Journal of Great Lakes Research最新文献

Invasive dreissenid mussels (Dreissena polymorpha and Dreissena rostriformis bugensis) have altered Great Lakes ecosystems through a multitude of effects on benthic habitats, food web structure, and nutrient cycling. This study explores whether spatially continuous geographic data of environmental factors can be utilized to predict Dreissena spp. spatial distributions on a lake-wide scale. Categorical variables were also assessed for significant relationships with Dreissena spp. biomass. Point observations from the 2017 Lake Huron benthic survey under the Cooperative Science and Monitoring Initiative (CSMI) were utilized for in situ measurements of dreissenid presence and biomass at 119 sites across Lake Huron. Basin, bathymetric zone, and tributary influence were found to have statistically significant relationships to dreissenid biomass. A boosted regression tree (BRT) model (ROC score 0.707) was developed to spatially predict dreissenid presence probability across Lake Huron from six environmental explanatory variables: April, May, and October chlorophyll, June dissolved organic carbon, January bottom temperature, and May bottom temperature. The importance of food availability and bottom temperature illuminated relationships between dreissenid mussels and periods of benthic-pelagic mixing in the spring and fall seasons. Future models could be improved through advancements in survey technology for improved geographic characterization of mussel habitat characteristics and environmental constraints.

Deepwater sculpin (Myoxocephalus thompsonii) were considered extirpated from Lake Ontario prior to the 1990s but have since resurged and are now an abundant offshore demersal species. As deepwater sculpin reproduction is poorly described, an investigation of their gonadal development and fecundity was conducted to better understand their reproductive biology. To evaluate spawning period duration and if females spawn multiple times during their spawning period, we compared deepwater sculpin gonadosomatic index (GSI), gonadal development, and fecundity using individuals collected in fall and spring from 2018 to 2021. Our analysis revealed female GSI was greater in fall (8.1 ± 6.2 %) than spring (4.4 ± 4.3 %). Absolute fecundity averaged 763 ± 246 oocytes and relative fecundity averaged 19 ± 6 oocytes per gram of fish. Histological analysis revealed the presence of only one batch of developing oocytes in the ovary (n = 60), indicating group-synchronous ovarian organization. Our findings suggest deepwater sculpin spawn once annually but have a protracted spawning season indicated by prolonged elevated GSI values. Therefore, protracted spawning in deepwater sculpin likely results in an extended period of larval emergence rather than the majority occurring in late spring as previously suggested. A longer timeframe for deepwater sculpin larval emergence may increase reproductive success and contribute to their population’s recovery.

Recent oligotrophication in Lake Michigan has contributed to reduced biomass of spring zooplankton and a shift in the zooplankton assemblage toward more calanoid copepods. These changes have likely altered prey availability for first feeding native fish species that hatch in early spring. While spring zooplankton densities and assemblage compositions are routinely monitored in offshore areas of Lake Michigan, zooplankton in nearshore areas such as shallow beach environments are less studied; and associated descriptions of diet characteristics among larval coregonine species are limited. In this study, we a) describe the nearshore (<1 m depth) zooplankton assemblage at four sites in northeastern Lake Michigan during early spring 2015–2019 and b) compare diets and diet selectivity of larval lake whitefish (Coregonus clupeaformis) and Cisco (C. artedi). Zooplankton composition varied among years, but calanoid copepods and copepod nauplii consistently dominated the zooplankton assemblage. Cisco and lake whitefish larvae were captured regularly, with Elk Rapids containing the highest proportion of ciscoes each year. For both species, calanoid and cyclopoid copepods were common in diets and were positively selected as prey. Although previous research has indicated high consumption of cyclopoid copepods by larval coregonines, our results provide new evidence that larval lake whitefish and Cisco in northeastern Lake Michigan will also consume and select for calanoid copepods when they are abundant. As such, should calanoid zooplankton continue to dominate the copepod community in Lake Michigan, larval coregonines appear capable of exploiting this abundant prey resource to improve their likelihood of survival to later life stages.

