Sensitivity of river fishes to climate change: The role of hydrological stressors on habitat range shifts

Shift towards Opportunistic Life-History of Sleeper in Response to Multi-Decadal Overfishing

Understanding long-term changes in life-history traits is central to assessing and managing freshwater fisheries. In this study, we explored how life-history traits have shifted in association with long-term change in population status for a native fish species (freshwater sleeper, Odontobutis sinensis, a by-catch species of shrimp traps) in the middle Yangtze lakes, China. We assessed the life-history traits of the species from Honghu Lake in 2016, where abundance had been dramatically lower following about 60 years of high fishing pressure, and made comparisons to similar data from Liangzi Lake (1957), when fishing intensity was low and abundance was high, and Bao’an Lake (1993–1994), when about 10 years of intense exploitation had occurred and abundance had greatly declined. Modern Honghu Lake sleeper exhibit life-history traits that are substantially more opportunistic compared to both of the historical populations. Modern fish were larger at age-1 and had significantly faster growth rates, a higher prevalence of sexually mature individuals and increased fecundities. Fish from the historical samples were larger and had higher age class diversity and delayed sexual maturation. Combined, the data suggest that faster growth towards early sexual maturation and reduced body sizes are associated with destabilized populations and ecosystems. Similar life-history patterns are common in other declined fish populations under exploitation. Recovering historic fish life-history dynamics requires conservation management policies aimed at reducing harvest and improving floodplain habitats.

Explore the relationship between fish community and environmental factors by machine learning techniques

In the face of multiple habitat alterations originating from both natural and anthropogenic factors, the fast-changing environments pose significant challenges for maintaining ecosystem integrity. Machine learning is a powerful tool for modeling complex non-linear systems through exploratory data analysis. This study aims at exploring a machine learning-based approach to relate environmental factors with fish community for achieving sustainable riverine ecosystem management. A large number of datasets upon a wide variety of eco-environmental variables including river flow, water quality, and species composition were collected at various monitoring stations along the Xindian River of Taiwan during 2005 and 2012. Then the complicated relationship and scientific essences of these heterogonous datasets are extracted using machine learning techniques to have a more holistic consideration in searching a guiding reference useful for maintaining river-ecosystem integrity. We evaluate and select critical environmental variables by the analysis of variance (ANOVA) and the Gamma test (GT), and then we apply the adaptive network-based fuzzy inference system (ANFIS) for an estimation of fish bio-diversity using the Shannon Index (SI). The results show that the correlation between model estimation and the biodiversity index is higher than 0.75. The GT results demonstrate that biochemical oxygen demand (BOD), water temperature, total phosphorus (TP), and nitrate-nitrogen (NO₃-N) are important variables for biodiversity modeling. The ANFIS results further indicate lower BOD, higher TP, and larger habitat (flow regimes) would generally provide a more suitable environment for the survival of fish species. The proposed methodology not only possesses a robust estimation capacity but also can explore the impacts of environmental variables on fish biodiversity. This study also demonstrates that machine learning is a promising avenue toward sustainable environmental management in river-ecosystem integrity.

FiCli, the Fish and Climate Change Database, informs climate adaptation and management for freshwater fishes

Inland fishes provide important ecosystem services to communities worldwide and are especially vulnerable to the impacts of climate change. Fish respond to climate change in diverse and nuanced ways, which creates challenges for practitioners of fish conservation, climate change adaptation, and management. Although climate change is known to affect fish globally, a comprehensive online, public database of how climate change has impacted inland fishes worldwide and adaptation or management practices that may address these impacts does not exist. We conducted an extensive, systematic primary literature review to identify peer-reviewed journal publications describing projected and documented examples of climate change impacts on inland fishes. From this standardized Fish and Climate Change database, FiCli (pronounced fick-lee), researchers and managers can query fish families, species, response types, or geographic locations to obtain summary information on inland fish responses to climate change and recommended management actions. The FiCli database is updatable and provides access to comprehensive published information to inform inland fish conservation and adaptation planning in a changing climate.

