Linking landscape heterogeneity with lake dissolved organic matter properties assessed through absorbance and fluorescence spectroscopy: Spatial and seasonal patterns in temperate lakes of Southern Andes (Patagonia, Argentina)

Science of the Total Environment 686 (2019) 223–235

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Linking landscape heterogeneity with lake dissolved organic matter properties assessed through absorbance and fluorescence spectroscopy: Spatial and seasonal patterns in temperate lakes of Southern Andes (Patagonia, Argentina) Claudia Queimaliños ⁎, Mariana Reissig, Gonzalo L. Pérez, Carolina Soto Cárdenas, Marina Gerea, Patricia E. Garcia, Daniel García, María C. Diéguez GESAP (Grupo de Ecología de Sistemas Acuáticos a escala de Paisaje), Instituto de Investigaciones en Biodiversidad y Medioambiente (INIBIOMA), Universidad Nacional del Comahue, CONICET, Quintral 1250, Bariloche (8400), Argentina

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Lake DOM concentration and quality tracked environmental gradients. • Spatial/seasonal precipitation patterns drive the timing of DOM allochthonous input. • Higher NDVI in piedmont and forested lakes reflect higher DOM terrestrial inputs. • Land-water hydrological connectivity influenced directly the CFOI. • Low CFOI and high C3/C2 ratio revealed DOM photobleaching under drier conditions.

a r t i c l e

i n f o

Article history: Received 21 March 2019 Received in revised form 24 May 2019 Accepted 25 May 2019 Available online 29 May 2019 Editor: Sergi Sabater Keywords: Landscape heterogeneity Lake dissolved organic matter UV–visible and fluorescent spectroscopy Climate seasonality

a b s t r a c t Hydrological connectivity between terrestrial and aquatic systems is influenced by landscape features. Topography, vegetation cover and type, lake morphometry and climate (seasonality, precipitation) drive the timing, concentration and quality of allochthonous dissolved organic matter (DOM) inputs to lakes, influencing lake metabolism. The impact of climate changes on terrestrial-aquatic linkages depends on regional trends and ecosystems properties. We examined how landscape heterogeneity affects lake DOM in pristine temperate headwater lakes located in sharp bioclimatic gradients at the leeward side of the southern Andes (Patagonia, Argentina), and predicted their potential responses to forecasted changes in regional climate. We assessed DOM properties of deep and shallow lakes spotted along precipitation and altitudinal gradients which reflect on vegetation heterogeneity. Lake DOM (concentration, and chromophoric and fluorescent properties) was related to terrestrial bioclimatic conditions, addressing also DOM bio- and photodegradation processes. Co-effects of climate and vegetation determined the quantity and quality of allochthonous DOM inputs. Higher terrestrial signs showed up at the wettest extreme of the gradient and during the rainy season, being attributable to higher hydrological

⁎ Corresponding author. E-mail address: [email protected] (C. Queimaliños).

https://doi.org/10.1016/j.scitotenv.2019.05.396 0048-9697/© 2019 Elsevier B.V. All rights reserved.

224 Allochthonous inputs South temperate lakes

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land-water connectivity, and dense vegetation cover. Under drier conditions, DOM displayed higher photobleaching signs at spatial and temporal scales. The ratio between non-humic and terrestrial humic substances indicated that DOM biodegradation dominates in shallow forested lakes and photodegradation prevails in deep ones, whereas coupled photo- and biological processing shaped the DOM pool of high altitude lakes. Overall, DOM optical metrics captured landscape heterogeneity. Under the forecasted climate changes for Patagonia (decreasing precipitation and increasing temperature), piedmont lakes may experience lower hydrological connectivity, lower terrestrial inputs and, enhanced photobleaching usually associated with longer water residence time. In high altitude lakes, terrestrial DOM inputs are expected to increase due to the upward expansion of native deciduous forests, thus becoming more similar to lakes located lower in the landscape. © 2019 Elsevier B.V. All rights reserved.

1. Introduction The functioning of freshwater ecosystems is strongly influenced by the regional climate and the surrounding landscape (e.g., Likens, 1985; Seekell et al., 2014, 2018; Lapierre et al., 2015). Dissolved organic matter (DOM) is the most important intermediate of the global C cycle on Earth (Battin et al., 2009; Kaiser and Kalbitz, 2012; Seekell et al., 2018), and its lateral transport within catchments depends on flow regimes that are driven by precipitation and runoff. This connectivity supports the coupling between land and water, as well as between the hydrological and C cycles (Cole et al., 2007; Sobek et al., 2007; Battin et al., 2009; Osburn et al., 2017; Gao et al., 2018). Freshwater ecosystems are extremely diverse in terms of climate conditions, geomorphology, and topography; therefore, large spatio-temporal variations are observed in the hydrological land-water connectivity. These in turn translate into an important heterogeneity in the concentration, molecular composition, age, and inputs of terrestrial organic C to individual aquatic systems (Jones et al., 2018; McCallister et al., 2018; McCullough et al., 2019). DOM exerts control on physical (i.e. light attenuation and thermal structure), photochemical, biochemical and biological processes, that overall regulate the metabolism and functioning of aquatic environments as well as the services they provide (Pace et al., 2004; Sobek et al., 2007; Prairie, 2008; Aiken, 2014). As lake DOC pools, comprising external (allochthonous) and internal (autochthonous) inputs, depend on the integrated environmental conditions, DOM concentration and quality act as key lake variables in response to climate and landscape regional-scale gradients (Lapierre et al., 2015; Osburn et al., 2017; Moser et al., 2019). The fraction of DOM that absorbs ultraviolet (UV) and visible light is referred to as chromophoric dissolved organic matter (CDOM) (Kirk, 1994; Helms et al., 2008). The organic chromophoric fraction that emits photons after the absorption of UV radiation is known as fluorescent DOM (FDOM) (Coble, 1996; Del Vecchio and Blough, 2004). Combined analyses of CDOM and FDOM provide insight on the quality and transformation processes of the DOM pool, giving information on the degree of DOM aromaticity, lignin content and molecular size. They also provide clues to distinguish between humic substances (fulvic and humic acids) and non-humic compounds (proteins, phenols, etc.), either allochthonous or autochthonous (Hernes et al., 2009; Aiken, 2014; Sepp et al., 2019). Biological and photochemical degradation are the two major processes that both transform and mineralize DOM, and their impact can be assessed through CDOM and FDOM optical properties (Stubbins et al., 2014; Hansen et al., 2016; Mostovaya et al., 2017; McCallister et al., 2018; Kellerman et al., 2018). For instance, absolute absorbance and fluorescence intensities, ratios at different wavelengths, DOCnormalized values and certain spectral slopes of CDOM absorption coefficients provide information on DOM sources and processing (e.g. Hansen et al., 2016). In particular, the absorbance at 350 nm (a350) is a good indicator of the concentration of lignin, substance which is exclusively biosynthesized by vascular plants on terrestrial systems (Waiser and Robarts, 2004; Fichot and Benner, 2012). The spectral slope between 275 and 295 nm (S275–295) is a reliable proxy of CDOM average

