Geology topics

Chris Castiglione

Publications and source records attributed to Chris Castiglione.

5 recordsLinked to original sources

USGS2026

Two hundred years of historical spawning and nursery data for coregonine fishes in the Laurentian Great Lakes

Historical data can provide critical ecological information for species across the globe, many of which are facing unprecedented rates of ecosystem change. Yet, historical information related to freshwater species, especially fishes, remains scattered, often in original formats, and underutilized for informing conservation and restoration activities. Here, we present a Data Descriptor called Coregonine Spawning History (CORHIST), a database designed to house diverse data related to past spawning and nursery areas for fishes in the family Salmonidae, subfamily Coregoninae (ciscoes and whitefishes), in the Laurentian Great Lakes and their tributaries. Data for 11 species of coregonines historically occurring in the Great Lakes are included in CORHIST. Over 3,400 occurrence records at the coordinate scale have been entered, over 2,200 of which are for Cisco ( Coregonus artedi ) and Lake Whitefish ( C. clupeaformis )—two focal species for which there is either multinational conservation interest or restoration efforts underway in the Laurentian Great Lakes. CORHIST is already proving useful for several studies developing habitat suitability models and delineating spatial units for conservation or restoration planning.

Laurentian Great Lakes↗

USGS2023

Gap analysis: A proposed methodology to describe and map historical and contemporary populations and habitats

This is a methodology paper that describes an approach for modeling and mapping historical and contemporary spawning areas for coregonine fishes in the Laurentian Great Lakes. Coregonines are a family of native whitefishes and ciscoes that are now greatly reduced or extirpated, but once served important roles for both the food web and society. This method can illustrate where habitats once existed and where they are today - critical information for restoration and conservation actions.

Great Lakes↗

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

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↗

USGS2006

A broadscale fish-habitat model development process: Genesee Basin, New York

We describe a methodology for developing species-habitat models using available fish and stream habitat data from New York State, focusing on the Genesee basin. Electrofishing data from the New York Department of Environmental Conservation were standardized and used for model development and testing. Four types of predictive models (multiple linear regression, stepwise multiple linear regression, linear discriminant analysis, and neural network) were developed and compared for 11 fish species. Predictive models used as many as 25 habitat variables and explained 35-91% of observed species abundance variability. Omission rates were generally low, but commission rates varied widely. Neural network models performed best for all species, except for rainbow trout Oncorhynchus mykiss , gizzard shad Dorosoma cepedianum , and brown trout Salmo trutta . Linear discriminant functions generally performed poorly. The species-environment models we constructed performed well and have potential applications to management issues.

Book chapter↗