From the 1980's, Indonesian shrimp production has continuously increased through a large expansion of cultured areas and an intensification of the production. As consequences of diseases and environmental degradations linked to this development, there are currently 250,000ha of abandoned ponds in Indonesia. To implement effective procedure to undertake appropriate aquaculture ecosystem assessment and monitoring, an integrated indicator based on four criteria using very high spatial optical satellite images, has been developed to discriminate active from abandoned ponds. These criteria were: presence of water, aerator, feeding bridge and vegetation. This indicator has then been applied to the Perancak estuary, a production area in decline, to highlight the abandonment dynamic between 2001 and 2015. Two risk factors that could contribute to explain dynamics of abandonment were identified: climate conditions and pond locations within the estuary, suggesting that a spatial approach should be integrated in planning processes to operationalize pond rehabilitation.

Background Microbial communities associated with animals are known to be key elements in the development of their hosts. In marine environments, these communities are largely under the influence of the surrounding water. In aquaculture, understanding the interactions existing between the microbiotas of farmed species and their rearing environment could help establish precise bacterial management. Method In light of these facts, we studied the active microbial communities associated with the eggs and the nauplii of the Pacific blue shrimp (Litopenaeus stylirostris) and their rearing water. All samples were collected in September 2018, November 2018 and February 2019. After RNA extractions, two distinct Illumina HiSeq sequencings were performed. Due to different sequencing depths and in order to compare samples, data were normalized using the Count Per Million method. Results We found a core microbiota made of taxa related to Aestuariibacter, Alteromonas, Vibrio, SAR11, HIMB11, AEGEAN 169 marine group and Candidatus Endobugula associated with all the samples indicating that these bacterial communities could be transferred from the water to the animals. We also highlighted specific bacterial taxa in the eggs and the nauplii affiliated to Pseudomonas, Corynebacterium, Acinetobacter, Labrenzia, Rothia, Thalassolituus, Marinobacter, Aureispira, Oleiphilus, Profundimonas and Marinobacterium genera suggesting a possible prokaryotic vertical transmission from the breeders to their offspring. This study is the first to focus on the active microbiota associated with early developmental stages of a farmed shrimp species and could serve as a basis to comprehend the microbial interactions involved throughout the whole rearing process.

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Scalable assessments of biodiversity are required to successfully and adaptively manage coastal ecosystems. Assessments must account for habitat variations at multiple spatial scales, including the small scales (<100 m) at which biotic and abiotic habitat components structure the distribution of fauna, including fishes. Associated challenges include achieving consistent habitat descriptions and upscaling from in situ‐monitored stations to larger scales. We developed a methodology for (a) determining habitat types consistent across scales within large management units, (b) characterizing heterogeneities within each habitat, and (c) predicting habitat from new survey data. It relies on clustering techniques and supervised classification rules and was applied to a set of 3,145 underwater video observations of fish and benthic habitats collected in all reef and lagoon habitats around New Caledonia. A baseline habitat typology was established with five habitat types clearly characterized by abiotic and biotic attributes. In a complex mosaic of habitats, habitat type is an indispensable covariate for explaining spatial variations in fish communities. Habitat types were further described by 26 rules capturing the range of habitat features encountered. Rules provided intuitive habitat descriptions and predicted habitat type for new monitoring observations, both straightforwardly and with known confidence. Images are convenient for interacting with managers and stakeholders. Our scheme is (a) consistent at the scale of New Caledonia reefs and lagoons (1.4 million km 2 ) and (b) ubiquitous by providing data in all habitats, for example, showcasing a substantial fish abundance in rarely monitored soft‐bottom habitats. Both features must be part of an ecosystem‐based monitoring strategy relevant for management. This is the first study applying data mining techniques to in situ measurements to characterize coastal habitats over regional‐scale management areas. This approach can be applied to other types of observations and other ecosystems to characterize and predict local ecological assets for assessments at larger scales.