Incorporating ecological knowledge into ecoinformatics: An example of modeling hierarchically structured aquatic communities with neural networks

Evaluation of Shallow Groundwater Quality at Regional Scales Using Adaptive Water Quality Indices

Groundwater, which is the main source of water for human consumption in many rural areas, has its quality determined by the complex interaction of environmental factors and anthropogenic activities. The present study evaluated the quality of shallow groundwater (1 to 25 m depth) in the rural area of the Târgovişte Plain, a densely populated area (200 inhabitants/km²) using 80 water samples collected from public wells. In order to explain the spatial distribution of the concentrations of the 19 physicochemical parameters considered (including heavy metals), the evaluation of groundwater quality for human consumption and potential impact on human health was conducted using the Water Quality Index (WQI), Integrated Weight Water Quality Index (IwWQI), Total Hazard Index (THI), and cumulative carcinogenic risk (CCR). For the WQI/IwWQI the comparative analysis of the two indices showed that for the WQI, it is important to select an optimal set of parameters, because use of a large number of physicochemical parameters can eclipse the values that exceed WHO guideline limits. In contrast, the use of entropy in the calculation of the IwWQI did not lead to eclipsing of exceedance, no matter the number of parameters used. Areas with poor and very poor groundwater quality according to the WQI/IwWQI overlapped, with a moderate risk to human health (THI > 1) for noncarcinogenic contaminants and also a risk of developing cancer according to the CCR average value (1.15 × 10^-2). The health of 43% of the rural population in the Târgovişte Plain can be affected if they drink contaminated groundwater, and it is estimated that about 600 people can develop cancer during their lifetime. If the risk of developing cancer is reduced only in the rural population that does not have access to a water source from a centralized and verified network, the results suggest that 385 people (1.15%) can develop cancer as a result of consuming groundwater contaminated with heavy metals based on the average value of CCR. This value is lower than the general mortality rate in areas with high CCR and below the average number of cancer patients in Romania (2.65%). The quality of groundwater and the risk of developing diseases and cancer due to water consumption is directly proportional to the intensity of agricultural land use and inversely proportional to the depth of the groundwater layer, the distance from the main hydrographic network and the reservoirs, and the distance from the main city, Târgovişte. The complex and integrated analysis of groundwater quality using quality indices and indicators of health risk for the population, validated by hot-spot analysis and compared to the mortality rate, is an approach with practical applicability. This integrated approach allows public authorities, policymakers, and health services to implement an efficient monitoring program and optimize anthropogenic activities in order to prevent groundwater contamination and finally improve the quality of life for the residents in the area of this study.

An interpretable machine learning method for supporting ecosystem management: Application to species distribution models of freshwater macroinvertebrates

Species distribution models (SDMs), in which species occurrences are related to a suite of environmental variables, have been used as a decision-making tool in ecosystem management. Complex machine learning (ML) algorithms that lack interpretability may hinder the use of SDMs for ecological explanations, possibly limiting the role of SDMs as a decision-support tool. To meet the growing demand of explainable MLs, several interpretable ML methods have recently been proposed. Among these methods, SHaply Additive exPlanation (SHAP) has drawn attention for its robust theoretical justification and analytical gains. In this study, the utility of SHAP was demonstrated by the application of SDMs of four benthic macroinvertebrate species. In addition to species responses, the dataset contained 22 environmental variables monitored at 436 sites across five major rivers of South Korea. A range of ML algorithms was employed for model development. Each ML model was trained and optimized using 10-fold cross-validation. Model evaluation based on the test dataset indicated strong model performance, with an accuracy of ≥0.7 in all evaluation metrics for all MLs and species. However, only the random forest algorithm showed a behavior consistent with the known ecology of the investigated species. SHAP presents an integrated framework in which local interpretations that incorporate local interaction effects are combined to represent the global model structure. Consequently, this framework offered a novel opportunity to assess the importance of variables in predicting species occurrence, not only across sites, but also for individual sites. Furthermore, removing interaction effects from variable importance values (SHAP values) clearly revealed non-linear species responses to variations in environmental variables, indicating the existence of ecological thresholds. This study provides guidelines for the use of a new interpretable method supporting ecosystem management.