molecular weight and indicative of DOM photodegradation (Helms et al., 2008). On the other hand, the relationship between the intensities of non-humic to terrestrial humic fluorophores provides information about DOM transformation (Hansen et al., 2016; Zhu et al., 2018). In nature, bio- and photodegradation act concomitantly transforming DOM composition and is difficult to evaluate their single effect. Nevertheless, basing on a broad experimental approach, Hansen et al. (2016) demonstrated that the ratio between the intensities of Peak C (terrestrial humic fluorophore) and Peak T (non-humic fluorophore) increased during biodegradation, and decreased during photodegradation in assays performed with DOM from different natural sources. Moreover, a suite of optical metrics has been combined in a DOCrelated climate forcing optical index (CFOI) that are appropriate to unveil DOC sources and transformation mechanisms in lakes, thereby allowing assessing the responses of lakes to climate fluctuation (e.g. temperature and precipitation) (Williamson et al., 2014; Warner and Saros, 2019). Thus, spectral analyses provide specific information about DOM sources and dynamics and are useful tools in environmental studies. In response to the increasing interest in understanding how ecosystem processes will be affected by regional to global changes (Heffernan et al., 2014; Lapierre et al., 2018), the landscape limnology can provide an appropriate framework to assess lake ecosystem processes and patterns across spatial extents. This approach considers lakes as part of a dynamic complex of terrestrial and aquatic elements rather than disconnected units in the landscape (Soranno et al., 2014, 2017; McCullough et al., 2019). In particular, mountain lakes along steep climate gradients allow detecting the impact of changing bioclimatic conditions (Moser et al., 2019). Inland waters have been recognized as important regulators of C processing along the land to ocean aquatic continuum (Cole et al., 2007; Battin et al., 2009; Tranvik et al., 2009; Biddanda, 2017). Indeed, lake's functioning controls the C transport and transformation, both lateral (hydrologic transport) and vertical (emission and burial), acting as a significant component of the global C cycle (Cole et al., 2007; Tranvik et al., 2009; Engel et al., 2018). Seekell et al. (2018) proposed a geographic framework for studying lake C cycling connecting internal C processes with external drivers, including climate and land cover. In turn, this investigation, among others, emphasizes that most of these assessments restrict to lakes from the Northern Hemisphere (Hestir et al., 2015; Seekell et al., 2018), highlighting the relative lack of information on aquatic environments of the Southern Hemisphere. In southern South America, the Patagonian region (39°–55°S) includes sharp environmental gradients determined by the influence of the Andes Cordillera on the regional climate. This mountainous range acts as a barrier for the humid air masses coming from the South Pacific subtropical anticyclone (southern westerlies), causing one of the most dramatic precipitation gradients on Earth, with a fivefold decrease in only 70 km from the Andes, at the leeward side (Barros et al., 1980; Garreaud et al., 2013). In northern Patagonia, the precipitation regime is markedly seasonal due to the southwards (summer) and northwards (winter) oscillations of the anticyclone (Paruelo et al., 1998; Barros et al., 2015). The topography of this region has been shaped by

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widespread Holocene volcanic activity and by Pleistocene glaciations (Veblen et al., 1996), which after the last glacial retreat (14–12 Kyr BP) originated the greatest lacustrine district of Argentina (Iriondo, 1989). In Andean Patagonia, high altitude lakes are present in glacial circa, while large and deep, as well as small and shallow lakes are situated within glacial valleys in piedmont areas. Nahuel Huapi National Park (NHNP; Northwestern Patagonia, Argentina) has many deep and shallow lakes, most of them transparent, oligotrophic systems ranging from very low to moderate DOC concentrations (Morris et al., 1995). Streams, shallow and deep lakes of NHNP receive variable lateral exports of DOM depending on seasonal precipitation (Queimaliños et al., 2012; Garcia et al., 2015a,b; Gerea et al., 2016; Soto Cárdenas et al., 2017), reflected in a marked DOC concentration and quality gradient related to their position in the landscape (Zagarese et al., 2017). Here, we focused on a set of deep and shallow lakes located at the eastern slopes of the Andes within NHNP located along the steepest section of the west-east precipitation and altitudinal gradients (Paruelo et al., 1998; Bianchi et al., 2016). These gradients translate into distinct vegetation units, contrasting among humid forested areas at the western sector, and drier ecosystems towards the east and the high altitude units above 1600 m a.s.l. (Ferreyra et al., 1998; Mermoz et al., 2009). In this investigation we applied a landscape limnology approach to address the effect of the sharp environmental gradients on lake DOM pools. We performed a characterization of the lakes integrating climate, landscape position, morphometry, thermal regime and surrounding terrestrial vegetation cover, aiming to: (i) analyze the quantity and quality of lake DOM applying absorbance and fluorescence spectroscopy in relation to landscape heterogeneity, (ii) evaluate DOM variables in terms of their suitability reflecting spatial and/or temporal patterns in order to generate practical monitoring tools, and (iii) discuss potential lake DOM responses to regional climate change forecasts. For these purposes, we analyzed the natural DOM pools from a representative suite of deep and shallow lakes. We focused on the relationship between DOC concentration, CDOM variables and FDOM components to assess spatial (geographic) and temporal (seasonal) variations in the origin, transformation and DOM composition among lakes. We applied different derived metrics to estimate DOM bio- and photodegradation and evaluate climate forcing at different ecosystem scales. We hypothesize that lake DOM quantity and quality parameters will reflect the marked landscape heterogeneity and climate seasonality in the environmental gradient.

2. Methods 2.1. Study area and lake data The study included deep and shallow lakes located within protected areas of Northwestern Patagonia (Argentina): the Nahuel Huapi National Park (NHNP, 40.145–41.592 S; 71.028–71.966 W; 7173 km2) and natural reserves placed in the suburban area of Bariloche city (41.15 S–71.30 W, 220 km2; Río Negro, Argentina) (Fig. 1a). For simplicity, hereinafter the study area will be referred to as NHNP. The topography of NHNP is characterized by Andean mountains and valleys at the western stretch, decreasing steadily in altitude towards an area of sierra and meseta at the east (Fig. 1b). The regional climate is cold temperate, with transitional oceanic-continental characteristics and dry warm seasons (Köppen Csb), depicted by two main climatic gradients: the altitudinal gradient in temperature and precipitation type, and a sharp longitudinal gradient in precipitation quantity caused by the “rain shadow effect” of the Andes. These important environmental features determine three bioclimatic units in a west-east transect of only 70 km from the Andes (Fig. 1b): the High Andes (“altoandino”), forested areas including hyperhumid, humid and subhumid Nothofagus forests, and the steppe (Ferreyra et al., 1998; Mermoz et al., 2009) (see Supplementary material for details).

