Estuaries and Coasts

, Volume 37, Supplement 1, pp 198–221 | Cite as

Mind the Data Gap: Identifying and Assessing Drivers of Changing Eutrophication Condition

  • Benjamin FertigEmail author
  • Michael J. Kennish
  • Gregg P. Sakowicz
  • Laura K. Reynolds


This study identified drivers of change in Barnegat Bay–Little Egg Harbor Estuary, NJ, USA over multiple long-term time periods by developing an assessment tool (an “Eutrophication Index”) capable of handling data gaps and identifying the condition of and relationships between ecosystem pressures, ecosystem state, and biotic responses. The Eutrophication Index integrates 15 indicators in 3 components: (1) water quality, (2) light availability, and (3) seagrass response. Annual quantitative assessments of condition and its consistency for three geographic segments range from 0 (highly degraded) to 100 (excellent condition). Eutrophication Index values significantly declined (p < 0.05) by 34 and 36 % in central and south segments from 73 and 71 in the early 1990s to 48 and 45 in 2010, respectively. Ongoing declines despite periods of improvement (e.g., 1989–1992, 1996–2002, and 2006–2008) suggest these estuarine segments are currently undergoing eutrophication. The north segment had highest nutrient loading and lowest Eutrophication Index values (2010 Eutrophication Index value = 37) but increased over time (from 14 in 1991 to 50 in 2009) in contrast to trends in central and south segments. Rapid initial declines of Eutrophication Index values with increasing loading highlight that the estuary is sensitive to loading. Ecosystem response to total nutrient loading, as described by the Index of Eutrophication, exhibited nonlinearity at loading rates of >1,200 and <5,000 kg TN km−2 year−1 and >100 and <250 kg TP km−2 year−1, values similar to responses of seagrass to nutrient loading in many ecosystems. While nutrient loading is initially a critical driver of ecosystem change, other factors, e.g., light availability and drive ecosystem condition, yield nonlinearity. Empirical evidence for switches in the driving factors of ecosystem stress adds complexity to the conceptualization of ecosystem resiliency due to feedback from multiple dynamic, nonlinear stressors.


Eutrophication Biotic indices Long-term data Data gaps Ecological status Multivariate analysis 



We acknowledge technical support and constructive comments by R. Baker, R. Nicholson, and C. Wieben (U.S. Geological Survey New Jersey Water Resources Center), R. Lathrop (Center for Remote Sensing and Spatial Analysis, Rutgers University), and D. Adams, T. Belton, R. Connell, M. Ferco, L.S. Hales, D. Hammond, S. King, D. Ringel, R. Schuster, B. Spinweber, and J. Vasslides (Technical Advisory Committee of the New England Interstate Water Pollution Control Commission, NEIWPCC). We thank E. Fertig for reviewing statistical sections of this contribution. This work was conducted with research awards from the Estuarine Reserves Division, Office of Ocean and Coastal Resource Management, National Ocean Service, National Oceanic and Atmospheric Administration, the New Jersey Department of Environmental Protection, and NEIWPCC. This is Contribution Number 13-257 of the Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey.

Supplementary material

12237_2013_9746_MOESM1_ESM.pdf (58 kb)
ESM 1 Annual means of nutrient loading, water quality indicators, light availability indicators, and seagrass indicators (PDF 57 kb)
12237_2013_9746_MOESM2_ESM.xlsx (25 kb)
ESM 2 XLSX 24 kb


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Copyright information

© Coastal and Estuarine Research Federation 2013

Authors and Affiliations

  • Benjamin Fertig
    • 1
    Email author
  • Michael J. Kennish
    • 1
  • Gregg P. Sakowicz
    • 1
  • Laura K. Reynolds
    • 1
  1. 1.Institute of Marine and Coastal SciencesRutgers UniversityNew BrunswickUSA

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