Geology topics

James E. McKenna

Publications and source records attributed to James E. McKenna.

At least 37 records · Page 2Linked to original sources

USGS2019

The Laurentian Great Lakes: A case study in ecological disturbance and climate change

Climate change effects are already significant, but can also magnify other ecological problems. This can be clearly seen in the Laurentian Great Lakes, which have suffered habitat degradation, fishery overharvest and dramatic alterations by invasive species. Thermal changes are expected to cause extensive loss of suitable fish habitat, and changing precipitation patterns will aggravate the problems with our highly modified lotic and lentic systems. A brief summary of the historic ecological context provided by the Great Lakes case is presented, followed by the descriptions of selected tools that help to understand and evaluate both ecological and climate change problems. Species distribution models and habitat classification combined with climate change predictions can identify the distribution and extent of optimal habitats, and identify which are most vulnerable to climate change. Ecological flow modelling can help to identify when critical flow changes are likely. Mechanistic simulation modelling specifies understanding of how aquatic systems function and can reveal cause and effect relationships. These tools can be used to help managers to protect optimal habitat, resist climate change effects to other habitats and adapt cultural systems to climate‐altered aquatic systems.

Great Lakes↗

USGS2018

An experimental comparison of composite and grab sampling of stream water for metagenetic analysis of environmental DNA

Use of environmental DNA (eDNA) to assess distributions of aquatic and semi-aquatic macroorganisms is promising, but sampling schemes may need to be tailored to specific objectives. Given the potentially high variance in aquatic eDNA among replicate grab samples, compositing smaller water volumes collected over a period of time may be more effective for some applications. In this study, we compared eDNA profiles from composite water samples aggregated over three hours with grab water samples. Both sampling patterns were performed with identical autosamplers paired at two different sites in a headwater stream environment, augmented with exogenous fish eDNA from an upstream rearing facility. Samples were filtered through 0.8 μm cellulose nitrate filters and DNA was extracted with a cetyl trimethylammonium bromide procedure. Eukaryotic and bacterial community profiles were derived by amplicon sequencing of 12S ribosomal, 16S ribosomal, and cytochrome oxidase I loci. Operational taxa were assigned to genus with a lowest common ancestor approach for eukaryotes and to family with the RDP Classifier software for prokaryotes. Eukaryotic community profiles were more consistent with composite sampling than grab sampling. Downstream, rarefaction curves suggested faster taxon accumulation for composite samples, and estimated richness was higher for composite samples as a set than for grab samples. Upstream, composite sampling produced lower estimated richness than grab samples, but with overlapping standard errors. Furthermore, a bimodal pattern of richness as a function of sequence counts suggested the impact of clumped particles on upstream samples. Bacterial profiles were insensitive to sample method, consistent with the more even dispersion expected for bacteria compared with eukaryotic eDNA. Overall, samples composited over 3 h performed equal to or better than triplicate grab sampling for quantitative community metrics, despite the higher total sequencing effort provided to grab replicates. On the other hand, taxon-specific detection rates did not differ appreciably and the two methods gave similar estimates of the ratio of the common fish genera Salmo and Coregonus at each site. Unexpectedly, Salmo eDNA dropped out substantially faster than Coregonus eDNA between the two sites regardless of sampling method, suggesting that differential settling affects the estimation of relative abundance. We identified bacterial patterns that were associated with eukaryotic diversity, suggesting potential roles as biomarkers of sample representativeness.

PeerJ↗

USGS2018

Measuring and evaluating ecological flows from streams to regions: Steps towards national coverage

