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Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework.

ABSTRACT: Nitrogen (N) and Phosphorus (P) are essential nutritional elements for life processes in water bodies. However, in excessive quantities, they may represent a significant source of aquatic pollution. Eutrophication has become a widespread issue rising from a chemical nutrient imbalance and is largely attributed to anthropogenic activities. In view of this phenomenon, we present a new geo-dataset to estimate and map the concentrations of N and P in their various chemical forms at a spatial resolution of 30 arc-second (?1?km) for the conterminous US. The models were built using Random Forest (RF), a machine learning algorithm that regressed the seasonally measured N and P concentrations collected at 62,495 stations across the US streams for the period of 1994-2018 onto a set of 47 in-house built environmental variables that are available at a near-global extent. The seasonal models were validated through internal and external validation procedures and the predictive powers measured by Pearson Coefficients reached approximately 0.66 on average.

SUBMITTER: Shen LQ

PROVIDER: S-EPMC7256043 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework.

Shen Longzhu Q LQ Amatulli Giuseppe G Sethi Tushar T Raymond Peter P Domisch Sami S

Scientific data 20200528 1

Nitrogen (N) and Phosphorus (P) are essential nutritional elements for life processes in water bodies. However, in excessive quantities, they may represent a significant source of aquatic pollution. Eutrophication has become a widespread issue rising from a chemical nutrient imbalance and is largely attributed to anthropogenic activities. In view of this phenomenon, we present a new geo-dataset to estimate and map the concentrations of N and P in their various chemical forms at a spatial resolut ...[more]

PMID: 32467642

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Project description:Changes in phosphorus concentrations affect periphytic diatom composition in streams, yet we rarely observe strong relationships between diatom richness and phosphorus. In contrast, changes in conductivity are strongly associated with differences in both diatom composition and richness. We hypothesised that we could better understand the mechanisms that control the phosphorus-richness relationship by examining relationships between phosphorus and the occurrence of individual diatom taxa, comparing these with relationships between conductivity and taxon occurrence, and documenting how niche breadths of taxa affect richness patterns. We estimated relationships between phosphorus and taxon occurrence using DNA metabarcoding data of diatoms collected from 1,811 sites distributed across the conterminous U.S.A. and contrasted patterns in these relationships with those between conductivity and taxon occurrence. The distribution of taxon optima for phosphorus was bimodal, with most optima located at either the maximum or minimum observed phosphorus concentration. The distribution of taxon optima for conductivity was unimodal. Niche breadths of taxa for phosphorus and for conductivity both generally increased with optimum values. The distribution of conductivity optima gave rise to a prominent hump-shaped relationship between richness and conductivity. The relationship between richness and phosphorus was also slightly hump-shaped, but this relationship would not be expected from the bimodal distribution of optima. Instead, we determined that broad niche breadths caused the hump-shaped relationship between richness and phosphorus. Our results highlight the nuanced effects that increased P loadings exert on diatom assemblages in rivers and streams and identify reasons that weak relationships between taxon richness and increased phosphorus have been observed. These findings allow us to better describe how excess phosphorus and subsets of taxa and their niche breadths contribute to patterns of taxa richness in diatom assemblages, and to improve the tools used to manage phosphorus pollution.

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Dataset Information

Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework.

Publications

Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework.

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