Using a hierarchical model framework to assess climate change and hydropower operation impacts on the habitat of an imperiled fish in the Jinsha River, China

Climate change and hydropower operations affect hydrological regimes at regional basin scales and impact hydrodynamics and habitat conditions for biota at the river reach scale. The present study proposes a hierarchical modeling framework for predicting and analyzing the impacts of climate change and hydropower on fish habitats. The approach couples multi-scale climate, hydrological, water temperature, hydrodynamic and habitat suitability models and was applied to a reach of the Jinsha River. Flow discharge and water temperature were predicted in the study area for a baseline scenario and three climate change scenarios, and each considered the presence and absence of impacts caused by hydropower operation. The impacts of flow discharge and water temperature variations on spawning and juvenile Coreius guichenoti, an imperiled warm-water fish in the Jinsha River Basin (JRB), were evaluated using a fuzzy logic-based habitat model. The results showed that habitat suitability and available usable area for the fish increased due to climate change, and water temperature rising was the main influencing factor. Water temperature decrease induced by hydropower operation in the spawning periods resulted in the reduction of available habitat area. However, climate change reduced the negative effects generated by hydropower operation, and the available habitat area for the fish would still be expected to increase under the combined impacts of climate change and hydropower operation in the future. It is predicted that water warming, as a result of climate change, is likely to eliminate the spawning postponement effect generated by hydropower operation on Coreius guichenoti as well as other warm-water fish species in the JRB. In contrast, water warming induced by climate change is likely to exacerbate the negative effects of hydropower operation on the spawning activity of cold-water fish species in the JRB. The present study provides a scheme to predict the impacts of climate change and hydropower on other organisms in river ecosystems. The results are beneficial for the development of long-term and adaptive conservation and restoration measures for aquatic ecosystems.

Potamodromous brown trout movements in the North of the Iberian Peninsula: Modelling past, present and future based on continuous fishway monitoring

Brown trout uses river flow and thermal regimens as main stimuli for initiating and maintaining behavioral reactions such as migration and spawning. Therefore, anthropogenic alterations on these factors may have strong impacts on its populations. The aim of this work is to understand these consequences by assessing potamodromous brown trout movements in the past and present, and to model future responses. For this, brown trout movements in a fishway in the Marin River (Bidasoa basin, Northern Iberian Peninsula) have been monitored from 2008 to 2017. Random forest regression has been used to assess the influence of environmental variables on brown trout movements and to model the response under hypothetical climatic and hydrological scenarios. Results show that brown trout uses the fishway during the whole year, with more upstream movements during the spawning season. The model is able to predict accurately the timing and number of migrants. Its use under hypothetical climate change and flow regulation scenarios shows a delay in the migration time. Therefore, modelling using large time series can be a powerful tool to define management and conservation strategies and prepare compensation measures for future scenarios.

Disentangling multiple pressures on fish assemblages in large rivers

European large rivers are exposed to multiple human pressures and maintained as waterways for inland navigation. However, little is known on the dominance and interactions of multiple pressures in large rivers and in particular inland navigation has been ignored in multi-pressure analyzes so far. We determined the response of ten fish population metrics (FPM, related to densities of diagnostic guilds and biodiversity) to 11 prevailing pressures including navigation intensity at 76 sites in eight European large rivers. Thereby, we aimed to derive indicative FPM for the most influential pressures that can serve for fish-based assessments. Pressures' influences, impacts and interactions were determined for each FPM using bootstrapped regression tree models. Increased flow velocity, navigation intensity and the loss of floodplains had the highest influences on guild densities and biodiversity. Interactions between navigation intensity and loss of floodplains and between navigation intensity and increased flow velocity were most frequent, each affecting 80% of the FPM. Further, increased sedimentation, channelization, organic siltation, the presence of artificial embankments and the presence of barriers had strong influences on at least one FPM. Thereby, each FPM was influenced by up to five pressures. However, some diagnostic FPM could be derived: Species richness, Shannon and Simpson Indices, the Fish Region Index and lithophilic and psammophilic guilds specifically indicate rhithralisation of the potamal region of large rivers. Lithophilic, phytophilic and psammophilic guilds indicate disturbance of shoreline habitats through both (i) wave action induced by passing vessels and (ii) hydromorphological degradation of the river channel that comes along with inland navigation. In European large rivers, inland navigation constitutes a highly influential pressure that adds on top of the prevailing hydromorphological degradation. Therefore, river management has to consider river hydromorphology and inland navigation to efficiently rehabilitate the potamal region of large rives.

