Influence of Intensive Agriculture on Benthic Macroinvertebrate

water

Article Inﬂuence of Intensive Agriculture on Benthic Macroinvertebrate Assemblages and Water Quality in the Aconcagua River Basin (Central Chile)

Pablo Fierro 1,* , Claudio Valdovinos 2, Carlos Lara 3 and Gonzalo S. Saldías 4,5

1 Instituto de Ciencias Marinas y Limnológicas, Universidad Austral de Chile, Valdivia 5090000, Chile 2 Departamento de Sistemas Acuáticos, Facultad de Ciencias Ambientales, Universidad de Concepción, y Centro de Ciencias Ambientales (EULA), Universidad de Concepcion, Concepcion 4070386, Chile; [email protected] 3 Departamento de Ecología, Facultad de Ciencias, Universidad Católica de la Santísima Concepción, Concepción 4090541, Chile; [email protected] 4 Departamento de Física, Facultad de Ciencias, Universidad del Bío-Bío, Concepción 4051381, Chile; [email protected] 5 Centro FONDAP de Investigación en Dinámica de Ecosistemas Marinos de Altas Latitudes (IDEAL), Valdivia 5090000, Chile * Correspondence: pablo.ﬁ[email protected]

Abstract: This study assessed natural variation in the macroinvertebrate assemblages (MIB) and water quality in one of the main basins with the largest agricultural activities in Chile (Aconcagua River Basin). We sampled throughout the annual cycle; nine sampling sites were established along Citation: Fierro, P.; Valdovinos, C.; the basin, classifying according to agricultural area coverage as least-disturbed, intermediate, and Lara, C.; Saldías, G.S. Influence of most-disturbed. We collected 56 macroinvertebrate taxa throughout the entire study area. Multi- Intensive Agriculture on Benthic variate analysis shows significant differences among the three disturbance categories in different Macroinvertebrate Assemblages and seasons, both water quality variables and the MIB structure. Distance-based linear model (DistLM) Water Quality in the Aconcagua River analysis for all seasons explained more than 95.9% of the macroinvertebrate assemblages, being Basin (Central Chile). Water 2021, 13, significantly explained by chemical oxygen demand, pH, total coliforms, nitrites, elevation, and 492. https://doi.org/10.3390/w1304 water temperature. ANOVA test revealed significant differences in the proportion of noninsect 0492 individuals, macroinvertebrates density, and the number of taxa among the three disturbance cate-

Academic Editor: Carla Sofia Santos gories (p < 0.05). In general, water temperature, conductivity, chemical oxygen demand, ammonium, Ferreira nitrites, and nitrates increased their values downstream in the basin. Our results indicate that the elevation gradient and increment in agricultural land use in the basin had a strong influence on water Received: 30 December 2020 quality and MIB. A better understanding of these ecosystems could help conservation and integrated Accepted: 5 February 2021 watershed management. Published: 14 February 2021 Keywords: Aconcagua; longitudinal pattern; biodiversity; bioindicators; MIB Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction Limnologists have studied natural changes in the aquatic community composition through altitude gradient for many decades [1]. In this sense, the River Continuum Concept-RCC [2] proposes a taxonomic composition change in macroinvertebrates along Copyright: © 2021 by the authors. the river continuum, from headwaters to mouth, increasing, for example, filter-feeding Licensee MDPI, Basel, Switzerland. species downstream due to an increase in dissolved organic matter. However, natural This article is an open access article changes in macroinvertebrates can be altered by anthropogenic activities, such as land-use distributed under the terms and changes [3]. Worldwide intensification of agriculture, one of the main stressors of aquatic conditions of the Creative Commons ecosystems worldwide, has adverse effects on water quality and therefore changing the Attribution (CC BY) license (https:// structure of streams communities, impacting the biodiversity and functions of freshwater creativecommons.org/licenses/by/ 4.0/). ecosystems negatively [4]. Lawrence et al. [5] found that an increasing agricultural area in

Water 2021, 13, 492. https://doi.org/10.3390/w13040492 https://www.mdpi.com/journal/water Water 2021, 13, 492 2 of 18

the watershed of California Mediterranean streams was negatively related to the diversity of streams communities and water quality. Similarly, Fierro et al. [6] recorded a negative relation among agriculture land-use percentage and habitat water quality and streams communities in Mediterranean Chilean streams. Thus, it is essential to evaluate if models predicted by RCC are fulfilled in streams affected by anthropogenic land use, to use this information in conservation policies that differentiate both natural and anthropogenic changes in stream communities. Macroinvertebrates have been used for decades as bioindicators because they respond to environmental changes as water quality and land use, increasing or decreasing their abundances, occasionally even disappearing [7,8]. However, because macroinvertebrate assemblages also change temporally by intrinsic factors (e.g., life-history cycles), studies comprising seasonal changes are essential to discriminate natural and anthropogenic effects on aquatic communities [9]. In southern South America, maximum abundances and richness have been reported in summer months (January, February, and March), related to seasonal fluctuations in hydrology, higher water temperatures, lower flows, and most stable conditions. In contrast, lower abundances and richness of aquatic insects occur in winter [10,11]. Agricultural land use often degrades riparian habitat, decreasing Canopy River, and commonly altering the water quality, increasing water temperature, nutrients, and fine sediments in streams [12,13]. Due to the unidirectional nature of the streams, nutrients and pollutants (e.g., fertilizers or agrochemicals) can accumulate downstream with higher concentrations in lower areas of the basin [14]. Moreover, reductions in streamflow associated with water diversions to irrigation canals are common in agricultural Mediterranean regions [15,16]. These environmental features associated with agricultural activities can negatively influence aquatic macroinvertebrates. Some agricultural land-use consequences are the decrease of sensible taxa (such as EPT), increased relative abundances of tolerant taxa such as Chironomids or oligochaetes, and noninsects invertebrates, such as snails [17,18]. Chilean Andean streams draining to the Pacific Ocean are relatively shorts (<400 km), with marked land use in the Mediterranean ecoregions. Uppers zones in these basins are dominated by native vegetation, such as scrublands, while central valleys are generally dominated by exotic forest plantations and agricultural land use. The lowlands of the basins are dominated almost exclusively by agricultural lands [6]. The Aconcagua River Basin is located in central Chile, comprises an elevation up to 6100 m above sea level, and it is one of the main basins that sustain the economy based on agriculture in Chile (supports 12% of Chile national agriculture), livestock and forestry production [19]. Thus, the Aconcagua basin provides an excellent study model to examine longitudinal patterns of macroinvertebrate assemblages associated with altitudinal and land-use agriculture gradients—agricultural areas increase toward lowlands of the basin. This study analyzes the seasonal structure of macroinvertebrate assemblages and water quality along the Aconcagua River Basin. We predict that landscape variables, such as altitude or catchment area, and environmental variables derived from agricultural activities, such as increased nutrients, are directly related to macroinvertebrate assemblages composition.

