Mangrove forests can ameliorate the impacts of typhoons and storms, but their extent is threatened by coastal development. The northern coast of Vietnam is especially vulnerable as typhoons frequently hit it during the monsoon season. However, temporal change information in mangrove cover distribution in this region is incomplete. Therefore, this study was undertaken to detect change in the spatial distribution of mangroves in Thanh Hoa and Nghe An provinces and identify reasons for the cover change. Landsat satellite images from 1973 to 2020 were analyzed using the NDVI method combined with visual interpretation to detect mangrove area change. Six LULC classes were categorized: mangrove forest, other forests, aquaculture, other land use, mudflat, and water. The mangrove cover in Nghe An province was estimated to be 66.5 ha in 1973 and increased to 323.0 ha in 2020. Mangrove cover in Thanh Hoa province was 366.1 ha in 1973, decreased to 61.7 ha in 1995, and rose to 791.1 ha in 2020. Aquaculture was the main reason for the loss of mangroves in both provinces. Overall, the percentage of mangrove loss from aquaculture was 42.5% for Nghe An province and 60.1% for Thanh Hoa province. Mangrove restoration efforts have contributed significantly to mangrove cover, with more than 1300 ha being planted by 2020. This study reveals that improving mangrove restoration success remains a challenge for these provinces, and further refinement of engineering techniques is needed to improve restoration outcomes.

Comprehensive understanding of the patterns and drivers of microbial diversity at a landscape scale is in its infancy, despite the recent ease by which soil communities can be characterized using massively parallel amplicon sequencing. Here we report on a comprehensive analysis of the drivers of diversity distribution and composition of the ecologically and economically important Phytophthora genus from 414 soil samples collected across Australia. We assessed 22 environmental and seven categorical variables as potential predictors of Phytophthora species richness, α and β diversity, including both phylogenetically and non-phylogenically explicit methods. In addition, we classified each species as putatively native or introduced and examined the distribution with respect to putative origin. The two most widespread species, P. multivora and P. cinnamomi, are introduced, though five of the ten most widely distributed species are putatively native. Introduced taxa comprised over 54% of Australia's Phytophthora diversity and these species are known pathogens of annual and perennial crop habitats as well as urban landscapes and forestry. Patterns of composition were most strongly predicted by bioregion (R 2 = 0.29) and ecoregion (R 2 = 0.26) identity; mean precipitation of warmest quarter, mean temperature of the wettest quarter and latitude were also highly significant and described approximately 21, 14 and 13% of variation in NMDS composition, respectively. We also found statistically significant evidence for phylogenetic over-dispersion with respect to key climate variables.This study provides a strong baseline for understanding biogeographical patterns in this important genus as well the impact of key plant pathogens and invasive Phytophthora species in natural ecosystems.

Abstract. State-and-transition models are increasingly used as a tool to inform management of post-disturbance succession and effective conservation of biodiversity in production landscapes. However, if they are to do this effectively, they need to represent faunal, as well as vegetation, succession. We assessed the congruence between vegetation and avian succession by sampling avian communities in each state of a state-and-transition model used to inform management of post-mining restoration in a production landscape in southwestern Australia. While avian communities differed significantly among states classified as on a desirable successional pathway, they did not differ between desirable and deviated states of the same post-mining age. Overall, we concluded there was poor congruence between vegetation and avian succession in this state-and-transition model. We identified four factors that likely contributed to this lack of congruence, which were that long-term monitoring of succession in restored mine pits was not used to update and improve models, states were not defined based on ecological processes and thresholds, states were not defined by criteria that were important in structuring the avian community, and states were not based on criteria that related to values in the reference community. We believe that consideration of these four factors in the development of state-and-transition models should improve their ability to accurately represent faunal, as well as vegetation, succession. Developing state-and-transition models that better incorporate patterns of faunal succession should improve the ability to manage post-disturbance succession across a range of ecosystems for biodiversity conservation.

DNA and RNA detected in soil using molecular techniques may originate from a living or dead organism. It is therefore of interest to know how long the DNA and RNA from a decaying organism can persist in soil, and how environmental conditions such as soil temperature, moisture, and microbial populations impact on the survival time. This study determined the difference between the persistence of Phytophthora cinnamomi mRNA and DNA in different soil types. DNA and RNA were extracted from P. cinnamomi and 10 ng/250 mg of soil was applied to five different soil types that were either air‐dried or maintained at 70% field capacity. The persistence of DNA at 20°C was tested after intervals of 0, 3, 7, 14, 90, 241, and 378 days, and for RNA at 0, 1, 3, and 7 days using qPCR and RT‐qPCR techniques, respectively. Persistence was longer in dry than moist soil, P. cinnamomi DNA could be readily detected in dry soil conditions for up to 90 days and was found at extremely low levels at 241 and 378 days. RNA was detected only on day 1, except for dry river sand, and moist sandy loam in which it persisted for 3 days; it was not detected after seven days. These results confirm that RNA degrades very quickly, making it a valuable tool for determining the presence of viable Phytophthora in soil. In contrast, DNA can be remarkably stable in some environments, and positive results could be obtained even after the death of the organism for a year or more prior to the test. For diagnostics, the use of an RNA‐based test avoids the possibility of such false positive results. In the context of the research project, this study is relevant to determining how long viable Phytophthora remains in soil after the eradication protocols have been instigated. In a broader context, the persistence of DNA is relevant to any study using environmental DNA for diagnostics or for metabarcoding when undertaking community ecology or microbiome studies. These results are relevant for studies using detection of P. cinnamomi nucleic acids in soils for purposes of diagnostics, ecological research, or projects on eradication.

Phytophthora cinnamomi is a pathogenic oomycete that causes plant dieback disease across a range of natural ecosystems and in many agriculturally important crops on a global scale. An annotated draught genome sequence is publicly available (JGI Mycocosm) and suggests 26,131 gene models. In this study, soluble mycelial, extracellular (secretome), and zoospore proteins of P. cinnamomi were exploited to refine the genome by correcting gene annotations and discovering novel genes. By implementing the diverse set of sub-proteomes into a generated proteogenomics pipeline, we were able to improve the P. cinnamomi genome annotation. Liquid chromatography mass spectrometry was used to obtain high confidence peptides with spectral matching to both the annotated genome and a generated 6-frame translation. Two thousand seven hundred sixty-four annotations from the draught genome were confirmed by spectral matching. Using a proteogenomic pipeline, mass spectra were used to edit the P. cinnamomi genome and allowed identification of 23 new gene models and 60 edited gene features using high confidence peptides obtained by mass spectrometry, suggesting a rate of incorrect annotations of 3% of the detectable proteome. The novel features were further validated by total peptide support, alongside functional analysis including the use of Gene Ontology and functional domain identification. We demonstrated the use of spectral data in combination with our proteogenomics pipeline can be used to improve the genome annotation of important plant diseases and identify missed genes. This study presents the first use of spectral data to edit and manually annotate an oomycete pathogen.