225

The lakes included in our survey are spotted in this narrow longitudinal range with pronounced environmental gradients (Fig. 1b). In general, lakes are oligotrophic or ultraoligotrophic, low DOC transparent systems; differing however in their morphometry (Table S1), landscape position (high altitude, mid altitude or piedmont lakes), and thermal regime (dimictic, polymictic or warm monomictic) (Table 1). The five high altitude lakes surveyed are shallow (5–25 m), dimictic, and occur either above, at or below the treeline (Fig. 1; Table 1), and remain frozen for 6–8 months every year (Zagarese et al., 2000) due to the very low mean annual temperature (MAT: ~2–4 °C). At high altitude, mean annual precipitation (MAP) ranges between 1000 and 2000 mm y−1 and is dominated by snowfall (Table 1; Fig. S1). The piedmont lakes studied (MAT: ~8 °C) show great differences in surface area and maximum depth. Since Lake Nahuel Huapi is the largest system (Area = 557 km2, Zmax = 464 m) and has a complex shoreline topography with seven arms spreading in the longitudinal gradient (Table S1; Fig. 1a), it was sampled at three different sites: Brazo Rincón, Bahía López and Dina Huapi (Table S1; Fig. 1a) (see Supplementary Material for details). Conductivity values are low in all the studied lakes (~8–80 μS cm−1), increasing from high altitude towards piedmont lakes. Among piedmont lakes, the headwater lakes Cántaros and Frías have very low conductivity values (Table S1 and references therein). According to their trophic conditions, chlorophyll a (Chla) concentration ranged from 0.3 to 2.4 μg L−1 in the ultraoligotrophic or oligotrophic lakes, reaching up to 5.7 μg L−1 only in the case of the mesotrophic Lake Verde (Table S1 and references therein). 2.2. Water collection and processing This research combines data collected in 13 lakes (15 sampling sites) during surveys performed from 2013 to 2015. Water samples were taken from late spring to early fall (dry period) when dimictic and warm monomictic lakes were thermally stratified. In addition, five of the studied lakes (Escondido, Morenito, Moreno West, Moreno East and Nahuel Huapi Brazo Rincón) were surveyed also during wet periods at least on 2 occasions during the annual cycle, to evaluate the effects of climate seasonality. Shallow lakes were sampled every 3 m from surface to the bottom, except in the case of the high altitude lakes Schmoll, Témpanos and Jakob where sampling was performed in the surface layer. Depth profiles of deep lakes were sampled every 10 m, from surface up to 40 m depth, which include the mixing and euphotic layers during the stratification period (Pérez et al., 2002; Queimaliños et al., 2012). Overall, 128 samples were obtained during the complete study period. Water samples were collected from a boat with a Kemmerer bottle (4.2 L), at the deepest point of the lake or lake's arm. The samples were poured into acid-cleaned polycarbonate carboys and stored thermally insulated and in darkness. Once in the laboratory and within 5 h from sampling, the water samples were filtered sequentially through pre-combusted 0.7 μm glass fiber filters (Munktell MF/F) and prerinsed 0.22 μm PVDF membranes (Millipore) to determine the concentration of DOC and to assess DOM characterization through absorbance and fluorescence spectroscopy. DOC was measured as non-purgeable organic carbon (NPOC) using a Shimadzu TOC-L high temperature analyzer suited with a high sensitivity catalyst, with a detection limit of 4 μg L−1. Each DOC concentration measured corresponds to the average (CV b 2%) of 3–5 sample injections (400 μL). Absorbance spectra (200 to 800 nm) from filtered lake water samples were obtained at 1 nm intervals with a spectrophotometer UV– visible (Hewlett–Packard 8453), in a 100 mm quartz cuvette. ASTM1 grade water (Milli-Q) was used as reference blank, subtracting it from each sample spectrum. The mean UV–visible absorbance between 700 and 800 nm was subtracted to each spectrum in order to correct for offsets due to instrument baseline effects after Helms et al. (2008). For the analysis of fluorescent DOM (FDOM) filtered water samples were

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Fig. 1. a) Location of the studied lakes inside Nahuel Huapi National Park (Northwestern Patagonia, Argentina). Dots: deep lakes; triangles: shallow lakes. Symbol's colors indicate the vegetation type in the catchment (precipitation conditions are included in the case of evergreen forests): HAV: high altitude vegetation (pink); TDF: temperate deciduous forest (blue); TEF: temperate evergreen forest (green); HTEF: hyperhumid temperate evergreen forest (orange) and, S: steppe (mustard). Map Projection: Transverse mercator, Datum: Posgar 94; b) Schematic landscape profile indicating lake position on the west-east gradients of precipitation, altitude and vegetation inside Nahuel Huapi National Park. Lake code in Table 1.

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Table 1 Bioclimatic conditions, landscape position and limnological features of the studied lakes belonging to the Nahuel Huapi National Park (Northwestern Patagonia, Argentina). References: VT: vegetation type with respective codes and identification colors, HAV: High Andean vegetation; TDF: temperate deciduous forest; TEF: temperate evergreen forest; HTEF: hyperhumid temperate evergreen forest; S: steppe; NDVI: Normalized Difference Vegetation Index; MAP: mean annual precipitation (mm y−1); MAT: mean annual temperature (°C); WM: warm monomictic, DM: dimictic, PM: polymictic; *MAP and MAT values were estimated following Mermoz et al. (2009), Pereyra and Roverano (2010) and Bianchi et al. (2016).

Lake Témpanos

Lake Code TEM

Morphometry

Altitude (m a.s.l.)

Shallow

1650

Landscape Position

VT Code

HAV Schmoll

SCH

Shallow

1950

Jakob

JAK

Shallow

1550

Toncek

TON

Shallow

1750

Verde

VER

Shallow

1545

Juventus

JUV

Shallow

1010

Moreno East

ME

Deep

Moreno West

MW

Deep

Morenito

MITO

Shallow

770.8

Escondido

ESC

Shallow

772.8

Nahuel Huapi Bahía López

NHBL

Deep

770.4

High altitude

Location area above treeline above treeline

NDVI range

NDVI

Precipitation gradient

MAP (mm y-1)*

Dominant Precipitation Type

Thermal regime

MAT (°C)*

0.000

Humid

2000

Snow

DM

4.0

0.018

Humid

1400

Snow

DM

2.1

<0.02

0.232

Humid

2000

Snow

DM

4.4

0.145

Humid

1400

Snow

DM

3.5

0.295

Humid

1000

Snow

DM

4.4

below treeline

0.370

Humid

1400

Rain

PM

6.9

770.8

forested

0.369

Humid

1700

Rain

WM

8.0

770.8

forested

0.385

Humid

1900

Rain

WM

8.0

forested

0.386

Humid

1700

Rain

PM

8.0

forested

0.372

Humid

1900

Rain

PM

8.0

0.354

Humid

2200

Rain

WM

8.0

forested

0.396

Hyperhumid

3500

Rain

WM

8.0

forested

0.316

Hyperhumid

3500

Rain

PM

8.0

forested

0.399

Hyperhumid

2800

Rain

WM

8.0

0.257

Dry

700

Rain

WM

8.0

Frías

FRI

Deep

790

Cántaros

CAN

Shallow

866

Nahuel Huapi Brazo Rincón

NHBR

Deep

770.4

Nahuel Huapi Dina Huapi

NHDH

Deep

770.4

at treeline

TDF

at treeline below treeline

Mid altitude

TEF

forested Piedmont

HTEF

S

steppe

scanned in a spectrofluorometer Perkin-Elmer LS55B (USA) equipped with a 150-W Xenon arc lamp and a Peltier temperature controller, using a 10 mm quartz fluorescence cell. Excitation–Emission Matrices (EEMs) were collected at specific wavelengths of excitation (240–450 nm, 5 nm intervals) and emission (300–600 nm, 0.5 nm intervals). The spectrofluorometer was set up with 10 nm excitation and emission slits and a scan speed of 1500 nm min−1. ASTM1 grade water was used as blank. The EEMs' processing is detailed in the Supplementary methods, section PARAFAC. 2.3. Data handling and calculations The precipitation data during the studied period was provided by meteorological stations belonging to the hydrometeorological network of AIC (Autoridad Interjuridiccional de Cuencas, Argentina; http:// www.aic.gov.ar/). MAP at each specific lake subcatchment was estimated following previously published data (see references in Table 1). The analyses of climate forcing at temporal (seasonal) scale, was performed using the cumulative precipitation of the 150 days previous to each sampling date (0–150 d), a fair proxy of the natural precipitation pattern (Gerea et al., 2016). The methodologies applied for GIS analysis and hydrogeomorphic lake features calculations are detailed in the Supplementary Material. DOM proxies and indexes were calculated from the absorbance and fluorescence spectra obtained from lake water samples. Three absorption coefficients (aλ) were calculated at three wavelengths in the UV region of the spectrum (254, 320 and 350 nm) and used as proxies of CDOM properties (Helms et al., 2008). The coefficients a254, a320 and a350 were calculated applying the formula: aλ = 2.303 Aλ/l, where a = Naperian absorption coefficient (m−1); λ = wavelength; Aλ = absorbance at a given wavelength (arbitrary units AU); l = path length of the quartz cuvette (m). The ratios a254:DOC (usually indicated as SUVA) and a350:DOC (a*350) were calculated as proxies of DOM aromaticity and lignin