2. Material and Methods 2.1. Study Area The Aconcagua River basin (32◦540 S, 71◦300 W) has a surface area of 7200 km2 (Figure1 ). The climate is typically Mediterranean, characterized by warm, dry sum- mers (between October and March) and wet, cool winters (mainly between May and August) with intense and irregular rainfall [20]. Temperatures and annual precipitation vary along the basin. Lowland average annual temperature is 14.5 ◦C, and precipitation reaches 395 mm/year; about 15.2 ◦C and 261 mm/year in the medium sector 14.1 ◦C and 467 mm/year in the highlands. The Aconcagua is 5th order river according to Strahler and is 142 km long, forming from Juncal and Blanco River’s conﬂuence. The basin has a Water 2021, 13, 492 3 of 18

mixed hydrological regime with rain and snow contributions, an average annual flow of 33.1 m3 s−1. The maximum flow peaks in November–January, whereas the minimum flow occurs between March and September [21].

Figure 1. Map of the study area and sampling sites on the Aconcagua River Basin. (a,b) Show the location in a geographical context in South America. (c) Show the basin with the elevations. Least-disturbed sites by agricultural activities are: JU, JU10, BL20, AC10; intermedium-disturbed sites by agricultural activities AC20, PU10; most-disturbed sites by agricultural activities PO10, AC30, AC40.

The Aconcagua River Basin is composed mainly of igneous rocks interbedded with marine and continental sediments, whose ages fluctuate between the Upper Triassic and the Upper Miocene [22]. The basin encompasses the three main geomorphological provinces of Chile: Andes mountains, central valley, and coastal mountains and can be divided into three parts according to its geomorphology and distribution of geological units [23]. In the upper section of the basin, high slopes do not allow a large amount of water to infiltrate the underground water tables. In the medium section, the terrain is smooth, and the slopes are covered with a detrital mantle accompanied by basement rocks in some places. Given the little rainfall, erosive effects are limited compared to the upper part of the basin. In this part of the basin, the detrital formations also influence the soils’ permeability capable of containing a large amount of water, constituting a large groundwater reservoir. In the lower section of the basin, the Aconcagua River narrows to only one watercourse. The Aconcagua valley is characterized by intensive irrigated agriculture. From the 145 inflows in the river, 90% are diverted for irrigation channels [21]. The basin is divided into four sections used to draw water to agricultural practices. The sections are: (i) from first agriculture terrains in the upper part of the basin (975 m asl) to San Felipe city (648 m asl), (ii) from San Felipe city to Puntilla Romeral (211 m asl), (iii) from Puntilla Romeral to Quillota city (127 m asl), and (iv) from Quillota city to Aconcagua River mouth in Concón bay. Aconcagua River Basin supports about 500,000 inhabitants, mainly from the cities San Felipe (71,000 inhabitants), Los Andes (63,000 inhabitants), and La Calera (50,000 inhabitants), all located in the middle and lower part of the basin. High nitrate concentrations are related to agriculture activities [24], whereas high concentrations of copper and molybdenum have been reported in the upper basin [25].

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2.2. Land Use and Sampling Sites The Aconcagua Valley is one of the most important agricultural regions of Chile [26]. We sampled streams highly impacted by agriculture on 9 activities, i.e., streams drain land that has been transformed mainly for agriculture use, close to the streams along the basin. A total of 12 types of agriculture can be recognized in the basin (Figure2,AppendixA), being the majority of the land used for vineyards, fruit trees, horticulture, and avocado plantations. The highest elevations are dominated by the typical evergreen sclerophyll forests and hillsides by dry xerophytic thorn scrub and forest shrublands [27].

Figure 2. Map of land uses and sampling sites on the Aconcagua River Basin. Name of sampling stations are shown in Figure1. The black circles represent the least-disturbed sites; lead squares the intermedium-disturbed sites; and white triangles the most-disturbed sites by agricultural activities.

We selected a total of nine sampling sites in the Aconcagua River Basin (Table1), according to a natural elevation gradient occurring in the basin (Figure1) and a gradient of agricultural land use (Figure2). Sample stations located at a higher elevation (>1096 m a.s.l.) and with agricultural land use < 0.2% (respect to the entire basin) corresponded to stations JU and JU10 (Juncal river), BL20 (Blanco river), and AC10 (Aconcagua river) and were categorized as least-disturbed (LD). Stations AC20 (Aconcagua river) and PU10 (Putaendo river) located at the middle part of the basin (650–604 m a.s.l.), categorized as intermediate-disturbed (ID) to have an agricultural land use between 6.8 and 7.5%. Finally, stations PO10 (Pocuro river), AC30, and AC40 (Aconcagua river), located at a lower elevation (<600 m a.s.l.), had the higher agricultural land use >10.8% being categorized as the most-disturbed (MD). BL20 sampled site was located downstream of the Blanco River watershed, where a mining plant (copper exploitation) is located in the upper part. AC20 sampled site was located approximately 1 km downstream of the sewage outﬂow of the San Felipe city.

