DOI: 10.5061/dryad.xpnvx0kns
title: Data from: Nonlinearities in phytoplankton groups across temperate high mountain lakes

description: 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.
description: 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 &gt;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 <sup>0</sup>C) 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 <em>et
 al.</em> 2006). The method works by algorithmic iteration and
 requires a first estimate of the marker pigment to Chl-a molar ratios
 (initial ratio matrix; H<sub>0</sub>) 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.
description: # Nonlinearities in phytoplankton groups across temperate high mountain
 lakes The data file
 contains the phytoplankton group distribution estimated using
 pigment-based chemotaxonomy across 82 lakes of the Pyrenees selected to
 cover the bedrock and elevation gradients. ## Description of the data and
 file structure The file PGMCHEMTAX-JEcol2023.xlsx contains a first raw
 with the variable names (19), followed by 79 raws with the data in
 columns. The variable names are self-descriptive, although we include a
 more detailed description below. There are no missing values; zeros (0)
 correspond to values below the detection limit of the method. ####
 Variables Lake_name: Lake name, which may differ depending on the
 cartographic reference used. Latitude_N: Latitude, North Longitude_E:
 Longitude, East Altitude_m: Elevation, meters above sea level.
 EMERGE_lake_code: Lake code used in the European project EMERGE. Lake_ID:
 Lake identification number used in Buchaca & Catalan (2024), Journal
 of Ecology. Chlorophytes_nmol_Chla/L: Chlorophyte biomass as concentration
 of chlorophyll-a, nmol per liter. Chrysophytes_nmol_Chla/L: Chrysophyte
 biomass as concentration of chlorophyll-a, nmol per liter.
 Cryptophytes_nmol_Chla/L: Cryptophyte biomass as concentration of
 chlorophyll-a, nmol per liter. Diatoms_nmol_Chla/L: Diatom biomass as
 concentration of chlorophyll-a, nmol per liter.
 Dinoflagellates_nmol_Chla/L: Dinoflagellate biomass as concentration of
 chlorophyll-a, nmol per liter. Cyanobacteria_nmol_Chla/L: Cyanobacteria
 biomass as concentration of chlorophyll-a, nmol per liter.
 Total_Chla_nmol/L: Total chlorophyll concentration in the sample, nmol per
 liter. Chlorophytes_%: Total biomass chlorophyte percentage
 Chrysophytes_%: Total biomass chrysophyte percentage Cryptophytes_%: Total
 biomass cryptophyte percentage Diatoms_%: Total biomass diatom percentage
 Dinoflagellates_%: Total biomass dinoflagellate percentage
 Cyanobacteria_%: Total biomass cyanobacteria percentage