Nonlinearities in phytoplankton groups across temperate high mountain lakes

1-High mountain lakes are increasingly recognized as sentinel ecosystems of global change. Monitoring phytoplankton changes or reconstructing their composition from sedimentary records can help identify systemic changes in these lakes and their catchments. 2- This study aimed to evaluate the distribution of the major phytoplankton groups in high mountain lakes across environmental gradients and identify tipping points in relative dominance. The phytoplankton groups were estimated using pigment-based chemotaxonomy in 79 lakes in the Pyrenees selected to cover the bedrock and elevation gradients. Fifty-four environment variables were considered, including in-lake and catchment descriptors. 3-Redundancy analyses showed that in-lake descriptors override the explicative capacity of landscape variables. Generalized additive models and multivariate regression trees showed that water hardness, trophic state, and food web descriptors were, in this order, the most influential factors determining phytoplankton group dominance. Calcium concentration of about 200 μeq L-1 defined the threshold between soft waters – with chrysophytes and chlorophytes showing a higher affinity for them – and harder waters that favour diatoms and cyanobacteria. Across the trophic gradient, there was a threshold at ~5 μg L-1 of total phosphorus (TP), chrysophytes being dominant below that TP value and cryptophytes above. The dominance of chlorophytes and cryptophytes increased with the density of macrozooplankton. Chrysophytes were significantly lower and diatoms higher in lakes with fish. 4- Synthesis. The relative abundance of phytoplankton groups in temperate high mountain lakes responds in a nonlinear way to the hardness of the water in the range 20 – 1195 Ca2+ μeq L-1 and the trophic state in the range 0.94 - 19 μg L TP-1. The thresholds across water hardness and trophic state gradients coincide with studies based on other organisms, pointing to a robust typology for mountain lakes that should be considered when selecting global-change sentinel lakes and anticipating abrupt transitions across these thresholds.

The study was based on a survey of 79 lakes from mid-July to the end of August 2000 across the entire range of the Pyrenees. During this period, summer stratification occurs in most lakes, and phytoplankton communities can be assumed to be in a similar successional stage. The lakes were selected to cover the main bedrock, elevation, and geographical variation within the range. Only one or, exceptionally, a few lakes were sampled in cirque basins that included many close lakes to reduce potential spatial covariance because of the proximity and also to facilitate covering the entire massif with the available resources. The environmental variables considered (54) were grouped into five categories to evaluate the partial and hierarchical influence on the phytoplankton groups. Water chemistry (15 variables) included nutrients, major ions, and DOC. The physical environment (12) was characterized by considering morphological, thermal, light, and littoral substrate characteristics. The biotic environment (8) included macrophyte and fish presence (assessed using ancillary information and visual inspection during the survey), the organic content of the top sediment (loss on ignition, LOI), and planktonic components (rotifers, macrozooplankton, and bacterial biomass). The description of the catchment (16) included the catchment area and geological characteristics determined using cartographic information (Spanish and French Geological Maps) and GIS techniques, estimates of precipitation and duration of the ice cover inferred by extrapolation methods, and vegetation categories evaluated during the survey. Finally, the geographic setting (3) was defined by coordinates and altitude.

 

Pigments were used for quantifying the relative dominance of high-rank taxonomic phytoplankton groups. The relationship between chlorophyll and phytoplankton biomass may be highly influenced by light conditions. To facilitate the lake comparison, we standardized pigment sampling by collecting water at an iso-irradiance depth in the deepest part of each lake at 1.5 times the Secchi disk depth. At this depth, between 1 and 10% subsurface irradiance penetrates in summer, usually coinciding with a typical deep chlorophyll maximum (DCM). The DCM results from a phytoplankton growth optimum at a depth balancing nutrient and light availability. In shallow lakes, with >10% surface irradiance reaching the bottom and weak stratification, DCM develops near the lake bottom, as early studies have already observed. Therefore, the sample was taken between 1 and 2 m above the surface sediment in 29 lakes where the Secchi depth reached the sediment surface. Water samples were collected using a polyethylene tube connected to a flask and a manual vacuum pump. A volume between 1.5 and 2 L was filtered using Whatman® GF / F filters (Maidstone, UK), which were wrapped in aluminium foil and kept cold before freezing (-20 ⁰C) within 3-6 hours. 

 

The pigments were extracted from frozen filters using a probe sonicator (50 W, 2 min) with 90% acetone. The extract was filtered through Whatman Anodisc filters (0.1 µm) and analysed by HPLC. The HPLC system was equipped with a Waters 600E solvent delivery system, a Waters 717 autosampler set at 4 °C, a C18 column (dimensions: 250 x 4.6 mm, particle size: 5µm; Spherisorb-ODS1, Waters Corporation, Milford, US) and a Waters 996 photodiode array detector. The detector was set at 440 and 660 nm to integrate the carotenoid and phorbin peaks, respectively. The pigments were separated based on modifying the method described by Kraay, Zapata and Veldhuis (1992). After injection of the sample (40 µL), pigments were eluted by a linear gradient from 100% solvent A (0.3 M ammonium acetate in methanol: acetonitrile: MilliQ water, 51:36:13 (v/v/v)) to 75% A and 25% B (ethyl acetate: acetonitrile, 70:30, (v/v)) for 5 min followed by 5 min and 20 min, respectively, of isocratic hold at 75% A and 100% solvent B. The flow rate was 1.2 mL min-1. The solvent composition was returned to initial conditions on a 5-minute gradient, followed by 5 minutes of system equilibration before injection of the following sample. Pigments were identified by comparison with a library of pigment spectra obtained from extracts of pure algae cultures from the Culture Collection of Algae and Protozoa (CCAP, Oban, Scotland, UK). Chl-a, Chl-b, and b,b-carotene standards were obtained from Sigma Chemical Co. Ltd. (UK). The extinction coefficients used for calculations were obtained from the literature (Rowan 1989; Jeffrey, Mantoura & Wright 1997).

 

The contribution of each algal group to phytoplankton biomass was estimated in terms of Chl-a using CHEMTAX (Mackey et al. 1996; Schlüter et al. 2006). The method works by algorithmic iteration and requires a first estimate of the marker pigment to Chl-a molar ratios (initial ratio matrix; H₀) appropriate for the algal classes expected in the sample. The matrix of the pigment ratio is varied by a small amount in each iteration, and the class abundance is recalculated. The class sum is checked against the measured total Chl-a. CHEMTAX gives the best fit of contributions of the predefined taxa to total Chl-a. The advantage of this method is that it distinguishes between algal groups with qualitatively identical pigment compositions by differences in pigment ratios. We used between 1 and 4 marker pigments per group, which included chlorophytes, chrysophytes, cryptophytes, diatoms, dinoflagellates, and cyanobacteria. 

 

For further details see the related publication.