2 Water 2021, 13, 492 5 of 18

Table 1. Catchment and habitat characteristics and land use at 9 sampling sites on the Aconcagua River basin, Chile. Elev = Elevation, CA = Catchment area, Bo = Boulder, Co = Cobble, Gra = Gravel. CA (ha) Site Code Elev (m asl) Stream Order % Shrublands % Agriculture % Water Body Substrate Type % Native Forest % No Vegetation % Forest Plantation % Urban-and-Industrial JU 1657 15779 3 Bo/Co 0.0 0.4 19.1 0.0 0.0 17.0 63.5 BL20 1521 38729 3 Bo/Co 0.0 1.2 10.3 0.0 4.6 25.1 58.9 JU10 1448 17312 3 Bo/Co 0.0 1.2 10.3 0.0 4.6 25.1 58.9 AC10 1096 44945 4 Gra/Co 0.2 4.0 16.1 0.0 1.7 18.1 60.0 AC20 650 164162 5 Gra/Co 6.8 7.3 29.6 0.0 1.8 14.6 40.0 PU10 604 103816 4 Gra 7.5 10.5 29.0 0.0 1.1 10.7 41.2 PO10 600 43814 4 Gra/Co 19.1 17.6 55.9 0.0 2.6 1.2 3.5 AC30 188 398540 5 Gra 10.8 15.9 30.0 0.1 1.6 10.1 31.6 AC40 12 538908 5 Gra 13.0 21.3 28.9 0.4 2.4 8.2 25.8

Land use cover (%) for each site was estimated by screening digitalized satellite images (1:30,000 scale, 2013 Landsat 8 sensor) freely available from the Satellite Information System, IDE Agriculture (http//www.ide.cl). Land-use types were determined using ArcGis10 [28]. We obtained the subcatchment area above the sampling site and the coverage (%) of land uses types.

2.3. Field Sampling and Data Collection Macroinvertebrates and water samples were sampled during winter 2014 (24–29 August), spring 2014 (24–29 November), summer 2015 (9–14 March), and fall 2015 (15–20 June). Macroinvertebrates at each site were sampled from the riffles, the most common habitat. For each season, macroinvertebrates samples consisting of 9 randomly located Surber samples (250 µm mesh netting; sampling area = 0.09 m2) were collected. The samples were fixed in situ with 70% ethanol and then taken to the Biodiversity Laboratory of the Universidad de Concepción in Concepción, Chile. The nine macroinvertebrate samples collected at each site were analyzed separately. A total of 315 replicates were analyzed. Station PU10 was not sampled in June because the stream was dried up. Samples were washed, sorted, and all organisms were separated from the detritus and preserved in 90% ethanol. Then organisms (larvae/pupae and adults in the case of riffle beetles) were identified and counted under stereo microscope (Zeiss, model Stemi Dv4, 32X magnification) to the lowest practical taxonomic resolution using dichotomous keys and taxonomic descriptions [29]. In Chile, there is insufficient information to identify all organisms to species. Most insects of the order Ephemeroptera were identified to species, whereas Trichoptera, Coleoptera, Odonata, Hemiptera, Lepidoptera, and Diptera were identified mostly to genus or family. Except for some rare taxa, most noninsects were identified to genus. The first author performed all identifications to main- tain consistency among sample sets. For each site, macroinvertebrate count data were converted to densities by square meter. At each site, 28 environmental and habitat variables were assessed (Tables1 and2). Water temperature (◦C), electrical conductivity (µS/cm), pH, and turbidity were measured in situ with a Hanna multiparameter instrument model HI 9828. Water quality samples Water 2021, 13, 492 6 of 18

were collected in duplicate from the center of the active channel below the water surface, de- posited in prewashed glass bottles (1 L), and transported in coolers to 4°C to the laboratory for chemical analyses. In the laboratory, the following water parameters were measured: DO-Dissolved Oxygen (mg/L), BDO5-Biological Oxygen Demand(mg/L), COD-Chemical Oxygen Demand (mg/L), Organic suspended solids (mg/L), Inorganic suspended solids (mg/L), total phosphorous (µg/L), ammonium (µg/L), nitrite (µg/L), nitrate (mg/L), Al, As, Cu, Fe, Mn, Mb, and Zn dissolved (µg/L), sulfate (mg/L), total hardness (mg/L), alkalinity (mg/L), chlorides (mg/L), fecal coliforms, and total coliforms (Nmp/100 mL). All analyses were carried out using standard methods for water and wastewater [30].

Table 2. Environmental features at 9 sampling sites on the Aconcagua River basin, Chile. Values represent average of four seasons (August and November 2014, March and June 2015).

Variable Abbreviation JU BL20 JU 10 AC 10 AC 20 PU 10 PO 10 AC 300 AC 40 Temperature (◦C) T° 6.3 8.5 17 13.4 17.0 15.6 15.2 16.7 14.4 Conductivity (µS/cm) Cond 367.8 720.8 303.2 326.5 643.0 653.4 493.0 621.3 938.5 pH pH 7.1 6.9 7.4 8.0 7.9 7.6 7.5 8.2 7.6 Turbidity (NTU) Tur 32.0 36.8 14.5 47.8 50.5 3.0 85.3 5.3 16.3 Dissolved oxygen (mg/L) DO 10.4 9.3 9.9 9.9 10.6 9.7 9.8 11.3 10.2

BDO5 (mg/L) BOD 1.1 5.8 1.1 1.0 2.9 1.0 2.0 1.4 1.5 COD (mg/L) COD 5.6 15.5 5.5 6.3 14.7 5.2 9.8 11.3 10.2 OSS (mg/L) SSO 1.6 22.0 2.5 1.8 7.1 1.0 11.8 1.1 2.5 ISS (mg/L) SSI 18.9 8.0 4.7 16.5 37.3 1.0 48.3 2.2 15.2 Total phosphorus (µg/L) TP 67.5 62.5 35.0 132.5 200.0 10.0 187.5 50.0 65.0 Ammonium (µg/L) Am 20.0 32.5 25.0 20.0 1067.5 20.0 142.5 22.5 57.5 Nitrite (µg/L) Nitri 22.5 133.5 24.5 26.3 852.5 33.3 87.3 36.5 153.5 Nitrate (mg/L) Nitra 1.0 4.4 1.4 1.4 3.1 17.7 2.6 11.7 5.7 Al dissolved (µg/L) Al 55.0 18.0 76.5 63.0 52.8 47.7 47.8 50.8 26.8 As dissolved (µg/L) As 3.5 3.7 4.2 4.3 1.8 0.7 3.2 1.9 2.3 Cu dissolved (µg/L) Cu 182.4 38.2 3.5 8.6 10.7 3.7 10.2 10.2 6.5 Fe dissolved (µg/L) Fe 21.5 16.0 30.3 18.3 17.0 12.7 28.5 11.8 27.5 Mn dissolved (µg/L) Mn 27.5 235.3 4.5 36.0 30.3 8.0 42.5 20.3 31.8 Mb dissolved (µg/L) Mb 3.3 20.3 2.3 2.3 2.5 1.3 3.8 1.8 1.8 Zn dissolved (µg/L) Zn 18.5 28.9 11.5 7.7 13.5 9.8 10.0 9.9 13.0 Sulfate (mg/L) Sulf 70.7 186.0 60.4 89.2 176.2 167.4 161.3 155.5 248.6 Total hardness (mg/L) TH 580.3 231.9 160.4 157.9 288.6 372.5 233.1 225.8 800.2 Alkalinity (mg/L) Alk 52.3 46.0 69.5 51.0 124.5 137.8 59.4 138.8 123.4 Chlorides (mg/L) Cl 18.3 64.3 19.9 15.7 49.1 26.4 42.1 20.4 53.6 Fecal coliforms (Nmp/100 mL) ColFe 25.1 190.8 90.2 44.0 16.1 1776.6 3025 512.5 892.3 Total coliforms (Nmp/100 mL) ColTo 65.4 1780.8 1760 116.5 493.3 5410.0 39,475.0 493.3 5280.0