0.140.30

0.300.40

0.26

content (tracers of terrigenous DOC), respectively, and expressed in L mg C−1 m−1 (Weishaar et al., 2003; Fichot and Benner, 2012). The spectral slope for the interval 275–295 nm (S275–295) was calculated from the absorption spectra by fitting the log-transformed spectral data to a linear model and expressed as positive number in nm−1. The S275–295 is sensitive to CDOM molecular weight and source. High molecular weight CDOM has lower S275–295, and is indicative of terrestrial origin, while higher slopes are indicative of low molecular weight CDOM, usually linked with photodegradation processes (Helms et al., 2008; Stedmon et al., 2011; Fichot and Benner, 2012). The Climate Forcing Optical Index (CFOI) was calculated as the ratio of the DOC-normalized absorption coefficient at 320 nm (a320:DOC) divided by the S275–295 and expressed in nm m2 (g C)−1 (Williamson et al., 2014). This index is derived from proxies of photodegradation (S275–295) (Helms et al., 2008) and allochthonous DOM (a320:DOC), allowing the evaluation of sunlight weathering and terrestrial DOM inputs. Low CFOI values indicate high sunlight exposure and low terrestrial DOC inputs, whereas increasing CFOI values indicate comparatively lower sunlight exposure and higher allochthonous inputs (Williamson et al., 2014). The ratio between the intensities of the non-humic and the terrestrial humic fluorophores (stated as C3 and C2, respectively) was analyzed as a proxy of biodegradation and photodegradation processes of DOM (Hansen et al., 2016; Zhu et al., 2018) (see Results and Discussion sections for further explanation). 2.4. Statistical analyses Data were tested for normal distribution and homoscedasticity. Correlation (Pearson correlation, r) and linear regression analyses were applied to explore the relationships between DOM optical parameters and DOC concentration. Linear models were fitted independently to the relationship a350 vs. DOC for the two lake groups (low DOC and higher DOC lakes), and their slopes were compared with a Student's t-test. One way analysis of variance was applied to compare the a*350 and

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SUVA between lake groups. In order to identify the environmental factors controlling the DOM features in North Patagonian Andean lakes, we conducted a redundancy analyses (RDA) with a quantitative matrix constructed with the DOM concentration and quality parameters (DOC concentration, CFOI, C3/C2 ratio, and DOC normalized fluorescence components), which were selected having into account the results of the correlation analyses aiming to reduce redundant variables. A second matrix was constructed with the corresponding environmental data: Chla, electrical conductivity, the normalized difference vegetation index (NDVI), MAP, MAT, altitude, Zmax, and lake area. The multivariate analysis was performed using the software CANOCO 4.5 (ter Braak and Šmilauer, 1998), applying forward selection. The significance of the canonical axes was tested through Monte Carlo permutation tests (Leps and Šmilauer, 2003). 3. Results The studied lakes were characterized through their morphometry, landscape position, thermal regime, and terrestrial surrounding bioclimatic conditions (Table 1). The surrounding vegetation cover, quantified through the NDVI, showed spatially explicit differences. In all the lakes surrounded by evergreen forests (the deep and shallow piedmont lakes, and the shallow mid-altitude Lake Juventus) the NDVI ranged from 0.30 to 0.40 (Table 1), while in the case of the steppe (S) surrounding Lake Nahuel Huapi at Dina Huapi was lower (0.26) (Table 1). At the piedmont, the warm monomictic deep lakes (Frías, Nahuel Huapi, Moreno East and Moreno West) and the polymictic shallow lakes (Cántaros, Escondido and Morenito) are placed along the longitudinal precipitation gradient from the hyperhumid sector at the west to the drier bioclimatic region at the east of the range (Table 1; Fig. 1a, b). The five dimictic high altitude lakes presented lower NDVI than the piedmont and mid altitude lakes, ranging from 0.14 to 0.30 in those surrounded by temperate deciduous forests (TDF) (Toncek, Jakob and Verde), and with values lower than 0.02 in those belonging to the High Andean subunit (with high altitude vegetation, HAV) (Schmoll and Témpanos) (Table 1; Fig. 1). 3.1. DOC concentration and CDOM-FDOM optical properties In all the studied lakes (including the three different sites sampled of Lake Nahuel Huapi), DOC concentrations and CDOM absorption coefficients at 350 nm (a350) were low, ranging from 0.267 to 4.140 mg L−1 and from 0.096 to 7.134 m−1, respectively (Table S2). All the deep lakes, and the shallow lakes located at or above the treeline showed DOC concentrations below 1 mg L−1; while in the shallow lakes below the treeline, the DOC ranged between 2.074 and 4.140 mg L−1 (Table S2). The low-DOC lakes (hereinafter referred to as DOCb1) presented a350 values up to 1.880 m−1, while in the lakes with higher DOC (hereinafter DOC2–5), the optical parameter varied between 0.483 and 7.134 m−1 (Table S2). In particular, Lake Cántaros had a low mean DOC concentration (0.594 ± 0.068 mg L−1), but exhibited the highest a350 among the DOCb1 lakes (Table S2). The bivariate analysis between a350 and DOC concentration showed a positive and significant linear relationship for both groups of lakes (DOCb1: R2 = 0.291, p = 0.013, n = 72, and DOC2–5: R2 = 0.542, p b 0.001, n = 56), with a higher slope in lakes DOC2–5 than in lakes DOCb1 (t-test: t = −4.507, p b 0.001, n = 128). The x-intercept (DOC concentration at zero a350) was higher for the lakes DOC2–5, and almost negligible in the lakes grouped in DOCb1 (Fig. 2), indicating the higher contribution of non-absorbing DOC in the former. As it was mentioned, the specific absorbance variables SUVA and a*350 were analyzed as proxies of the degree of aromaticity and lignin content of the DOM pool, respectively. A high correlation coefficient between SUVA and a*350 was obtained (r = 0.90, p b 0.001, n = 128) (Fig. S3), and both variables registered significantly higher values in lakes DOC2–5 (SUVA:

Fig. 2. a) Relationship between the absorption coefficient at 350 nm (log a350) and the concentration of dissolved organic carbon (log DOC); b) Relationship between the DOCnormalized absorption coefficient at 350 nm (a*350) and the absorption coefficient at 350 nm (a350), in the 13 Andean Patagonian studied lakes (15 sampling sites). White dots: deep lakes grouped in DOCb1; white triangles: shallow lakes grouped in DOCb1; and, black triangles: shallow lakes grouped in DOC2–5.

