Sovan Lek

Predicting assemblages and species richness of endemic fish in the upper Yangtze River.

The present work describes the ability of two modeling methods, Classification and Regression Tree (CART) and Random Forest (RF), to predict endemic fish assemblages and species richness in the upper Yangtze River, and then to identify the determinant environmental factors contributing to the models. The models included 24 predictor variables and 2 response variables (fish assemblage and species richness) for a total of 46 site units. The predictive quality of the modeling approaches was judged with a leave-one-out validation procedure. There was an average success of 60.9% and 71.7% to assign each site unit to the correct assemblage of fish, and 73% and 84% to explain the variance in species richness, by using CART and RF models, respectively. RF proved to be better than CART in terms of accuracy and efficiency in ecological applications. In any case, the mixed models including both land cover and river characteristic variables were more powerful than either individual one in explaining the endemic fish distribution pattern in the upper Yangtze River. For instance, altitude, slope, length, discharge, runoff, farmland and alpine and sub-alpine meadow played important roles in driving the observed endemic fish assemblage structure, while farmland, slope grassland, discharge, runoff, altitude and drainage area in explaining the observed patterns of endemic species richness. Therefore, the various effects of human activity on natural aquatic ecosystems, in particular, the flow modification of the river and the land use changes may have a considerable effect on the endemic fish distribution patterns on a regional scale.

Strengthening the link between climate, hydrological and species distribution modeling to assess the impacts of climate change on freshwater biodiversity.

To understand the resilience of aquatic ecosystems to environmental change, it is important to determine how multiple, related environmental factors, such as near-surface air temperature and river flow, will change during the next century. This study develops a novel methodology that combines statistical downscaling and fish species distribution modeling, to enhance the understanding of how global climate changes (modeled by global climate models at coarse-resolution) may affect local riverine fish diversity. The novelty of this work is the downscaling framework developed to provide suitable future projections of fish habitat descriptors, focusing particularly on the hydrology which has been rarely considered in previous studies. The proposed modeling framework was developed and tested in a major European system, the Adour-Garonne river basin (SW France, 116,000 km(2)), which covers distinct hydrological and thermal regions from the Pyrenees to the Atlantic coast. The simulations suggest that, by 2100, the mean annual stream flow is projected to decrease by approximately 15% and temperature to increase by approximately 1.2 °C, on average. As consequence, the majority of cool- and warm-water fish species is projected to expand their geographical range within the basin while the few cold-water species will experience a reduction in their distribution. The limitations and potential benefits of the proposed modeling approach are discussed.

Effects of damming on population sustainability of Chinese sturgeon, Acipenser sinensis : evaluation of optimal conservation measures

The numbers of spawning sites for Chinese sturgeon have been drastically reduced since the construction of the Gezhouba Dam across the Yangtze River. This dam has blocked migration of Chinese sturgeon to their historic spawning ground causing a significant decline of the Chinese sturgeon population. We conducted a VORTEX population viability analysis to estimate the sustainability of the population and to quantify the efficiency of current and alternative conservation procedures. The model predicted the observed decline of Chinese sturgeon, resulting from the effect of the Gezhouba Dam. These simulations demonstrated the potential interest of two conservation measures: increasing spawning area and reducing predation on sturgeon eggs. The simulations also demonstrated that the actual restocking program is not sufficient to sustain sturgeon population as the artificial reproduction program induce the loss of more wild mature adults that the recruitment expected by the artificial reproduction.

Patterning ecological risk of pesticide contamination at the river basin scale

Ecological risk assessment was conducted to determine the risk posed by pesticide mixtures to the Adour-Garonne river basin (south-western France). The objectives of this study were to assess the general state of this basin with regard to pesticide contamination using a risk assessment procedure and to detect patterns in toxic mixture assemblages through a self-organizing map (SOM) methodology in order to identify the locations at risk. Exposure assessment, risk assessment with species sensitivity distribution, and mixture toxicity rules were used to compute six relative risk predictors for different toxic modes of action: the multi-substance potentially affected fraction of species depending on the toxic mode of action of compounds found in the mixture (msPAF CA(TMoA) values). Those predictors computed for the 131 sampling sites assessed in this study were then patterned through the SOM learning process. Four clusters of sampling sites exhibiting similar toxic assemblages were identified. In the first cluster, which comprised 83% of the sampling sites, the risk caused by pesticide mixture toward aquatic species was weak (mean msPAF value for those sites<0.0036%), while in another cluster the risk was significant (mean msPAF<1.09%). GIS mapping allowed an interesting spatial pattern of the distribution of sampling sites for each cluster to be highlighted with a significant and highly localized risk in the French department called Lot et Garonne. The combined use of the SOM methodology, mixture toxicity modelling and a clear geo-referenced representation of results not only revealed the general state of the Adour-Garonne basin with regard to contamination by pesticides but also enabled to analyze the spatial pattern of toxic mixture assemblage in order to prioritize the locations at risk and to detect the group of compounds causing the greatest risk at the basin scale.

