Geology topics

P.M. Kocovsky

Publications and source records attributed to P.M. Kocovsky.

5 recordsLinked to original sources

USGS2011

Influence of landscape-scale factors in limiting brook trout populations in Pennsylvania streams

Landscapes influence the capacity of streams to produce trout through their effect on water chemistry and other factors at the reach scale. Trout abundance also fluctuates over time; thus, to thoroughly understand how spatial factors at landscape scales affect trout populations, one must assess the changes in populations over time to provide a context for interpreting the importance of spatial factors. We used data from the Pennsylvania Fish and Boat Commission's fisheries management database to investigate spatial factors that affect the capacity of streams to support brook trout Salvelinus fontinalis and to provide models useful for their management. We assessed the relative importance of spatial and temporal variation by calculating variance components and comparing relative standard errors for spatial and temporal variation. We used binary logistic regression to predict the presence of harvestable-length brook trout and multiple linear regression to assess the mechanistic links between landscapes and trout populations and to predict population density. The variance in trout density among streams was equal to or greater than the temporal variation for several streams, indicating that differences among sites affect population density. Logistic regression models correctly predicted the absence of harvestable-length brook trout in 60% of validation samples. The r 2-value for the linear regression model predicting density was 0.3, indicating low predictive ability. Both logistic and linear regression models supported buffering capacity against acid episodes as an important mechanistic link between landscapes and trout populations. Although our models fail to predict trout densities precisely, their success at elucidating the mechanistic links between landscapes and trout populations, in concert with the importance of spatial variation, increases our understanding of factors affecting brook trout abundance and will help managers and private groups to protect and enhance populations of wild brook trout. ?? Copyright by the American Fisheries Society 2006.

Transactions of the American Fisheries Society↗

USGS2010

Night sampling improves indices used for management of yellow perch in Lake Erie

Catch rate (catch per hour) was examined for age-0 and age-1 yellow perch, Perca flavescens (Mitchill), captured in bottom trawls from 1991 to 2005 in western Lake Erie: (1) to examine variation of catch rate among years, seasons, diel periods and their interactions; and (2) to determine whether sampling during particular diel periods improved the management value of CPH data used in models to project abundance of age-2 yellow perch. Catch rate varied with year, season and the diel period during which sampling was conducted as well as by the interaction between year and season. Indices of abundance of age-0 and age-1 yellow perch estimated from night samples typically produced better fitting models and lower estimates of age-2 abundance than those using morning or afternoon samples, whereas indices using afternoon samples typically produced less precise and higher estimates of abundance. The diel period during which sampling is conducted will not affect observed population trends but may affect estimates of abundance of age-0 and age-1 yellow perch, which in turn affect recommended allowable harvest. A field experiment throughout western Lake Erie is recommended to examine potential benefits of night sampling to management of yellow perch. Published 2010. The article is a US Government work and is in the public domain in the USA.

Fisheries Management and Ecology↗

USGS2009

Prioritizing removal of dams for passage of diadromous fishes on a major river system

Native diadromous fishes have been extirpated from much of the Susquehanna River system for nearly a century. Recent restoration efforts have focused on removal of dams, but there are hundreds of dams and presently there is no biologically based system to assist in prioritizing their removal. We present a new method that uses existing habitat suitability index models (HSI) for American shad Alosa sapidissima, alewife A. pseudoharengus, blueback herring A. aestivalis, and American eel Anguilla rostrata to prioritize the removal of non-hydropower dams within the Susquehanna River system. We ranked HSI scores for each of the four species, association between a landscape-scale factor and HSIs, length of river opened by removing a dam, and distance from the mouth at Chesapeake Bay for each dam and then calculated a mean rank prioritization for dam removal by averaging the ranks for the seven criteria. This prioritization method is resistant to outliers, is not strongly affected by somewhat arbitrary decisions on metrics included in the analysis, and provides a biologically based prioritization for dam removal that can be easily amended to include other metrics or adapted to other river systems and that complements other social and economic considerations that must be included in decisions to remove dams.

