Interest in climate change-related water level fluctuations and their effects on aquatic communities has increased. Chironomids are reliable indicators of extreme conditions, yet whether climate-induced compositional changes occur in lakes with artificially regulated water levels, such as Lake Tisza, is unclear. We studied the direct and indirect effects of hydrological extremes and climatic factors on chironomid assemblages from 2009 to 2022, hypothesising that extremes drive compositional shifts and reduced diversity, with indirect effects more pronounced. Results confirmed this hypothesis, revealing a remarkable assemblage change over 14 years. Flooding during the growing period significantly altered habitat diversity, causing notable structural changes in chironomid assemblages. Compositional variability was more closely linked to environmental variables than to discharge or climatic variables. Oligotrophication emerged as the main driver of long-term trends. Diversity responded to environmental changes, declining after summer floods, but recovery was rapid, with no lasting diversity loss despite structural shifts. Projected increases in climate extremes suggest further alterations in chironomid composition and diversity. These processes may ultimately affect the food web and compromise the lake's conservation function.
Improving the accuracy of subsurface heterogeneity characterization remains a key component in better understanding groundwater flow and contaminant transport. Heat tracer tests can provide temperature measurements, in addition to head data, that can be used for mapping heterogeneity. Here, the performance of head and temperature data in characterizing the hydraulic conductivity (K) distribution is investigated with a three-dimensional highly parameterized model using the pilot point method. The performance results are evaluated qualitatively and quantitatively in various aspects, including K fields comparison, head and temperature matches for both model calibration and validation, as well as through identifiability and sensitivity analyses. Results of this study reveal that: (a) K fields obtained by inverting head data show finer details of heterogeneity, while small scale heterogeneity is smoothed when inverting temperature data; (b) combination of heat and temperature data improves the prediction of heat tracer tests; (c) increasing data density yields more heterogeneity information and further improves prediction performance; and (d) identifiability and sensitivity analyses suggest that head and temperature data contain nonredundant information of K heterogeneity. These results jointly suggest that the integration of transient head and temperature data shows promising potential in improving the delineation of subsurface K distribution and obtaining reliable predictions of head responses and heat plume migration.
Fish sounds are a significant component of marine soundscapes. Recently, passive acoustic monitoring (PAM) arose as a promising tool for ecological monitoring, but a good characterization of fish acoustic communities is still needed. This study is the first to characterize the fish acoustic community at a biogeographic transition zone in the Northeast Atlantic. The research was conducted in a marine protected area (MPA) along the Portuguese mainland coast. Based on a literature review, we identified 29 (19.3%) sound-producing fish species present at this MPA, while 70 species (46.7%) were considered potentially soniferous. Using in situ acoustic recordings to detect potential fish sounds, we found 33 putative fish sounds that were categorized using a simple dichotomous classification. The temporal and spectral features of the 13 most prevalent sound types were characterized and compared among them and with available recordings to identify similarities. Finally, hydrophone recordings coupled with baited remote underwater video systems were tested as a method to identify sound sources. This study provides the first fish sound catalogue from the Portuguese mainland coast, laying the foundations to survey fish communities in coastal habitats with PAM.
Developing species distribution models (SDMs) requires high‐quality species occurrence records. These records, stemming from various sources with different sampling procedures, are often archived in open‐access databases, making automated data quality checks inevitable. Temporal, geographic, and taxonomic quality checks are usually conducted in SDM workflows, but checking for records distant in environmental space, i.e. outliers, is often ignored. Here, we present ‘specleanr', an R package that contains 20 outlier detection methods (ODMs) that can be ensembled to identify potential outliers in environmental predictors. These methods are categorized into 1) species‐specific ecological range, 2) univariate, and 3) multivariate ODMs. All potential outliers flagged by the different methods are pooled to identify absolute outliers (records appearing in multiple methods). The local regression (LOESS) method is then used to automatically set a threshold that optimally identifies the absolute outliers. Additionally, clustering records into poor, fair, moderate, very strong, and perfect outliers, as well as non‐outliers, is possible based on each record's likelihood as a potential outlier, which allows expert assessment. We demonstrated the approach to 15 fish species from the Danube River Basin, including native, alien, threatened, and common species. We fitted SDMs using bioclimatic and hydromorphological parameters. We compared the model area under the curve (AUC) before and after outlier removal using three scenarios: 1) the LOESS method, 2) removing very strong outliers, and 3) removing perfect outliers. The results showed a significant improvement in the model AUC, with generally small to moderate effect sizes after outlier removal. ‘specleanr' is generalizable across taxonomic groups, data types, ecological realms, and geographic regions. Beyond SDMs, it can also be broadly used in general data analysis where outlier detection is essential. We provide detailed vignettes to support package use. ‘specleanr' offers a user‐friendly and reproducible approach for handling outliers in biogeographical modeling and general data analysis workflows.
Freshwater fish such as juvenile salmon often rely on dynamic and diverse habitats such as wetlands. Although juvenile salmon wetland use is well documented, their use of freshwater wetlands in large river networks that vary in isolation and connection is not well known. We studied juvenile coho salmon use of three wetland sites along the North Thompson River, British Columbia, Canada, from May 2021 to October 2023 to understand how seasonal variation in wetland connectivity and water quality (temperature and dissolved oxygen) influence juvenile coho salmon habitat use. We used monthly mark–recapture sampling to estimate juvenile coho salmon abundance and density. Seasonal abundance and growth of juvenile coho salmon in wetlands were intertwined with connectivity and abiotic conditions. Age-0 juvenile coho salmon were recruited to wetlands during high spring flows and used wetland habitats year-round. Periods of high density and low oxygen were associated with lower growth and abundance. Our study also provides information on the timing of juvenile coho salmon use of wetland habitats, which can be used to inform habitat managers of times of year that pose the greatest risk and benefits to these fish.