Early detection of aquatic invasive species (AIS) is vital to cost-effective prevention of their spread in the Great Lakes. Unfortunately, AIS surveillance has been generally too slow and geographically limited to support this purpose. Environmental DNA (eDNA) detection using quantitative polymerase chain reaction (qPCR) offers more rapid and affordable detection of likely AIS presence, but it does not directly discern live/dead status. Vital status verification using conventional surveys following positive eDNA qPCR detections could resolve this barrier, but only if the latter are adequately reliable and sensitive. Here we explore the reliability and sensitivity of eDNA qPCR monitoring for the bloody red shrimp (Hemimysis anomala), an AIS established in the southern Great Lakes but not yet widely distributed in Lake Superior, against conventional microscopy-based methods. We conducted this comparison using 1) harbor water from Muskegon Lake, MI where H. anomala is established, and 2) raw ballast water from ships transporting ballast from lower Lake Michigan to western Lake Superior. Our studies showed positive eDNA qPCR detections of H. anomala in all harbor and ballast samples for which conventional detection results were positive, and in some samples for which conventional results were negative. These results suggest that qPCR assays with adequate specificity could be an important tool in support of more effective and affordable early detection of target species in Great Lakes water, especially when combined with confirmatory conventional monitoring.

Invasive grass carp (Ctenopharyngodon idella) are currently reproducing in several tributaries to Lake Erie and threatening the Great Lakes ecosystem and fisheries. Grass carp are pelagic river spawners whose fertilized eggs drift downstream from the spawning site, developing as they drift. Variability in spawning time and location together with nonuniform velocities in natural rivers leads to egg age variability in field samples at downstream sampling sites. In this study, the Fluvial Egg Drift Simulator (FluEgg) model was used to simulate the transport of grass carp eggs collected in 12 samples at 9 sites in the lower Sandusky River (Ohio, USA) on July 12, 2017, to replicate the observed variability in egg-age distributions present in field samples. The variability in egg ages in virtual samples compare well to field samples. The most plausible explanations for differences between virtual and field samples are the existence of multiple spawning locations, including a spawning area approximately 8 km upstream from the river mouth, and idealized flow fields derived from a one-dimensional hydraulic model. Despite multiple sources of uncertainty and the deficiency in prescribing detailed spawning activities in the simulations, the results validate the utility of FluEgg together with ichthyoplankton data to identify plausible spawning areas and interpret age variability in field samples. A comprehensive discussion of model limitations and ichthyoplankton sample interpretation provides guidance for those using drift models to inform management actions for control of invasive carp in North America and to protect and restore carp populations in their native range in Asia.

Many terminal lakes in agricultural basins are prone to eutrophication due to restricted inflows and receiving excess nutrients from their basin. The synergy of using satellite data and machine learning models is a low-cost way to monitor the root-cause water quality variables (WQVs) of eutrophication. This study investigates the potential of remote sensing-based machine learning algorithms to model chlorophyll-a (Chl-a), total phosphorus (TP), Secchi disk depth (SD), and Carlson trophic state index (CTSI) in the north part of Lake Urmia (LU). The multiple linear regression (MLR) and artificial neural network (ANN) models were developed using Landsat-8 (L8) and Sentinel-2 (S2) data with nearly concurrent in-situ WQVs of the north part of LU from February 2016 to January 2017. Results showed that models based on L8 were superior to those with S2. Moreover, the ANN models based on L8 for Chl-a, SD, and TP having NSE = 0.75, 0.98, and 0.96, respectively, outperformed MLRs (with NSE = 0.74, 0.81, 0.58). Applying atmospheric correction (i.e., ACOLITE, C2RCC, and C2RCCX) enhances the models. The resultant Chl-a and SD maps indicated an inverse spatiotemporal pattern that agrees with the variation of the abiotic condition in the lake (e.g., surface temperature and total suspended sediments). According to the CTSI maps, the north part of LU was mesotrophic in February and March and eutrophic between June and October 2016. Our study indicates the promising application of remote sensing-based machine learning algorithms to model the spatiotemporal variation of eutrophication in LU, which provides valuable insights into cost-effective lake monitoring.