River Research and Applications↗

USGS2008

Linking landscapes and habitat suitability scores for diadromous fish restoration in the susquehanna river basin

Dams within the Susquehanna River drainage, Pennsylvania, are potential barriers to migration of diadromous fishes, and many are under consideration for removal to facilitate fish passage. To provide useful input for prioritizing dam removal, we examined relations between landscape-scale factors and habitat suitability indices (HSIs) for native diadromous species of the Susquehanna River. We used two different methods (U.S. Fish and Wildlife Service method: Stier and Crance [1985], Ross et al. [1993a, 1993b, 1997], and Pardue [1983]; Pennsylvania State University method: Carline et al. [1994]) to calculate HSIs for several life stages of American shad Alosa sapidissima, alewives Alosa pseudoharengus, and blueback herring Alosa aestivalis and a single HSI for American eels Anguilla rostrata based on habitat variables measured at transects spaced every 5 km on six major Susquehanna River tributaries. Using geographical information systems, we calculated land use and geologic variables upstream from each transect and associated those data with HSIs calculated at each transect. We then performed canonical correlation analysis to determine how HSIs were linked to geologic and land use factors. Canonical correlation analysis identified the proportion of watershed underlain by carbonate rock as a positive correlate of HSIs for all species and life stages except American eels and juvenile blueback herring. We hypothesize that potential mechanisms linking carbonate rock to habitat suitability include increased productivity and buffering capacity. No other consistent patterns of positive or negative correlation between landscape-scale factors and HSIs were evident. This analysis will be useful for prioritizing removal of dams in the Susquehanna River drainage, because it provides a broad perspective on relationships between habitat suitability for diadromous fishes and easily measured landscape factors. This approach can be applied elsewhere to elucidate relationships between fine- and coarse-scale variables and suitability of habitat for fishes. ?? Copyright by the American Fisheries Society 2008.

North American Journal of Fisheries Management↗

USGS2007

Application of two tests of multivariate discordancy to fisheries data sets

The generalized (Mahalanobis) distance and multivariate kurtosis are two powerful tests of multivariate discordancies (outliers). Unlike the generalized distance test, the multivariate kurtosis test has not been applied as a test of discordancy to fisheries data heretofore. We applied both tests, along with published algorithms for identifying suspected causal variable(s) of discordant observations, to two fisheries data sets from Lake Erie: total length, mass, and age from 1,234 burbot, Lota lota; and 22 combinations of unique subsets of 10 morphometrics taken from 119 yellow perch, Perca flavescens. For the burbot data set, the generalized distance test identified six discordant observations and the multivariate kurtosis test identified 24 discordant observations. In contrast with the multivariate tests, the univariate generalized distance test identified no discordancies when applied separately to each variable. Removing discordancies had a substantial effect on length-versus-mass regression equations. For 500-mm burbot, the percent difference in estimated mass after removing discordancies in our study was greater than the percent difference in masses estimated for burbot of the same length in lakes that differed substantially in productivity. The number of discordant yellow perch detected ranged from 0 to 2 with the multivariate generalized distance test and from 6 to 11 with the multivariate kurtosis test. With the kurtosis test, 108 yellow perch (90.7%) were identified as discordant in zero to two combinations, and five (4.2%) were identified as discordant in either all or 21 of the 22 combinations. The relationship among the variables included in each combination determined which variables were identified as causal. The generalized distance test identified between zero and six discordancies when applied separately to each variable. Removing the discordancies found in at least one-half of the combinations (k=5) had a marked effect on a principal components analysis. In particular, the percent of the total variation explained by second and third principal components, which explain shape, increased by 52 and 44% respectively when the discordancies were removed. Multivariate applications of the tests have numerous ecological advantages over univariate applications, including improved management of fish stocks and interpretation of multivariate morphometric data. ?? 2007 Springer Science+Business Media B.V.

Environmental Biology of Fishes↗