As corals in tropical regions are threatened by increasing water temperatures, poleward range expansion of reef-building corals has been observed, and temperate regions are expected to serve as refugia in the face of climate change. To elucidate the important indicators of the sustainability of coral populations, we examined the genetic diversity and connectivity of the common reef-building coral Acropora hyacinthus along the Kuroshio Current, including recently expanded (<50 years) populations. Among the three cryptic lineages found, only one was distributed in temperate regions, which could indicate the presence of Kuroshio-associated larval dispersal barriers between temperate and subtropical regions, as shown by oceanographic simulations as well as differences in environmental factors. The level of genetic diversity gradually decreased towards the edge of the species distribution. This study provides an example of the reduced genetic diversity in recently expanded marginal populations, thus indicating the possible vulnerability of these populations to environmental changes. This finding underpins the importance of assessing the genetic diversity of newly colonized populations associated with climate change for conservation purposes. In addition, this study highlights the importance of pre-existing temperate regions as coral refugia, which has been rather underappreciated in local coastal management.

Background: Seagrass beds are essential habitats in coastal ecosystems, providing valuable ecosystem services, but are threatened by various climate change and human activities. Seagrass monitoring by remote sensing have been conducted over past decades using satellite and aerial images, which have low resolution to analyze changes in the composition of different seagrass species in the meadows. Recently, unmanned aerial vehicles (UAVs) have allowed us to obtain much higher resolution images, which is promising in observing fine-scale changes in seagrass species composition. Furthermore, image processing techniques based on deep learning can be applied to the discrimination of seagrass species that were difficult based only on color variation. In this study, we conducted mapping of a multispecific seagrass bed in Saroma-ko Lagoon, Hokkaido, Japan, and compared the accuracy of the three discrimination methods of seagrass bed areas and species composition, i.e., pixel-based classification, object-based classification, and the application of deep neural network. Methods: We set five benthic classes, two seagrass species (Zostera marina and Z. japonica), brown and green macroalgae, and no vegetation for creating a benthic cover map. High-resolution images by UAV photography enabled us to produce a map at fine scales (<1 cm resolution). Results: The application of a deep neural network successfully classified the two seagrass species. The accuracy of seagrass bed classification was the highest (82%) when the deep neural network was applied. Conclusion: Our results highlighted that a combination of UAV mapping and deep learning could help monitor the spatial extent of seagrass beds and classify their species composition at very fine scales.

The Asia‐Pacific Biodiversity Observation Network (APBON) was launched in 2009, in response to the establishment of the Biodiversity Observation Network under the Group on Earth Observations in 2008. APBON's mission is to increase exchange of knowledge and know‐how between institutions and researchers concerning biodiversity science research in the Asia‐Pacific (AP) region and thereby contribute to evidence‐based decision‐making and policy‐making. Here we summarize APBON activities and achievements in its first 10 years. We review how APBON has developed networks, facilitated communication for sharing knowledge, and built capacity of researchers and stakeholders through workshops and publications as well as discuss the network plan. Key findings by APBON members include descriptions of species new to science, mapping tropical forest cover change, evaluating impacts of hydropower dams and climate change on fish species diversity in the Mekong, and mapping “Ecologically and Biologically Significant Areas” in the oceans. APBON has also contributed to data collection, sharing, analysis, and synthesis for regional and global biodiversity assessment. A highlight was contributing to the “Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services” regional report. New strategic plans target the development of national‐level BONs and interdisciplinary research to address the data and knowledge gaps and increase data accessibility for users and for meeting societal demands. Strengthening networks in AP region and capacity building through APBON meetings will continue. By promoting monitoring and scientific research and facilitating the dialogue with scientists and policymakers, APBON will contribute to the implementation of conservation and sustainable use of biodiversity in the entire AP region.

Few studies have investigated the long-term temporal dynamics of seagrass beds, especially in Southeast Asia. Remote sensing is one of the best methods for observing these dynamic patterns, and the advent of deep learning technology has led to recent advances in this method. This study examined the feasibility of applying image classification methods to supervised classification and deep learning methods for monitoring seagrass beds. The study site was a relatively natural seagrass bed in Hat Chao Mai National Park, Trang Province, Thailand, for which aerial photographs from the 1970s were available. Although we achieved low accuracy in differentiating among various densities of vegetation coverage, classification related to the presence of seagrass was possible with an accuracy of 80% or more using both classification methods. Automatic classification of benthic cover using deep learning provided similar or better accuracy than that of the other methods even when grayscale images were used. The results also demonstrate that it is possible to monitor the temporal dynamics of an entire seagrass area, as well as variations within sub-regions, located in close proximity to a river mouth.

Data papers, such as those published by Ecological Research , encourage the retrieval and archiving of valuable unpublished, undigitized ecological observational data. However, scientists remain hesitant to submit their data to such forums. In this perspective paper, we describe lessons learned from the Long‐Term Ecological Research, the Global Biodiversity Information Facility and marine biological databases and discuss how data sharing and publication are both powerful and important for ecological research. Our aim is to encourage readers to submit their unpublished, undigitized ecological observational data then the data may be archived, published and used by other researchers to advance knowledge in the field of ecology. Coupling data sharing and syntheses with the development of innovative informatics would allow ecology to enter the realm of big science and provide seeds for a new and robust agenda of future ecological studies.