A predictive index based on environmental filters for the bioassessment of river basins without reference areas in Atlantic Forest biome, Brazil

Abstract: Biological assessments that use the reference condition approach are based on the concept of comparing a site's observed biology to sites where disturbance is minimal or absent. However, in many regions of the world, such areas are scarce or nonexistent. In this study, an alternative approach proposed by Chessman and Royal for bioassessment without reference areas based on environmental filters was tested in Brazil. This approach assumes that key environmental features act in the selection of potential colonists, from a regional pool of taxa, based on the ecological traits (tolerances) possessed by each taxon. We developed the approach by: 1) determining the regional pool, based on a large Atlantic Forest biome database; 2) selecting environmental filters (elevation, original vegetation and soil type); and 3) including information on the tolerance and preferences of aquatic insects to these filters. With this information we were able to determine the expected taxon under natural conditions and compare with observed taxon, developing a predictive index (Observed/Expected). Although the model was intended to predict the fauna in regions without reference sites, we included reference areas to test the model responsiveness, precision and sensitivity. Our results indicated that the index was able to discriminate impairment classes (F=56.9; p<0,001), it has high precision due to low standard deviation across reference sites values (SD=0.098) and high sensitivity due the correlation with environmental variables that are sensitive to human alteration (r=0.74, p<0.01). Also, it was strongly correlated with multimetric indices developed for multiple watersheds in the state, showing agreement between the methods in relation to ecological quality classification. Even though the predictive index had performed well in our study, we make some considerations that may help to improve its sensitivity of similar methods that are being tested using the environmental filters approach.

Modelling native fish richness to evaluate the effects of hydromorphological changes and river restoration (Júcar River Basin, Spain)

The richness of native fish is considered to be an indicator of aquatic ecosystem health, and improving richness is a key goal in the management of river ecosystems. An artificial neural network (ANN) model based on field data from 90 sample sites distributed throughout the Júcar River Basin District was developed to predict the native fish species richness (NFSR). The Levenberg-Marquardt learning algorithm was used for model training. When constructing the model, we tried different numbers of neurons (hidden layers), compared different transfer functions, and tried different k values (from 3 to 10) in the k-fold cross-validation method. This process and the final selection of key variables with relevant ecological meaning support the reliability and robustness of the final ANN model. The partial derivatives method was applied to determine the relative importance of input environmental variables. The final ANN model combined variables describing riparian quality, water quality, and physical habitat and helped identify the primary drivers of the NFSR patterns in Mediterranean rivers. In the second part of the study, the model was used to evaluate the effectiveness of two restoration actions in the Júcar River: the removal of two abandoned weirs and the progressive increase in the proportion of riffles. The model indicated that the combination of these actions produced a rise in NFSR, which ultimately reached the maximum values observed in the reference site of that river ecotype (sensu the European Water Framework Directive). The results demonstrate the importance of longitudinal connectivity and riffle proportion for improving NFSR and the power of ANNs to help decisions in the management and ecological restoration of Mediterranean rivers. Furthermore, this model at the basin scale is the first step for further research on the effects of water scarcity and global change on Mediterranean fish communities.

Fish response to the temporal hierarchy of the natural flow regime in the Daly River, northern Australia

In this study, relationships between flow variation across multiple temporal scales and the distribution and abundance of three fish species, western rainbowfish Melanotaenia australis, sooty grunter Hephaestus fuliginosus and barramundi Lates calcarifer were examined at eight sampling reaches in the Daly River, Northern Territory, Australia. Discharge was highly seasonal during the study period of 2006-2010 with a distinct wet-dry discharge pattern. Significant catchment-wide correlations were identified between species abundance and hydrologic variables across several scales describing the magnitude and variability of flow. A Bayesian hierarchical model which accounted for >80% of variation in abundances for all species and age classes (i.e. juvenile and adult), identified the extent to which the influence of short-term flow variation was dependent upon the historical flow regime. There were distinct ontogenetic differences in these relationships for H. fuliginosus, with variability of recent flows having a negative effect on juveniles which was stronger at locations with higher historical mean daily flow. Lates calcarifer also displayed ontogenetic differences in relationships to flow variation with adults showing a positive association with increase in recent flows and juveniles showing a negative one. The effect of increased magnitude of wet-season flows on M. australis was negative in locations with lower historical mean daily flow but positive in locations with higher historical mean daily flow. The results highlighted how interactions between multiple scales of flow variability influence the abundance of fish species according to their life-history requirements.

Beyond data management: how ecoinformatics can benefit environmental monitoring programs

We review ways in which the new discipline of ecoinformatics is changing how environmental monitoring data are managed, synthesized, and analyzed. Rapid improvements in information technology and strong interest in biodiversity and sustainable ecosystems are driving a vigorous phase of development in ecological databases. Emerging data standards and protocols enable these data to be shared in ways that have previously been difficult. We use the U.S. Environmental Protection Agency's National Coastal Assessment (NCA) as an example. The NCA has collected biological, chemical, and physical data from thousands of stations around the U.S. coasts since 1990. NCA data that were collected primarily to assess the ecological condition of the U.S. coasts can be used in innovative ways, such as biogeographical studies to analyze species invasions. NCA application of ecoinformatics tools leads to new possibilities for integrating the hundreds of thousands of NCA species records with other databases to address broad-scale and long-term questions such as environmental impacts, global climate change, and species invasions.