Living aquatic communities are largely determined and maintained by the volume and quality of flowing waters, both within lotic systems and in receiving waters of coastal systems. However, flow is one of the most frequently and extensively altered features of rivers and streams; alteration effects are likely to be exacerbated by climate change. Lotic systems vary and different fish species need different environmental conditions, and distinct problems are evident at various spatial scales. New synoptic flow and biological information now make it possible to evaluate the effects of altered flows throughout the Great Lakes Region at scales from the stream reach to the Region. We used estimates of river and streamflow and observed fish abundances to develop tools that specify the response of fish to alterations in those flows. We fit the logistic model to a cumulative fish abundance curve as a function of yield providing an empirical means to develop models of the response of cumulative fish abundance to flows. Response zones of yield for each species in each system type (based on size and thermal class) illustrate how criteria may be developed that can be used in decision‐making for management of flows. In our example application, we evaluate both the general response of brook trout ( Salvelinus fontinalis ) abundances (and fish diversity) to changes in flows and assess the sensitivity of each stream fish community to flow alteration. Mapping stream sensitivity to flow alteration throughout the US Great Lakes Region with a multiscale spatial framework showed how regional variability in sensitivity for any fish species or assemblage may be evaluated and provides managers with information to help determine where the best opportunities for protection or restoration of streamflows and associated communities exist. These results provide valuable tools and critical information to managers responsible for balancing water uses and maintaining high quality lotic ecosystems. These methods may be applied to any geographic region and can be extended nationally or globally, where flow, temperature, fish and landscape data are available.

Illinois, Indiana, Michigan, Minnesota, New York, ↗

USGS2018

Great Lakes coastal fish habitat classification and assessment

Basin-scale assessment of fish habitat in Great Lakes coastal ecosystems would increase our ability to prioritize fish habitat management and restoration actions. As a first step in this direction, we identified key habitat factors associated with highest probability of occurrence for several societally and ecologically important coastal fish species as well as community metrics, using data from the Great Lakes Aquatic Habitat Framework (GLAHF), Great Lakes Environmental Indicators (GLEI) and Coastal Wetland Monitoring Program (CWMP). Secondly, we assessed whether species-specific habitat was threatened by watershed-level anthropogenic stressors. In the southern Great Lakes, key habitat factors for determining presence/absence of several species of coastal fish were chlorophyll concentrations, turbidity, and wave height, whereas in the northern ecoprovince temperature was the major habitat driver for most of the species modeled. Habitat factors best explaining fish richness and diversity were bottom slope and chlorophyll a . These models could likely be further improved with addition of high-resolution submerged macrophytecomplexity data which are currently unavailable at the basin-wide scale. Proportion of invasive species was correlated primarily with increasing maximum observed inorganic turbidity and chlorophyll a . We also demonstrate that preferred habitat for several coastal species and high-diversity areas overlap with areas of high watershed stress. Great Lakes coastal wetland fish are a large contributor to ecosystem services as well as commercial and recreational fishery harvest, and scalable basin-wide habitat models developed in this study may be useful for informing management actions targeting specific species or overall coastal fish biodiversity.

Great Lakes↗

USGS2018

Ecosystem classification and mapping of the Laurentian Great Lakes

Owing to the enormity and complexity of the Laurentian Great Lakes, an ecosystem classification is needed to better understand, protect, and manage this largest freshwater ecosystem in the world. Using a combination of statistical analyses, published knowledge, and expert opinion, we identified key driving variables and their ecologically relevant thresholds and delineated and mapped aquatic systems for the entire Great Lakes. We identified and mapped 77 aquatic ecological units (AEUs) that depict unique combinations of depth, thermal regime, hydraulic, and landscape classifiers. Those 77 AEU types were distributed across 1997 polygons (patches) ranging from 1 to >48 000 km 2 in area and were most diverse in the nearshore (35 types), followed by the coastal margin (26), and then the offshore (16). Our classification and mapping of ecological units captures gradients that characterize types of aquatic systems in the Great Lakes and provides a geospatial accounting framework for resource inventory, status and trend assessment; research for ecosystem questions; and management and policy-making.