2.4. Data Analyses The following macroinvertebrate assemblages metrics descriptors were calculated using the level taxa (such as [6,16,31]), averaging all seasons for each sampling site: insects in the disturbance sensitive orders Ephemeroptera, Plecoptera, and Trichoptera (% EPT individuals and number of EPT taxa) and % noninsects individuals, abundance descriptors (i.e., total density macroinvertebrates), species richness (i.e., total number taxa), and Shannon–Weaver diversity index (H’). These metric values for low, medium, and highly disturbed agricultural sites were compared using an analysis of variance (ANOVA), followed by a Tukey test to identify different groups. The differences were considered to be signiﬁcantly different at p < 0.05. Water 2021, 13, 492 7 of 18

The environmental variables were elevation and catchment area (described in Table1 ) and the 26 variables described in Table2. We used canonical analysis of principal coordinates— CAP [32]—to test for differences in macroinvertebrates taxonomic composition and environmental variables among the land-use types. CAP is a variant of principal coordinates analysis (PCOA), which can find axes through the multivariate cloud of points to discriminate among a priori groups [33]. The analysis can be based on any resemblance matrix. We used Bray–Curtis dissimilarity for biological data and Euclidean distances for environmental data. Biological data were square-root transformed before performing the CAP. The diagnostic test in CAP to allocate the sites to correct classification groups and test for agriculture intensification differences was run in macroinvertebrate composition using random permutations. We also tested the null hypothesis of no differences among group centroids (agriculture intensification type) using 999 permutations [34]. Relationships between macroinvertebrate community composition and environmental variables were examined using distance-based redundancy analysis (dbRDA) and distance- based linear models (DistLM) [33](α = 0.05; 999 permutations). These analyses were based on Bray–Curtis dissimilarity matrices of macroinvertebrate data and the whole set of environmental variables. Before dbRDA and DistLM analysis, the environmental features (except pH) were square-root transformed, and highly correlated variables (Pearson’s r > 0.9) were removed. In winter, conductivity, turbidity, Zn, and alkalinity were removed; spring: conductivity, turbidity, SSO, SSI, P, Mn, Mb, sulfate, fecal coliforms, and elevation; summer: turbidity, BOD5, nitrite, sulfates, total hardness; fall: turbidity, BOD5, SSO, Zn, Mn, and Clorures. The dbRDA and DistLM were run for the macroinvertebrates and environmental data among the 4 seasons. We reported the adjusted R² and cumulative proportion for environmental variables from the constrained ordinations. We ran ANOVAs using the R software [35], whereas CAPs, DistLMs, and dbRDAs were conducted in PERMANOVA+ for PRIMER [33].

3. Results 3.1. Environmental Variables and Water Quality The elevation of sampling sites ranged from 12 m a.s.l. to 1657 m a.s.l., and Strahler order ranged from 3 to 5, reaching this last value in the central valley. The main substrate types were sand, gravel, cobble, and boulders, with the mostly size substrates in the higher altitude sampling sites (BL20, JU, JU10) (Table1). Agricultural land use characterized the intermedium and most-disturbed sites, ranging from 6.8% to 19.1% of basin land, recorded agricultural sites at altitudes below 604 m a.s.l. (Table1). Generally, water temperature, conductivities, COD, ammonium, nitrites, and nitrates were lower in LD stations (stations higher altitude), increasing downstream. Conversely, metal concentrations were higher in LD stations, speciﬁcally at BL20 (mouth of Blanco River), decreasing downstream (Al, Cu, Mn, Mb). Sulfates, total hardness, alkalinity, chlorides, fecal coliforms, and total coliforms followed the same pattern: increasing their concentrations downstream, reaching maximum values near the river mouth, and minimum values around headwaters. The pH, turbidity, DO, BOD5, organic suspended solids, and inorganic suspended solids varied through the basin without a clear altitudinal trend (Table2, AppendixB).

3.2. Macroinvertebrate Assemblages We collected 56 macroinvertebrate taxa in the study area across all the year from rifﬂe habitats, of which 36 were insects (mostly Diptera taxa) and 20 noninsects. Among insects, Diptera was the most diverse order (11 taxa), followed by Ephemeroptera (7), Trichoptera (6), and Coleoptera (5). Numerically Chironomids were the taxa most abundant and found at 100% of sites throughout the year. Noninsects were represented by crustaceans, spiders, snails, worms, free-living ﬂatworms, leeches, and nematodes, of which snails Physa chilensis, Litorridina sp., leeches Glossiphonidae and worms Tubifex sp. were found in greater abundance in intermedium and most-disturbed sites. Water 2021, 13, 492 8 of 18

Seasonally, 39 taxa were reported in winter with 23,782 individuals, 36 in spring with 29,885 individuals, 34 in summer with 17,512 individuals, and 38 in fall with 15,644 individuals. Most-disturbed agricultural sites had the highest richness of macroinvertebrates, both aquatic insects and noninsects yearlong. Surprisingly, juvenile life stages of Plecoptera were not recorded in the study area, whereas other disturbance-sensitive orders, Ephemeroptera and Trichoptera were uncommon in least-disturbed sites. In contrast, the Trichopteran Metrichia sp. and Smicridea sp., the Ephemeropteran Meridialaris laminata, Meridialaris chilooense, Massartellopsis irarrazavali, and the Dipteran Blephabericeridae, were taxa founded only in least-disturbed sites. A clear pattern was observed in the % noninsect individuals, densities, and the number of taxa, which increased significantly from least-disturbed agricultural sites to most-disturbed sites (Figure3). Statistical analysis (ANOVA, Tukey’s test, p < 0.05) showed statistically significant differences in % of noninsect individuals, densities, and the number of taxa among least and most agricultural disturbed sites. Despite %EPT individuals and Shannon–Weaver diversity shown their highest median values in most perturbed sites, these were not significant (p < 0.05). A similar situation occurred with EPT richness, which did not vary between levels of agricultural sites (Figure3).