4.820 ± 1.339 L mg C−1 m−1; a*350: 0.939 ± 0.430 L mg C−1 m−1) than in lakes DOCb1 (SUVA: 3.680 ± 1.175 L mg C−1 m−1; a*350: 0.733 ± 0.545 L mg C−1 m−1) (ANOVA, p b 0.001 in both cases). From a landscape perspective, high aromaticity and lignin content (higher SUVA and a*350) were observed in the DOM pool of the piedmont lakes located at the hyperhumid temperate evergreen forest (HTEF) (Cántaros, Frías and Nahuel Huapi Brazo Rincón) and in the two shallow lakes surrounded by the humid temperate evergreen forest (TEF) (Escondido and Morenito). The lowest values were recorded in the high altitude Lake Schmoll, located above the treeline (Table S2). Regarding the spectral slope S275–295, the values varied between 0.011 and 0.032 nm−1, although DOC2–5 lakes fell within a narrower range (0.016 and 0.026 nm−1) than those belonging to DOCb1 (0.012 and 0.033 nm−1). The lowest S275–295 value indicative of higher molecular weight DOM was registered in Lake Cántaros, while the highest slope (lower molecular weight) was recorded in the Dina Huapi site of Lake Nahuel Huapi (Table S2). A significant negative trend was found between a*350 and S275–295, two CDOM qualitative variables independent of DOC concentration (Fig. 3). Lakes with high a*350 and low S275–295 (those in the hyperhumid forest and the shallow piedmont lakes in the humid forest) showed less photodegraded DOM. Comparatively, lakes with lower a*350 and high S275–295 (deep piedmont lakes in the humid evergreen forest and in the steppe) reflected greater exposure to photochemical degradation (Fig. 3). The mid-altitude lake and most piedmont lakes (excepting Lake Frías) fell along a main trend line (R2 = 0.947, p b 0.0001, n = 116), signaling the west-east vegetation and precipitation gradients (Table 1; Fig. 3). The remaining samples, included in the trend line at a lower position in the plot, displayed proportionally lower a*350 at similar S275–295 (R2 = 0.817, p b 0.0001, n = 12). These samples corresponded to Lake Frías, a deep glacial fed lake, and to the five high altitude dimictic lakes surrounded by deciduous forests or High Andean vegetation (Table 1; Fig. 3). In relation to fluorescent DOM, the PARAFAC analysis of the EEM spectra identified three fluorescent components (C1, C2 and C3). The components C1 and C2 have been previously recognized as humic compounds, a combination of components of peaks A + M and A + C, respectively; while C3 (peak T) has been associated with non-humic and aliphatic compounds (e.g., Stubbins et al., 2014; Kellerman et al., 2015) (Table S3, see Supplementary Results for details). The total fluorescence intensity (sum of intensities of the three components, TFDOM) showed a similar pattern to that observed in the a350 along

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Fig. 3. Relationship between the DOC-normalized absorption coefficient at 350 nm (log a*350) and the spectral slope between 275 and 295 nm (S275–295) including all the samples obtained in the 13 Andean Patagonian lakes studied (15 sampling sites). The arrow indicates the theoretical trend of the relationship according to the conceptualization proposed by Anderson and Stedmon (2007), Fichot and Benner (2012) and Williamson et al. (2014) (see the text for further explanation). The asterisk (*) highlights data from Lake Frías. The dashed line represents the main trend including all the lakes, the lower dotted like depicts the trend of high altitude lakes and Lake Frías, and the upper dotted line depicts the trend in the rest of the lakes. Symbol's colors indicate the vegetation type in the catchment, HAV: high altitude desert TDF: temperate deciduous forest; TEF: temperate evergreen forest; HTEF: hyperhumid temperate evergreen forest, and S: steppe. WRT: water residence time.

the increasing DOC gradient, with very low values in DOCb1 compared to DOC2–5 lakes (Fig. S2a). Among DOCb1 lakes, the lowest TFDOM values were recorded in the high altitude lakes Schmoll and Témpanos located above the treeline, while the highest TFDOM value was observed at Lake Cántaros in the hyperhumid evergreen forest. On the other hand, among the lakes DOC2–5, Lake Verde in the deciduous forest and Lake Juventus in the transition between deciduous and evergreen forests, showed lower total fluorescence than the shallow piedmont lakes Morenito and Escondido located in the evergreen forest (Fig. S2a). The analysis of the relative contribution of the fluorescent components (as DOC normalized values) allowed the comparative assessment of FDOM composition among the studied lakes. In most of the lakes DOCb1, C1 and C3 prevailed contributing similarly, whereas in lakes Cántaros and Frías, C3 had a lower contribution (Fig. S2b). The FDOM of the lakes DOC2–5 was dominated by the C1 (Fig. S2b). For the whole data set, C1:DOC was positively correlated with C2: DOC (r = 0.91, p b 0.001, n = 128), and negatively with C3:DOC (r = −0.19, p b 0.036, n = 128) (Fig. S3). The relationships of the fluorescent components´ contributions with a*350 and the spectral slope S275–295 (as proxies of terrestrial DOM and photobleached DOM, respectively), showed contrasting patterns (Fig. S4). Positive correlation coefficients were found between a*350 and C1:DOC and C2:DOC (r = 0.71, p b 0.001, and r = 0.76, p b 0.036, n = 128, respectively) (Fig. S3 and S4), indicating the higher lignin content of these humic fluorophores, whereas the opposite pattern was found with the S275–295. C3 related negatively ith a*350 (r = −0.19, p b 0.031, n = 128), indicating a different DOM quality (Fig. S3 and S4). Moreover, the relationship between a*350 vs. the C3/C2 ratio (Fig. 4) revealed different patterns, with shallow lakes in the evergreen forest displaying low C3/C2 values (b1) within a wide range of a*350. Comparatively, the high altitude lakes (surrounded alternatively by high altitude vegetation or deciduous forests) and the deep lakes showed higher values of C3/C2 ratios (~1–2.5 and 0.6–4.4, respectively) along a narrower range of a*350 (Fig. 4). The seasonal analysis of the fluorescent components in deep and shallow lakes showed the decline of C1:DOC and C2:DOC, and a concomitant increment of C3:DOC and C3/C2 towards the dry season (Fig. S5a–d).

3.2. Spatial and seasonal trends of the CFOI The variability of the Climate Forcing Optical Index (CFOI) was analyzed spatially and seasonally. The analysis of the west-east precipitation gradient was perfomed comparing the CFOI values of the piedmont lakes sampled during summer. The CFOI of both deep and shallow piedmont lakes decreased along the precipitation gradient (i.e., from west to east, Fig. 5). All lakes located at the hyperhumid forest showed higher CFOI values, while the site Dina Huapi (Lake Nahuel Huapi), at the easternmost and driest extreme of the gradient, showed the lowest (Fig. 5). Among systems located inside the temperate humid evergreen forest, the deep lakes showed relatively lower CFOI values compared to the shallow ones (Fig. 5). The seasonal trends of the CFOI were analyzed in three deep and two shallow lakes, comparing the wet and dry seasons. In all cases, the CFOI

Fig. 4. Relationship between the 350 nm (log a*350) and the ratio of the fluorescent components C3/C2 identified by the PARAFAC model. All the samples obtained in the 13 Andean Patagonian studied lakes (15 sampling sites) are included. Symbol's colors indicate the vegetation type in the catchment, HAV: high altitude desert TDF: temperate deciduous forest; TEF: temperate evergreen forest; HTEF: hyperhumid temperate evergreen forest, and S: steppe.

