Seagrass ecosystem trajectory depends on the relative timescales of resistance, recovery and disturbance
O'Brien, Katherine R. ; Waycott, Michelle ; Maxwell, Paul ; Kendrick, Gary A. ; Udy, James W. ; Ferguson, Angus J.P. ; Kilminster, Kieryn ; Scanes, Peter ; McKenzie, Len J. ; McMahon, Kathryn ; Adams, Matthew P. ; Samper-Villarreal, Jimena ; Collier, Catherine ; Lyons, Mitchell ; Mumby, Peter J. ; Radke, Lynda ; Christianen, Marjolijn J.A. ; Dennison, William C. - \ 2018
Marine Pollution Bulletin 134 (2018). - ISSN 0025-326X - p. 166 - 176.
Colonizing - Opportunistic - Persistent - Recovery - Resilience - Resistance - Seagrass - Trajectory
Seagrass ecosystems are inherently dynamic, responding to environmental change across a range of scales. Habitat requirements of seagrass are well defined, but less is known about their ability to resist disturbance. Specific means of recovery after loss are particularly difficult to quantify. Here we assess the resistance and recovery capacity of 12 seagrass genera. We document four classic trajectories of degradation and recovery for seagrass ecosystems, illustrated with examples from around the world. Recovery can be rapid once conditions improve, but seagrass absence at landscape scales may persist for many decades, perpetuated by feedbacks and/or lack of seed or plant propagules to initiate recovery. It can be difficult to distinguish between slow recovery, recalcitrant degradation, and the need for a window of opportunity to trigger recovery. We propose a framework synthesizing how the spatial and temporal scales of both disturbance and seagrass response affect ecosystem trajectory and hence resilience.
Free radical reaction pathway, thermohemistry of peracetic acid homolysis and its application for phenol degradation: spectroscopic sti=udy and quantum chemistry calculations
Rokhina, E.V. ; Makarova, K. ; Golovina, E.A. ; As, H. van; Virkutyte, J. - \ 2010
Environmental Science and Technology 44 (2010)17. - ISSN 0013-936X - p. 6815 - 6821.
bond-dissociation energies - biological-systems - peroxyacetic acid - waste-water - spin-trap - n-oxide - oxidation - oxygen - disinfection - simulation
The homolysis of peracetic acid (PAA) as a relevant source of free radicals (e.g., •OH) was studied in detail. Radicals formed as a result of chain radical reactions were detected with electron spin resonance and nuclear magnetic resonance spin trapping techniques and subsequently identified by means of the simulation-based fitting approach. The reaction mechanism, where a hydroxyl radical was a primary product of O-O bond rupture of PAA, was established with a complete assessment of relevant reaction thermochemistry. Total energy analysis of the reaction pathway was performed by electronic structure calculations (ab initio and semiempirical methods) at different levels and basis sets [e.g., HF/6-311G(d), B3LYP/6-31G(d)]. Furthermore, the heterogeneous MnO2/PAA system was tested for the elimination of a model aromatic compound, phenol from aqueous solution. An artificial neural network (ANN) was designed to associate the removal efficiency of phenol with relevant process parameters such as concentrations of both the catalyst and PAA and the reaction time. Results were used to train and test ANN to identify an optimized network structure, which represented the correlations between the operational parameters and removal efficiency of phenol