1 1.10 Application of estuarine and coastal classifications in marine ...

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continuation of high resolution seafloor bathymetric mapping and benthic habitat mapping to ensure that special, sensitive, or unique estuarine and marine life and habitats could be identified and protected, habitat evaluation methodology can be refined and ecosystem values and services can be evaluated and mapped. Where data is available increased data sharing and development of data portals with detailed metadata will facilitate the effective application of spatial data in marine spatial management. In addition, more information is needed to better understand the temporal variability of seascape characteristics and to assess their vulnerability to perturbations, resilience and recovery times. Where spatial data gaps exist, spatial predictive modeling is emerging as an accurate and cost-effective tool to fill spatial information gaps and help elucidate on macroecological drivers in support of decision making in marine and coastal management (Leathwick et al. 2008, Pittman et al. 2009, Valavanis et al. 2008). Landscape ecology concepts and tools offer great promise in determining the ecological relevance of the spatial patterning depicted in seascape maps including functional connectivity, synergistic interactions among adjacent seascape types such as complementation effects and other functions of multiple seascape types. Landscape ecology has developed analytical techniques such as pattern metrics specifically for quantifying the spatial complexity in landscape or seascape composition, spatial configuration (Robbins and Bell 1994, Wedding et al. in press). Furthermore, a new generation of surface metrics can be applied to examine the three-dimensional spatial gradients in surface morphology of the environment (Pittman et al. 2009; Pittman & Brown in press). Marine and terrestrial regions are typically interlinked through ecosystem flows such as run-off, fishing pressure and other direct human activity that can be a function of population density, therefore classifications and data products including decision making must rapidly move toward an integrated land-sea spatial planning 70
approach. New classifications will be required that represent land-sea interactions and are spatially seamless between landscapes and seascapes. 1.10.12.2 Understanding and Communicating Errors and Uncertainty An important challenge for management is to ensure that decision making where ecological goals and objectives are central, such as conservation prioritization, are objective and science driven and not driven primarily by data availability or technological advancement. In reality the desire for a rapid, quantitative and defensible approach can sometimes override uncertainty in the data. Very rarely are the uncertainties quantified and communicated in a spatially explicit way before marine classifications are applied in management. For robust decision making to take place, more effort must be focused on evaluating data errors, bias and uncertainty and this will become very important in MSP where a diverse array of spatial datasets are being assembled together with novel untested procedures for developing classified maps. Uncertainty comes in a variety of forms and representations and requires different techniques for presentation. It is important to acknowledge that a classification scheme is a model of reality and in many cases is a 2 nd or even 3 rd derivative of the original data. Misclassifications occur and can be due to spatial accuracy, observer bias, environmental variability, processing techniques, thematic, temporal and spatial resolution, etc. Some will say that all maps are a “lie” and that decisions can be made at various stages to make maps “lie” (Monmonier 1996). Inevitably some maps represent more of the true reality than others. Error is particularly problematic when a highly dynamic system is represented by a static map, yet management decisions will and must be made on best-available data. As noted by Alfred Russell Wallace in 1876 “nothing like a 71
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1.10 Application of estuarine and c
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SYNOPSIS Coastal and marine classif
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government and non-governmental man
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paucity of species and habitat data
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and many sub-catastrophic disturban
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conservation practitioners, industr
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levels of the hierarchy are defined
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distributions, both within and surr
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In a hierarchical framework for the
Page 19 and 20: 1.10.2.3 Australian Coastal Classif
Page 21 and 22: Ecological theory predicts that the
Page 23 and 24: outbreaks in the late 1970s (Green
Page 25 and 26: intertidal zones, and shallow coast
Page 27 and 28: 1.10.3.1 Identifying Priority Conse
Page 29 and 30: y the Massachusetts Oceans Act (200
Page 31 and 32: eight color coded zones to provide
Page 33 and 34: appropriate place to investigate fe
Page 35 and 36: 1.10.4.3 Massachusetts Ocean Plan T
Page 37 and 38: One of the foundational concepts un
Page 39 and 40: ecosystems has shown that considera
Page 41 and 42: In May 2004, Germany was the first
Page 43 and 44: improve the design and interpretati
Page 45 and 46: NOAA’s CoastWatch Change Analysis
Page 47 and 48: ground resolution of 30 m. Each cla
Page 49 and 50: also be used to assess the role of
Page 51 and 52: States (Gundlach and Hayes 1978). T
Page 53 and 54: information at the species level. I
Page 55 and 56: 1.10.8.4 Classifying and Mapping Hu
Page 57 and 58: map of the entire Australian shorel
Page 59 and 60: within 11 regions, leading to an ov
Page 61 and 62: enhancement of certain functions in
Page 63 and 64: achieved by identifying barriers to
Page 65 and 66: ecosystem service values extracted
Page 67 and 68: 1.10.12 FUTURE DIRECTIONS AND PRIOR
Page 69: 1.10.12.1 Linking Patterns and Proc
Page 73 and 74: Arundel, H. and Mount, R. 2007. Nat
Page 75 and 76: Connell, J. H. 1978. Diversity in t
Page 77 and 78: Duke. N.C., Meynecke, J.O., Dittman
Page 79 and 80: Green, A.L., C.E. Birkeland, R.H. R
Page 81 and 82: Hiddink, J. G., Jennings, S., Kaise
Page 83 and 84: Kostylev, V., Todd, B.J., Fader, G.
Page 85 and 86: Maxwell, D.L., Stelzenmüller, V.,
Page 87 and 88: Spatial and temporal patterns in fi
Page 89 and 90: Sharples, C. 2006. Indicative Mappi
Page 91 and 92: Wells, S., Ravilous, C., Corcoran,
Page 93 and 94: FIGURE LEGENDS Figure 1. A) Classes
Page 95 and 96: Figure 10. Ecological evaluation in
Page 97 and 98: Figure 21. Combining 'intolerance'
Page 99 and 100: selected planning units in gray ins
Page 101 and 102: Box 2. Classifications as thematic
Page 103 and 104: Figure 1. 1
Page 117 and 118: Figure 15. Fishing effort (hrs/day)
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Figure 19. 100% Conversion. 80% 60%
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Figure 21. 21
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Figure 23. 23
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Figure 25. 25
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Figure 27. 27
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Figure 29. 29
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Figure 31. 31
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Figure 33. 33
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