Key Features

fingerPro is a flexible framework for sediment source fingerprinting that integrates data exploration, tracer selection, and unmixing to estimate, visualize, and validate source apportionments.

Data Exploration

Box and whiskers plot:
box_plot function creates a series of box and whisker plots to visualize the distribution and variability of individual tracers within a dataset.
Correlation matrix chart:
correlation_plot function displays a correlation matrix of each od the tracers divided by the different sources.
Linear discriminant analysis chart:
LDA_plot function performs a linear discriminant analysis and displays the data in the relevant dimensions.
Principal component analysis chart:
PCA_plot function performs a principal components analysis on the data and displays a biplot of the results for each source.
Consensus ranking (CR) method:
CR function computes the CR method, an ensemble technique to identify non-conservative and dissenting tracers. The CR score, which ranges from 100 to 0, indicates a tracer’s rank in terms of consensus and conservativeness. Tracers are ordered by score, with high scores indicating conservative tracers and low scores indicating dissenting ones.
Ternary diagrams:
ternary_diagram function creates ternary diagrams to visualize the results of the individual tracer analysis. For three sources, each tracer is represented in a single ternary plot which represents the predicted apportionments for a specific tracer, making interpretation straightforward. For four sources, each tracer is represented by six ternary plots since a ternary plot can only represent three sources, the function groups sources and generates six triangles, and the visualization becomes more complex and the interpretation becomes less intuitive. For more sources, this type of visualization is not recommended.

- Range test:

range_test function identify tracers of the sediment mixture that are outside the minimum and maximum values of the sediment sources.

Tracer Selection Methods

Consistent tracer selection (CTS) method:
CTS_explore function estimate all possible minimal tracer combinations (tracers_seeds) and represents the initial step from a specific seed in building a consistent tracer selection within a sediment fingerprinting study. The function evaluates these combinations by solving the corresponding systems of equations and assessing their variability, which provides an indication of their discriminant capacity.

CTS_select function builds on and extends a minimal tracer combination (seed) selected from the tracers_seeds obtained in CTS_explore, ensuring its mathematical consistency to identify an optimal set of tracers for unmixing. To apply this function, the user must: 1) select a specific seed from all tracers_seeds estimated by CTS_explore, and (2) define an error threshold (typically 5%, i.e., 0.05).

Unmixing and Results

Unmixing:
unmix function assesses the relative contribution of potential sources to each mixture using a mass balance approach. It supports both unconstrained and constrained optimization. The output is a data frame with the relative contributions of sources to each mixture across all iterations.
Plotting results:
plot_results function generates a plot showing the relative contribution of sediment sources to each mixture. It can use either violin charts or density plots.
Validate resuts:
validate_test function evaluate the mathematical consistency of a tracer selection for an apportionment solution. Assess the mathematical consistency of a tracer selection for an apportionment result by computing the normalized error between the predicted and observed tracer concentrations in the virtual mixture. A low normalized error for all tracers indicates a consistent tracer selection. This function can be used to diagnose problems in the results of fingerprinting models.

Isotopic Ratios Analysis

Conservative balance (CB) method:
CB function transforms isotopic ratio and content data into virtual elemental tracers. This allows isotopic tracers to be analysed with classical unmixing models and combined with scalar tracers to potentially increase discriminant capacity.

Database Formats

The package supports four main database formats, each with specific column requirements:

‘raw’ format: Contains individual measurements for scalar tracers. It must have columns for ID, samples, and tracer1, tracer2, …
‘isotopic raw’ format: Contains individual measurements for isotopic tracers, which require both ratio and content data. It must have columns for ID, samples, ratio1, ratio2, …, and cont_ratio1, cont_ratio2, …
‘averaged’ format: Contains statistical summaries of the scalar tracer data. It must have columns for ID, samples, mean_tracer1, mean_tracer2, …, sd_tracer1, sd_tracer2, …, and n.
‘isotopic averaged’ format: Contains statistical summaries for isotopic tracers. It must have columns for ID, samples, mean_ratio1, mean_ratio2, …, mean_cont_ratio1, mean_cont_ratio2, …, sd_ratio1, sd_ratio2, …, sd_cont_ratio1, sd_cont_ratio2, …, and n.