High intraspecific variability in the functional niche of a predator is associated with ontogenetic shift and individual specialization

Investigations on the functional niche of organisms have primarily focused on differences among species and tended to neglect the potential effects of intraspecific variability despite the fact that its potential ecological and evolutionary importance is now widely recognized. In this study, we measured the distribution of functional traits in an entire population of largemouth bass (Micropterus salmoides) to quantify the magnitude of intraspecific variability in functional traits and niche (size, position, and overlap) between age classes. Stable isotope analyses (δ13C and δ15N) were also used to determine the association between individual trophic ecology and intraspecific functional trait variability. We observed that functional traits were highly variable within the population (mean coefficient variation: 15.62% ± 1.78% SE) and predominantly different between age classes. In addition, functional and trophic niche overlap between age classes was extremely low. Differences in functional niche between age classes were associated with strong changes in trophic niche occurring during ontogeny while, within age classes, differences among individuals were likely driven by trophic specialization. Each age class filled only a small portion of the total functional niche of the population and age classes occupied distinct portions in the functional space, indicating the existence of ontogenetic specialists with different functional roles within the population. The high amplitude of intraspecific variability in functional traits and differences in functional niche position among individuals reported here supports the recent claims for an individual-based approach in functional ecology.

Evidence of Water Quality Degradation in Lower Mekong Basin Revealed by Self-Organizing Map.

To reach a better understanding of the spatial variability of water quality in the Lower Mekong Basin (LMB), the Self-Organizing Map (SOM) was used to classify 117 monitoring sites and hotspots of pollution within the basin identified according to water quality indicators and US-EPA guidelines. Four different clusters were identified based on their similar physicochemical characteristics. The majority of sites in upper (Laos and Thailand) and middle part (Cambodia) of the basin were grouped in two clusters, considered as good quality water with high DO and low nutrient levels. The other two clusters were mostly composed of sites in Mekong delta (Vietnam) and few sites in upstream tributaries (i.e., northwestern Thailand, Tonle Sap Lake, and swamps close to Vientiane), known for moderate to poor quality of water and characterized by high nutrient and dissolved solid levels. Overall, we found that the water in the mainstream was less polluted than its tributaries; eutrophication and salinity could be key factors affecting water quality in LMB. Moreover, the seasonal variation of water quality seemed to be less marked than spatial variation occurring along the longitudinal gradient of Mekong River. Significant degradations were mainly associated with human disturbance and particularly apparent in sites distributed along the man-made canals in Vietnam delta where population growth and agricultural development are intensive.

Assessment of stream biological responses under multiple-stress conditions.

Background, aim and scopeDue to the numerous anthropogenic stress factors that affect aquatic ecosystems, a better understanding of the adverse consequences on the biological community of combined pressures is needed to attain the objectives of the European Water Framework Directive. In this study we propose an innovative approach to assess the biological impact of toxicants under field conditions on a large spatial scale.Materials and methodsArtificial Neural Network (ANN) analyses, focusing on impacts at the community level, were carried out to identify the relative importance of environmental and toxic stress factors on the patterns observed in the aquatic invertebrate fauna from the Scheldt basin (Belgium).Results and discussionOur results show that the use of the backpropagation algorithm of the ANN is a promising method to highlight the relationship between environmental pollution and biological responses. This method allows the effects of chemical exposure to be distinguished from the effects caused by other stressors in running waters. Moreover, the use of an overall estimate for toxic pressure in predictive models enables the links between toxicants and community alterations in the field to be clarified. The ANN correctly predicts 74% of samples with an area under the curve of 0.89 and a Cohen’s κ coefficient of 0.64. Organic load, oxygen availability, water temperature and the nitrate concentration appeared important factors in predicting aquatic invertebrate assemblages. On the other hand, toxic pressure did not seem relevant for these assemblages, suggesting that the water quality characteristics were therefore more important than exposure to toxicants in the water phase for the aquatic invertebrate communities in the study area. However, we suggest that the high organic load encountered in the Scheldt basin may lead to an underestimation of the impact of toxicity.

