The DOI matches its Crossref record.
The Seasonal Dynamics of The Chesapeake Bay Ecosystem
Abstract
The full suite of carbon exchanges among the 36 most important components of the Chesapeake Bay mesohaline ecosystem is estimated to examine the seasonal trends in energy flow and the trophic dynamics of the ecosystem. The networks provide information on the rates of energy transfer between the trophic components in a system wherein autochthonous production is dominated by phytoplankton production. A key seasonal feature of the system is that the summer grazing of primary producers by zooplankton is greatly reduced due to top—down control of zooplankton by ctenophores and sea nettles. Some of the ungrazed phytoplankton is left to fuel the activities of the pelagic microbial community, and the remainder falls to the bottom where it augments the deposit—feeding assemblage of polychaetes, amphipods, and blue crabs. There is a dominant seasonal cycle in the activities of all subcommunities, which is greatest in the summer and least in the cold season. However, the overall topology of the ecosystem does not appear to change substantially from season to season. Matrix operations can be employed to assess the various direct and indirect pathways by which each trophic group obtains energy. Often, indirect linkages reveal interesting differences. For example, although the bluefish and striped bass are both piscivorous predators, 63% of bluefish intake depends indirectly on benthic organisms, whereas striped bass depends mainly on planktonic organisms. Nearly all higher trophic species exhibit significant indirect dependencies upon the upper components of the microbial loop, especially during summer. The complicated trophic network can be mapped into an eight—level trophic chain in the sense of Lindeman. Such analysis reveals that detritivory is about 10 times greater than herbivorous grazing in the Chesapeake system and that 70% of detritus results from internal recycle. Annual efficiencies of trophic levels decrease as one ascends the chain. Major seasonal shifts in trophic efficiencies at higher levels appear to be modulated by how effectively microscopic zooplankton (mostly ciliates) are cropped by their predators. Average trophic efficiency is 9.6%. Despite the existence of eight trophic levels, the average level at which each species feeds always remains below 5. One "pest" species (the coelenterate Chrysaora quinquecirrha) feeds rather high on the trophic pyramid and may exert a heretofore unappreciated level of control on the planktonic food chain. The number of cycles present in the network is surprisingly few, despite the fact that a relatively large and seemingly constant amount (23.2%) of total system activity is devoted to recycling. This combination of factors possibly indicates a stressed ecosystem. A study of the rate—limiting links in the seasonal networks of recycling of material within the plankton reconfirms the shift of predator control from crustaceous zooplankton in springtime to the sea nettle (Chrysaora quinquecirrha) during summer months. The collection of cycles present in the system is disjoint; there is no overlap between the cycles among the planktonic community and the circulations among the deposit feeders and nekton. The filter—feeding benthos and fish do not participate in any cycling, but serve rather as bridges to shift carbon and energy from the planktonic community into the benthic—nektonic subsystems. Neither do most of the members of the microbial loop engage in any recycle of carbon, functioning instead as a dissipative shunt of energy out of the system.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Community structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
Spreading dead zones and consequences for marine ecosystems
Diaz, R. J., & Rosenberg, R. (2008) · Science · doi:10.1126/science.1156401 · Cited by 6,626
The nested assembly of plant–animal mutualistic networks
Bascompte, J., Jordano, P., Melián, C. J., & Olesen, J. M. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1633576100 · Cited by 2,497
Eutrophication of Chesapeake Bay: historical trends and ecological interactions.
Kemp, W., Boynton, W., Adolf, J., Boesch, D., Boicourt, W., Brush, G., Cornwell, J., Fisher, T., Glibert, P., Hagy, J., et al. (2005) · Marine Ecology Progress Series · doi:10.3354/meps303001 · Cited by 1,468
Rose, K. A., Cowan, J. H., Winemiller, K. O., Myers, R. A., & Hilborn, R. (2001) · Fish and Fisheries · doi:10.1046/j.1467-2960.2001.00056.x · Cited by 696
Effects of seasonality and fish movement on tropical river food webs
Winemiller, K. O., & Jepsen, D. B. (1998) · Journal of Fish Biology · doi:10.1111/j.1095-8649.1998.tb01032.x · Cited by 508
Coen, L. D., & Luckenbach, M. W. (2000) · Ecological Engineering · doi:10.1016/s0925-8574(00)00084-7 · Cited by 276
Influence of Eastern Oysters on Nitrogen and Phosphorus Regeneration in Chesapeake Bay, USA
Newell, R. I. E., Fisher, T. R., Holyoke, R. R., & Cornwell, J. C. (2005) · NATO Science Series IV: Earth and Environmental Series · doi:10.1007/1-4020-3030-4_6 · Cited by 270
Compilation and Network Analyses of Cambrian Food Webs
Dunne, J. A., Williams, R. J., Martinez, N. D., Wood, R. A., & Erwin, D. H. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060102 · Cited by 269
Origin of compartmentalization in food webs
Guimerà, R., Stouffer, D. B., Sales-Pardo, M., Leicht, E. A., Newman, M. E. J., & Amaral, L. A. N. (2010) · Ecology · doi:10.1890/09-1175.1 · Cited by 201
Parallel ecological networks in ecosystems
Olff, H., Alonso, D., Berg, M. P., Eriksson, B. K., Loreau, M., Piersma, T., & Rooney, N. (2009) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2008.0222 · Cited by 188
Food web models: a plea for groups.
Allesina, S., & Pascual, M. (2009) · Ecology Letters · doi:10.1111/j.1461-0248.2009.01321.x · Cited by 149
Jung, S., & Houde, E. D. (2003) · Estuarine, Coastal and Shelf Science · doi:10.1016/s0272-7714(03)00085-4 · Cited by 96
Ecological subsystems via graph theory: the role of strongly connected components
Allesina, S., Bodini, A., & Bondavalli, C. (2005) · Oikos · doi:10.1111/j.0030-1299.2005.13082.x · Cited by 71
Ecological forecasting in Chesapeake Bay: using a mechanistic-empirical modeling approach.
Brown, C. W., Hood, R. R., Long, W., Jacobs, J., Ramers, D. L., Wazniak, C., Wiggert, J. D., Wood, R., & Xu, J. (2013) · Journal of Marine Systems · doi:10.1016/j.jmarsys.2012.12.007 · Cited by 72
The roles and impacts of human hunter-gatherers in North Pacific marine food webs
Dunne, J. A., Maschner, H., Betts, M. W., Huntly, N., Russell, R., Williams, R. J., & Wood, S. A. (2016) · Scientific Reports · doi:10.1038/srep21179 · Cited by 70
Global Systematic Review of Methodological Approaches to Analyze Coastal Shelf Food Webs
Kytinou, E., Sini, M., Issaris, Y., & Katsanevakis, S. (2020) · Frontiers in Marine Science · doi:10.3389/fmars.2020.00636 · Cited by 17
Metadata
- Authors
- Baird, D., & Ulanowicz, R. E.
- Venue
- Ecological Monographs
- Publisher
- Wiley
- Published
- 1989-12
- Volume
- 59
- Issue
- 4
- Pages
- 329-364
- ISSN
- 0012-9615 (ISSN-L)
- 1557-7015
- OpenAlex
- W1972501876
- Cited by
- 824
- References
- 0
- FWCI
- 11.98
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.