The Influence of Landscape Position and Catchment Characteristics on Aquatic Biogeochemistry in High-Elevation Lake-Chains |
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Authors: | Steven Sadro Craig E. Nelson John M. Melack |
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Affiliation: | (1) The Earth Research Institute, University of California, Santa Barbara, California 93106, USA |
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Abstract: | To examine the influence of landscape characteristics and landscape position on aquatic biogeochemistry, we sampled a total
of 76 lakes within 14 different lake-chains spanning the latitudinal extent of the high-elevation Sierra Nevada (California).
We measured water chemistry, dissolved organic matter (DOM), nutrients, and biotic variables in study catchments that encompassed
representative ranges of area (3–22 km2), elevation (2,200–3,700 m.a.s.l), elevation change (50–700 m), and average slope (13°–26°). Hierarchical models were used
to account for variability in biogeochemistry because they explicitly maintain the framework of lakes within individual lake-chains
while accounting for variation among lake-chains. Unconditional means models, where lake-chain was a random effect, revealed
significant differences among lake-chains for nearly all biogeochemical variables. Models explained 42–95% of this variability,
with the majority of the variation (70%) explained by the among lake-chain component. To explore the amount of additional
variation explained by lake landscape position, we added lake network number (LNN) to models. LNN explained a significant
amount of additional variation (7% average) in 8 of 23 biogeochemical parameters. However, it explained more variability within
individual lake-chains (75%), where among lake-chain differences did not obscure patterns. Patterns of increase with LNN were
found for dissolved organic carbon and nitrogen, fluorescence of DOM, alkalinity, and bacterioplankton abundance, whereas
nitrate and nitrogen to phosphorus nutrient ratios decreased. LNN explained variation because it served as a proxy for underlying
catchment characteristics that changed consistently along downstream flow paths. To characterize the amount of variation explained
by catchment characteristics alone, we fit a third model that included lake-chain as a random effect and landscape or lake
morphometry attributes as fixed effects. Catchment characteristics explained about as much additional variation (6%) as LNN,
but for substantially more biogeochemical parameters (18 out of 23). The catchment characteristics most predictive of biogeochemistry
were land-cover factors delineating alpine and subalpine zones (elevation, slope, or proportions of rock or shrub cover).
In general, catchment characteristics were stronger predictors of biogeochemistry than characteristics of lake morphometry,
emphasizing the relative importance of landscape processes in snowmelt-dominated lake ecosystems. |
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