Landscape drivers of regional variation in the relationship between total phosphorus and chlorophyll in lakes

Authors

  • TYLER WAGNER,

    1. U.S. Geological Survey, Pennsylvania Cooperative Fish & Wildlife Research Unit, Pennsylvania State University, University Park, PA, U.S.A.
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  • PATRICIA A. SORANNO,

    1. Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, U.S.A.
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  • KATHERINE E. WEBSTER,

    1. Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, U.S.A.
    2. School of Biological Sciences, Queen’s University, Belfast, U.K.
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  • KENDRA SPENCE CHERUVELIL

    1. School of Biological Sciences, Queen’s University, Belfast, U.K.
    2. Lyman Briggs College, Michigan State University, East Lansing, MI, U.S.A.
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Tyler Wagner, U.S. Geological Survey, Pennsylvania Cooperative Fish & Wildlife Research Unit, Pennsylvania State University, 402 Forest Resources Bldg, University Park, PA 16802, U.S.A. E-mail: txw19@psu.edu

Summary

1. For north temperate lakes, the well-studied empirical relationship between phosphorus (as measured by total phosphorus, TP), the most commonly limiting nutrient and algal biomass (as measured by chlorophyll a, CHL) has been found to vary across a wide range of landscape settings. Variation in the parameters of these TP–CHL regressions has been attributed to such lake variables as nitrogen/phosphorus ratios, organic carbon and alkalinity, all of which are strongly related to catchment characteristics (e.g. natural land cover and human land use). Although this suggests that landscape setting can help to explain much of the variation in ecoregional TP–CHL regression parameters, few studies have attempted to quantify relationships at an ecoregional spatial scale.

2. We tested the hypothesis that lake algal biomass and its predicted response to changes in phosphorus are related to both local-scale features (e.g. lake and catchment) and ecoregional-scale features, all of which affect the availability and transport of covarying solutes such as nitrogen, organic carbon and alkalinity. Specifically, we expected that land use and cover, acting at both local and ecoregional scales, would partially explain the spatial pattern in parameters of the TP–CHL regression.

3. We used a multilevel modelling framework and data from 2105 inland lakes spanning 35 ecoregions in six US states to test our hypothesis and identify specific local and ecoregional features that explain spatial heterogeneity in TP–CHL relationships. We include variables such as lake depth, natural land cover (for instance, wetland cover in the catchment of lakes and in the ecoregions) and human land use (for instance, agricultural land use in the catchment of lakes and in the ecoregions).

4. There was substantial heterogeneity in TP–CHL relationships across the 35 ecoregions. At the local scale, CHL was negatively and positively related to lake mean depth and percentage of wooded wetlands in the catchment, respectively. At the ecoregional scale, the slope parameter was positively related to the percentage of pasture in an ecoregion, indicating that CHL tends to respond more rapidly to changes in TP where there are high levels of agricultural pasture than where there is little. The intercept (i.e. the ecoregion-average CHL) was negatively related to the percentage of wooded wetlands in the ecoregion.

5. By explicitly accounting for the hierarchical nature of lake–landscape interactions, we quantified the effects of landscape characteristics on the response of CHL to TP at two spatial scales. We provide new insight into ecoregional drivers of the rate at which algal biomass responds to changes in nutrient concentrations. Our results also indicate that the direction and magnitude of the effects of certain land use and cover characteristics on lake nutrient dynamics may be scale dependent and thus likely to represent different underlying mechanisms regulating lake productivity.

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