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It is thought that recovery of marine habitats from uncontrollable disturbance may be faster in marine reserves than in unprotected habitats. But which marine habitats should be protected, those areas at greatest risk or those at least risk? We first defined this problem mathematically for 2 alternate conservation objectives. We then analytically solved this problem for both objectives and determined under which conditions each of the different protection strategies was optimal. If the conservation objective was to maximize the chance of having at least 1 healthy site, then the best strategy was protection of the site at lowest risk. On the other hand, if the goal was to maximize the expected number of healthy sites, the optimal strategy was more complex. If protected sites were likely to spend a significant amount of time in a degraded state, then it was best to protect low-risk sites. Alternatively, if most areas were generally healthy then, counterintuitively, it was best to protect sites at higher risk. We applied these strategies to a situation of cyclone disturbance of coral reefs on Australia's Great Barrier Reef. With regard to the risk of cyclone disturbance, the optimal reef to protect differed dramatically, depending on the expected speed of reef recovery of both protected and unprotected reefs. An adequate consideration of risk is fundamental to all conservation actions and can indicate surprising routes to conservation success. [ABSTRACT FROM AUTHOR]

The article offers information on the study conducted by Edward T. Game and colleagues on ways of mitigating and controlling catastrophic marine events for the Great Barrier Reef in Queensland. Research shows that large-scale catastrophic events such as cyclones, disease epidemics, algal blooms and hypoxia events lie beyond the control of local management and prevent marine reserves from achieving biodiversity outcomes. To address the issue, researchers formulate and solve the problem of minimizing the impact of large-scale coral bleaching events on the reserve system for the reef. Discussed are the details of the issue.

Globally, the area of agricultural land is shrinking in part due to environmental degradation. Acquisition and restoration of degraded lands no longer used for agriculture may present a major conservation opportunity with minimal social and political opposition. The ability to efficiently and accurately identify these lands from regional to global scales will aid conservation management, ultimately enhancing the global prospects of achieving the Sustainable Development Goals (SDGs). Remote Sensing provides a potential tool to identify areas where surface property changes can be mapped and linked with land degradation. In this study, we begin to tackle a small section of this challenge by presenting novel approach to mapping changes in vegetation cover amounts at the pixel level (30 m), using Google Earth Engine (GEE). We illustrate our approach across large-scale rangelands in Queensland Australia, using three decades of Landsat satellite imagery (1988–2017) along with field observations of land condition scores for validation. The approach used an existing method for dynamic reference cover to remove the rainfall variability and focused on the human management effects on the vegetation cover changes. Results showed the identified vegetation cover changes could be categorized into five classes of decrease, increase or stable cover compared with a set reference level, which was obtained from locations of the most persistent ground cover across all dry years. In total, vegetation cover decrease was observed in 20% of our study area, with similar portion of lands recovering and the rest (~60%) staying stable. The lands with decrease in vegetation cover, covering a considerable area of ~2 × 105 km2, exhibited a markedly reduced resilience to droughts. The accuracy assessment yielded an overall classification accuracy of 82.6% (±3.32 standard error) with 75.0% (±5.16%) and 70.0% (±4.13%) producer's and user's accuracy for areas experiencing a significant decrease in vegetation cover, respectively. Identifying areas of degraded land will require multiple stages of spatial data analysis and this work provided the first stage for identifying vegetation cover changes in large-scale rangeland environment, and provides a platform for future research and development to identify degraded lands and their utility for achieving conservation endeavours. • We identified vegetation cover changes in large-scale rangelands with Landsat data. • Dynamic reference cover method was used to separate grazing and rainfall effects. • The resulting map can be used to set user-specified threshold for land degradation. • The approach offers a pathway to identify degraded lands for conservation. • Google Earth Engine can improve mapping of large-scale land surface change. [ABSTRACT FROM AUTHOR]