Scripts from: A framework to diagnose the causes of river ecosystem deterioration using biological symptoms

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