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  • 1
    Publication Date: 2022-05-25
    Description: Author Posting. © Ecological Society of America, 2013. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Applications 23 (2013): 959–971, doi:10.1890/12-0447.1.
    Description: The biological benefits of marine reserves have garnered favor in the conservation community, but “no-take” reserve implementation is complicated by the economic interests of fishery stakeholders. There are now a number of studies examining the conditions under which marine reserves can provide both economic and ecological benefits. A potentially important reality of fishing that these studies overlook is that fishing can damage the habitat of the target stock. Here, we construct an equilibrium bioeconomic model that incorporates this habitat damage and show that the designation of marine reserves, coupled with the implementation of a tax on fishing effort, becomes both biologically and economically favorable as habitat sensitivity increases. We also study the effects of varied degrees of spatial control on fisheries management. Together, our results provide further evidence for the potential monetary and biological value of spatial management, and the possibility of a mutually beneficial resolution to the fisherman–conservationist marine reserve designation dilemma.
    Description: M. G. Neubert acknowledges the support of the National Science Foundation (DMS-0532378, OCE-1031256) and a Thomas B. Wheeler Award for Ocean Science and Society. H. V. Moeller acknowledges support from a National Science Foundation Graduate Research Fellowship. This research is based in part on work supported by Award No. USA 00002 made by King Abdullah University of Science and Technology (KAUST).
    Keywords: Bioeconomics ; Destructive fishing practices ; Fisheries ; Habitat damage ; Marine protected areas ; Marine reserves ; Optimal control ; Optimal harvesting ; Spatial management
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2022-05-25
    Description: Author Posting. © Ecological Society of America, 2010. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Applications 20 (2010): 1285–1301, doi:10.1890/09-0876.1.
    Description: Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter performance, but it did not improve performance when used individually. The EnKF estimates of leaf area followed the expected springtime canopy phenology. However, there were also diel fluctuations in the leaf-area estimates; these are a clear indication of a model deficiency possibly related to vapor pressure effects on canopy conductance.
    Description: This material is based upon work supported by the U.S. National Science Foundation under grants OPP-0352897, DEB-0423385, DEB-0439620, DEB-0444592, and OPP- 0632139.
    Keywords: Alaska, USA ; Data assimilation ; Ecosystem carbon balance ; Ecosystem models ; Eddy covariance ; Kalman filter ; Net ecosystem carbon exchange
    Repository Name: Woods Hole Open Access Server
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  • 3
    Publication Date: 2022-05-26
    Description: © The Author(s), 2012. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecosphere 3, no 1 (2012): art4, doi:10.1890/ES11-00202.1.
    Description: Understanding the carbon dioxide and water fluxes in the Arctic is essential for accurate assessment and prediction of the responses of these ecosystems to climate change. In the Arctic, there have been relatively few studies of net CO2, water, and energy exchange using micrometeorological methods due to the difficulty of performing these measurements in cold, remote regions. When these measurements are performed, they are usually collected only during the short summer growing season. We established eddy covariance flux towers in three representative Alaska tundra ecosystems (heath tundra, tussock tundra, and wet sedge tundra), and have collected CO2, water, and energy flux data continuously for over three years (September 2007–May 2011). In all ecosystems, peak CO2 uptake occurred during July, with accumulations of 51–95 g C/m2 during June–August. The timing of the switch from CO2 source to sink in the spring appears to be regulated by the number of growing degree days early in the season, indicating that warmer springs may promote increased net CO2 uptake. However, this increased uptake in the spring may be lost through warmer temperatures in the late growing season that promote respiration, if this respiration is not impeded by large amounts of precipitation or cooler temperatures. Net CO2 accumulation during the growing season was generally lost through respiration during the snow covered months of September–May, turning the ecosystems into net sources of CO2 over measurement period. The water balance from June to August at the three ecosystems was variable, with the most variability observed in the heath tundra, and the least in the tussock tundra. These findings underline the importance of collecting data over the full annual cycle and across multiple types of tundra ecosystems in order to come to a more complete understanding of CO2 and water fluxes in the Arctic.
    Description: This research was funded by the National Science Foundation Office of Polar Programs (OPP 0632264), with a grant during the International Polar Year, ‘Collaborative Research on Carbon, Water, and Energy Balance of the Arctic Landscape at Flagship Observatories and in a PanArctic Network’. Tracy
    Keywords: Arctic tundra ; Ecosystem respiration ; Eddy covariance ; Evapotranspiration ; Gross primary production ; Net ecosystem exchange ; Water balance ; Water use efficiency
    Repository Name: Woods Hole Open Access Server
    Type: Article
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