Geology topics

James E. Breck

Publications and source records attributed to James E. Breck.

3 recordsLinked to original sources

USGS2026

The Great Lakes aquatic tissue analysis repository (GLATAR)

Empiricists and modellers use information on energy density, proximate composition, stable isotopes, fatty acids, thiamine, and bioaccumulative tracers (e.g., PCBs and mercury) to understand the state and inter-relationships of aquatic food webs. Data exist in many published and unpublished sources, but are not consolidated in an easily accessible database that would serve as a vital resource to i) provide basic estimates of these diet-derived measures of body composition, ii) understand sources of variation in the underlying data, iii) facilitate exploration and development of data proxies, and iv) assist in study design. We designed GLATAR (Great Lakes Aquatic Tissue Analysis Repository, glatar.org ) to address this need. GLATAR is an open-access, searchable database linked to a web-based toolbox to visualise and generate user-defined summaries on diet-derived ecological metrics, with a focus on taxa of importance to the Great Lakes. GLATAR currently contains over 50,000 records on energy density, chemical tracers, and proximate body composition from 67 species of fish and 72 invertebrate taxa. We hope this user-friendly interface will entice others to upload their published and unpublished data to the repository, enriching the breadth of data accessible to researchers. In this way, GLATAR will become a ‘living’ and interactive resource for empiricists and modellers working in freshwater ecosystems as they make critical decisions related to growth, production, and consumption across a diverse group of economically and ecologically important aquatic species.

Journal of Great Lakes Research↗

USGS2020

The geometry of reaction norms yields insights on classical fitness functions for Great Lakes salmon

Life history theory examines how characteristics of organisms, such as age and size at maturity, may vary through natural selection as evolutionary responses that optimize fitness. Here we ask how predictions of age and size at maturity differ for the three classical fitness functions–intrinsic rate of natural increase r , net reproductive rate R 0 , and reproductive value V x −for semelparous species. We show that different choices of fitness functions can lead to very different predictions of species behavior. In one’s efforts to understand an organism’s behavior and to develop effective conservation and management policies, the choice of fitness function matters. The central ingredient of our approach is the maturation reaction norm (MRN), which describes how optimal age and size at maturation vary with growth rate or mortality rate. We develop a practical geometric construction of MRNs that allows us to include different growth functions (linear growth and nonlinear von Bertalanffy growth in length) and develop two-dimensional MRNs useful for quantifying growth-mortality trade-offs. We relate our approach to Beverton-Holt life history invariants and to the Stearns-Koella categorization of MRNs. We conclude with a detailed discussion of life history parameters for Great Lakes Chinook Salmon and demonstrate that age and size at maturity are consistent with predictions using R 0 (but not r or V x ) as the underlying fitness function.

Great Lakes↗

USGS2017

Fish Bioenergetics 4.0: An R-based modeling application

Bioenergetics modeling is a widely used tool in fisheries management and research. Although popular, currently available software (i.e., Fish Bioenergetics 3.0) has not been updated in over 20 years and is incompatible with newer operating systems (i.e., 64‐bit). Moreover, since the release of Fish Bioenergetics 3.0 in 1997, the number of published bioenergetics models has increased appreciably from 56 to 105 models representing 73 species. In this article, we provide an overview of Fish Bioenergetics 4.0 (FB4), a newly developed modeling application that consists of a graphical user interface (Shiny by RStudio) combined with a modeling package used in the R computing environment. While including the same capabilities as previous versions, Fish Bioenergetics 4.0 allows for timely updates and bug fixes and can be continuously improved based on feedback from users. In addition, users can add new or modified parameter sets for additional species and formulate and incorporate modifications such as habitat‐dependent functions (e.g., dissolved oxygen, salinity) that are not part of the default package. We hope that advances in the new modeling platform will attract a broad range of users while facilitating continued application of bioenergetics modeling to a wide spectrum of questions in fish biology, ecology, and management.

Fisheries Magazine↗