Figure 3. Biological indicators based on macroinvertebrate assemblages. Boxplots show the maximum and minimum as well as the interquartile ranges (25–75%) with solids lines representing median values. p indicates level of signiﬁcance among the levels of agricultural disturbance in the one-way ANOVA.

3.3. Multivariate Analysis: Environmental Variables and Macroinvertebrate Assemblages The CAPs based on environmental features showed that sampling sites were not statistically different among the three agriculture disturbed types across the year (Table3 ). Despite this, the percentage of correct classification of sites to their parental groups reached 100% in least and intermediate agricultural streams yearlong. Sites classified as most- disturbed reached 66.7% of correct classification because site PO10 should have been classified as least-disturbed. Water 2021, 13, 492 9 of 18

Table 3. Summary of Canonical Analysis of Principal Coordinates (CAP) for the three agriculture intervention types, environmental datasets, and macroinvertebrate assemblages in Aconcagua River Basin.

Percentage Correct Classiﬁcations Among Land Use Types Differences CAP Least Intermediate Most Trace P Environmental variables Winter 100 100 66.67 0.587 0.064 Spring 100 100 66.67 0.566 0.055 Summer 100 100 66.67 0.566 0.057 Fall 100 100 66.67 0.583 0.079 Macroinvertebrate assemblages Winter 50 0 100 1.444 0.080 Spring 100 100 100 1.943 0.002 Summer 100 0 66.67 1.490 0.008 Fall 75 0 66.67 1.216 0.094

The CAPs based on macroinvertebrate assemblages showed that sampling sites were statistically different among the three agriculture intervention types, both in spring and summer (Table3). In winter, the percentage of correct site classification to their parental groups reached 50%, and none of them was correctly classified as an intermediate-disturbed site. In the fall, the percentage of correct classification of sites to their parental groups reached <75% (Table3). Environment-assemblage relationships varied across the seasons, as shown by differences of both the significant predictor variables and the predicted power (adjusted R²), recorded both natural (e.g., temperature, elevation, catchment area) and anthropogenic variables (e.g., total coliforms, nitrates, nitrites) (Table4). DistLM explained 96.9% of the variation in the macroinvertebrate assemblages in winter, and contributions from seven environmental variables achieved the best solution, in which COD and temperature were significant (Table4). DistLM for spring explained 97.7% with contribution from seven variables in which temperature and pH were significant. DistLM for summer explained 97% with contribution from seven variables to the best solution in which elevation and total coliforms were significant. Finally, DistLM for fall explained 95.9%, and six environmental variables achieved the best solution. The elevation, total coliforms, and nitrites were significant (Table4). The dbRDA model (Forward) indicated that the least and most-disturbed sites were strongly separated along axis 1 and 2, and intermediate-disturbed sites (depending on the seasons) were closer to the least or most-disturbed sites. For winter (Figure4), vectors for Mn concentration, COD, and fecal coliforms were closely aligned parallel to axis 1 and intermediate and most-disturbed sites were scattered in this direction. The vector for nitrates was oriented along axis 2, also close to intermediate and most-disturbed sites. On the dbRDA plot for spring (Figure4), vectors for catchment area, nitrates, and total coliforms were closely aligned parallel with axis 1, and vectors for pH, temperature, Fe, and Nitrites were in orientation to axis 2. Intermediate and most-disturbed sites were close to these vectors. On the dbRDA plot for summer (Figure4), vectors for catchment area, fecal coliforms, elevation, and SSO were closely aligned parallel with axis 1, and total coliforms, COD, and Zn were in orientation to axis 2. Intermediate disturbance sites were partially intermingled with least and most-disturbed sites and scattered almost equally along axis 1 and 2. The dbRDA plot for fall (Figure4) vector for pH was closely aligned parallel with axis 1, whereas elevation, fecal coliforms, total coliforms, ammonium, and nitrites were principally aligned with axis 2. Vectors for fecal and total coliforms were linked to most-disturbed sites. Water 2021, 13, 492 10 of 18

Figure 4. Ordination plots of Distance-based Redundancy Analysis (dbRDA) of macroinvertebrate assemblages to environmental variables between seasons of the year. Vectors indicate the direction of the effects of quantitative variables in the ordination plot. Abbreviations for environmental variables are described in Table2.

Table 4. DistLM sequential test results giving signiﬁcant environmental variables explain macroinvertebrates assemblage variation to winter and spring 2014 and summer and fall 2015. Variables selection was based on the option “Forward”. Adj.R2 = cumulative adjusted R2.

Variable Adjust.R2 Pseudo-F P Cumulative Proportion Winter CDOM 0.289 2.856 0.013 0.289 Temperature 0.528 3.029 0.012 0.528 Fecal coliforms 0.657 1.884 0.052 0.657 As 0.75 1.646 0.13 0.757 Cl 0.843 1.651 0.211 0.843 Nitrates 0.911 1.546 0.267 0.912 Mn 0.969 1.902 0.322 0.970 Spring Temperature 0.425 5.174 0.005 0.425 pH 0.589 2.401 0.018 0.589 Nitrates 0.697 1.776 0.139 0.697 Catchment area 0.793 1.868 0.152 0.793 Total coliforms 0.857 1.971 0.144 0.875 Fe 0.931 1.634 0.274 0.931 Nitrites 0.977 2.091 0.33 0.977 Water 2021, 13, 492 11 of 18

Table 4. Cont.