Laurentian Great Lakes↗

USGS2017

Simulation of rapid ecological change in Lake Ontario

Lower trophic level processes are integral to proper functioning of large aquatic ecosystems and have been disturbed in Lake Ontario by various stressors including exotic species. The invasion of benthic habitats by dreissenid mussels has led to systemic changes and native faunal declines. Size-dependent physiological rates, spatial differences and connectivity, competition, and differential population dynamics among invertebrate groups contributed to the change and system complexity. We developed a spatially explicit, individual-based mechanistic model of the benthic ecosystem in Lake Ontario, with coupling to the pelagic system, to examine ecosystem dynamics and effects of dreissenid mussel invasion and native fauna losses. Benthic organisms were represented by functional groups; filter-feeders (i.e., dreissenid mussels), surface deposit-feeders (e.g., native amphipod Diporeia spp.), and deposit-feeders (e.g., oligochaetes and other burrowers). The model was stable, represented ecological structure and function effectively, and reproduced observed effects of the mussel invasion. Two hypotheses for causes of Diporeia loss, competition or disease-like mortality, were tested. Simple competition for food did not explain observed declines in native surface deposit-feeders during the filter-feeder invasion. However, the elevated mortality scenario supports a disease-like cause for loss of the native amphipod, with population changes in various lake areas and altered benthic biomass transfers. Stabilization of mussel populations and possible recovery of the native, surface-deposit feeding amphipod were predicted. Although further research is required on forcing functions, model parameters, and natural conditions, the model provides a valuable tool to help managers understand the benthic system and plan for response to future disruptions.

Lake Ontario↗

USGS2017

Habitat use by juvenile salmonids in Lake Ontario tributaries-species, age, diel and seasonal effects

Understanding the habitat needs of fish and how these requirements may change seasonally over a 24-h period is important, especially for highly managed sport species. Consequently, we examined the diel and seasonal habitat use of four juvenile salmonid species in streams in the Lake Ontario watershed. For juvenile Atlantic salmon Salmo salar and juvenile rainbow trout Oncorhynchus mykiss , differences in day versus night habitat use were more profound than seasonal differences. Observed differences in day versus night habitat for all species and age classes were mainly due to the use of less object oriented cover at night and to a lesser extent to the use of slower velocities and smaller substrate at night. Seasonal differences in habitat use were also observed, likely due to increased fish size, and included movement to deeper and faster water and the use of larger substrate and more cover from summer to winter. Different habitat variables were important to individual species. Juvenile Atlantic salmon were associated with higher water velocities, juvenile rainbow trout with larger substrate and more cover, and subyearling Chinook salmon O. tshawytscha and subyearling coho salmon O. kisutch with small substrate and less cover. Our observations demonstrate that habitat partitioning occurs and likely reduces intraspecific and interspecific competition which may increase the potential production of all four species in sympatry. Consequently, these findings provide important information for resource managers charged with managing, protecting, and enhancing Great Lakes tributaries where all or some of these species occur.

New York↗

USGS2017

Intra-reach headwater fish assemblage structure

Large-scale conservation efforts can take advantage of modern large databases and regional modeling and assessment methods. However, these broad-scale efforts often assume uniform average habitat conditions and/or species assemblages within stream reaches.

The Open Ecology Journal↗

USGS2016

FishVis, A regional decision support tool for identifying vulnerabilities of riverine habitat and fishes to climate change in the Great Lakes Region

Climate change is expected to alter the distributions and community composition of stream fishes in the Great Lakes region in the 21st century, in part as a result of altered hydrological systems (stream temperature, streamflow, and habitat). Resource managers need information and tools to understand where fish species and stream habitats are expected to change under future conditions. Fish sample collections and environmental variables from multiple sources across the United States Great Lakes Basin were integrated and used to develop empirical models to predict fish species occurrence under present-day climate conditions. Random Forests models were used to predict the probability of occurrence of 13 lotic fish species within each stream reach in the study area. Downscaled climate data from general circulation models were integrated with the fish species occurrence models to project fish species occurrence under future climate conditions. The 13 fish species represented three ecological guilds associated with water temperature (cold, cool, and warm), and the species were distributed in streams across the Great Lakes region. Vulnerability (loss of species) and opportunity (gain of species) scores were calculated for all stream reaches by evaluating changes in fish species occurrence from present-day to future climate conditions. The 13 fish species included 4 cold-water species, 5 cool-water species, and 4 warm-water species. Presently, the 4 cold-water species occupy from 15 percent (55,000 kilometers [km]) to 35 percent (130,000 km) of the total stream length (369,215 km) across the study area; the 5 cool-water species, from 9 percent (33,000 km) to 58 percent (215,000 km); and the 4 warm-water species, from 9 percent (33,000 km) to 38 percent (141,000 km). Fish models linked to projections from 13 downscaled climate models projected that in the mid to late 21st century (2046–65 and 2081–2100, respectively) habitats suitable for all 4 cold-water species and 4 of 5 cool-water species under present-day conditions will decline as much as 86 percent and as little as 33 percent, and habitats suitable for all 4 warm-water species will increase as much as 33 percent and as little as 7 percent. This report documents the approach and data used to predict and project fish species occurrence under present-day and future climate conditions for 13 lotic fish species in the United States Great Lakes Basin. A Web-based decision support mapping application termed “FishVis” was developed to provide a means to integrate, visualize, query, and download the results of these projected climate-driven responses and help inform conservation planning efforts within the region.