Examples Datasets

The package includes four example datasets:

example_geochemical_3s_raw.csv ‘raw’ format: Scalar tracers (17 geochemical elements, 3 sources and 1 mixture).
example_isotopic_3s_raw.csv ‘isotopic raw’ format: Isotopic tracers (5 CSSI ratios and their corresponding contents, 3 sources and 1 mixture).
example_geochemical_3s_mean.csv ‘averaged’ format: Scalar tracers (17 geochemical elements, 3 sources and 1 mixture).
example_isotopic_3s_mean.csv ‘isotopic averaged’ format: Isotopic tracers (5 CSSI ratios and their corresponding contents, 3 sources and 1 mixture).

References

Unmixing model

Latorre, B., Lizaga, I., Gaspar, L., Navas, A. 2025. Evaluating the Impact of High Source Variability and Extreme Contributing Sources on Sediment Fingerprinting Models. Water Resources Management 39, 4589–4603. https://doi.org/10.1007/s11269-025-04169-8
Lizaga, I., Latorre, B., Gaspar, L., Navas, A. 2020. FingerPro: an R package for tracking the provenance of sediment. Water Resources Management 34, 3879–3894. https://doi.org/10.1007/s11269-020-02650-0
Palazón, L., Latorre, B., Gaspar, L., Blake, W.H., Smith, H.G., Navas, A., 2015. Comparing catchment sediment fingerprinting procedures using an auto-evaluation approach with virtual sample mixtures. Science of The Total Environment 532, 456–466. https://doi.org/10.1016/j.scitotenv.2015.05.003

Understanding individual tracers and tracer selection | CTS and CR methods

Latorre, B., Lizaga, I., Gaspar, L., Navas, A. 2021. A novel method for analysing consistency and unravelling multiple solutions in sediment fingerprinting. Science of The Total Environment 789, 147804. https://doi.org/10.1016/j.scitotenv.2021.147804
Lizaga, I., Latorre, B., Gaspar, L., Navas, A. 2020. Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studies. Science of The Total Environment 720, 137537. https://doi.org/10.1016/j.scitotenv.2020.137537

Artificial samples for testing FingerPro model

Gaspar, L., Blake, W.H., Smith, G.H., Lizaga, I., Navas, A. 2019. Testing the sensitivity of a multivariate mixing model using geochemical fingerprints with artificial mixtures. Geoderma 337, 498-510. https://doi.org/10.1016/j.geoderma.2018.10.005

Combining geochemistry and isotopic tracers | CB method

Lizaga, I., Latorre, B., Gaspar, L., Navas, A. 2022. Combined use of geochemistry and compound-specific stable isotopes for sediment fingerprinting and tracing. Science of The Total Environment 832, 154834. https://doi.org/10.1016/j.scitotenv.2022.154834

Particle size effect

Gaspar, L., Blake, W.H., Lizaga, I., Latorre, B., Navas, A. 2022. Particle size effect on geochemical composition of experimental soil mixtures relevant for unmixing modelling. Geomorphology 403, 108178. https://doi.org/10.1016/j.geomorph.2022.108178

Exceptional storm events effects

Gaspar, L., Lizaga, I., Blake, W.H., Latorre, B., Quijano, L., Navas, A. 2019. Fingerprinting changes in source contribution for evaluating soil response during an exceptional rainfall in Spanish pre-pyrenees. Journal of Environmental Management 240, 136-148. https://doi.org/10.1016/j.jenvman.2019.03.109
Lizaga, I., Gaspar, L., Blake, W.H., Latorre, B., Navas, A. 2019. Fingerprinting changes of source apportionments from mixed land uses in stream sediments before and after an exceptional rainstorm event. Geomorphology 341, 216-229. https://doi.org/10.1016/j.geomorph.2019.05.015