Variable Adjust.R2 Pseudo-F P Cumulative Proportion Summer Elevation 0.268 2.569 0.01 0.268 Total coliforms 0.443 1.888 0.024 0.443 ColFe 0.586 1.726 0.08 0.586 SSO 0.706 1.632 0.171 0.706 Catchment area 0.812 1.704 0.176 0.812 Zn 0.891 1.439 0.301 0.891 COD 0.97 2.635 0.277 0.97 Fall Elevation 0.2810 2.3453 0.0210 0.2810 Total coliforms 0.5211 2.5050 0.0310 0.5211 Nitrites 0.7083 2.5670 0.0410 0.7083 pH 0.8193 1.8430 0.1220 0.8193 Ammonium 0.8982 1.5480 0.2620 0.8982 Fecal coliforms 0.9599 1.5400 0.4050 0.9599

4. Discussion Land-use changes, such as those associated with agricultural activities, degrade the habitat and the water quality in various rivers around the world [36,37]. However, such changes can be masked by the natural changes that occur in the basins, such as the altitudinal gradients that occur along a river, influencing the water’s quality and the aquatic communities [38,39]. We evaluated these effects in the Aconcagua River Basin since the increase in the proportion of agricultural activity is related to an increase in the basin’s size and a decrease in altitude. We found that water quality changes were mainly reflected in increased temperature and nutrients (e.g., nitrates, nitrites) downstream. The increase (decrease) in agricultural activity (altitude) was also associated with the increase in noninsect individuals and the increase in macroinvertebrates’ density and richness. The increase in agricultural activity impacted the macroinvertebrate assemblages, reflected in the CAP analysis for the spring and summer seasons, and significant differences for three of the six evaluated metrics. These results are consistent with numerous studies related to the impact of agriculture [17], which relate an increase in the proportion of noninsect individuals and an increase in the density of macroinvertebrates. On the other hand, macroinvertebrates richness was higher in most-disturbed sites by agriculture. These results are contrary to those reported in the literature, where lower species richness is expected in places with vast agricultural land [6,40]. However, this increase in richness is due to the decrease in altitude. Studies in rivers covering a wide altitudinal gradient, such as the Colorado River in the USA, have shown that as altitude decreases, there is an increase in the richness of macroinvertebrates [41]. The richness of macroinvertebrate taxa was similar throughout the year. Simultane- ously, the density increased notably in spring, resulting from the seasonal changes of life cycles of insects such as Trichoptera Metrichia, Smicridea and the diptera Chironomidae in intermedium and most-disturbed stations. The EPT index also remained stable along the continuous river, so that this index would be a poor indicator of the environmental status of the Aconcagua River Basin. The nonvariation in the index values results from the high abundance in all sampling stations of some ephemeropterans (Andesiops torrens and A. peruvianus) and trichopterans (Hydroptilidae and Smicridea sp.). Both species of Andesiops are widely distributed in southern South America, from high-altitude rivers up to river mouths at sea level [42]. These results suggest that A. peruvianus and A. torrens seem to be tolerant to the increase in nitrites (ranged from 22.5–852.5 µg/L), nitrates (ranged from 1–17.7 mg/L), total phosphorous (ranged from 10–200 µg/L), water temperature (ranged from 6.3–17 ◦C), and other environmental variables in the Aconcagua River Basin. Water 2021, 13, 492 12 of 18

Although the Plecoptera order is characteristic of mountain rivers, especially in Andean rivers, no species were reported in the study area. Similar results found Scheibler et al. [43] in a river at the same latitude as the Aconcagua river but on the other side of the Andes, in Argentina, which only recorded one Plecoptera (Gripopterygidae) taxa. Probably the absence of stoneflies nymphs in the upper part of the Aconcagua River is due to the small entry of woody residues or leaf-litter from the riverine vegetation, the main food of these insects, which is naturally scarce in the upper part of the basin, where the use of bare soil and rocky outcrops (see Figure2). In the upper part of the basin (>1096 m asl), where there is a low percentage of agricultural land use, the macroinvertebrate assemblages were represented by chironomids. Furthermore, the presence of insects sensitive to pollution, only present in this sector of the basin, were represented by ephemeropterans Meridialaris laminata, Meridialaris chilooense, Massartellopsis irarrazavali, and the Dipteran Blephabericeridae. These taxa are known to prefer mountain rivers with cold, well-oxygenated waters [10,44]. On the other hand, in the middle sector of the basin, sites moderately disturbed (660–604 m asl), and in the lower sector with sites most-disturbed by agricultural land use (<600 m asl), the assemblage was again represented by Chironomids and a high abundance of the insects Smicridea and Hydroptilidae. Both taxa are characteristic for being found in sites with a high organic material content, low DO, high conductivities, and warm temperatures [45,46]. Our results showed statistical differences among the three agricultural disturbed types based on macroinvertebrates assemblages in spring and summer. On the other hand, non statistical differences were founded based on environmental features. These results indicate that ecological indicators are more sensitive that environmental features to detect different degrees of disturbance based on agricultural disturbances, as described in other Chilean streams [47]. Notice that site PO10 was incorrectly classified in its parental group (most-disturbed) based on environmental features, which should have been classified as leas-disturbed according to CAP analysis. Pocuro stream (where PO10 site was located) has the highest proportion of agricultural land use (19.1%), principally grapes and parron-vid (Figure2, AppendixA). However, it also has the highest proportion of shrublands land use (55.9%). Thus, it seems that this stream’s elevated agricultural activities are not enough to classify this sampling station as a most-disturbed site based on environmental features. From the 28 environmental variables included in the DistLM for each station, six to seven variables contributed to the model’s best solution (Table4). The significant variables with a high percentage of contribution (>26.8%) throughout the year were the DOC, temperature, pH, altitude, total coliforms, and nitrites. Several studies have shown that high levels of DOC (an indicator of organic pollution) and high pH values can affect the structure of the macroinvertebrate assemblages in rivers associated with the use of agricultural land [48,49]. Thus, our significant variables follow the general models of agricultural basins, having strong effects on the structure of aquatic biodiversity. Altitude is an environmental factor that affects macroinvertebrate assemblages and water quality, for example, by altering temperature and dissolved oxygen [50]. Vannote et al. [2] suggests that the highest species richness occurs in the basins middle reaches. However, we found the highest richness in the lower reaches of the basin. Thus, the expected assemblage of macroinvertebrates by the RCC would be modified by the effects of agriculture in the Aconcagua River Basin. The Aconcagua River Basin decreased its flow in recent years, being declared to have water scarcity by the Chilean government [51]. The dramatic decrease in river flow is associated with climate change and activities related to agriculture, such as excessive water diversions for irrigation canals in the middle and lower part of the basin, which implies a decrease in the main river flow and consequent increase in its temperature. The water temperature is a critical variable in the macroinvertebrate assemblage structuring, which naturally varies along the continuous river related to the basin’s altitude and size. In our study, the highest altitude station had an average of 6.3 ◦C throughout the year, whereas the lowest stations had an average temperature range between 14.4 and 17 ◦C. Rivers with the highest proportion of agricultural soils generally have little riparian vegetation. This lack of vegetation causes rivers to be more exposed to solar radiation, and therefore, an increase Water 2021, 13, 492 13 of 18

