Assessing long-term rainfall trends and changes in a tropical watershed Brantas, Indonesia: an approach for quantifying the agreement among satellite-based rainfall data, ground rainfall data, and small-scale farmers questionnaires.

The agreement between meteorological data and societal perception is essential in supporting a robust policy making and its implementation. In humid tropic watersheds like Brantas, such consensus is important for water resources management and policies. This study exemplifies an effort to understand the long-term rainfall characteristics within the watershed and to build a common link among the differing data sources: CHIRPS rainfall satellite data, rain gauge data, and farmers perceptions. Six rainfall characteristics were derived using statistical measures from the scientific data and then were translated to a series of structured questionnaires given to small-scale farmers. A consensus matrix was built to examine the level of agreement among three data sources, supporting the spatial pattern of the meteorological data and farmers perception. Two rainfall attributes were classified with high agreement, four with moderate and one with low agreement. The agreements and discrepancies of rainfall characteristics were found in the study area. The discrepancies originated from the accuracy in translating scientific measurements to practical meanings for farmers, complexity of the farming system, the nature of phenomena in questions, and farmers' ability to record long-term climatic events. This study shows an implication that a combined approach to link scientific data and societal data is needed to support powerful climate policy making.

Sponge diversification in marine lakes: Implications for phylogeography and population genomic studies on sponges.

The relative influence of geography, currents, and environment on gene flow within sessile marine species remains an open question. Detecting subtle genetic differentiation at small scales is challenging in benthic populations due to large effective population sizes, general lack of resolution in genetic markers, and because barriers to dispersal often remain elusive. Marine lakes can circumvent confounding factors by providing discrete and replicated ecosystems. Using high-resolution double digest restriction-site-associated DNA sequencing (4826 Single Nucleotide Polymorphisms, SNPs), we genotyped populations of the sponge Suberites diversicolor (n = 125) to test the relative importance of spatial scales (1-1400 km), local environmental conditions, and permeability of seascape barriers in shaping population genomic structure. With the SNP dataset, we show strong intralineage population structure, even at scales <10 km (average F ST = 0.63), which was not detected previously using single markers. Most variation was explained by differentiation between populations (AMOVA: 48.8%) with signatures of population size declines and bottlenecks per lake. Although the populations were strongly structured, we did not detect significant effects of geographic distance, local environments, or degree of connection to the sea on population structure, suggesting mechanisms such as founder events with subsequent priority effects may be at play. We show that the inclusion of morphologically cryptic lineages that can be detected with the COI marker can reduce the obtained SNP set by around 90%. Future work on sponge genomics should confirm that only one lineage is included. Our results call for a reassessment of poorly dispersing benthic organisms that were previously assumed to be highly connected based on low-resolution markers.

Assessment of the dynamics of urban surface temperatures and air pollution related to COVID-19 in a densely populated City environment in East Java.

The COVID-19 pandemic that has hit the whole world has caused losses in various aspects. Several countries have implemented lockdowns to curb the spread of the SARS-CoV-2 virus that caused death. However, for developing countries such as Indonesia, it is not suitable for lockdown because it considers the economic recession. Instead, the Large-scale Social Restrictions (LSSR) regulation is applied, the same as the partial lockdown. Thus, it is hypothesized that implementing LSSR that limits anthropogenic activities can reduce heat emissions and air pollution. Utilization of remote sensing data such as Terra-MODIS LST and Sentinel-5P images to investigate short-term trends (i.e., comparison between baseline year and COVID-19 year) in surface temperature, Surface Urban Heat Islands Intensity (SUHII), and air pollution such as NO2, CO, and O3 in Malang City and Surabaya City, East Java Province. Spatial downscaling of LST using the Random Forest Regression technique was also carried out to transform the spatial resolution of the Terra-MODIS LST image to make it feasible on a city scale. Raster re-gridding was also implemented to refine the Sentinel-5P spatial resolution. The accuracy of LST spatial downscaling results is quite satisfactory in both cities. Surface temperatures in both cities slightly decreased (below 1 °C) during LSSR was applied (P < 0.05). SUHII in both cities experienced a slight increase in both cities during LSSR. NO2 gas was reduced significantly (P < 0.05) in Malang City (∼38%) and Surabaya City (∼28%) during LSSR phase due to reduced vehicle traffic and restrictions on anthropogenic activities. However, CO and O3 gases did not indicate anomaly during LSSR. Moreover, this study provides insight into the correlation between SUHII change and the distribution of air pollution in both cities during the pandemic year. Air temperature and wind speed are also added as meteorological factors to examine their effect on air pollution. The proposed models of spatial downscaling LST and re-gridding satellite-based air pollution can help decision-makers control local air quality in the long and short term in the future. In addition, this model can also be applied to other ecological research, especially the input variables for ecological spatial modeling.