scholarly journals Seasonal to Inter-Annual Variability of Primary Production in Chesapeake Bay: Prospects to Reverse Eutrophication and Change Trophic Classification

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Lawrence W. Harding ◽  
Michael E. Mallonee ◽  
Elgin S. Perry ◽  
W. David Miller ◽  
Jason E. Adolf ◽  
...  
2016 ◽  
Vol 73 (9) ◽  
pp. 2238-2251 ◽  
Author(s):  
Edward D. Houde ◽  
Eric R. Annis ◽  
Lawrence W. Harding ◽  
Michael E. Mallonee ◽  
Michael J. Wilberg

Abstract The abundance of prerecruit, age-0 Atlantic menhaden (Brevoortia tyrannus), declined to low levels in Chesapeake Bay in the 1990s, after two decades of high abundances in the 1970s–1980s. Environmental factors and trophodynamics were hypothesized to control age-0 menhaden abundance. Data on age-0 menhaden abundance from seine and trawl surveys were analysed with respect to primary productivity, chlorophyll a (Chl a), and environmental variables. Abundance from 1989 to 2004 was strongly correlated with metrics of primary production and euphotic-layer Chl a, especially during spring months when larval menhaden transform into filter-feeding, phytoplanktivorous juveniles. Correlation, principal components, and multiple regression analyses were conducted that identified factors associated with age-0 menhaden abundance. Primary production, Chl a, and variables associated with freshwater flow, e.g. Secchi disk depth and zooplankton assemblages, were correlated with age-0 menhaden abundance. Lengths of age-0 menhaden were positively related to mean levels of annual primary production. However, lengths were negatively related to age-0 menhaden abundance, indicating that growth may be density-dependent. The identified relationships suggest that numbers of menhaden larvae ingressing to Chesapeake Bay and environmental factors that subsequently control primary productivity and food for juveniles within the Bay may control recruitment levels of Atlantic menhaden.


2014 ◽  
Vol 144 ◽  
pp. 109-119 ◽  
Author(s):  
SeungHyun Son ◽  
Menghua Wang ◽  
Lawrence W. Harding

Estuaries ◽  
1994 ◽  
Vol 17 (2) ◽  
pp. 403 ◽  
Author(s):  
Jeroen Gerritsen ◽  
A. Frederick Holland ◽  
David E. Irvine

1970 ◽  
Vol 11 (2) ◽  
pp. 117 ◽  
Author(s):  
David A. Flemer

2013 ◽  
Vol 10 (5) ◽  
pp. 3313-3340 ◽  
Author(s):  
D. I. Kelley ◽  
I. C. Prentice ◽  
S. P. Harrison ◽  
H. Wang ◽  
M. Simard ◽  
...  

Abstract. We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover; composition and height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, and are compared to scores based on the temporal or spatial mean value of the observations and a "random" model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global vegetation models (DGVMs). In general, the SDBM performs better than either of the DGVMs. It reproduces independent measurements of net primary production (NPP) but underestimates the amplitude of the observed CO2 seasonal cycle. The two DGVMs show little difference for most benchmarks (including the inter-annual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change impacts and feedbacks.


2005 ◽  
Vol 50 (2) ◽  
pp. 480-492 ◽  
Author(s):  
M. Roman ◽  
X. Zhang ◽  
C. McGilliard ◽  
W. Boicourt

2012 ◽  
Vol 9 (11) ◽  
pp. 15723-15785 ◽  
Author(s):  
D. I. Kelley ◽  
I. Colin Prentice ◽  
S. P. Harrison ◽  
H. Wang ◽  
M. Simard ◽  
...  

Abstract. We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover, composition and height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, and are compared to scores based on the temporal or spatial mean value of the observations and a "random" model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), and the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global vegetation models (DGVMs). SDBM reproduces observed CO2 seasonal cycles, but its simulation of independent measurements of net primary production (NPP) is too high. The two DGVMs show little difference for most benchmarks (including the inter-annual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change impacts and feedbacks.


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