Source Regions of the First Immigration of Fall Armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae) Invading Australia

Fall armyworm is recognized as one of most highly destructive global agricultural pests. In January 2020, it had first invaded Australia, posing a significant risk to its biosecurity, food security, and agricultural productivity. In this study, the migration paths and wind systems for the case of fall armyworm invading Australia were analyzed using a three-dimensional trajectory simulation approach, combined with its flight behavior and NCEP meteorological reanalysis data. The analysis showed that fall armyworm in Torres Strait most likely came from surrounding islands of central Indonesia on two occasions via wind migration. Specifically, fall armyworm moths detected on Saibai and Erub Islands might have arrived from southern Sulawesi Island, Indonesia, between January 15 and 16. The fall armyworm in Bamaga most likely arrived from the islands around Arafura Sea and Sulawesi Island of Indonesia, between January 26 and 27. The high risk period for the invasion of fall armyworm is only likely to have occurred in January–February due to monsoon winds, which were conducive to flight across the Timor Sea towards Australia. This case study is the first to confirm the immigration paths and timing of fall armyworm from Indonesia to Australia via its surrounding islands.

Deoxygenation of the eastern Indonesian waters and its variability

Long-term ocean deoxygenation could lead to decline biological productivity and alter biogeochemical cycles. Ocean warming contributions to ocean deoxygenation are reasonably understood, however, there is a challenge to reveal the gaps about other modifying factors to explain different regional patterns and predicts the condition in the coming century. This study aimed to identify the deoxygenation areas in the eastern Indonesian waters, understand the variability of physical and chemical parameters as the deoxygenation drivers, and investigate the correlation between parameters. In-situ and satellite-derived data from 1995 to 2020 were analyzed with statistical methods and remote sensing techniques to enhance deoxygenation measures in higher spatial and temporal resolutions. Our findings revealed that significant deoxygenation was detected around the Arafura Sea. The oxygen minimum zone extended at 133.5° – 136.8° E in the depth of 350 – 1,000 meters, with less than 20 mmol/m3 of dissolved oxygen concentration. Nitrate, phosphate, and temperature were identified to have a strong reversed relationship with the oxygen concentration in the study area. This study also developed multiple regression model algorithms to estimate the oxygen concentration in specified depths.

Dissolved oxygen variability of Indonesian seas over decades as detected by satellite remote sensing

The dissolved oxygen (DO) decrease in the ocean is a notable issue because of its potential impacts on marine biogeochemical cycles and ecosystem services. Satellite remote sensing application to support in-situ measurement is a time and cost-saving on wide scales DO monitoring. This study aims to determine the DO variability from 1993 to 2020, identify the potential areas to experience deoxygenation, and investigate the correlation between DO and other ocean parameters in Indonesian seas. The validation between in-situ and satellite-derived DO shows the determination coefficient of 0.73, indicating the satellite dataset reliability for the entire analysis. The multiple regression analysis among the long-term satellite-derived ocean parameters shows that the in-situ DO can be estimated by the combination of the potential temperature, total chlorophyll-a, and salinity. The potential temperature was statistically identified as the parameter with the highest correlation and influence on DO. The results of DO variability analysis show the overall decreasing trend with significant decreases in 1998, 2010, and 2016. There is a distinct difference in DO’s seasonal patterns in the southwestern and northeastern regions. The potential of ocean deoxygenation is detected in western Sumatra waters and the Arafura Sea at the 200–1,000 meters depth.

The effect of Ekman mass transport and Ekman pumping velocity on the variability of sea surface temperature in the Arafura Sea

Arafura sea is located between the southern part of Papua Island and Aru Island. Previous studies on Sea Surface Temperature (SST) have described that the SST is strongly affected by the upwelling, but the effect of Ekman Mass Transport (EMT) and Ekman Pumping Velocity (EPV) has not yet been studied. In other areas, it has been shown that EMT and EPV generated by the winds could affect the SST. Thus, further research is needed to better understand the role of the winds on the variability of SST through the mechanism of EMT and EPV in the Arafura Sea. This study used SST data from a high-resolution satellite image (GHRSST) and wind data from a scatterometer satellite image (MetOp A ASCAT). The data were processed using the composite and time-series correlation. This study shows that the higher the wind speed, causes the colder the SST in the Arafura Sea. In contrast, when the wind speed is lower, the SST tends to be warmer. The variabilities of the SST are mostly related to the mixing process associated with the magnitude of EMT. In the shallow water where the calculated Ekman depth is deeper than the actual depth, EMT is more influencing than EPV. On the deeper water at the northeast of the Island of Aru, the negative EPV induces upwelling, bringing the colder water to the sea surface. Statistically, the correlation between EMT (EPV) and the SST in the shallow water of the Arafura Sea is considered strong (weak). On the other hand, at the deep water of the Arafura Sea (northeast of the Island of Aru) offers a strong correlation between the EPV and the SST, whereas the EMT and the SST correlation is considered weak.

