DOI: 10.5061/dryad.37pvmcvh6
title: Scripts from: A framework to diagnose the causes of river ecosystem deterioration using biological symptoms

description: River assessments are predominantly based upon biological metrics and
 indices selected or designed to integrate the impact of multiple causes of
 deterioration (stressors) operating at various spatial scales. Yet, the
 integrative nature of many bioassessment systems does not allow for
 tracing back individual stressors and their influence on the overall
 assessment result. Thus, river managers often fail to link bioassessment
 with programmes of management measures, to improve ecological quality.
 Here, we present a novel diagnostic approach that allows to estimate the
 probability of individual stressors being causal for biological
 degradation at the scale of individual riverine ecosystems. Similar to
 medical diagnosis, we use
 various symptoms (macroinvertebrate metrics) and
 probabilistically link them to various
 potential causes of ecological status degradation
 (stressors). Symptoms and causes are informed by a training dataset of 157
 samples (stressors, taxa lists) from central European lowland rivers and
 are linked through a Bayesian Network (BN). Three separate BNs addressing
 three different spatial scales (catchment, reach and site) are
 presented.  Water quality-related causes are most influential at
 the catchment scale, while hydromorphological causes prevail at finer
 scales. Causes indicating riparian degradation are most influential at the
 reach scale. Many symptoms show strong linkages to causes and reveal
 ecologically meaningful relationships, thus pointing at the potential
 diagnostic utility of the symptoms selected. BNs are validated using an
 independent dataset of 47 samples. Overall, model accuracies
 range 53–58% for the three BNs, while for individual nodes (causes and
 symptoms) up to 100% concordance of predicted and actual node states in
 the validation data is achieved. The BNs are implemented as interactive
 online diagnostic tools to allow end users an easy application. 
 Synthesis and applications. Our results confirm that Bayesian
 inference can greatly assist the diagnosis of potential causes of river
 deterioration based upon a selection of diagnostic biological metrics. If
 integrated into a Bayesian Network, symptoms and potential causes can be
 linked and inform management decisions on appropriate measures, to improve
 ecological quality. Diagnostic Bayesian Networks thus support end users
 bridge the gap between biological monitoring and appropriate programmes of
 management measures. 28 July 2020
description: The .zip archive contains a selection of files and data required
 to calculate the <strong>catchment</strong> Bayesian Network
 and to implement the network as an interactive online
 application.  The selection includes the following
 files and folders:<br> - file app.R: R script to run the online
 application via Shiny <br> - file code_for_shiny.R: R script to
 calculate the conditional probabilities in the catchment Bayesian Network
 (BN)<br> - file data_for_shiny_catchment.Rdata: R data file
 containing the catchment model's data<br> - folder data and
 text: collection of .csv files containing numerical and
 textual information required to run the online application<br> -
 folder network file: contains the .net version of the BN file as
 exported from the GUI "GeNie"; this allows to develop (and
 illustrate) a BN via a GUI (e.g. GeNie or Netica);<br> the resulting
 network file can be directly imported into R and used to calculate the
 posterior probabilities for the online application
description: The reach and the site scale Diagnostic Tools run accordingly and
 are not covered here. We also do not present here the additional
 information provided,<br> when a user selects a candidate
 cause's name in the online application. This information can be
 easily included via .html files (one file per cause to be <br>
 stored in a folder named html_files) and an illustration or photo (to be
 stored in a folder named www). The three online
 applications are available
 at: http://www.freshwaterplatform.eu/index.php/mars-diagnostic-tools.html  Additonal information is available in the attached Readme.txt