Home > Explore Data & Reports > Evidence of climate-driven ecosystem reorganization in the Gulf of Mexico

Citation:

Karnauskas, M., M.J. Schirripa, J.K. Craig, G.S. Cook, C.R. Kelble, J.J. Agar, B.A. Black, D.B. Enfield, D. Lindo-Atichati, B.A. Muhling, K.M. Purcell, P.M. Richards, and C. Wang. 2015. Evidence of climate-driven ecosystem reorganization in the Gulf of Mexico. Global Change Biology, 21(7):2554-2568. https://doi.org/10.1111/gcb.12894

Data/Report Type:

Sponsored Research

Description

The Gulf of Mexico is one of the most ecologically and economically valuable marine ecosystems in the world and is affected by a variety of natural and anthropogenic phenomena including climate, hurricanes, coastal development, agricultural runoff, oil spills, and fishing. These complex and interacting stressors, together with the highly dynamic nature of this ecosystem, present challenges for the effective management of its resources. We analyze a compilation of over 100 indicators representing physical, biological, and economic aspects of the Gulf of Mexico and find that an ecosystem-wide reorganization occurred in the mid-1990s. Further analysis of fishery landings composition data indicates a major shift in the late 1970s coincident with the advent of US national fisheries management policy, as well as significant shifts in the mid-1960s and the mid-1990s. These latter shifts are aligned temporally with changes in a major climate mode in the Atlantic Ocean: the Atlantic Multidecadal Oscillation (AMO). We provide an explanation for how the AMO may drive physical changes in the Gulf of Mexico, thus altering higher-level ecosystem dynamics. The hypotheses presented here should provide focus for further targeted studies, particularly in regard to whether and how management should adjust to different climate regimes or states of nature. Our study highlights the challenges in understanding the effects of climatic drivers against a background of multiple anthropogenic pressures, particularly in a system where these forces interact in complex and nonlinear ways.

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