in water temperature, nutrients, fine sediments, and pesticides is expected [52–54]. In our study, the total and fecal coliform variables, nitrates, nitrites, increased their concentrations in intermedium and most-disturbed areas. For example, the average values of phosphates and ammonium reached 100 µg/L and 1067 µg/L, respectively, closest to the river mouth. Higher values of the total N-content in the water have been recorded in rainy seasons in the study area, associated with agricultural sources [24]. Nutrients play an essential role in structuring aquatic communities [55]. One possible mechanism is that the increase in nutrients recorded in this study in the middle and lower areas of the basin (i.e., phosphorus, ammonium nitrites, nitrates) promotes an increase in the biomass of the periphyton, the main food for browsing macroinvertebrates [6]. Indeed, a large number of gastropod snails were recorded in these areas of the basin. This would also explain the lack of statistical differences in most-disturbed sites based on the Shannon–Wiever diversity index. Heino et al. [56] found that taxonomic richness indices could not differentiate disturbed sites from reference sites based on macroinvertebrate assemblages. The replacement of species from least- to most-disturbed sites was reflected by the increase in the richness and density of noninsect taxa in the middle and lower basin. These results are consistent with studies carried out in the lower area of the Aconcagua River Basin, where sites affected by agriculture dominated flatworms, hirudineans, worms, snails, and chironomids [57]. The high concentration of heavy metals in the upper basin is consistent with the natural sources of metals in the environment (include metallic minerals and parent rocks) and the presence of a mining company of copper and molybdenum in the Blanco River (BL20 station) [58]. The decrease in the concentration of metals downstream may result from their dissolution by contributing other tributaries to the main river and the natural dispersion with an increase in distance from the source [59]. Heavy metals As, Mn, Fe, and Zn were nonsignificant variables but were included in the model throughout the year. These parameters were generally high in the upper basin, and their concentrations decreased downstream. Our results are similar to those reported by Alvial et al. [60] in a nearby basin with heavy metals naturally present and variables that change naturally through a continuum river such as altitude and dissolved oxygen explaining the structure of the macroinvertebrate assemblages. Given the limited knowledge of altitude and agriculture’s effects on these Mediter- ranean ecosystems, our findings are relevant for the basin’s management and regulation. Agricultural activities affect water quality parameters and benthic communities. However, their impacts can be underestimated because of the river’s natural changes due to altitude gradients. The Aconcagua river basin is one of the main basins of central Chile affected by climate change, which is reflected in drought conditions. Thus, we suggest that decision- makers regulate agricultural activity in the region, emphasizing the river’s flow and its tributaries to preserve aquatic biodiversity.

5. Conclusions The altitude gradient and the increase in agricultural activity downstream in the Aconcagua River Basin affected several physicochemical parameters, which varied throughout the year and led to increased nutrient levels in the areas mostly disturbed by agricultural activities. These changes were also reﬂected in the macroinvertebrate assemblages structure, associated with an increase in noninsect taxa and greater abundance and richness of macroinvertebrates downstream. Considering that a large part of the basins of the Mediterranean area of Chile follows the same land use pattern (the upper basin has little human intervention, while agricultural activities dominate the middle and lower areas), we expect similar results in other rivers. Integrated watershed management must consider the effect of various anthropogenic stressors on water quality and aquatic biota, including the changes that occur naturally by altitude gradients and the seasonal variability of water quality and biota. Water 2021, 13, 492 14 of 18

Author Contributions: Conceptualization, P.F. and C.V.; methodology and data curation, P.F., C.V., G.S.S., and C.L.; writing—original draft preparation, P.F and C.V.; writing—review and editing, P.F., C.V., G.S.S., and C.L.; funding acquisition, P.F and C.V. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by FONDECYT grant number 11190631 and DAND Codelco- Andina. CL has been funded by UPWELL Millenium Nucleus (grant ICM NCN 19153) and red GeoLIBERO–CYTED. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data sets are contained within the article. Further data and materials requests should be addressed to pablo.fi[email protected]. Acknowledgments: We thank W. San Martin, A. Jara, C. Cerna, J. González, and A. Manosalva for his assistance in sampling. Three anonymous reviewers and an editor provided comments that improved our manuscript. Conflicts of Interest: The authors declare no conflict of interest.

Appendix A

Figure A1. Map of land uses and sampling sites on the Aconcagua River Basin. Direct land use corresponds to land use adjacent to watercourses, principally anthropogenic land use. Indirect land use corresponds to land use in a wider catchment area. Name of sampling stations in Figure1, black circles, least-disturbed sites; lead squares, intermedium-disturbed sites; white triangles, most- disturbed sites by agricultural activities. Water 2021, 13, 492 15 of 18

Appendix B

Table A1. Environmental features at 9 sampling sites on the Aconcagua River basin, Chile. Values represent average of four seasons (August and November 2014, March and June 2015). Minimal and maximum (in brackets) are consigned.