Sediment source fingerprinting in Mediterranean Environments

Lizaga, I., Gaspar, L., Latorre, B., Navas, A. 2020. Variations in transport of suspended sediment and associated elements induced by rainfall and agricultural cycle in a Mediterranean agroforestry catchment. Journal of Environmental Management 272, 111020. https://doi.org/10.1016/j.jenvman.2020.111020
Palazón, L., Gaspar, L., Latorre, B., Blake, W.H., Navas, A., 2015. Identifying sediment sources by applying a fingerprinting mixing model in a Pyrenean drainage catchment. Journal of Soils Sediments 15, 2067–2085. https://doi.org/10.1007/s11368-015-1175-6

Sediment source fingerprinting in Glacial Landscapes

Navas, A., Ramírez, E., Gaspar, L., Lizaga, I., Stott, T., Rojas, F., Latorre, B. and Dercon, G. 2024. The impact of glacier retreat on Andean high wetlands: Assessing the geochemical transfer and sediment provenance in the proglacial area of Huayna-Potosí (Bolivia). Geomorphology 460, 109250. https://doi.org/10.1016/j.geomorph.2024.109250
Golosov, V., Navas, A., Castillo, A., Mavlyudov, B., Kharchenko, S., Lizaga, I., Gaspar, L., Dercon, G. 2024. Sediment source analysis in the korabelny stream catchment, King George Island, maritime Antarctica: Geomorphological survey, fingerprinting and delivery rate assessment. Geomorphology 461, 109312. https://doi.org/10.1016/j.geomorph.2024.109312
Navas, A., Lizaga, I., Santillán, N., Gaspar, L., Latorre, B., Dercon, G. 2022. Targeting the source of fine sediment and associated geochemical elements by using novel fingerprinting methods in proglacial tropical highlands (Cordillera Blanca, Perú). Hydrological Processes 36(8), 4662. https://doi.org/10.1002/hyp.14662
Navas, A., Lizaga, I., Gaspar, L., Latorre, B., Dercon, G. 2020. Unveiling the provenance of sediments in the moraine complex of Aldegonda Glacier (Svalbard) after glacial retreat using radionuclides and elemental fingerprints. Geomorphology 367, 107304. https://doi.org/10.1016/j.geomorph.2020.107304

Combining catchment modelling and sediment fingerprinting

Palazón, L., Latorre, B., Gaspar, L., Blake, W.H., Smith, H.G., Navas, A. 2016. Combining catchment modelling and sediment fingerprinting to assess sediment dynamics in a Spanish Pyrenean river system. Science of the Total Environment 569, 1136-1148. https://doi.org/10.1016/j.scitotenv.2016.06.189
Palazón, L., Gaspar, L., Latorre, B., Blake, W.H., Navas, A. 2014. Evaluating the importance of surface soil contributions to reservoir sediment in alpine environments: a combined modelling and fingerprinting approach in the Posets-Maladeta Natural Park. Solid Earth 5, 963–978. https://doi.org/10.5194/se-5-963-2014

Pollutants

Mohammadi, M., Egli, M., Kavian, A., Lizaga, I., 2023. Static and dynamic source identification of trace elements in river and soil environments under anthropogenic activities in the Haraz plain, Northern Iran. Science of the Total Environment, 892, 164432. https://doi.org/10.1016/j.scitotenv.2023.164432

Mining

Crespo, J., Holley, E., Guillen, M., Lizaga, I., Ticona, S., Simon, I., Garcia-Chevesich, P.A. Martínez, G. 2023. Tracking Sediment Provenance Applying a Linear Mixing Model Approach Using R’s FingerPro Package, in the Mining-Influenced Ocoña Watershed, Southern Peru. Sustainability 15(15), 11856. https://doi.org/10.3390/su151511856

About FingerPro

Tips (Methodological Principles)