Great Lakes Region↗

USGS2016

Predation on Chinook Salmon parr by hatchery salmonids and Fallfish in the Salmon River, New York

Naturally reproduced Chinook Salmon Oncorhynchus tshawytscha contribute substantially to the fishery in Lake Ontario. The Salmon River, a Lake Ontario tributary in New York, produces the largest numbers of naturally spawned Chinook Salmon, with parr abundance in the river often exceeding 10 million. In the spring of each year, large numbers of hatchery salmonid yearlings—potential predators of Chinook Salmon parr—are released into the Salmon River by the New York State Department of Environmental Conservation. We sought to examine predation on Chinook Salmon parr in the Salmon River during May and June prior to out-migration. Over the 4 years examined (2009–2012), annual consumption of Chinook Salmon parr by hatchery-released yearling steelhead O. mykiss and Coho Salmon O. kisutch ranged from 1.5 to 3.3 million and from 0.4 to 2.1 million, respectively. In 2009, Fallfish Semotilus corporalis were estimated to consume 2.9 million Chinook Salmon parr. Predation was higher in May, when the average TL of Chinook Salmon parr was 44.5 mm, than in June. Fallfish were also important predators of naturally reproduced steelhead subyearlings, consuming an estimated 800,000 steelhead in 2009. Hatchery-released yearling salmonids consumed 13.8–15.3% of the Chinook Salmon parr that were estimated to be present in the Salmon River during 2010–2012. Earlier releases of hatchery salmonid yearlings could reduce the riverine consumption of Chinook Salmon parr by facilitating the out-migration of yearlings prior to Chinook Salmon emergence.

New York↗

USGS2016

Summer-autumn habitat use of yearling rainbow trout in two streams in the Lake Ontario watershed

Understanding the habitat requirements of salmonids in streams is an important component of fisheries management. We examined the summer and autumn habitat use of yearling Rainbow Trout Oncorhynchus mykiss in relation to available habitat in two streams in the Lake Ontario watershed. Little interstream variation in trout habitat use was observed; the variation that did occur was largely due to differences between streams in available habitat in the autumn. In both streams, yearling Rainbow Trout utilized pool habitat and during periods of high stream discharge were associated with larger substrate that may provide a velocity barrier. These findings may assist resource managers in their efforts to protect and restore habitat for migratory Rainbow Trout in the Lake Ontario watershed.

New York↗

USGS2015

Dam removal, connectivity, and aquatic resources in the St. Regis River Watershed, New York

The decommissioning and planned removal of the Hogansburg Dam on the St. Regis River in New York has stimulated interest in the potential effects of that barrier removal on the St. Regis watershed. There will be immediate and systemic effects of the Hogansburg Dam removal, which may include inundation of habitats below the dam or dewatering of habitats above the dam, possibly affecting local fish assemblages and (or) local native mussel assemblages; and expansion of stream network connectivity, which has the potential to open a large area of the watershed to migratory aquatic species. Information was collected about biota, water quality, sediment distribution, riverbed dimensions in the vicinity of the dam, and habitat characteristics of headwater sample sites. Complete fish assemblages were collected, but species of special concern associated with the connectivity changes included, American Eel, Atlantic Salmon, Brook Trout, Eastern Sand Darter, and Lake Sturgeon. Freshwater mussels in the vicinity of the dam also were examined and may be at risk of exposure (without a rescue plan) after dam removal. Reservoir sediment will be transported downstream and will alter aquatic habitat as it moves through the system. The dam removal will open more than 440 kilometers of stream habitat to migratory species, allowing them to more easily complete their life cycles. Fish assemblages above the dam may be altered by migrating fishes, but resident Brook Trout are not expected to be adversely affected.

