Premium
Effects of Temperature on Age‐0 Atlantic Menhaden Growth in Chesapeake Bay
Author(s) -
Humphrey Jennifer,
Wilberg Michael J.,
Houde Edward D.,
Fabrizio Mary C.
Publication year - 2014
Publication title -
transactions of the american fisheries society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.696
H-Index - 86
eISSN - 1548-8659
pISSN - 0002-8487
DOI - 10.1080/00028487.2014.931299
Subject(s) - menhaden , chesapeake bay , bay , environmental science , growing degree day , fishery , growth model , oceanography , fish <actinopterygii> , biology , estuary , ecology , mathematics , geology , phenology , mathematical economics , fish meal
Atlantic Menhaden Brevoortia tyrannus is an economically and ecologically important forage fish in the western Atlantic Ocean. In the Chesapeake Bay, its recruitment has been low since the late 1980s, prompting questions on how environmental factors may affect its productivity. Growth is an important component of production, but causes of spatial and temporal variability in growth of age‐0 Atlantic Menhaden are not fully understood. Our objective was to quantify the effect of temperature on spatial and temporal variability in growth of age‐0 Atlantic Menhaden in Chesapeake Bay. We analyzed data on mean length and temperature for years 1962–2011 from nine regions of Chesapeake Bay. We developed a linear model that relates mean total length of Atlantic Menhaden to cumulative growing degree‐days (GDDs) in Chesapeake Bay and validated the model using data that were withheld from the initial parameter estimation. The temperature threshold that best described variability in growth was 14°C, a temperature substantially higher than the physiological threshold for growth. The GDD model explained almost 80% of the variability in mean length over time (within and among years) and among regions. In a model validation exercise, it accurately predicted mean length in tributary subregions of the bay not included in the original model fitting. The GDD model requires only temperature data to effectively predict growth, making it simpler to apply than models requiring more complex approaches. Received August 22, 2013; accepted May 30, 2014