostrata (Oestrup) Hustedt Achnanthes laterostrata Hustedt Achnanthes minutissima Kützing Achnanthes subatomoides (Hustedt) Lange-Bertalot & Archibald Achnanthes suchlandtii Hustedt Amphora inariensis Krammer Amphora libyca Ehrenberg Amphora pediculus (Kützing) Grunow ex A. Schmidt Asterionella formosa Hassall Aulacoseira alpigena (Grunow) Krammer Aulacoseira ambigua (Grunow) Simonsen Aulacoseira granulata (Ehrenberg) Simonsen Aulacoseira subarctica (O. Müller) Haworth Brachysira vitrea (Grunow) Ross Cocconeis neothumensis Krammer Cocconeis placentula Ehrenberg Cocconeis placentula var. euglypta (Ehrenberg) Grunow Cyclostephanos cf. tholiformis Stoermer, Håkansson & Theriot Cyclostephanos sp.#1 Cyclostephanos tholiformis f. #1 Cyclotella aff. comta var. unipunctata Hustedt Cyclotella bodanica var. aff. lemanica (O. Müller ex Schröter) Bachmann Cyclotella comensis Grunow Cyclotella michiganiana Skvortzow Cyclotella ocellata Pantocsek Cyclotella pseudostelligera Hustedt Cyclotella stelligera Cleve & Grunow Cymbella microcephala Grunow
19 35 3 11 20 15 4 61 5 7 4 10 35 62 5 53 27 53 11 18 11 27 10 11 11 23 48 30 37 21 48 14 35
#occ 13.3 23.1 2.9 9.4 17.7 13.7 3.4 49.0 4.8 6.3 3.5 9.4 27.7 50.0 4.3 39.3 22.7 40.7 10.0 16.9 9.5 22.1 7.6 9.2 7.9 14.1 38.0 21.1 27.7 14.8 31.3 10.0 27.1
N2 0.016 0.018 0.012 0.022 0.019 0.018 0.017 0.016 0.015 0.009 0.022 0.022 0.018 0.014 0.034 0.016 0.019 0.014 0.022 0.014 0.020 0.020 0.021 0.012 0.020 0.010 0.011 0.010 0.014 0.012 0.013 0.011 0.016
TP (mg L–1)
8.14 8.20 8.14 7.50 8.13 8.06 8.24 8.39 8.23 8.26 8.28 7.97 8.29 8.03 8.18 7.89 7.66 8.06
8.08 8.15 7.41 8.06 8.11 8.04 8.10 8.01 8.01 8.05 8.23 8.20 8.17 7.99
pH 0.452 0.494 0.380 0.553 0.492 0.495 0.488 0.459 0.406 0.365 0.529 0.562 0.481 0.423 0.668 0.469 0.508 0.455 0.502 0.431 0.479 0.538 0.534 0.431 0.546 0.423 0.382 0.406 0.426 0.443 0.394 0.355 0.474
TN (mg L–1) 16.5 18.4 19.7 12.3 14.2 13.7 13.9 20.6 16.9 39.0 8.9 13.3 19.2 24.0 9.1 19.4 14.4 26.2 20.9 25.4 18.9 18.9 25.0 42.9 26.6 37.5 23.7 30.7 23.4 23.2 23.2 21.3 20.7
MaxZ (m)
1.67 1.78 1.66 1.85 1.81 1.78 1.83 1.71 1.48 1.50 1.97 1.87 1.72 1.66 2.40 1.72 1.81 1.67 1.79 1.62 1.82 1.95 2.01 1.60 2.17 1.64 1.53 1.57 1.63 1.70 1.58 1.47 1.71
Chla (mg L–1)
Table 6. List of the 96 diatom taxa in the training set, the number of lakes in which they appeared (#occ), their effective number of occurrences (N2) and their optima for five important environmental variables. Missing values indicate that in the smaller, 45-lake dataset, those taxa were not sufficiently common to warrant calculations of optima. Species numbers correspond to those used in Figure 3. Optima for transformed variables (MaxZ, Chla) have been back-transformed to normal values. Hill’s N2 (Hill, 1973) is the inverse of the Shannon Weaver index of diversity, and it gives a better indication of a species’ occurrence by giving less weight to samples in which the species was rare. For example, Cyclotella stelligera occurred in 14 lakes, but in 4 of those samples it was rare (< 1%), so its optima were effectively determined by the 10 samples in which it occurred in greater relative abundance. Therefore the N2 for this taxon was 10