New York↗

USGS2015

Diel resource partitioning among juvenile Atlantic Salmon, Brown Trout, and Rainbow Trout during summer

Interspecific partitioning of food and habitat resources has been widely studied in stream salmonids. Most studies have examined resource partitioning between two native species or between a native species and one that has been introduced. In this study we examine the diel feeding ecology and habitat use of three species of juvenile salmonids (i.e., Atlantic Salmon Salmo salar , Brown Trout Salmo trutta , and Rainbow Trout Oncorhynchus mykiss ) in a tributary of Skaneateles Lake, New York. Subyearling Brown Trout and Rainbow Trout fed more heavily from the drift than the benthos, whereas subyearling Atlantic Salmon fed more from the benthos than either species of trout. Feeding activity of Atlantic Salmon and Rainbow Trout was similar, with both species increasing feeding at dusk, whereas Brown Trout had no discernable feeding peak or trough. Habitat availability was important in determining site-specific habitat use by juvenile salmonids. Habitat selection was greater during the day than at night. The intrastream, diel, intraspecific, and interspecific variation we observed in salmonid habitat use in Grout Brook illustrates the difficulty of acquiring habitat use information for widespread management applications.

North American Journal of Fisheries Management↗

USGS2015

A spatial classification and database for management, research, and policy making: The Great Lakes aquatic habitat framework

Managing the world's largest and most complex freshwater ecosystem, the Laurentian Great Lakes, requires a spatially hierarchical basin-wide database of ecological and socioeconomic information that is comparable across the region. To meet such a need, we developed a spatial classification framework and database — Great Lakes Aquatic Habitat Framework (GLAHF). GLAHF consists of catchments, coastal terrestrial, coastal margin, nearshore, and offshore zones that encompass the entire Great Lakes Basin. The catchments captured in the database as river pour points or coastline segments are attributed with data known to influence physicochemical and biological characteristics of the lakes from the catchments. The coastal terrestrial zone consists of 30-m grid cells attributed with data from the terrestrial region that has direct connection with the lakes. The coastal margin and nearshore zones consist of 30-m grid cells attributed with data describing the coastline conditions, coastal human disturbances, and moderately to highly variable physicochemical and biological characteristics. The offshore zone consists of 1.8-km grid cells attributed with data that are spatially less variable compared with the other aquatic zones. These spatial classification zones and their associated data are nested within lake sub-basins and political boundaries and allow the synthesis of information from grid cells to classification zones, within and among political boundaries, lake sub-basins, Great Lakes, or within the entire Great Lakes Basin. This spatially structured database could help the development of basin-wide management plans, prioritize locations for funding and specific management actions, track protection and restoration progress, and conduct research for science-based decision making.

Great Lakes↗

USGS2014

Development of a spatially universal framework for classifying stream assemblages with application to conservation planning for Great Lakes lotic fish communities

Classifications are typically specific to particular issues or areas, leading to patchworks of subjectively defined spatial units. Stream conservation is hindered by the lack of a universal habitat classification system and would benefit from an independent hydrology-guided spatial framework of units encompassing all aquatic habitats at multiple spatial scales within large regions. We present a system that explicitly separates the spatial framework from any particular classification developed from the framework. The framework was constructed from landscape variables that are hydrologically and biologically relevant, covered all space within the study area, and was nested hierarchically and spatially related at scales ranging from the stream reach to the entire region; classifications may be developed from any subset of the 9 basins, 107 watersheds, 459 subwatersheds, or 10,000s of valley segments or stream reaches. To illustrate the advantages of this approach, we developed a fish-guided classification generated from a framework for the Great Lakes region that produced a mosaic of habitat units which, when aggregated, formed larger patches of more general conditions at progressively broader spatial scales. We identified greater than 1,200 distinct fish habitat types at the valley segment scale, most of which were rare. Comparisons of biodiversity and species assemblages are easily examined at any scale. This system can identify and quantify habitat types, evaluate habitat quality for conservation and/or restoration, and assist managers and policymakers with prioritization of protection and restoration efforts. Similar spatial frameworks and habitat classifications can be developed for any organism in any riverine ecosystem.

