Changes between Version 9 and Version 10 of EwEugSpatialOptimizationProcedures
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 20101122 22:44:11 (10 years ago)
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EwEugSpatialOptimizationProcedures
v9 v10 4 4 This section contains the methodology and scientific material for the ''Spatial optimizations'' tool in Ecospace. This routine is implemented using the ''Spatial optimizations'' form (''Spatial dynamic (Ecospace) > Tools > Spatial optimizations''). For instructions on implementing the routine, see [wiki:EwEugSpatialOptimizations Spatial optimizations]. 5 5 6 We describe two approaches for spatial optimization of protected area placement, both based on maximizing an objective function that incorporates ecological, social, and economical criteria. Of these, a seed cell selection procedure works by evaluating potential cells for protection one by one, picking the one that maximizes the objective function, add seed cells, and continue to full protection. The other is a Monte Carlo approach, which uses a likelihood sampling procedure based on weighted importance layers of conservation interest (similar to Marxan ’s) to evaluate alternative protected area sizing and placement. The two approaches are alternative options in a common spatial optimization module, which uses the time and spatial dynamic Ecospace model for the evaluations. The optimizations are implemented as components of the Ecopath with Ecosim approach and software. In a case study, we find that there can be protected area zoning that will increase economical and social factors, without causing ecological deterioration. We also find a tradeoff between including cells of special conservation interest and the economical and social interest, and while this does not need to be a general feature, it emphasizes the need to use modeling techniques to evaluate the tradeoff.6 We describe two approaches for spatial optimization of protected area placement, both based on maximizing an objective function that incorporates ecological, social, and economical criteria. Of these, a seed cell selection procedure works by evaluating potential cells for protection one by one, picking the one that maximizes the objective function, add seed cells, and continue to full protection. The other is a Monte Carlo approach, which uses a likelihood sampling procedure based on weighted importance layers of conservation interest (similar to Marxan's) to evaluate alternative protected area sizing and placement. The two approaches are alternative options in a common spatial optimization module, which uses the time and spatial dynamic Ecospace model for the evaluations. The optimizations are implemented as components of the Ecopath with Ecosim approach and software. In a case study, we find that there can be protected area zoning that will increase economical and social factors, without causing ecological deterioration. We also find a tradeoff between including cells of special conservation interest and the economical and social interest, and while this does not need to be a general feature, it emphasizes the need to use modeling techniques to evaluate the tradeoff. 7 7 8 8 The most widely used approach for spatial planning with a conservation perspective is the Marxan approach and software, (http://www.uq.edu.au/marxan/) developed primarily by Hugh Possingham and colleagues at the Ecology Centre, University of Queensland. Marxan is a very flexible approach capable of incorporating large data sources and use categories, it is computationally efficient, and lends itself well to enabling stakeholder involvement in the site selection process. … … 36 36 where [[Image(wiki:EwEugImages:03000011.png)]] is baseline Ecopath biomass for group ([[Image(wiki:EwEugImages:03000005.png)]]), and [[Image(wiki:EwEugImages:03000012.png)]] equals the group biomass [[Image(wiki:EwEugImages:03000007.png)]] if [[Image(wiki:EwEugImages:03000013.png)]] is lower than the mandated biomass, [[Image(wiki:EwEugImages:03000014.png)]] for the group, and [[Image(wiki:EwEugImages:03000014.png)]] if it is not. 37 37 38 The mandated rebuilding objective can be used to set ‘minimum biological acceptable levels’ (or MBAL as commonly used). By setting high mandated biomasses ([[Image(wiki:EwEugImages:03000014.png)]]) for a group it can also be used to capture ‘existence values,’e.g., of marine mammals of interest for a whale watching industry. We do not discount the mandated rebuilding structure over time.38 The mandated rebuilding objective can be used to set 'minimum biological acceptable levels' (or MBAL as commonly used). By setting high mandated biomasses ([[Image(wiki:EwEugImages:03000014.png)]]) for a group it can also be used to capture 'existence values,' e.g., of marine mammals of interest for a whale watching industry. We do not discount the mandated rebuilding structure over time. 39 39 40 40 The ecosystem structure objective is meant to capture that mature (Ktype) ecosystems tend to be dominated by longlived species and individuals (Odum, 1969). We seek to capture this characteristic through the inverse production/biomass ratio, estimating for each time step ([[Image(wiki:EwEugImages:03000001.png)]]) … … 46 46 The ecosystem structure objective is not discounted over time; having longlived species in the future being deemed as important as having them now. 47 47 48 As a measure of biomass diversity, we used a modified version of Kempton ’s Q75 index, originally was developed to describe species diversity (Kempton, 2002). We here used a biomass diversity indicator following Ainsworth and Pitcher (2006), albeit slightly modified. We estimate the biomass diversity index ([[Image(wiki:EwEugImages:0300001B.png)]] from48 As a measure of biomass diversity, we used a modified version of Kempton's Q75 index, originally was developed to describe species diversity (Kempton, 2002). We here used a biomass diversity indicator following Ainsworth and Pitcher (2006), albeit slightly modified. We estimate the biomass diversity index ([[Image(wiki:EwEugImages:0300001B.png)]] from 49 49 50 50 [[Image(wiki:EwEugImages:0300001C.png)]] … … 72 72 This optimization method is based on a previous study (Beattie 2001; Beattie et al. 2002), in which a very simple optimization scheme was used to evaluate tradeoff between proportion of area protected and the ecosystemlevel objective function. We have modified the previous approach by securing a better program flow, and notably by changing the objective function from considering only profit from fishing and existence value of biomass groups to the more detailed function described above (Equation 2). 