38
Species number 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73
Species
Cymbella silesiaca Bleisch Diatoma tenuis Agardh Diploneis oculata (Brébisson) Cleve Fragilaria brevistriata Grunow Fragilaria brevistriata var.#1 SLR Fragilaria capucina var. gracilis (Oestrup) Hustedt Fragilaria capucina var. mesolepta (Rabenhorst) Rabenhorst Fragilaria capucina var. vaucheriae (Kützing) Lange-Bertalot sensu lato Fragilaria construens (Ehrenberg) Grunow Fragilaria construens f. binodis (Ehrenberg) Hustedt Fragilaria construens f. venter (Ehrenberg) Hustedt Fragilaria crotonensis Kitton Fragilaria cyclopum (Brutschy) Lange-Bertalot Fragilaria nanana Lange-Bertalot Fragilaria parasitica (W. Smith) Grunow Fragilaria pinnata Ehrenberg Fragilaria pinnata var acuminatum A. Mayer Fragilaria pinnata var. lancettula (Schumann) Hustedt Fragilaria sp.#5 PIRLA Fragilaria tenera (W. Smith) Lange-Bertalot Fragilaria ulna (Nitzsch) Lange-Bertalot Gomphonema angustatum (Kützing) Rabenhorst Gomphonema angustum Agardh Gomphonema minutum (Agardh) Agardh Gomphonema parvulum Kützing Gomphonema pumilum (Grunow) Reichardt & Lange-Bertalot Mastogloia smithii Thwaites Navicula cari Ehrenberg Navicula cryptotenella Lange-Bertalot Navicula cryptotenelloides Lange-Bertalot Navicula ignota var. palustris (Hustedt) Lund Navicula kuelbsii Lange-Bertalot Navicula minima Grunow Navicula pseudoventralis Hustedt Navicula pupula Kützing Navicula radiosa Kützing Navicula schadei Krasske Navicula seminulum Grunow Navicula subatomoides Hustedt Navicula submuralis Hustedt
Table 6. continued
15 11 5 35 11 27 12 36 30 9 22 63 9 49 8 44 10 9 14 7 33 4 7 5 5 19 7 8 35 18 8 6 13 11 19 3 10 7 7 32
#occ
13.6 10.5 4.7 28.4 10.4 24.7 6.6 29.1 19.0 7.1 18.9 55.9 6.8 36.9 7.5 31.8 9.0 7.6 10.2 6.3 30.7 3.8 5.7 3.8 4.4 17.0 6.3 7.4 31.3 16.1 7.5 5.2 12.0 9.2 17.6 2.9 9.0 6.6 6.5 27.1
N2
0.016 0.019 0.017 0.017 0.013 0.015 0.027 0.017 0.018 0.013 0.018 0.014 0.011 0.013 0.015 0.016 0.017 0.024 0.013 0.015 0.018 0.026 0.014 0.020 0.021 0.022 0.024 0.016 0.016 0.022 0.017 0.013 0.015 0.024 0.013 0.014 0.019 0.016 0.021 0.017
TP (mg L–1)
8.17 8.05 8.29 8.19 8.48 8.18 8.18 8.19 8.11 8.10 7.58 8.18 7.96 7.88 8.08 7.73 7.68 7.94
8.23 8.39 7.83 8.02 7.93 8.06 8.55 8.24 8.08 8.03 8.05 8.10 8.17 8.03 8.22 8.05 7.80 8.02 7.77 8.25 8.28
pH