JU BL 20 JU 10 AC 10 AC 20 PU 10 PO 10 AC 30 AC 40 Temperature (°C) 6.3 (3–10.3) 8.5 (4.5–14.9) 10.2 (6.9–12.6) 13.4 (10–16.2) 17 (10.9–21.7) 15.6 (12.1–17.7) 15.2 (7.3–22.8) 16.7 (10.2–20.4) 14.4 (5.4–18.9) Electrical conductivity (µS/cm) 367.8 (204.2–560) 720.8 (362–1122) 303.2 (235.2–375) 326.5 (257.2–432) 643 (452–794) 653.7 (558–721) 493 (379–585) 621.3 (594–663) 938.5 (813–1089) pH 7.1 (6.3–8) 6.9 (6–7.5) 7.4 (6.6–8) 8 (7.5–8.4) 7.9 (7–8.6) 7.6 (7.4–7.8) 7.5 (6.9–8) 8.2 (7.9–8.6) 7.6 (7.3–8.3) Turbidity (NTU) 32 (11–55) 36.8 (5–100) 14.5 (2–47) 47.8 (5–170) 50.5 (6–111) 3 (1–5) 85.3 (45–132) 5.3 (4–9) 16.3 (3–44) Dissolved oxygen (mg/L) 10.4 (9.6–11.6) 9.3 (8.4–11.8) 9.9 (9.5–10.3) 9.9 (8.8–10.8) 10.6 (9.6–11.2) 9.7 (7.9–10.8) 9.8 (8.5–11.8) 11.3 (10.4–12) 10.2 (6–13.2) BDO5 (mg/L) 1.1 (1–1.3) 5.8 (2.6–7.8) 1.1 (1–1.4) 1 (1–1.1) 2.9 (2.2–3.4) 1 (1–1.1) 2 (1.2–2.6) 1.4 (1–1.6) 1.5 (1.1–1.9) COD (mg/L) 5.6 (2.5–8.5) 15.5 (9.7–24.1) 5.5 (2.5–7.6) 6.3 (2.5–11.5) 14.7 (3.3–23.9) 5.2 (2.7–8.5) 9.8 (4–12.3) 8.6 (5.4–12.3) 10.5 (6.8–14.2) Organic suspended solids (mg/L) 1.6 (1.2–2.5) 22 (1–3.8) 2.5 (1–7) 1.8 (1–3.5) 7.1 (3.1–11.8) 1 11.8 (6.3–24) 1.1 (1–1.3) 2.5 (1–4.2) Inorganic suspended solids (mg/L) 18.9 (10.1–35.8) 8 (1–15.7) 4.7 (1–11.8) 16.5 (2.6–57.4) 37.3 (3.1–79.2) 1 (1–1.1) 48.3 (31.3–62.8) 2.2 (1.1–3.9) 15.2 (2–42.2) Total phosphorus (µg/L) 67.5 (20–120) 62.5 (10–150) 35 (10–70) 132.5 (10–420) 200 (70–270) 10 187.5 (120–240) 50 (10–90) 65 (20–120) Ammonium (µg/L) 20 32.5 (20–70) 25 (20–40) 20 1067.5 (60–1920) 20 142.5 (20–320) 22.5 (20–30) 57.5 (30–90) Nitrite (µg/L) 22.5 (15–45) 133.5 (15–478) 24.5 (15–53) 26.3 (15–60) 852.5 (114–1889) 33.3 (15–0) 87.3 (36–174) 36.5 (15–59) 153.5 (24–366) Nitrate (mg/L) 1 (0.9–1.2) 4.4 (1.4–11.9) 1.4 (0.9–1.8) 1.4 (1–2.2) 3.1 (1.8–5.9) 17.7 (11.9–23.8) 2.6 (1.2–4.4) 11.7 (7.7–17) 5.7 (0.4–13.7) Al dissolved (µg/L) 55 (13–128) 18 (5–57) 76.5 (5–195) 63 (30–90) 52.8 (8–140) 47.7 (27–71) 47.8 (22–93) 50.8 (5–81) 26.8 (5–83) As dissolved (µg/L) 3.5 (1–5) 3.7 (1.4–7.1) 4.2 (3–5.7) 4.3 (2.2–7.5) 1.8 (1–2.7) 0.7 (0.2–1) 3.2 (2.7–3.7) 1.9 (0.5–3) 2.3 (1.4–3.5) Cu dissolved (µg/L) 182.4 (0.5–617.4) 38.2 (13.1–63.8) 3.5 (0.5–8.9) 8.6 (0.5–13.8) 10.7 (5.7–16.2) 3.7 (0.5–6.9) 10.2 (4.3–17.5) 10.2 (4.2–15.3) 6.5 (0.5–13.8) Fe dissolved (µg/L) 21.5 (3–35) 16 (1–38) 30.3 (18–45) 18.3 (1–27) 17 (5–36) 12.7 (1–22) 28.5 (17–38) 11.8 (1–32) 27.5 (5–70) Mn dissolved (µg/L) 27.5 (6–47) 235.3 (68–570) 4.5 (1–7) 36 (4–94) 30.3 (20–46) 8 (4–11) 42.5 (3–129) 20.3 (5–43) 31.8 (3–63) Mb dissolved (µg/L) 3.3 (1–5) 20.3 (1–65) 2.3 (1–6) 2.3 (1–5) 2.5 (1–6) 1.3 (1–2) 3.8 (1–7) 1.8 (1–3) 1.8 (1–3) Zn dissolved (µg/L) 18.5 (6.7–34.9) 28–9 (7.2–67.6) 11.5 (2.5–24.3) 7.7 (5–13.8) 13.5 (0.9–34.5) 9.8 (0.2–22.5) 10 (5.1–17.1) 9.9 (3.1–21.9) 13 (0.2–39.5) Sulfate (mg/L) 70.7 (51–102) 186 (93.5–337.6) 60.4 (50–66.7) 89.2 (55.4–116.8) 176.2 (127–234.3) 167.4 (152.7–176.4) 161.3 (116.9–223.9) 155.5 (148.2–161.4) 248.6 (215.2–266.3) Total hardness (mg/L) 580.3 (103.6–1875.5) 231.9 (177–324.1) 160.4 (125.1–187.1) 157.9 (106–217.3) 288.6 (200.4–367) 372.5 (330.9–404.7) 233.1 (175.4–315) 325.8 (279.6–362) 800.2 (388.3–1961.5) Alkalinity (mg/L) 52.3 (45–65) 46 (36–55) 69.5 (65–73) 51 (43–56) 124.5 (95–145) 137.8 (97–172.5) 59.4 (50–72.5) 138.8 (115–155) 123.4 (52.5–159) Chlorides (mg/L) 18.3 (10–24.8) 64.3 (24.1–125.1) 19.9 (15.9–25.1) 15.7 (9.1–23.9) 49.1 (25.8–79.5) 26.4 (26.2–26.6) 42.1 (20.6–54.8) 20.4 (20–20.8) 53.6 (39.4–66.9) Fecal coliforms (Nmp/100 mL) 25.1 (4.5–46) 190.8 (33–330) 90.2 (7.8–220) 44 (2–130) 16.1 (4.5–26) 1776.6 (240–4600) 3025 (1100–7900) 512.5 (170–920) 892.3 (79–1700) Total coliforms (Nmp/100 mL) 65.4 (4.5–130) 1780.8 (33–3300) 1760 (280–4600) 116.5 (17–330) 493.3 (13–1300) 5410 (330–11,000) 39475 (7900–79,000) 493.3 (13–1300) 5280 (330–17,000) Water 2021, 13, 492 16 of 18

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