Jones EA, Would JA, Nesbitt LVR, Rear LA, Pisaric MFJ, Grooms C, Michelutti N, Smol JP. 2026. Assessing the limnological effects of 20th-century silver mining, piscicide use, and recent climate warming in central Yukon, Canada. Lake Reserv Manage. XXX-XXX.Paleolimnological techniques and modern water quality measurements were used to determine whether metal-laden dust generated by mining operations near Keno City (central Yukon) has been deposited into nearby lakes. These lakes are important freshwater resources located on the traditional territory of the First Nation of Na-Cho Ny & auml;k Dun. Present-day water chemistry analyses from 8 of these lakes, situated 13.5 to similar to 40 km from Keno City, do not suggest a clear pattern of metal enrichment caused by mining. Downcore metal analyses of sediment cores from 3 study sites (Hanson Lakes, McQuesten Lake, and Roop Lakes) showed no clear evidence of aerially transported dust following the onset of mining around the 1920s. Increases in sedimentary metal(loid)s recorded in the McQuesten and Roop sediment cores over the past similar to 50 yr do not track the onset of large-scale mining near Keno City and are likely related to mining activities that occurred later within their respective catchments. A downcore assessment of biological indicators in Hanson Lakes, the site nearest Keno City (13.5 km), shows that cladocerans assemblages were likely restructured by the application of piscicides in 1963, whereas diatoms responded slightly later to climate-related changes in lake ice and mixing regimes after ca. 1960. Overall primary production also increased in response to both stressors. Collectively, our data suggest dust deposition from silver mining near Keno City minimally affected regional lakes and that climate and other stressors (piscicides, local placer mining) were larger drivers of aquatic ecological change.
Bukaveckas PA, Barnett J, Cameron A. 2026. Contributions of suspended particulate matter and dissolved color to light attenuation and trends in water clarity among mid-latitude reservoirs. Lake Reserv Manage. XXX-XXX.Reservoirs account for almost half of all lakes in the conterminous United States and are the dominant lake type in the southeastern United States. Reservoirs are optically complex systems in which diverse factors affect water clarity, including both algal and non-algal suspended particulate matter, as well as dissolved color. Data obtained from 7 reservoirs in central Virginia were used to partition the effects of suspended particulate matter and dissolved color on light attenuation. Their relative importance differed among ecoregions as dissolved color was the dominant component of light attenuation in Coastal Plain lakes, whereas suspended particulate matter was the largest contributor to light attenuation among Piedmont lakes. Multidecadal Secchi data from over 100 Virginia reservoirs were used to assess status and trends in water clarity. Although phytoplankton were a minor component of suspended particulate matter, chlorophyll a (Chl-a) was an important predictor of interlake, seasonal, longitudinal and long-term variation in water clarity. Our findings show that although algae are a small contributor to suspended particulate matter, the large spatial and temporal variation in Chl-a (relative to chromophoric dissolved organic matter and total suspended solids) increases their importance in explaining variability in light attenuation.
Fricke RM, Gawel JE, Shinneman A, Martin L, Bogstie S, Olden JD. 2026. Motivations to visit and perceived barriers to accessing the cultural ecosystem services offered by urban lakes. Lake Reserv Manage. XXX-XXX.Urban blue-green spaces (lakes with adjacent park space) provide numerous cultural ecosystem services to human populations, yet these contributions are infrequently measured and can be underappreciated in public resource planning. Here, we asked active and potential visitors to public urban blue-green spaces in western Washington, USA, to rate how likely they are to use activities and amenities at blue-green spaces and to identify problems or barriers that may prevent them from using blue-green spaces more. We collected data across 2 surveys, one disseminated primarily in person at 18 local lakeside parks in summer 2021 (n = 667), and the other via a summer 2022 Qualtrics email campaign that intended to capture a population sample reflecting local demographics (n = 756). Our factor analysis of blue-green space users' ratings of their likelihood to participate in different blue-green space activities identified 3 latent factors: the activities respondents participate in, the ecosystem where these activities occur (lake versus park), and whether these activities are more social or solitary. These explained 24%, 12%, and 8% of variance in responses, respectively. In addition, survey responses suggest that barriers such as limited public transportation availability and water safety disproportionately affect underrepresented sociodemographic groups. Cumulatively, these findings will support lake-specific improvements in communication and accessibility by county water resource management partners.
Riner AE, Glueckert JS, Leary J, Sperry BP, Macdonald B, Elrahman A-A. 2026. Drone-based aerial surveillance for detecting water hyacinth (Pontederia crassipes) using deep learning models. Lake Reserv Manage. XXX-XXX.Water hyacinth is a rapidly growing, floating aquatic invasive plant that threatens biodiversity, impedes navigation, and increases flood risk across the southeastern United States. Traditional water hyacinth management involves frequent monitoring and herbicide application from airboats; however, escaped plants lead to reinfestation, requiring continuous efforts. Surveys using unoccupied aerial systems (UAS) can potentially provide increased efficiency for large-scale monitoring, and deep learning can potentially enhance plant detection in aerial imagery. In this study, we evaluated 3 deep learning models (UPerNet, Unet, and DeepLabv3) for water hyacinth detection using red, green, and blue (RGB) imagery. To assess model transferability, we evaluated trained models under 3 conditions: (1) on datasets from the same site and time period as the training images, (2) on datasets from the same site but a different season than the training images, and (3) on datasets from a different site but the same time period as the training images. All models exhibited higher recall than precision, indicating strong plant detection capabilities, but frequently misclassified other species as water hyacinth. DeepLabv3 outperformed other models on images similar to the training data (69% accuracy), Unet outperformed other models on in a different lake (62%) and in a different season (57%) than the training set. UPerNet exhibited the lowest accuracy across all conditions. These findings highlight the potential of UAS and deep learning for water hyacinth surveillance, offering a scalable approach to invasive species management.
Scherbaum S, Mirbach S, H & uuml;bl F, Fuchs Y, Haun S, Schneider J. 2026. Separating and analyzing wind- and boat-induced waves: a filtering framework for hydraulic lake monitoring. Lake Reserv Manag. XXX-XXX.Separating artificially induced waves by motorboats from natural wind waves on lakes is essential to assess their damage potential for lake ecosystems and to implement effective protective measures. A novel filtering framework, combining a classification of wave packet by peak period with high- and low-pass filter and wavelet denoising methods, enabled a reliable classification of small motorboat-induced and wind-induced waves. Wind waves were determined using a calibrated numerical SWAN model, while waves induced by motorboats were identified using synchronized drone footage in a field campaign at an Austrian lake. Thereby, motorboat-induced waves accounted for 83% of the total wave energy, and wind-generated waves accounted for 3%. The remaining 14% could not be classified, likely due to low signal strength or disturbances caused by other vehicles and reflections that were not identifiable as boat-induced. The framework was subsequently applied to 7 wave recordings obtained along shorelines characterized by contrasting hydraulic conditions. Results consistently indicated that motorboat-induced waves dominated the wave energy budget, and increased wind exposure resulted in a higher contribution of wind-generated waves. Pronounced buffering effects of wave-protection fences and sheltered bays were observed, whereas wave energy increased substantially near reflective shore structures compared to dissipative shorelines. Filtered boat-induced waves were characterized by higher wave heights, longer periods, and greater energy flux, with clear distinctions between planing and semi-planing modes visible in the wavelet spectrograms. The developed framework enhances the evaluation of wave recordings, enabling robust monitoring and assessment of anthropogenic wave exposure, which supports evidence-based management of lake ecosystems.