73 73 74 The procedure takes as its starting point the designation of one, more, or all spatial cells as ‘seed cells’, i.e. cells that are to be considered as potential protected cells in the next program iteration. The procedure will then run the Ecospace model repeatedly between two time steps, closing one of the seeds cells in each run, while storing the ecosystem objective function value. The seed cell that results in the highest objective function is then closed for fishing, and its four neighboring cells (above, below, and to either side) are then turned into seed cells, unless they are so already, or already are protected, or are land cells. This procedure will continue until all cells are protected.74 The procedure takes as its starting point the designation of one, more, or all spatial cells as 'seed cells', i.e. cells that are to be considered as potential protected cells in the next program iteration. The procedure will then run the Ecospace model repeatedly between two time steps, closing one of the seeds cells in each run, while storing the ecosystem objective function value. The seed cell that results in the highest objective function is then closed for fishing, and its four neighboring cells (above, below, and to either side) are then turned into seed cells, unless they are so already, or already are protected, or are land cells. This procedure will continue until all cells are protected. 75 75 76 76 The time over which the selection procedure is run is chosen dependent on the application. Typically, an ecosystem model is initially developed and tuned using time series data to cover a certain time period, e.g., from 1950 to 2005. Subsequently, the model is used in a scenario development mode to evaluate for instance protected area placement covering the period 20062020. … … 82 82 An advantage of the seed cell modeling approach described above is that it allows a comprehensive overview of the tradeoff between proportion of area closed to fishing, and the ecological, social, and economical benefit and costs of the closures. This is done, based on the information already included in the EwE modeling approach, with no new information being needed. While this may be an advantage from one perspective, it does not allow use of other form for information, notably in form of geospatial data, such as, for instance, critical fish habitat layers from GIS. 83 83 84 To address this shortcoming, we have developed an alternative optimization routine for the Ecospace model, which uses spatial layers of conservation interest ( ‘importance layers’) to set likelihoods for spatial cells being considered for protection. The optimizations are performed using a Monte Carlo (MC) approach where the importance layers are used for the initial cell selection in each MC realization. The Ecospace model is then run, the objective function (Equation 2) is evaluated, and the results, including which cells were protected, are stored for each run (see Figure 1).84 To address this shortcoming, we have developed an alternative optimization routine for the Ecospace model, which uses spatial layers of conservation interest ('importance layers') to set likelihoods for spatial cells being considered for protection. The optimizations are performed using a Monte Carlo (MC) approach where the importance layers are used for the initial cell selection in each MC realization. The Ecospace model is then run, the objective function (Equation 2) is evaluated, and the results, including which cells were protected, are stored for each run (see Figure 1). 85 85 86 The importance layers are defined as raster layers, with dimensions similar to the base map layers in the underlying Ecospace model, i.e. they are rectangular cells in a grid with a certain number of rows and columns. Each cell in a given layer has a certain ‘importance’for conservation, expressed, e.g., as the probability of occurrence for an endangered species. For each importance layer ([[Image(wiki:EwEugImages:0300002C.png)]]), we initially scale the importance layer values to sum to unity, and then calculate an overall cell weighting ([[Image(wiki:EwEugImages:0300002D.png)]]) for each cell ([[Image(wiki:EwEugImages:03000026.png)]]) from86 The importance layers are defined as raster layers, with dimensions similar to the base map layers in the underlying Ecospace model, i.e. they are rectangular cells in a grid with a certain number of rows and columns. Each cell in a given layer has a certain 'importance' for conservation, expressed, e.g., as the probability of occurrence for an endangered species. For each importance layer ([[Image(wiki:EwEugImages:0300002C.png)]]), we initially scale the importance layer values to sum to unity, and then calculate an overall cell weighting ([[Image(wiki:EwEugImages:0300002D.png)]]) for each cell ([[Image(wiki:EwEugImages:03000026.png)]]) from 87 87 88 88 [[Image(wiki:EwEugImages:0300002E.png)]] '''Equation 3''' … … 113 113 114 114  '''Objective'''  '''Description'''  115  Profit  Estimated by ‘fleet’, and summed over all such 115  Profit  Estimated by 'fleet', and summed over all such  116 116  Jobs  Estimated from value of fisheries, and relative number of jobs/value  117 117  Mandated rebuilding  A minimum acceptable level, by group 