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3.3. Multivariate analysis: RDA The multivariate redundancy analysis RDA selected five out of the eight environmental variables considered in the model (Chla, electrical conductivity, NDVI, MAP and lake area). Basing on the results of the correlation analyses (Fig. S3) and intending to prevent redundant optical parameters, the response variables utilized were: DOC concentration, CFOI, C3/C2 and DOC normalized fluorescent components (C1:DOC, C2:DOC and C3:DOC). The first and second canonical axes of the RDA explained 83.5% of the total observed variation (0.613) (Axis 1 = 54.7%; Axis 2 = 28.8%). The Monte Carlo unrestricted permutation test on the first eigenvalue indicated that the environmental variables were significantly correlated with the first axis (p = 0.020) as well as with all the canonical axes (p = 0.010). The first axis ordinated the lakes along the west-east precipitation gradient, whereas the second axis separated the two groups of lakes according to DOC concentration and conductivity (concentrated vs. diluted lakes). Lakes in the westernmost extreme showed high values of CFOI and DOC specific fluorescent components (C1:DOC and C2:DOC), whereas lakes towards the east showed decreasing values of these parameters. DOC and C3/C2 discriminated shallow lakes below the treeline from deep and shallow lakes at and above the treeline (Fig. S6). The former group showed greater DOC levels and DOM mainly composed by humic compounds of high molecular weight, whereas the other set of lakes with lower DOC, included a higher proportion of aliphatic compounds product of photodegradation (Fig. S6). 4. Discussion 2

−1

Fig. 5. a) Variation of the Climate Forcing Optical index [CFOI (nm m (g C) ; Williamson et al., 2014] in piedmont lakes along the west-east precipitation and vegetation gradients inside Nahuel Huapi National Park. Symbol's color indicates the vegetation cover of the catchment, HAV: high altitude desert; TDF: temperate deciduous forest; TEF: temperate evergreen forest; HTEF: hyperhumid temperate evergreen forest, and S: steppe; b) Variation of the mean annual precipitation in the longitudinal range inside Nahuel Huapi National Park.

decreased from the wet towards the dry season (Fig. 6). The highest seasonal variation of the CFOI was observed in the shallow Lake Escondido [ΔCFOI = 95.86 nm m2 (g C)−1], followed by Lake Morenito and the deep lakes. The lowest variation of the CFOI was recorded in Lake Moreno West [ΔCFOI = 26.36 nm m2 (g C)−1] (Fig. 6).

The distinctive DOM concentration and quality properties of the studied lakes reflected clearly the landscape heterogeneity within the Nahuel Huapi National Park. The observed DOC concentration pattern was likely associated with the interaction of environmental and hydrogeomorphic drivers: terrestrial vegetation, lake morphometry (deep and shallow lakes), mean annual temperature, precipitation type and regime and land-water hydrological connectivity (Table 1). In this scenario, two groups of lakes were discriminated by DOC concentration and conductivity (DOCb1 and DOC2–5; Fig. S6). At a comparative vegetation cover, deep lakes are more diluted than shallow ones. In turn, at comparable lake size, lakes surrounded by bare soil (i.e., high altitude systems) are more diluted than systems surrounded by forests, as has been observed in other regions of the world (Riera et al., 2000; Webster et al., 2008). Lake Cántaros falls between the previous two groups because, as a shallow lake in a forested area, it has much lower DOC due to its higher water flux (influenced by the high precipitation in this sector), which may translate into a shorter water residence time preventing the concentration of dissolved compounds. Such dilution/concentration effect has been described for other systems in the Northern Hemisphere (Anderson and Stedmon, 2007; Kothawala et al., 2014; Kellerman et al., 2015). 4.1. DOC-CDOM relationship

Fig. 6. Variation of the Climate Forcing Optical index [CFOI (nm m2 (g C)−1; Williamson et al., 2014] between consecutive wet and dry seasons in the deep lakes Moreno West (MW), Moreno East (ME), Nahuel Huapi Brazo Rincón (NHBR) and in the shallow lakes Escondido (ESC) and Morenito (MITO).

The relationship between DOC and CDOM is usually applied to track inputs of terrigenous DOC, as CDOM absorption is a fair proxy of DOC concentration (i.e., Baker et al., 2008; Spencer et al., 2008; Garcia et al., 2015a). However, optical properties of CDOM are not conservative and change due to photochemical reactions and bacterial degradation, which introduce variability to the CDOM-DOC relationships (RochelleNewall and Fisher, 2002; Del Vecchio and Blough, 2004; Kowalczuk et al., 2010). In our survey, significant different slopes were found in both groups of lakes, with a higher x-intercept in the lakes DOC2–5, and close to zero in lakes DOCb1 (Fig. 2). The value of DOC concentration at zero absorbance (x-intercept) has been widely described as the contribution of non-chromophoric fraction of DOM (non-CDOM) for different inland and marine waters (Spencer et al., 2008; Kowalczuk et al., 2010;

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DeVilbiss et al., 2016; Zhou et al., 2015). Therefore, our results indicate a much higher contribution of non-absorbing DOC to the DOM pool in the DOC2–5 lakes. Probable explanation to the differential contribution of non-CDOM between the two groups of lakes is that in the shallow forested lakes of group DOC2–5, macrophytes (which are absent in the lakes DOCb1) may contribute with non-absorbing substances to the DOM pool, as it has been previously observed (i.e., Lapierre and Frenette, 2009; He et al., 2018). Thus, our results confirmed that CDOM absorption is not always sensitive to DOC differences, particularly when comparing water masses differing in DOM processing (i.e., Bracchini et al., 2010; Harvey et al., 2015; Soto Cárdenas et al., 2017), being this an important issue to consider for the DOC estimation through remote sensing at both regional and global scales (Hestir et al., 2015). 4.2. Relationships between DOM qualitative variables: a*350 and S275-295 The negative relationship between a*350 and S275-295 (Fig. 3) showed that the DOM pools of lakes located at the wettest extreme of the gradient share similar properties with soil-derived DOM (“fresh” DOM with high a*350 and low S275-295). These systems with higher MAP likely present stronger hydrological connectivity and greater catchments inputs, associated with shorter water residence time (WRT). Whereas, lakes with lower MAP show weaker hydrological connectivity and a longer WRT. In this scenario, DOM with lower a*350 and higher S275–295 accumulates as a result of microbial and/or photochemical processing of soil-derived and/or autochthonous DOM (Anderson and Stedmon, 2007; Sobek et al., 2007; Helms et al., 2008, 2014; Fichot and Benner, 2012; Osburn et al., 2017). CDOM loss (i.e., lower values of a*350) due to photobleaching has been widely observed in natural systems (i.e. Helms et al., 2008; Fichot and Benner, 2012). Also, experimental studies performed with Andean Patagonian lake water have shown that natural and induced photodegradation reduced DOM absorptivity (decreased a*350) and the molecular weight (increased S275-295) (Diéguez et al., 2013; Soto Cárdenas et al., 2017). Interestingly, the lakes located at the temperate deciduous forest and high altitude vegetation displayed lower a*350 than those surrounded by humid and hyperhumid temperate evergreen forests (lower trend line in Fig. 3). Terrestrial vegetation DOM inputs have been shown to influence DOM optical metrics (Fichot et al., 2016). Indeed, the diverse vegetation types along the spatial gradients of NHNP likely provide DOM of different quality, as suggested by the wide specific a*350 values obtained in our lake survey. The lignin-CDOM relationship depends tightly on lignin concentration and composition. At low lignin concentrations, lignin absorptivity is higher on the UV-C region, decreasing towards the UV-A region. Further, different lignin compounds (p-hydroxy, vanillyl, and syringyl phenols) have specific CDOM absorption coefficients and depend on vegetation type (Fichot et al., 2016). On the other hand, lake connectivity with the terrestrial surrounding contrasted between high altitude and piedmont systems. Rainfall dominates down in the landscape, resulting in higher terrestrial inputs to piedmont and mid-altitude lakes through surface runoff during the rainy period (fall-winter). Instead, at high altitude, snowfall prevails (Table 1), concentrating the terrestrial inputs to lakes during the snowmelt period. Interestingly, this “asynchrony” in terrestrial inputs among lakes at different altitudes may explain the observed idiosyncratic behavior of DOM processing in high altitude lakes. 4.3. FDOM dynamics The analysis of the dynamics of the three fluorescent components provided additional insight on DOM quality differences among lakes. In particular, the relative contribution of the humic components C1 and C2 showed a decreasing trend with the S275-295, while the nonhumic component C3 displayed the opposite pattern (Fig. S4). These

