DOI: 10.5061/dryad.5x69p8d17
title: Data 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-Jul-2020
description: The dataset consists of river benthic macroinvertebrate metrics
 and accompanying environmental covariates sampled between 2000 and 2005
 from sites in the central European lowlands. Macroinvertebrate metrics
 encompass assessment indices, diversity measures and traits (e.g. feeding
 types). The metrics were calculated from taxalists (mainly species-level
 identification) using the traits and ecological characteristics provided
 by www.freshwaterecology.info. Macroinvertebrate taxa lists were obtained
 by multi-habitat sampling at 50–100 m long river stretches using a hand
 net (frame 50 x 50 cm, mesh: 500 µm). Samples were preserved in ethanol
 (70% end concentration) and stored for determination in the
 lab.  Environmental covariates encompass
 physical-chemical, hydrological, morphological (incl. microhabitats) and
 land cover variables at various levels of intensity, i.e at different
 levels of human-induced stress imposed on the riverine macroinvertebrate
 community. Site and reach-scaled variables (physical-chemical,
 morphological, hydrological) were recorded in parallel to biological
 sampling, while land cover and other catchment-scale data were obtained
 through maps in a GIS. Field sampling methodologies and
 lab procedures including the chemical analysis of water samples, the
 determination of macroinvertebrates and the calculation of metrics are
 described in detail in two publications: <br> - Feld, C.K. (2004)
 Identification and measure of hydromorphological degradation in Central
 European lowland streams. Hydrobiologia, 516, 69–90.<br> - Feld,
 C.K. & Hering, D. (2007) Community structure or function: effects
 of environmental stress on benthic macroinvertebrates at different spatial
 scales. Freshwater Biology, 52, 1380–1399.
description: The dataset is provided as an Excel file. The first table
 contains the explanation of column codes, the variables units and the
 references to the variables, if available. The second
 table contains the matrix of 144 samples (rows) by environmental variables
 and biological metrics (columns) used to develop and train the Bayesian
 Networks. The third table contains a similar matrix of
 47 independent samples and environmental/biological variables used to
 validate the Bayesian Networks. This validation set contains a number of
 missing values (coded NA).  Please note that the data
 contain original measurements and records, respectively, which may require
 discretisation, if to be used for the training and validation of a
 discretised Bayesian Network. For further details,
 please contact christian.feld@uni-due.de.