Voltine Ecotypes of the Asian Corn Borer and Their Response to Climate Warming

In the Asian corn borer (ACB), Ostrinia furnacalis (Guenée), diapause is governed by a multigenetic constitution that responds to daylength and temperature with seasonality. The ACB displays uni- or multivoltinism, depending on its geographic specificity. Hence, warmer temperatures may result in alternation of voltinism in the ACB, which will help in understanding the ecological consequences of climate warming on insects. In the present study, we investigated the voltinism in two natural populations from Harbin (H) and Gongzhuling (G) as well as a laboratory (L) population (established from the H population in 2017) of the ACB, at ambient and elevated atmospheric CO2 (aCO2 390 μL/L and eCO2 750 μL/L) and temperature (aT and Et = At + 2 °C). From the diapause response, both the uni- and multivoltine ecotypes were coexisting in the H population. The neonate occurrence date of 50% individuals that induced diapause was ca. 10 days later in the G population than in the H population, but it was about 10 days earlier than in the L population. Comparing to the dates of onset and the peak of diapause induction, the G and L populations were less variable than the H population in response to a short and/or shortening daylength in the field. The univoltine individuals could not be eliminated completely after 19 generations of selection. Diapause incidence decreased with a climate-warming scenario, which was temporally specific and could be overridden by significantly low daily average temperatures. The eCO2 did not directly impact the voltinism. On the basis of voltinism, the H population was sympatric for uni- and multivoltine ecotypes, with multivoltinism being dominant. The univoltinism trait was recessive. Climate warming could significantly override the effect of photoperiod, which was yearly dependent. Warmer temperatures and a decreased latitude (shortened daylength), and their interaction, would drive ACB evolution toward diapause homogeneity for multivoltinism.

Adapting the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms to map rainfall-triggered landslides in the West-Cameroon highlands (Central-Africa)

Background – NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This method uses the Landsat 8 satellite images and daily rainfall recordings for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods – The SLIP algorithm is adapted, by integrating the inverse NDVI to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948-2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate the occurrence date of the landslide with the rainfall since the first known event in Cameroon, through the Cox model. Results – From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96%. Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99%, between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions – The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime.

Adapting the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms to map rainfall-triggered landslides in the West-Cameroons’ highlands (Central-Africa)

Background – NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This method uses the Landsat 8 satellite images and daily rainfall recordings for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods – The SLIP algorithm is adapted, by integrating the inverse NDVI to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948-2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate the occurrence date of the landslide with the rainfall since the first known event in Cameroon, through the Cox model.Results – From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96 % . Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99% , between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions – The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime.

Adapting the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms to map rainfall-triggered landslides in the West-Cameroons’ highlands (Central-Africa)

Background – NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This method uses the Landsat 8 satellite images and daily rainfall recordings for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods – The SLIP algorithm is adapted, by integrating the inverse NDVI to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and the intersections are used to propose sets of dichotomous variables before (1948-2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlating the occurrence date of the landslide with the rainfall since the first known event in Cameroon, through the Cox model. Results – The outcome of the SLIP adapted model is the Landslide Hazard Zonation (LHZ) map, with an overall accuracy of 96%. Further, the outcome of the DRIP adapted model states that the probability of rainfall-triggered landslides is 99.99%, for 6/9 ranges of probability between June and October. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions – The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime.

Comparing Apple and Pear Phenology and Model Performance: What Seven Decades of Observations Reveal

Based on observations for the beginning of the flowering stage of Malus domestica (apple) and Pyrus communis (pear) for the 1950–2018 period, phenological trends in north-eastern Belgium were investigated in function of temperatures during dormancy. Moreover, two different phenological models were adapted and evaluated. Median flowering dates of apple were on average 9.5 days earlier following warm dormancy periods, and 11.5 days for pear, but the relationship between bloom date and temperature was found not to be linear, suggesting delayed fulfilment of dormancy requirements due to increased temperatures during the chilling period. After warm chilling periods, an average delay of 5.0 and 10.6 days in the occurrence date of dormancy break was predicted by the phenological models while the PLSR reveals mixed signals regarding the beginning of flowering. Our results suggest overlapping chilling and forcing processes in a transition phase. Regarding the beginning of flowering, a dynamic chill model coupled to a growing degree days estimation yielded significantly lower prediction errors (on average 5.0 days) than a continuous chill-forcing model (6.0 days), at 99% confidence level. Model performance was sensitive to the applied parametrization method and limitations for the application of both models outside the past temperature ranges became apparent.

ANALYSIS OF THE RED TIDE OCCURRENCE PATTERN WHEN HIGH WATER TEMPERATURE

In 1982, the red tide caused by Cochlodimium polykrikodies occurred at first in Jinhae Bay in Korea. Since then, it causes serious fisheries damage every year. In 2018, red tide occurred when high water temperature. We analyzed red tide occurrence pattern when high water temperature. Red tide occurrence date, occurrence area caused by the Cochlodinium polykrikoides were used provided by National Institute of Fisheries Science. SST data were used the GHRSST Level 4 OSTIA data provided by NOAA. The red tide occurred July 14 to September 2 in 2013, July 29 to October 4 in 2014, August 5 to September 23 in 2015, and July 23 to August 8 in 2018 in Korea. The nearest SST data from the red tide occurrence area were extracted. When red tide occurred, SST was 22~28 °C in 2013, 23~25 °C in 2014, 21~27 °C in 2015, 26~28 °C in 2018. SST in 2013 was increasing trend and 2015 was downward trend. SST in 2018 occurred at high water temperatures above 25 °C. The spatial pattern by using the self-organizing map (3x3 map), node 4 was the highest frequency (16.9%). It is considered that it appears at the beginning and end of red tide occurrence. Node 1 was 16.3% frequency, it showed at the end of 2014 and 2015. SST of node 1 and 4 maintained 22~23 °C on the southern sea of Korea. Node 9 was 13.9%, which is the late 2013 and 2018. Node 9 showed high temperature pattern of more than 26 °C in the southern sea of Korea.