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results clearly reflect the effects of photodegradation, which reduce the absorption per unit DOC, the molecular weight and humic compounds (Tank et al., 2011; Soto Cárdenas et al., 2017). Photobleaching produces a faster loss of C2 compared to C1 since C2 is more photoreactive than C1 (Ishii and Boyer, 2012; Stubbins et al., 2014), resulting in a higher persistence of the latter in the aquatic system (Kothawala et al., 2014; Kellerman et al., 2015). Overall, the direct relationships between a*350, SUVA, C1:DOC and C2:DOC indicate that C1 and C2 are terrestrially-derived. In general, terrestrial DOM inputs associated with precipitation in forested areas have a high content of stable aromatic structures containing lignin compounds, which are found exclusively within terrestrial vascular plants and are related to humic C2 (Hernes et al., 2009; Kalbitz et al., 2003; Stubbins et al., 2014; Kellerman et al., 2015). Furthermore, the humic component C1 has been associated with microbially transformed products derived from terrestrial organic substances (Stedmon and Markager, 2005; Zhang et al., 2010; Zhou et al., 2015; Kellerman et al., 2015). In contrast, the significant correlations between C3:DOC and other DOM optical parameters (Fig. S3), suggest that C3 is a comparatively less chromophoric, and non-aromatic, low molecular weight DOM component. The C3 may contain protein-like compounds and non-proteinaceous substances like small phenols derived from different sources and processes (Maie et al., 2007; Hernes et al., 2009; Kellerman et al., 2015), and products of the photodegradation of larger polyphenolic compounds attributed to the humic component C2 (Galgani et al., 2011). In several of the studied lakes, the concomitant decrease of C2:DOC and the increase of C3:DOC have been previously observed after exposure to artificial and natural photobleaching (Diéguez et al., 2013; Soto Cárdenas et al., 2017), supporting the idea that the non-humic C3 is, at least partially, a product of the photodegradation of C2. Also, the seasonal trends in the FDOM components during this study evidenced a lower C2:DOC, higher C3:DOC and C3/C2 values towards the dry season associated with higher solar radiation exposure (Fig. S5). In this context, C1 appears as the most photoresistant fluorophore, C2 as the most photoreactive and C3 as a photoproduct, resulting in higher persistence of C1 and C3 and the loss of C2 from the DOM pool. This seems to be the scenario of most of the studied DOCb1 lakes in which C1 and C3 prevailed (Fig. S2b). On the other hand, DOC2–5 lakes were all dominated by C1 displaying low C3, suggesting a different internal DOM processing. Particularly, C3 is recognized as the most bioreactive component, and may be consumed and produced simultaneously by bacterioplankton, as has been suggested by Guillemette and del Giorgio (2012). However, as the bacterial metabolism depends on nutrient availability, the interaction between nutrients and DOM may ultimately regulate DOM utilization by bacteria (Guillemette and del Giorgio, 2012; Guillemette et al., 2013). Although DOM metabolism has complex dynamics, low nutrient levels of DOCb1 lakes (TP b 6 μg L−1, Diaz et al., 2007; Garcia et al., 2015b) might limit the consumption of C3 by bacteria, while higher nutrient concentrations of DOC2–5 lakes (TP 7–20 μg L−1, Garcia et al., 2015b; Gerea et al., 2016) support higher bacterial metabolism, thereby decreasing the C3 in their DOM pools. Nevertheless, differential inputs from snowmelt and/or groundwater, typically with low fluorescence and rich in C3 (Burns et al., 2016), cannot be ruled out. In this context, the C3/C2 ratio, which may decrease through biodegradation and/or increase through photodegradation (Hansen et al., 2016), showed up as a good proxy to indirectly estimate the relative importance of these transforming processes in the different lake groups, both spatially and seasonally (Fig. 4 and S4, respectively). Our results suggest that FDOM is more influenced by biological degradation in shallow lakes below the treeline (in line with the dominance of C1), and that photochemical degradation impacts more strongly in deep lakes (supported by the prevalence of C3). In particular, coupled photochemical and biological processes shaped the CDOM pool of high altitude lakes (Fig. 4), pattern also found by Zhu et al. (2018) in marine waters. The fact that photodegradation appears to have a lower impact on DOM properties

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in high altitude lakes compared to deep piedmont lakes, despite they are all very transparent (Morris et al., 1995), may be associated with their particular land-water hydrological connectivity. Pereyra and Roverano (2010) indicated the presence of a “seasonal permafrost” (“discontinuos permafrost” according to Haeberli, 2005) at the High Andean unit, meaning that the horizontal hydrological connectivity is constrained during the cold periods. In contrast, this connectivity reaches its maximum during the snowmelt period throughout late spring and summer (depending on the accumulated snowpack), just when the lakes are exposed to enhanced solar radiation levels (Zagarese et al., 1997). Thus, the period with relatively higher terrestrial inputs coincides with that of high solar radiation levels favoring photobleaching, contrasting with the pattern observed in piedmont lakes. Our results are in line with the concept that lake conditions integrate not only the weather effects of the current season but also may depend upon external and internal forces operating in previous seasons (Hampton et al., 2017).

lakes DOCb1, diminishing towards the lakes DOC2–5. The C3/C2 ratio reflected the variability of the DOM transformation processes, tracking a higher impact of biodegradation in shallow lakes below the treeline, and a major photodegradation effect in the deep lakes and high altitude lakes. Among the shallow lakes, the altitudinal gradient was better reflected by the parameters DOC and C3/C2, with lakes at and above the treeline showing lower DOC and higher C3/C2 than lakes below the treeline. As a whole, the results obtained through the multivariate analysis together with the optical metrics applied indicated that DOM of lakes DOCb1 is mostly chromophoric, largely constituted by less absorbing substances with a higher proportion of low molecular weight aliphatic compounds. Whereas, the lakes DOC2–5 present a considerable proportion of non-CDOM and a CDOM with a greater content of aromatic humic compounds. 4.6. Local and regional implications and potential lake DOM responses to climate change