0.460 0.512 0.448 0.470 0.390 0.438 0.554 0.498 0.497 0.402 0.479 0.437 0.347 0.422 0.426 0.464 0.460 0.599 0.396 0.465 0.493 0.545 0.442 0.474 0.573 0.572 0.578 0.499 0.478 0.544 0.474 0.473 0.432 0.573 0.416 0.432 0.515 0.436 0.492 0.442
TN (mg L–1) 21.8 21.7 21.2 17.2 23.6 19.0 25.8 20.3 13.5 16.1 15.7 23.7 25.3 22.6 16.3 16.2 16.3 10.9 30.4 21.1 22.1 19.0 20.8 28.0 12.7 12.1 16.1 17.0 18.9 18.0 22.5 10.1 18.6 12.6 14.6 10.3 11.5 16.9 16.0 18.6
MaxZ (m)
1.70 1.98 1.71 1.66 1.61 1.66 2.16 1.82 1.68 1.56 1.73 1.67 1.44 1.66 1.61 1.63 1.64 1.91 1.54 1.82 1.88 2.16 1.65 1.84 2.16 1.97 2.03 1.75 1.76 1.95 1.65 1.68 1.65 1.92 1.54 1.80 1.69 1.66 1.80 1.65
Chla (mg L–1)
39
Species number 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
Species
Navicula subrotundata Hustedt Navicula vitabunda Hustedt Nitzschia amphibia Grunow Nitzschia dissipata (Kützing) Grunow Nitzschia draveillensis Coste & Ricard Nitzschia fonticola Grunow Nitzschia gracilis Hantzsch Nitzschia lacuum Lange-Bertalot Nitzschia palea (Kützing) W. Smith Nitzschia paleacea (Grunow) Grunow Nitzschia pura Hustedt Rhizosolenia eriensis H. L. Smith Stephanodiscus alpinus Hustedt Stephanodiscus binderanus var. oestrupii Cleve-Euler Stephanodiscus hantzschii Grunow Stephanodiscus hantzschii var. tenuis (Hustedt) Håkansson Stephanodiscus medius Håkansson Stephanodiscus minutulus (Kützing) Cleve & Möller Stephanodiscus niagarae Ehrenberg Stephanodiscus parvus Stoermer & Håkansson Tabellaria flocculosa (Roth) Kützing str. 3 sensu Koppen Tabellaria flocculosa (Roth) Kützing str. 3P sensu Koppen Tabellaria quadriseptata Knudson
Table 6. Continued
11 16 8 20 14 4 15 11 14 19 3 17 9 3 7 2 32 44 34 15 8 50 6
#occ 9.7 12.8 5.9 18.2 11.3 3.4 14.3 9.6 12.5 17.5 2.8 15.1 8.2 2.8 6.7 2.0 27.7 29.4 29.8 9.2 41.1 6.7 5.3
N2 0.017 0.021 0.023 0.015 0.011 0.013 0.016 0.020 0.016 0.016 0.016 0.013 0.019 0.012 0.024 0.022 0.016 0.015 0.017 0.028 0.012 0.013 0.020
TP (mgL–1) 8.32 8.06 8.15 8.07 8.10 8.25 8.25 8.04 7.95 7.91 8.25 7.93 8.38 8.26 8.38 8.48 8.26 8.24 8.20 8.24 7.64 7.98 7.97
ph 0.505 0.551 0.562 0.467 0.391 0.540 0.454 0.512 0.458 0.440 0.468 0.431 0.530 0.414 0.605 0.534 0.456 0.461 0.483 0.568 0.363 0.410 0.466
TN (mg L–1) 15.4 12.1 13.5 20.1 20.2 14.2 23.4 14.9 18.9 19.8 12.6 21.4 32.6 33.5 16.1 40.1 28.7 26.2 18.7 30.2 29.6 24.8 20.1
MaxZ (m)
1.64 1.79 1.96 1.80 1.54 1.96 1.71 1.73 1.76 1.75 1.68 1.78 1.91 1.50 2.13 2.54 1.72 1.71 1.79 2.13 1.59 1.60 1.80
Chla (mg L–1)
40
41
Figure 5. Scatter diagrams of the relative abundance of 21 diatom taxa (those with Hill’s N2 > 25 effective occurrences) along the total phosphorus gradient. Gaussian logit curves were fitted using quasi-likelihood modeling in the computer program CALIBRATE (Juggins & Ter Braak, 1993). The estimated WA optimum for each taxon is shown as a vertical line at the top of each plot.