Notes on two rare species of brachyuran crabs (families Matutidae and Parthenopidae) from Indonesian waters with new distribution records

This research aimed to report two species of Brachyuran crabs that were found rarely from Indonesian waters. The latest report was more than one hundred years ago, and the study from Indonesia before was not so clear, both information about specimens and location details. We conducted our study in Southern Aru Island and Malacca Strait using trawl during the cruise research which was held by the Research Institute for Marine Fisheries, Ministry of Marine Affairs and Fisheries. Our findings were Izanami reticulata, New Record (family Matutidae) from Southern Aru Island, and Cryptopodia fornicata (family Parthenopidae) from Malacca Strait. The two locations are located close to the location where the species was found in previous studies, namely the Arafura Sea which is adjacent to the Aru Islands, and the Malacca Strait which is adjacent to Borneo. We suspect that the presence of these two species in western and eastern Indonesia was due to their distribution during the pelagic larval stage through the sea current. This article also provided the specific habitat for both species which has not been stated. In addition, this article will contribute to strengthening Indonesia as a mega biodiversity country with initiate compiling the database of Brachyura in Indonesian waters.

FISHING BOAT DISTRIBUTION ESTABLISHED BY COMPARING VMS AND VIIRS DATA AROUND THE ARU ISLANDS IN MALUKU INDONESIA

Marine protected areas (MPAs) and no take zones (NTZs) are essential for the preservation of marine ecosystems. However, these important areas can be severely harmed by illegal fishing. All vessels above 30 gross tons are required to use vessel monitoring systems (VMSs) that enable vessel tracking by sending geographic data to satellites in each specific time period. The Visible Infrared Radiometer Suite (VIIRS) is a sensor on the National Oceanic and Atmospheric Administration (NOAA)-20 satellite that can detect the light-emitting diode (LED) light used by fishing vessels from space during the night time. In this research, VMS and VIIRS fishery data were combined in order to identify fishing vessels that were detected by the VIIRS sensor of the NOAA-20 satellite. The research was focused on an area near the Aru Islands in the Arafura Sea in Indonesia. Data on LED light used by the fishing techniques of purse seine and bouke ami were obtained for the whole of 2018. The data were then processed using R software. An R package called LLFI (LED Light Fisheries Identifier) was created, containing several R-functions that calculate VMS vessel position during satellite overpass time and then combine the VMS and VIIRS data attributes, resulting in a dataset comprising vessels identified from the VIIRS dataset. Out of all the estimated VMS fishing vessel positions during the VIIRS satellite overpass, approximately 51% could be assigned to fishing vessels detected from the VIIRS dataset. For bouke ami, the identification rate was approximately 87%, while that for small purse seine was around 39%. Ultimately, the LLFI package created daily paths for each identified fishing vessel, displaying all its movements during the day of its’identification. These daily paths did not show any activity within MPA or NTZ. The LLFI package was successful in combining VMS and VIIRS data, estimating VMS vessel positions during the VIIRS satellite overpass, identifying a percentage of the vessels, and creating a daily path for each identified vessel.

Seasonal Variability of Waves Within the Indonesian Seas and Its Relation With the Monsoon Wind

The previous studies have simulated the variability of the wave within the Indonesian seas which showed that the variability of wave follows the seasonal pattern. However, their analysis only consider the influence of local wind forcings. The bias and error of their simulated wave were also unclear. In the present study, we investigate the variability of wave within the Indonesian seas and its relation with the surface wind speed using the combination of reanalysis and remote sensing data with high accuracies. We split the analysis into swell and wind wave to obtain the influence of local and remote wind forcings. We show that at the inner seas (i.e., the South China Sea, Java Sea, Flores Sea, Banda Sea and Arafura Sea), the variability of significant wave height (SWH) is majorly influenced by the variability of the speed of monsoon wind. The maximum SWH during Northwest monsoon (NWM) season is located at the South China Sea while during Southeast monsoon (SEM) season is at Arafura Sea. This indicates that the wind wave (sea) is dominant at the inner seas. At the open seas (i.e., Pacific Ocean and Indian Ocean) the variability of SWH less corresponds to the the speed of monsoon wind. The remote wind forcings control the wave variability in the open ocean area. This indicates that swell is dominant at the open seas. In general, the magnitude of SWHswell is also more than SWHsea within the Indonesian seas.