Michigan, Minnesota, New York, Ohio, Wisconsin↗

USGS2014

Developing and testing temperature models for regulated systems: a case study on the Upper Delaware River

Water temperature is an important driver of many processes in riverine ecosystems. If reservoirs are present, their releases can greatly influence downstream water temperatures. Models are important tools in understanding the influence these releases may have on the thermal regimes of downstream rivers. In this study, we developed and tested a suite of models to predict river temperature at a location downstream of two reservoirs in the Upper Delaware River (USA), a section of river that is managed to support a world-class coldwater fishery. Three empirical models were tested, including a Generalized Least Squares Model with a cosine trend (GLScos), AutoRegressive Integrated Moving Average (ARIMA), and Artificial Neural Network (ANN). We also tested one mechanistic Heat Flux Model (HFM) that was based on energy gain and loss. Predictor variables used in model development included climate data (e.g., solar radiation, wind speed, etc.) collected from a nearby weather station and temperature and hydrologic data from upstream U.S. Geological Survey gages. Models were developed with a training dataset that consisted of data from 2008 to 2011; they were then independently validated with a test dataset from 2012. Model accuracy was evaluated using root mean square error (RMSE), Nash Sutcliffe efficiency (NSE), percent bias (PBIAS), and index of agreement (d) statistics. Model forecast success was evaluated using baseline-modified prime index of agreement (md) at the one, three, and five day predictions. All five models accurately predicted daily mean river temperature across the entire training dataset (RMSE = 0.58–1.311, NSE = 0.99–0.97, d = 0.98–0.99); ARIMA was most accurate (RMSE = 0.57, NSE = 0.99), but each model, other than ARIMA, showed short periods of under- or over-predicting observed warmer temperatures. For the training dataset, all models besides ARIMA had overestimation bias (PBIAS = −0.10 to −1.30). Validation analyses showed all models performed well; the HFM model was the most accurate compared other models (RMSE = 0.92, both NSE = 0.98, d = 0.99) and the ARIMA model was least accurate (RMSE = 2.06, NSE = 0.92, d = 0.98); however, all models had an overestimation bias (PBIAS = −4.1 to −10.20). Aside from the one day forecast ARIMA model (md = 0.53), all models forecasted fairly well at the one, three, and five day forecasts (md = 0.77–0.96). Overall, we were successful in developing models predicting daily mean temperature across a broad range of temperatures. These models, specifically the GLScos, ANN, and HFM, may serve as important tools for predicting conditions and managing thermal releases in regulated river systems such as the Delaware River. Further model development may be important in customizing predictions for particular biological or ecological needs, or for particular temporal or spatial scales.

Delaware, New York, Pennsylvania↗

USGS2014

Model distribution of Silver Chub ( Macrhybopsis storeriana ) in western Lake Erie

Silver Chub ( Macrhybopsis storeriana ) was once a common forage fish in Lake Erie but has declined greatly since the 1950s. Identification of optimal and marginal habitats would help conserve and manage this species. We developed neural networks to use broad-scale habitat variables to predict abundance classes of Silver Chub in western Lake Erie, where its largest remaining population exists. Model performance was good, particularly for predicting locations of habitat with the potential to support the highest and lowest abundances of this species. Highest abundances are expected in waters >5 m deep; water depth and distance to coastal habitats were important model features. These models provide initial tools to help conserve this species, but their resolution can be improved with additional data and consideration of other ecological factors.

American Midland Naturalist↗

USGS2014

Comparison of fishes in nearshore areas of the St. Lawrence River, New York over 35 years

Fishes of the nearshore waters of the St. Lawrence River provide forage for valuable sport fisheries and are important biological indicators of condition and change. This fish community differs slightly among various reaches of the St. Lawrence River from New York to Quebec (Carlson et al. 2006, Eckert and Hanlon 1977, Kapuscinski 2011, LaViolette et al. 2003, Mandrak et al. 2006, McKenna et al. 2005). Nearshore habitat has been described by McKenna et al. (2012), and others have suggested that there were changes over the last few decades (Clapsadl 1993, Kapuscinski and Farrell 2013). More definitive work needs to be completed on submerged aquatic vegetation habitats. In this paper, changes in the nearshore fish species composition for the New York reach from Cape Vincent to Moses-Saunders Dam are examined through comparison of results from 2009-2010 (McKenna et al. 2012) and 1976 surveys (Eckert and Hanlon 1977).

New York↗