Lake indicates that flooding had a significant impact on the lake’s ecology (research in progress).
Conclusions Although a number of environmental variables are strongly influencing the structure of diatom communities, our TP transfer function performs well and has good predictive ability across the range of measured spring TP in Southern Ontario (0.004 – 0.054 mg L–1). Hence it is suitable to infer past phosphorus concentrations from fossil diatom assemblages in sediment cores, and thus to reconstruct the eutrophication histories of Southern Ontario. The DI-TP model should be particularly effective in paleolimnological investigations of oligotrophic and mesotrophic lakes in Southern Ontario (research in progress). Future work will aim to increase the geographical range of the model, and improve its predictive ability, by sampling lakes in other regions of Ontario.
Acknowledgements We thank Joanne Little, Saloni Clerk and Rideau Lakes Authority personnel for help with field work. This work was funded by a Natural Sciences and Engineering
Research Council strategic grant to J.P.S. Andy Gemza provided some total phosphorus and Secchi depth data as part of the Ministry of Environment’s Southern Ontario Lake Partner Program (Dip-In project). Don Galloway (Ontario Ministry of Environment and Energy, Kingston office) helped with collection of background data for our study lakes. Personnel at the Ontario Ministry of Environment (Etobicoke, Ontario) performed some of the water chemistry analyses. Three anonymous reviewers provided useful comments on the manuscript.
References Anderson, D. S., R. B. Davis & M. S. Ford, 1993. Relationships of sedimented diatom species (Bacillariophyceae) to environmental gradients in dilute northern New England lakes. J. Phycol. 29: 264–277. Anderson, N. J., 1995. Diatom-based phosphorus transfer functions – errors and validation. In Patrick, S. T. & N. J. Anderson (eds), Ecology and Paleoecology of lake eutrophication. Service report 7, Geological Survey of Denmark, Copenhagen: 39–40. Bennion, H., 1993. A diatom-phosphorus transfer function for eutrophic ponds in southeast England. PhD thesis, Department of Geography, University College London: 429 pp. Bennion, H., 1994. A diatom-phosphorus transfer function for shallow, eutrophic ponds in southeast England. Hydrobiologia 275/276: 391–410.
42 Birks, H. J. B., 1995. Quantitative palaeoenvironmental reconstructions. In Maddy, D. & J. S. Brew (eds), Statistical modelling of Quaternary science data. Technical Guide 5, Quaternary Research Association, Cambridge: 161–254. Birks, H. J. B., J. M Line, S. Juggins, A. C. Stevenson & C. J. F. ter Braak, 1990. Diatoms and pH reconstruction. Phil. Trans. r. Soc., Lond. 327: 263–278. Camburn, K. E., J. C. Kingston & D. F. Charles, 1984–1986. PIRLA Diatom Iconograph. PIRLA Unpublished Report Series 3. Indiana University, Bloomington. Chapman, L. J. & D. F. Putnam, 1966. The physiography of southern Ontario. University of Toronto Press, Toronto: 386 pp. Charles, D. F., J. P Smol & D. R. Engstrom, 1994. Paleolimnological approaches to biological monitoring. In Loeb S. L. & A. Spacie (eds), Biological Monitoring of Aquatic Systems. CRC Press, Boca Raton, Florida: 233–293. Christie, C. E. & J. P. Smol, 1993. Diatom assemblages as indicators of lake trophic status in southeastern Ontario lakes. J. Phycol. 