Understanding multiple stressors in a Mediterranean basin: Combined effects of land use, water scarcity and nutrient enrichment

River basins are extremely complex hierarchical and directional systems that are affected by a multitude of interacting stressors. This complexity hampers effective management and conservation planning to be effectively implemented, especially under climate change. The objective of this work is to provide a wide scale approach to basin management by interpreting the effect of isolated and interacting factors in several biotic elements (fish, macroinvertebrates, phytobenthos and macrophytes). For that, a case study in the Sorraia basin (Central Portugal), a Mediterranean system mainly facing water scarcity and diffuse pollution problems, was chosen. To develop the proposed framework, a combination of process-based modelling to simulate hydrological and nutrient enrichment stressors and empirical modelling to relate these stressors - along with land use and natural background - with biotic indicators, was applied. Biotic indicators based on ecological quality ratios from WFD biomonitoring data were used as response variables. Temperature, river slope, % of agriculture in the upstream catchment and total N were the variables more frequently ranked as the most relevant. Both the two significant interactions found between single hydrological and nutrient enrichment stressors indicated antagonistic effects. This study demonstrates the potentialities of coupling process-based modelling with empirical modelling within a single framework, allowing relationships among different ecosystem states to be hierarchized, interpreted and predicted at multiple spatial and temporal scales. It also demonstrates how isolated and interacting stressors can have a different impact on biotic quality. When performing conservation or management plans, the stressor hierarchy should be considered as a way of prioritizing actions in a cost-effective perspective.

Environmental and socio-economic methodologies and solutions towards integrated water resources management

Semi-arid regions are facing the challenge of managing water resources under conditions of increasing scarcity and drought. These are recently pressured by the impact of climate change favoring the shifting from using surface water to groundwater without taking sustainability issues into account. Likewise, water scarcity raises the competition for water among users, increasing the risk of social conflicts, as the availability of fresh water in sufficient quality and quantity is already one of the major factors limiting socio-economic development. In terms of hydrology, semi-arid regions are characterized by very complex hydro- and hydrogeological systems. The complexity of the water cycle contrasts strongly with the poor data availability, (1) which limits the number of analysis techniques and methods available to researchers, (2) limits the accuracy of models and predictions, and (3) consequently challenges the capabilities to develop appropriate management measures to mitigate or adapt the environment to scarcity and drought conditions. Integrated water resources management is a holistic approach to focus on both environmental as well as on socio-economic factors influencing water availability and supply. The management approaches and solutions adopted, e.g. in form of decision support for specific water resources systems, are often highly specific for individual case studies.

A data-mining framework for exploring the multi-relation between fish species and water quality through self-organizing map

The steep slopes of rivers can easily lead to large variations in river water quality during typhoon seasons in Taiwan, which may poses significant impacts on riverine eco-hydrological environments. This study aims to investigate the relationship between fish communities and water quality by using artificial neural networks (ANNs) for comprehending the upstream eco-hydrological system in northern Taiwan. We collected a total of 276 heterogeneous datasets with 8 water quality parameters and 25 fish species from 10 sampling sites. The self-organizing feature map (SOM) was used to cluster, analyze and visualize the heterogeneous datasets. Furthermore, the structuring index (SI) was adopted to determine the relative importance of each input variable of the SOM and identify the indicator factors. The clustering results showed that the SOM could suitably reflect the spatial characteristics of fishery sampling sites. Besides, the patterns of water quality parameters and fish species could be distinguishably (visually) classified into three eco-water quality groups: 1) typical upstream freshwater fishes that depended the most on dissolved oxygen (DO); 2) typical middle-lower reach riverine freshwater fishes that depended the most on total phosphorus (TP) and ammonia nitrogen; and 3) low lands or pond (reservoirs) freshwater fishes that depended the most on water temperature, suspended solids and chemical oxygen demand. According to the results of the SI, the representative indicators of water quality parameters and fish species consisted of DO, TP and Onychostoma barbatulum. This grouping result suggested that the methodology can be used as a guiding reference to comprehensively relate ecology to water quality. Our methods offer a cost-effective alternative to more traditional methods for identifying key water quality factors relating to fish species. In addition, visualizing the constructed topological maps of the SOM could produce detailed inter-relation between water quality and the fish species of stream habitat units.

Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?

Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms.

Analysing the impact of multiple stressors in aquatic biomonitoring data: A 'cookbook' with applications in R

Multiple stressors threaten biodiversity and ecosystem integrity, imposing new challenges to ecosystem management and restoration. Ecosystem managers are required to address and mitigate the impact of multiple stressors, yet the knowledge required to disentangle multiple-stressor effects is still incomplete. Experimental studies have advanced the understanding of single and combined stressor effects, but there is a lack of a robust analytical framework, to address the impact of multiple stressors based on monitoring data. Since 2000, the monitoring of Europe's waters has resulted in a vast amount of biological and environmental (stressor) data of about 120,000 water bodies. For many reasons, this data is rarely exploited in the multiple-stressor context, probably because of its rather heterogeneous nature: stressors vary and are mixed with broad-scale proxies of environmental stress (e.g. land cover), missing values and zero-inflated data limit the application of statistical methods and biological indicators are often aggregated (e.g. taxon richness) and do not respond stressor-specific. Here, we present a 'cookbook' to analyse the biological response to multiple stressors using data from biomonitoring schemes. Our 'cookbook' includes guidance for the analytical process and the interpretation of results. The 'cookbook' is accompanied by scripts, which allow the user to run a stepwise analysis based on his/her own data in R, an open-source language and environment for statistical computing and graphics. Using simulated and real data, we show that the recommended procedure is capable of identifying stressor hierarchy (importance) and interaction in large datasets. We recommend a minimum number of 150 independent observations and a minimum stressor gradient length of 75% (of the most relevant stressor's gradient in nature), to be able to reliably rank the stressor's importance, detect relevant interactions and estimate their standardised effect size. We conclude with a brief discussion of the advantages and limitations of this protocol.

Riparian responses to extreme climate and land-use change scenarios

Climate change will induce alterations in the hydrological and landscape patterns with effects on riparian ecotones. In this study we assess the combined effect of an extreme climate and land-use change scenario on riparian woody structure and how this will translate into a future risk of riparian functionality loss. The study was conducted in the Tâmega catchment of the Douro basin. Boosted Regression Trees (BRTs) were used to model two riparian landscape indicators related with the degree of connectivity (Mean Width) and complexity (Area Weighted Mean Patch Fractal Dimension). Riparian data were extracted by planimetric analysis of high spatial-resolution Word Imagery Layer (ESRI). Hydrological, climatic and land-use variables were obtained from available datasets and generated with process-based modeling using current climate data (2008-2014), while also considering the high-end RCP8.5 climate-change and "Icarus" socio-economic scenarios for the 2046-2065 time slice. Our results show that hydrological and land-use changes strongly influence future projections of riparian connectivity and complexity, albeit to diverse degrees and with differing effects. A harsh reduction in average flows may impair riparian zones while an increase in extreme rain events may benefit connectivity by promoting hydrologic dynamics with the surrounding floodplains. The expected increase in broad-leaved woodlands and mixed forests may enhance the riparian galleries by reducing the agricultural pressure on the area in the vicinity of the river. According to our results, 63% of river segments in the Tâmega basin exhibited a moderate risk of functionality loss, 16% a high risk, and 21% no risk. Weaknesses and strengths of the method are highlighted and results are discussed based on a resilience perspective with regard to riparian ecosystems.