An overview of remote monitoring methods in biodiversity conservation

Exploring the uptake of advanced digital technologies in environmental assessment practice - Experiences from Austria and Germany

Environmental assessment (EA) evaluates the environmental impacts of proposed projects, plans or policies to inform decision making. While several studies have highlighted the potential and opportunities of digitalisation for EA, few have explored practitioners' perceptions using a mixed methods approach in order to discover concerns and risks identified by EA of novel technological approaches. In addition, this initial exploratory study examines the perception of benefits and contributions to quality and effectiveness of advanced digital approaches, such as the introduction of artificial intelligence, in EA practice. The research process was based on focus group discussions and exploratory interviews with EA consultants, environmental authorities, researchers, environmental associations and NGOs. Relevant technologies were identified from the existing scientific literature and their applicability, benefits and use were discussed in context of real-world experience made by the practitioner. It became evident that the majority of practitioners in the field of EA in Austria and Germany are not familiar with advanced digital approaches and tools. While other planning disciplines are exploiting the potential of advanced digital tools, EA practitioners still share concerns about data quality, security, legal uncertainties, but also skills and know-how. The study identifies a gap and a need for training and confidence building. It aims to contribute to the promotion of inter- & transdisciplinary exchange involving the wider EA community.

Forest fragmentation causes an isolated population of the golden takin (Budorcas taxicolor bedfordi Thomas, 1911) (Artiodactyla: Bovidae) in the Qinling Mountains (China)

Budorcas taxicolor bedfordi is a rare animal uniquely distributed in the Qinling Mountains (China). Human disturbance and habitat fragmentation have directly affected the survival of B. t. bedfordi. It is urgent to clarify the genetic diversity and genetic structure of the B. t. bedfordi population and implement effective conservation measures. In this study, 20 new polymorphic microsatellite loci were isolated by Illumina sequencing. The genetic diversity and population structure of 124 B. t. bedfordi individuals from three populations (Niubeliang population, Zhouzhi population, and Foping population) were analysed according to these 20 microsatellite loci. Our results indicated that B. t. bedfordi had a low level of genetic variability and that there was inbreeding in the three populations. The population genetic structure analyses showed that the Niubeliang population had a trend of differentiation from other populations. National roads can affect population dispersal, while ecological corridors can promote population gene exchange. None of the three B. t. bedfordi populations experienced bottleneck effects. For conservation management plans, the Zhouzhi population and Foping population should be considered one management unit, and the Niubeliang population should be considered another management unit. We suggest building an ecological corridor to keep the habitat connected and formulating tourism management measures to reduce the influence of human disturbance on B. t. bedfordi.

Advances and applications of machine learning and deep learning in environmental ecology and health

Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.