Scale-dependent effects of land cover on water physico-chemistry and diatom-based metrics in a major river system, the Adour-Garonne basin (South Western France)

The scale dependence of ecological phenomena remains a central issue in ecology. Particularly in aquatic ecology, the consideration of the accurate spatial scale in assessing the effects of landscape factors on stream condition is critical. In this context, our study aimed at assessing the relationships between multi-spatial scale land cover patterns and a variety of water quality and diatom metrics measured at the stream reach level. This investigation was conducted in a major European river system, the Adour-Garonne river basin, characterized by a wide range of ecological conditions. Redundancy analysis (RDA) and variance partitioning techniques were used to disentangle the different relationships between land cover, water-chemistry and diatom metrics. Our results revealed a top-down cascade effect indirectly linking diatom metrics to land cover patterns through water physico-chemistry, which occurred at the largest spatial scales. In general, the strength of the relationships between land cover, physico-chemistry, and diatoms was shown to increase with the spatial scale, from the local to the basin scale, emphasizing the importance of continuous processes of accumulation throughout the river gradient. Unexpectedly, we established that the influence of land cover on the diatom metric was of primary importance both at the basin and local scale, as a result of discontinuous but not necessarily antagonist processes. The most detailed spatial grain of the Corine land cover classification appeared as the most relevant spatial grain to relate land cover to water chemistry and diatoms. Our findings provide suitable information to improve the implementation of effective diatom-based monitoring programs, especially within the scope of the European Water Framework Directive.

Temporal Patterns of Larval Fish Occurrence in a Large Subtropical River

Knowledge of temporal patterns of larval fish occurrence is limited in south China, despite its ecological importance. This research examines the annual and seasonal patterns of fish larval presence in the large subtropical Pearl River. Data is based on samples collected every two days, from 2006 to 2013. In total, 45 taxa representing 13 families and eight orders were sampled. The dominant larval family was Cyprinidae, accounting for 27 taxa. Squaliobarbus curriculus was the most abundant species, followed by Megalobrama terminalis, Xenocypris davidi, Cirrhinus molitorella, Hemiculter leuscisculus and Squalidus argentatus. Fish larvae abundances varied significantly throughout the seasons (multivariate analyses: Cluster, SIMPROF and ANOSIM). The greatest numbers occurred between May and September, peaking from June through August, which corresponds to the reproductive season. In this study, redundancy analysis was used to describe the relationship between fish larval abundance and associated environmental factors. Mean water temperature, river discharge, atmospheric pressure, maximum temperature and precipitation play important roles in larval occurrence patterns. According to seasonal variations, fish larvae occurrence is mainly affected by water temperature. It was also noted that the occurrence of Salanx reevesii and Cyprinus carpio larvae is associated with higher dissolved oxygen (DO) concentrations, higher atmospheric pressure and lower water temperatures which occur in the spring. On the other hand, M. terminalis, X. davidi, and C. molitorella are associated with high precipitation, high river discharge, low atmospheric pressure and low DO concentrations which featured during the summer months. S. curriculus also peaks in the summer and is associated with peak water temperatures and minimum NH3–N concentrations. Rhinogobius giurinus occur when higher atmospheric pressure, lower precipitation and lower river discharges occur in the autumn. Dominant fish species stagger their spawning period to avoid intraspecific competition for food resources during early life stages; a coexistence strategy to some extent. This research outlines the environmental requirements for successful spawning for different fish species. Understanding processes such as those outlined in this research paper is the basis of conservation of fish community diversity which is a critical resource to a successful sustainable fishery in the Pearl River.

diatSOM: a R-package for diatom biotypology using self-organizing maps

Owing to the high complexity of diatom community data, there is a special need for methods accounting for complex non-linear gradients. A Kohonens self-organizing map (SOM) is a neural network with unsupervised learning. It allows both unbiased classification of the communities and visualization of biological gradients on a two-dimensional plane. However, as with other neural networks, many parameters must be set. A new R-package with a SOM parameterization specifically suited to diatom communities has been developed. Further developments will consist of creating a graphical user interface in order to make this method easier to use for the scientific community.