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Research about New York, Ohio, Pennsylvania

Source-linked reports with geographic coverage including New York, Ohio, Pennsylvania.

4 recordsLinked to original sources
USGS2026

Bayesian hierarchical model of lake whitefish cohort strength from sparse trawl data

Recruitment indices for rare or intermittently recruiting fishes are needed to compare year classes and evaluate recruitment drivers, but sparse trawl data with many zero-catch observations complicate estimation. We used fall bottom trawl data from New York, Pennsylvania, and Ohio surveys in Lake Erie's central and eastern basins to estimate annual relative cohort strength of age-0 lake whitefish ( Coregonus clupeaformis ) from 1992 to 2021 and evaluate whether a Bernoulli-Bernoulli presence-absence model retained enough information for an annual relative cohort strength index compared with a Binomial-Poisson count model. We fixed detection probability at 0.31 in the primary analysis and refit both models using alternative fixed values in sensitivity analyses. Among 2879 tows, 173 were positive and 368 fish were collected, with positive catches ranging from 1 to 20 fish. Annual catch per unit area and both models recovered a similar recruitment pattern, with variable recruitment from 1992 to 2005, little to no recruitment from 2006 to 2014, and renewed recruitment in most years from 2015 to 2021. Cohort rank order was stable across fixed detection values (Spearman r s = 0.996 to 1.000), and annual median estimates maintained high agreement with the primary analysis (Pearson r = 0.966 to 1.000). However, Bernoulli-Bernoulli estimates were not one-to-one with Binomial-Poisson estimates, and relative magnitude depended on assumed detection probability. These results indicate that the Bernoulli-Bernoulli simplification is adequate for recovering cohort strength patterns, but the Binomial-Poisson model is more appropriate for distinguishing relative cohort strength among years.

New York, Ohio, Pennsylvania↗
USGS2014

Influence of hyporheic exchange, substrate distribution, and other physically-linked hydrogeomorphic characteristics on abundance of freshwater mussels

Both endangered and non-endangered unionid mussels are heterogeneously distributed within the Allegheny River, Pennsylvania. Mussel populations vary from high to low density downstream of Kinzua Dam, and the direction, amount, and range of hyporheic exchange (seepage) at the sediment–water interface were suspected to influence their distribution and abundance. Nineteen hydrogeomorphic variables, including the quantification of seepage metrics, substrate size, river stage, river discharge, and shear stress, were measured at five reaches on the Allegheny River within 80 km downstream of Kinzua Dam. Analysis revealed significant (α = 0·05) non-linear correlations between mussel population density and directional mean seepage (positive relationship), river width (positive relationship), and median substrate size (negative relationship). Specifically, seepage findings showed that increases in upward seepage and decreases in the overall range of seepage related to increases in mussel population density. River width, directional mean seepage, and median substrate size were also found to co-vary with marginal significance (α = 0·1), making their individual influences on mussel population density uncertain. Absolute mean seepage, water depth, hydraulic head, temperature differences between the surface water and substrate, and other substrate metrics besides median grain size were not found to significantly correlate to mussel population density. Considering the physical processes often linking seepage to other explanatory variables, future research in seepage–mussel relationships should work to isolate the mechanistic influence of hyporheic exchange independently from its common covariation with substrate size and geomorphology. Copyright © 2014 John Wiley & Sons, Ltd.

New York, Ohio, Pennsylvania↗
USGS2011

Variation in spring harvest rates of male wild turkeys in New York, Ohio, and Pennsylvania

Spring harvest rates of male wild turkeys ( Meleagris gallapavo ) influence the number and proportion of adult males in the population and turkey population models have treated harvest as additive to other sources of mortality. Therefore, hunting regulations and their effect on spring harvest rates have direct implications for hunter satisfaction. We used tag recovery models to estimate survival rates, investigate spatial, temporal, and demographic variability in harvest rates, and assess how harvest rates may be related to management strategies and landscape characteristics. We banded 3,266 male wild turkeys throughout New York, Ohio, and Pennsylvania during 2006–2009. We found little evidence that harvest rates varied by year or management zone. The proportion of the landscape that was forested within 6.5 km of the capture location was negatively related to harvest rates; however, even though the proportion forested ranged from 0.008 to 0.96 across our study area, this corresponded to differences in harvest rates of only 2–5%. Annual survival was approximately twice as high for juveniles as adults . In turn, spring harvest rates for adult turkeys were greater for adults than juveniles . We estimated the population of male turkeys in New York and Pennsylvania ranged from 104,000 to 132,000 in all years and ranged from 63,000 to 75,000 in Ohio. Because of greater harvest rates for adult males, the proportion of adult males in the population was less than in the harvest and ranged from 0.40 to 0.81 among all states and years. The high harvest rates observed for adults may be offset by greater recruitment of juveniles into the adult age class the following year such that these states can sustain high harvest rates yet still maintain a relative high proportion of adult males in the harvest and population.

New York, Ohio, Pennsylvania↗