Landscape patterns influence nutrient concentrations in aquatic systems: citizen science data from Brazil and Mexico.

Studies of the effects of landscape configuration on nutrient concentrations in aquatic systems, apart from land cover percentages, remain limited. Understanding these influences is important to guide land use planning and avoid the undesirable consequences of artificial eutrophication. We investigated how land use and natural landscape attributes such as edge density, mean shape index, cohesion, and contagion were related to nitrate (N-NO3) and phosphate (P-PO4) concentrations in Brazilian streams and Mexican lakes. Data on nutrient concentrations were collected by citizen science volunteers from 2013 to 2016, and we calculated land use classes and landscape metrics for each watershed. We developed models to predict nutrient concentrations based on landscape metrics, watershed slope, and season after excluding autocorrelated predictors. We used the Generalized Additive Model for Location, Shape and Scale framework and found the distribution (gamma or lognormal) that provided the best fit to the data based on the Akaike Information Criterion. The best predictors were selected following a stepwise strategy. We found relatively high N-NO3 (5–10 mg/L) and P-PO4 (0.5–1.0 mg/L) concentrations in the watersheds in both countries. Landscape composition (percentages of urban and agricultural areas) and configuration (mean shape indexes for urban and agricultural land use) metrics were the key predictors in the model for P-PO4 in Brazilian streams. In Mexican lakes, the predictors of nutrient concentrations were configuration metrics such as contagion and edge density of natural areas for P-PO4, and cohesion of urban areas for N-NO3. Our findings can be used as a starting point for land use planning, as well as for helping managers predict nutrient enrichment in watersheds within existing urban and agricultural areas. Our study highlights the importance of community-based monitoring that supplements regular monitoring initiatives because we were able to use data collected by citizen scientists to assess potential drivers of nutrient pollution and differences between countries. [ABSTRACT FROM AUTHOR]