4.4. Application of the CFOI metric The application of the CFOI metric, which links two variables in one (a*320 and S275–295), allowed us to analyze an integrated DOM quality signal in relation to environmental variables, both spatially and seasonally (Figs. 5 and 6), in the steep environmental gradient studied and under the marked seasonality in the precipitation regime. This index has been shown to increase with higher inputs of light-absorbing terrestrially derived DOC (a*320) favored by wetter conditions and lower photobleaching effect (Williamson et al., 2014; Warner and Saros, 2019). The bioclimatic patterns present in NHNP were consistently reflected by the CFOI along the west-east gradient, with higher values in the precipitation-dominated lakes surrounded by hyperhumid forests at the west, and decreasing towards drier and less complex vegetation cover at eastern sites (Fig. 5). In addition, in a similar spatial unit, shallow lakes showed higher CFOI values than deep lakes, suggesting that they have a stronger terrestrial influence due to their smaller volume, as has been pointed out in several studies of the Northern Hemisphere (Webster et al., 2008; Adrian et al., 2009; Williamson et al., 2009). The marked seasonality in precipitation was also reproduced by the DOM pools of deep and shallow piedmont lakes, which showed higher CFOI values during the wet season, indicating increased inputs of terrestrial DOC (Fig. 6). In contrast, the lower CFOI values recorded in the dry season reflected lower allochthonous inputs due to the reduced connectivity with the terrestrial surrounding, and also enhanced solar radiation acting on reduced water masses. The higher CFOI values of shallow piedmont lakes may be due to the fact that they concentrate more terrestrial DOM with high humic content and high photoreactivity (Fig. 5), which can be inferred through the decrease of C2:DOC and the increase in C3:DOC observed from the wet towards the dry season (Fig. S5). This pattern results from a high photobleaching impact in lakes with strong terrestrial inputs, as has been described for Northern freshwater ecosystems (Lapierre et al., 2013). The analysis of the CFOI and the FDOM components convey to the idea that shallow lakes respond more rapidly than deep ones to changes in the catchment, including natural climate fluctuations such as the precipitation regime and/or the impact of solar radiation and photobleaching. 4.5. Integrating outcomes The multivariate analysis (Fig. S6) summarized several patterns at a spatial scale basing on a reduced number of DOM optical metrics. The analysis evidenced the two groups of lakes according to DOC concentration. The CFOI captured the west-east gradient in precipitation and vegetation cover, with higher values in lakes at the wettest extreme, diminishing towards the east. A similar trend was observed in the relative contribution of the humic fluorescent components C1 and C2. In the case of the non-humic C3, its relative contribution was higher in the

All the discussed environmental processes are part of the factors controlling the functioning at the catchment level and provide valuable information about the C lateral transport in these southern temperate aquatic environments. Lakes of NHNP constitute the headwater of the largest watershed of North Patagonia, contributing with a large volume of freshwater flowing through one of the most arid landscapes of the world (the Patagonian steppe), finally reaching the Atlantic Ocean. Undoubtedly, natural and/or anthropogenically-induced changes in these headwater networks would affect locally and regionally the availability and/or quality of the resource. Although only 4.5% of the surface of NHNP is covered by urban and suburban areas, further research will be needed to analyze the effects of the growing human populations on certain lakes of this protected area. Temperate regions of South America are showing the impact of Climate Change (Intergovernmental Panel on Climate Change, 2013; Barros et al., 2015). In particular, a decreasing pattern of the westerlies' intensities is causing a drying trend over northern and central Patagonia causing profound hydroclimatic changes (Masiokas et al., 2008; Garreaud et al., 2013; Barros et al., 2015). If sustained over time, these changes could operate differentially on lake metabolism depending on lake type. Thus, it may be expected that the deep and shallow piedmont lakes will undergo a bleaching process, due to a decrease of terrestrial inputs product of lower precipitation, and a greater impact of photobleaching promoted by longer WRT. Such response is opposite to the browning observed in aquatic environments of the Northern Hemisphere (i.e., Monteith et al., 2007; Larsen et al., 2011; Thrane et al., 2014). In contrast, high altitude lakes would experience a “homogenization” with shallow lakes of lower altitudes, since an upward expansion of the deciduous Nothofagus forests is expected due to warming, with a concomitant shift of the treeline to higher altitudes (Mathiasen and Prémoli, 2016), potentially increasing terrestrial DOM inputs. 5. Conclusions • DOC concentration and CDOM and FDOM properties of the studied lakes captured the sharp environmental gradients and climate seasonality within NHNP, validating our hypothesis. • Two groups of lakes with different DOC concentrations were described along the study area: lakes DOCb1 (b1 mg L−1) and lakes DOC2–5 (2 to 5 mg L−1). The group DOCb1 is represented by deep lakes and by high altitude lakes located at or above the treeline, while the shallow lakes below the treeline constitute the group DOC2–5. • In lakes DOCb1, DOM is mostly chromophoric with a substantial contribution of aliphatic compounds, while lakes DOC2–5 present a considerable fraction of non-CDOM and humic, aromatic CDOM.

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• In piedmont lakes, precipitation exerts a chief control by promoting land-lake flux, thereby driving the bulk and timing of the allochthonous contribution, revealed by the higher CFOI and NDVI values at the west extreme of the gradient (precipitation-dominated and forested lakes). • The ratio between non-humic and terrestrial humic components C3/ C2, used as a proxy of the biological and photochemical DOM degradation, showed up different patterns among lake types. Shallow lakes below the treeline were dominated by DOM biodegradation reflected in their low values of C3/C2, while high altitude and the deep lakes showed higher values, indicative of a stronger effect of photodegradation. • High altitude lakes with dimictic thermal regime revealed coupled biodegradation and photodegradation processes, probably due to the “asynchrony” in their hydrological behavior compared to the piedmont and mid altitude lakes. In high altitude lakes, the period of higher hydrological connectivity with the terrestrial surrounding occurs during the ice-free period (late spring and early summer), while in the lakes lower in the landscape is maximum during the rainy period (fall and winter). • The marked seasonality of precipitation in Patagonia influences the quantity and quality of lake DOM, with terrestrial inputs concentrating in the cold wet season and internal DOM transformation processes favored in dry periods coinciding with reduced water levels. This pattern is reflected by the decreasing CFOI values and an increasing trend in the C3/C2 ratio between the wet and dry seasons. • Thus, the CFOI and the C3/C2 ratio revealed as fair metrics depicting spatial and seasonal DOM dynamics in regions with sharp environmental gradients, being valuable as monitoring tools. Also, the a*350 and S275–295 reflected the gradients and may be useful for comparing with other studies since they are widely used, while the application of the CFOI is more recent.

Overall, this study underscores the effectiveness of DOM optical metrics to characterize the state and functioning of lakes, allowing capturing spatial and temporal environmental changes. These findings are valuable for future monitoring studies for conservation and management. Besides, they enhance the available information about the effect of Climate Change on mountain ecosystems of the Southern Hemisphere, which are under-represented in a global context. Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.05.396. Acknowledgments This investigation was funded by the Agencia Nacional de Promoción Científica y Técnica (PICTs 2012-1200, 2013-1384 and 2015-3496), and by Universidad Nacional del Comahue (UNComahue 04/B194). We are grateful to Dr. Horacio Zagarese and to the two anonymous reviewers for their valuable comments and suggestions. Dr. Sergio Ribeiro Guevara and his research group are specially acknowledged for facilitating us the sampling in the Lake Nahuel Huapi. We thank the National Parks Bureau of Argentina and to San Carlos de Bariloche Town Council for granting permission to sample the lakes within their jurisdictions. C. Soto Cárdenas, and R.D. Garcia are CONICET Postdoctoral fellows. C. Queimaliños, M. Reissig, G.L. Pérez, M. Gerea, P.E. Garcia, and M.C. Diéguez are CONICET researchers (Argentina). References Adrian, R., O'Reilly, C.M., Zagarese, H., Baines, S.B., Hessen, D.O., Keller, W., Livingstone, D.M., Sommaruga, R., Straile, D., Van Donk, E., Weyhenmeyer, G.A., Winder, M., 2009. Lakes as sentinels of climate change. Limnol. Oceanogr. 54, 2283–2297. Aiken, G., 2014. Fluorescence and dissolved organic matter: a chemist's perspective. In: Coble, P., Lead, J., Baker, A., Reynolds, D.M., Spencer, R.G.M. (Eds.), Aquatic Organic Matter Fluorescence. Cambridge University Press, New York, pp. 35–74.

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