Comparison of marine spatial planning methods in Madagascar demonstrates value of alternative approaches

Abstract

The Government of Madagascar plans to increase marine protected area coverage by over one million hectares. To assist this process, we compare four methods for marine spatial planning of Madagascar's west coast. Input data for each method was drawn from the same variables: fishing pressure, exposure to climate change, and biodiversity (habitats, species distributions, biological richness, and biodiversity value). The first method compares visual color classifications of primary variables, the second uses binary combinations of these variables to produce a categorical classification of management actions, the third is a target-based optimization using Marxan, and the fourth is conservation ranking with Zonation. We present results from each method, and compare the latter three approaches for spatial coverage, biodiversity representation, fishing cost and persistence probability. All results included large areas in the north, central, and southern parts of western Madagascar. Achieving 30% representation targets with Marxan required twice the fish catch loss than the categorical method. The categorical classification and Zonation do not consider targets for conservation features. However, when we reduced Marxan targets to 16.3%, matching the representation level of the "strict protection" class of the categorical result, the methods show similar catch losses. The management category portfolio has complete coverage, and presents several management recommendations including strict protection. Zonation produces rapid conservation rankings across large, diverse datasets. Marxan is useful for identifying strict protected areas that meet representation targets, and minimize exposure probabilities for conservation features at low economic cost. We show that methods based on Zonation and a simple combination of variables can produce results comparable to Marxan for species representation and catch losses, demonstrating the value of comparing alternative approaches during initial stages of the planning process. Choosing an appropriate approach ultimately depends on scientific and political factors including representation targets, likelihood of adoption, and persistence goals.

Figure 1. Map of study area on Madagascar's West Coast, and locations mentioned in the text.

Study area is shown in grey with black outline Most of the study area is in Madagascar's Exclusive Economic Zone with the exception of small areas that fall in Glorieuses and Juan de Nova.

Figure 2. Non-biological inputs to the analysis.

A: Fishing pressure from fish catch model , units are metric tons/km²/yr.; B: Environmental exposure probability .

Figure 3. Mangroves, reef geomorphology and bioregions.

Figure 4. Additional biological inputs into the analysis.

A: Biological richness showing number of fish species per 25 km² grid cell; B: Biodiversity value measured using Zonation algorithm.

Figure 5. Two results of RGB visual overlay of primary variables (biodiversity, fishing pressure, exposure).

A: Biodiversity value expressed as fish species richness; B: Biodiversity value measured using the Zonation algorithm. Key shows classification in 3-dimensional RGB color cube, with biodiversity (letter B in the key) assigned to Red (z-axis), fishing (F) assigned to Green (y-axis), and exposure (E) assigned to Blue (x-axis). Only the colors formed on the inner and outer planes of the cube are visible. On the inner planes, one variable is always 0. On the outer planes, one variable is always 255. The inner corner (black) has 0 values for all variables. The outer corner (white) has values of 255 for all variables.

Figure 6. Two views of conservation and management priorities.

A: results of the categorical classification (see Table 3 for class descriptions); B: target-based priority-setting with Marxan.

Figure 7. Weighted Zonation result.

This map shows a continuous ranking of conservation value by the Zonation algorithm. Higher ranked cells are more important for species representation, and tend to have lower fishing pressure and exposure values.

Figure 8. Comparison of results.

A: Strict protection class of the categorical classification; B: Marxan 30% solution; C: Marxan 16% solution; D: Zonation 16% solution.

Figure 9. Overlap between results.

The number (one, two or three) indicates the number of 16% solutions represented; in other words, the number of times a planning unit has been selected by either the strict protection, Marxan 16% or Zonation 16% result.