Comparison of Marine Spatial Planning Methods in Madagascar Demonstrates Value of Alternative Approaches

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

Reimaanlok: A national framework for conservation area planning in the Marshall Islands

The development of Reimaanlok, a national framework for the planning and establishment of community-based conservation areas in the Marshall Islands, is outlined. A team composed of international experts and local resource management professionals selected and modified an ecoregional planning approach, defined key concepts, selected conservation features and targets, compiled biogeographical information from scientific and local knowledge and carried out a national-level ecological gap assessment. Past development of community-based fisheries and conservation plans was reviewed and the lessons learned informed the development of a robust community-based planning process for the design and establishment of conservation areas on individual atolls, integrating ecosystem based management (EBM) theory, traditional knowledge and management, and the particular socio-economic needs of island communities. While specific geographic, historical, cultural and economic characteristics of the Marshall Islands have created a framework that is unique, several aspects of this process offer ideas for national strategic conservation planning in other Small Island Developing States where there is a paucity of scientific data, significant and increasing threats, and where decision-making about the use of natural resources occurs primarily at the local level

Securing a long-term future for coral reefs

Rapid ocean warming as a result of climate change poses a key risk for coral reefs. Even if the goals of the Paris Climate Agreement are achieved, coral reefs are likely to decline by 70-90% relative to their current abundance by midcentury. Although alarming, coral communities that survive will play a key role in the regeneration of reefs by mid-to-late century. Here, we argue for a coordinated, global coral reef conservation strategy that is centred on 50 large (500 km(2)) regions that are the least vulnerable to climate change and which are positioned to facilitate future coral reef regeneration. The proposed strategy and actions should strengthen and expand existing conservation efforts for coral reefs as we face the long-term consequences of intensifying climate change

Comparison of results.

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

Weighted Zonation result.

<p>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.</p

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

<p>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.</p

Additional biological inputs into the analysis.

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

Two views of conservation and management priorities.

<p>A: results of the categorical classification (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0028969#pone-0028969-t003" target="_blank">Table 3</a> for class descriptions); B: target-based priority-setting with Marxan.</p

Data used in the study (I), sources, methods (M), and an overview of the comparisons (C).

<p>Data used in the study (I), sources, methods (M), and an overview of the comparisons (C).</p

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

<p>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.</p