Characterization of biological responses under different environmental conditions: A hierarchical modeling approach

Discovering Ecological Relationships in Flowing Freshwater Ecosystems

Knowledge of ecological responses to changes in the environment is vital to design appropriate measures for conserving biodiversity. Experimental studies are the standard to identify ecological cause-effect relationships, but their results do not necessarily translate to field situations. Deriving ecological cause-effect relationships from observational field data is, however, challenging due to potential confounding influences of unmeasured variables. Here, we present a causal discovery algorithm designed to reveal ecological relationships in rivers and streams from observational data. Our algorithm (a) takes into account the spatial structure of the river network, (b) reveals the complete network of ecological relationships, and (c) shows the directions of these relationships. We apply our algorithm to data collected in the US state of Ohio to better understand causes of reductions in fish and invertebrate community integrity. We found that nitrogen is a key variable underlying fish and invertebrate community integrity in Ohio, likely negatively impacting both. We also found that fish and community integrity are each linked to one physical habitat quality variable. Our algorithm further revealed a split between physical habitat quality and water quality variables, indicating that causal relations between these groups of variables are likely absent. Our approach is able to reveal networks of ecological relationships in rivers and streams based on observational data, without the need to formulate a priori hypotheses. This is an asset particularly for diagnostic assessments of the ecological state and potential causes of biodiversity impairment in rivers and streams.

Habitat Suitability Curves for Freshwater Macroinvertebrates of Tropical Andean Rivers

Sustainable river management requires a thorough understanding of the response of aquatic biota to riverine microhabitat variability. The purpose of this study was to assess macroinvertebrate hydraulic-habitat suitability in Ecuadorian Andean rivers to support habitat modelling for sustainable ecosystem management. 597 macroinvertebrate samples were collected from ten sampling stations the Yanuncay River, Ecuador. Physical, chemical, hydraulic and habitat variables were measured/calculated. Froude number, Reynolds number, substrate index and algae coverage were major drivers of macroinvertebrate response, and were used to develop suitability curves for Baetodes, Andesiops, Camelobaetidius, Ecuaphlebia, Anacroneuria, Atopsyche, Simulium and Palpomyia using General Additive Models. Standardised density contours of taxa as functions of hydraulic and habitat variables were also developed. Taxonomic response was related to body structures/shapes and feeding habits. Baetodoes, Simulium, Anacroneuria and Atopsyche preferred fast flowing waters, and thus, they could be significantly affected in case of flow reduction. Similar habitat suitability curves were developed from the main river and the tributaries, possibly due to the short distance between the sampling stations. This study fills a major knowledge gap by developing macroinvertebrate habitat suitability curves for future physical habitat simulations and environmental flow assessments in the Andean region.

Habitat usage of Daubenton's bat (Myotis daubentonii), common pipistrelle (Pipistrellus pipistrellus), and soprano pipistrelle (Pipistrellus pygmaeus) in a North Wales upland river catchment

Distributions of Daubenton's bat (Myotis daubentonii), common pipistrelle, (Pipistrellus pipistrellus), and soprano pipistrelle (Pipistrellus pygmaeus) were investigated along and altitudinal gradient of the Lledr River, Conwy, North Wales, and presence assessed in relation to the water surface condition, presence/absence of bank-side trees, and elevation. Ultrasound recordings of bats made on timed transects in summer 1999 were used to quantify habitat usage. All species significantly preferred smooth water sections of the river with trees on either one or both banks; P. pygmaeus also preferred smooth water with no trees. Bats avoided rough and cluttered water areas, as rapids may generate high-frequency echolocation-interfering noise and cluttered areas present obstacles to flight. In lower river regions, detections of bats reflected the proportion of suitable habitat available. At higher elevations, sufficient habitat was available; however, bats were likely restricted due to other factors such as a less predictable food source. This study emphasizes the importance of riparian habitat, bank-side trees, and smooth water as foraging habitat for bats in marginal upland areas until a certain elevation, beyond which bats in these areas likely cease to forage. These small-scale altitudinal differences in habitat selection should be factored in when designing future bat distribution studies and taken into consideration by conservation planners when reviewing habitat requirements of these species in Welsh river valleys, and elsewhere within the United Kingdom.

Assessing the impact of multiple stressors on aquatic biota: the receptor's side matters

Aquatic ecosystems are confronted with multiple stress factors. Current approaches to assess the risk of anthropogenic stressors to aquatic ecosystems are developed for single stressors and determine stressor effects primarily as a function of stressor properties. The cumulative impact of several stressors, however, may differ markedly from the impact of the single stressors and can result in nonlinear effects and ecological surprises. To meet the challenge of diagnosing and predicting multiple stressor impacts, assessment strategies should focus on properties of the biological receptors rather than on stressor properties. This change of paradigm is required because (i) multiple stressors affect multiple biological targets at multiple organizational levels, (ii) biological receptors differ in their sensitivities, vulnerabilities, and response dynamics to the individual stressors, and (iii) biological receptors function as networks, so that actions of stressors at disparate sites within the network can lead via indirect or cascading effects, to unexpected outcomes.

Prediction of biological integrity based on environmental similarity--revealing the scale-dependent link between study area and top environmental predictors

Indices of Biological integrity (IBI) are considered valid indicators of the overall health of a water body because the biological community is an endpoint within natural systems. However, prediction of biological integrity using information from multi-parameter environmental observations is a challenging problem due to the hierarchical organization of the natural environment, the existence of nonlinear inter-dependencies among variables as well as natural stochasticity and measurement noise. We present a method for predicting the Fish Index of Biological Integrity (IBI) using multiple environmental observations at the state-scale in Ohio. Instream (chemical and physical quality) and offstream parameters (regional and local upstream land uses, stream fragmentation, and point source density and intensity) are used for this purpose. The IBI predictions are obtained using the environmental site-similarity concept and following a simple to implement leave-one-out cross validation approach. An IBI prediction for a sampling site is calculated by averaging the observed IBI scores of observations clustered in the most similar branch of a dendrogram--a hierarchical clustering tree of environmental observations--built using the rest of the observations. The standardized Euclidean distance is used to assess dissimilarity between observations. The constructed predictive model was able to explain 61% of the IBI variability statewide. Stream fragmentation and regional land use explained 60% of the variability; the remaining 1% was explained by instream habitat quality. Metrics related to local land use, water quality, and point source density and intensity did not improve the predictive model at the state-scale. The impact of local environmental conditions was evaluated by comparing local characteristics between well- and mispredicted sites. Significant differences in local land use patterns and upstream fragmentation density explained some of the model's over-predictions. Local land use conditions explained some of the model's IBI under-predictions at the state-scale since none of the variables within this group were included in the best final predictive model. Under-predicted sites also had higher levels of downstream fragmentation. The proposed variables ranking and predictive modeling methodology is very well suited for the analysis of hierarchical environments, such as natural fresh water systems, with many cross-correlated environmental variables. It is computationally efficient, can be fully automated, does not make any pre-conceived assumptions on the variables interdependency structure (such as linearity), and it is able to rank variables in a database and generate IBI predictions using only non-parametric easy to implement hierarchical clustering.