29: 575–586. Cumming, B. F., S. E. Wilson, R. I. Hall & J. P. Smol, 1995. Diatoms from British Columbia (Canada) Lakes and their Relationship to Salinity, Nutrients, and other Limnological Variables. Bibliotheca Diatomologica. J. Cramer, Berlin: 207 pp. Conservation Authorities Branch, Ontario, 1970. History of the Rideau Waterway. Toronto: 83 pp. Dixit, S. S., J. P. Smol, J. C. Kingston & D. F. Charles, 1992. Diatoms: powerful indicators of environmental change. Environ. Sci. Technol. 26: 22–33. Dixit, S. S., J. P. Smol, D. F. Charles, R. M. Hughes, S. G. Paulsen & G. B. Collins, 1999. Assessing water quality changes in the lakes of the northern United States using sediment diatoms. Can. J. Fish. Aquat. Sci. 56: 131–152. Glew, J. R., 1988. A portable extruding device for close interval sectioning of unconsolidated core samples. J. Paleolim. 1: 229– 234. Glew, J. R., 1989. A new trigger mechanism for sediment samplers. J. Paleolim. 2: 241–243. Hall, R. I. & J. P. Smol, 1999. Diatoms as indicators of lake eutrophication. In Stoermer E. F. & J. P. Smol (eds). The Diatoms: Applications for the Environmental and Earth Sciences. Cambridge University Press, Cambridge: 128–168. Hill, M. O., 1973. Reciprocal averaging: an eigenvalue method of ordination. J. Ecol. 61: 237–249. Hill, M. O. & H. G. Gauch, 1980. Detrended correspondence analysis,
an improved ordination technique. Vegetatio 42: 47–58. Janhurst, S. (ed), 1998. 1995 and 1996 performance report: general chemistry and microbiology analyses section. Ontario Ministry of Environment report: 306 pp. Juggins, S. & C. J. F. ter Braak, 1993. CALIBRATE -unpublished computer program. Environmental Change Research Centre, University College, London. Krammer, K. & H. Lange-Bertalot, 1986–1991. Bacillariophyceae. In Ettl H., J. Gerloff, H. Hyenig & D. Mollenhauer (eds), Süsswasserflora von Mitteleuropa 2/1–4. Fischer, Stuttgart. Line, J. M., C. J. F. Ter Braak & H. J. B. Birks, 1994. WACALIB version 3.3 – a computer program to reconstruct environmental variables from fossil assemblages by weighted averaging and to derive sample-specific errors of prediction. J. Paleolim. 10: 147–152. Patrick, R. & C. Reimer, 1966. The Diatoms of the United States, Vol. 1. Academy of Natural Sciences, Philadelphia: 668 pp. Reavie, E. D., J. P. Smol & R. I. Hall, 1995. An expanded weightedaveraging model for inferring past total phosphorus concentrations from diatom assemblages in eutrophic British Columbia (Canada) lakes. J. Paleolim. 14: 49–67. Reavie, E. D. & J. P. Smol, 1998. Freshwater diatoms from the St. Lawrence River. Bibliotheca Diatomologica, Band 41. J. Cramer, Berlin: 137 pp. Schindler, D. W., 1977. Evolution of phosphorus limitation in lakes. Science 195: 4275. Smol, J. P., 1995. Application of chrysophytes to problems in paleoecology. In Sandgren C. D., J. P. Smol & J. Kristiansen (eds), Chrysophyte Algae: Ecology, Phylogeny and Development. Cambridge University Press, Cambridge, UK: 303–329. Stoermer, E. F. & J. P. Smol (eds), 1999. The Diatoms: Applications for the Environmental and Earth Sciences. Cambridge University Press, Cambridge: 484 pp. Ter Braak, C. J. F., 1995. Ordination. In Jongman R. H., C. J. F. ter Braak & O. F. T. van Tongeren (eds), Data Analysis in Community Ecology. Pudoc, Wageningen: 91–173. Ter Braak, C. J. F. & P. `milauer, 1998. CANOCO reference manual and user’s guide to CANOCO for Windows: Software for canonical community ordination (version 4). Microcomputer Power, Ithaca, New York: 352 pp. Walker, I. R., 1987. Chironomidae (Diptera) in paleoecology. Quat. Sci. Rev. 6: 29–40. Zar, J. H., 1984. Biostatistical Analysis. 2nd. ed. Prentice-Hall, Englewood Cliffs, N.J.: 718 pp.
