Long and short-term trends of stream hydrochemistry and high frequency surveys as indicators of the influence of climate change, agricultural practices and internal processes (Aurade agricultural catchment, SW France)

In a first of this kind, this paper examines the issue of prior return effect in Indian stock market in intra-day analysis using high frequency data. We document that in Indian stock market, security returns exhibit a reversal in their direction within few minutes of extreme price rises as well as price falls. However the speed with which the correction takes place is slightly different for good news events and bad news events. Indian investors tend to be optimistic as they immediately bring stock prices up following unjustified price falls but take time to bring stock prices down following unjustified price rises. These findings lend a further support to short-term overreaction literature. More importantly, these findings serve as a proof of predictability of the direction of future stock prices and consequent returns on an intra-day basis. It forwards important investment implications for traders, fund managers, and investors at large.

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The legacy of climate change effects: previous drought increases short-term litter decomposition rates in a temperate mixed grass- and shrubland

Plant and Soil ◽

10.1007/s11104-016-2913-2 ◽

2016 ◽

Vol 408 (1-2) ◽

pp. 183-193 ◽

Cited By ~ 2

Author(s):

Merian Skouw Haugwitz ◽

Anders Michelsen ◽

Anders Priemé

Keyword(s):

Climate Change ◽

Litter Decomposition ◽

Decomposition Rates ◽

Short Term ◽

Climate Change Effects ◽

Mixed Grass

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Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model

Forests ◽

10.3390/f12010095 ◽

2021 ◽

Vol 12 (1) ◽

pp. 95

Author(s):

Yuan Gong ◽

Christina L. Staudhammer ◽

Susanne Wiesner ◽

Gregory Starr ◽

Yinlong Zhang

Keyword(s):

Climate Change ◽

Soil Water ◽

Prescribed Fire ◽

Water Availability ◽

Longleaf Pine ◽

Growing Season ◽

Soil Water Availability ◽

Future Climate Change ◽

Short Term ◽

Phenological Model

Understanding plant phenological change is of great concern in the context of global climate change. Phenological models can aid in understanding and predicting growing season changes and can be parameterized with gross primary production (GPP) estimated using the eddy covariance (EC) technique. This study used nine years of EC-derived GPP data from three mature subtropical longleaf pine forests in the southeastern United States with differing soil water holding capacity in combination with site-specific micrometeorological data to parameterize a photosynthesis-based phenological model. We evaluated how weather conditions and prescribed fire led to variation in the ecosystem phenological processes. The results suggest that soil water availability had an effect on phenology, and greater soil water availability was associated with a longer growing season (LOS). We also observed that prescribed fire, a common forest management activity in the region, had a limited impact on phenological processes. Dormant season fire had no significant effect on phenological processes by site, but we observed differences in the start of the growing season (SOS) between fire and non-fire years. Fire delayed SOS by 10 d ± 5 d (SE), and this effect was greater with higher soil water availability, extending SOS by 18 d on average. Fire was also associated with increased sensitivity of spring phenology to radiation and air temperature. We found that interannual climate change and periodic weather anomalies (flood, short-term drought, and long-term drought), controlled annual ecosystem phenological processes more than prescribed fire. When water availability increased following short-term summer drought, the growing season was extended. With future climate change, subtropical areas of the Southeastern US are expected to experience more frequent short-term droughts, which could shorten the region’s growing season and lead to a reduction in the longleaf pine ecosystem’s carbon sequestration capacity.

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Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

Journal of Marine Science and Engineering ◽

10.3390/jmse9060651 ◽

2021 ◽

Vol 9 (6) ◽

pp. 651

Author(s):

Yan Yan ◽

Hongyan Xing

Keyword(s):

High Frequency ◽

Short Term Memory ◽

Detection Method ◽

Low Frequency ◽

Sea Clutter ◽

Short Term ◽

Term Memory ◽

Memory Network ◽

Long Short Term Memory ◽

Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

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Does the adaptation of climate-smart agricultural practices increase farmers’ resilience to climate change?

Environmental Science and Pollution Research ◽

10.1007/s11356-021-12425-8 ◽

2021 ◽

Author(s):

Ihsan Jamil ◽

Wen Jun ◽

Bushra Mughal ◽

Muhammad Haseeb Raza ◽

Muhammad Ali Imran ◽

...

Keyword(s):

Climate Change ◽

Agricultural Practices

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The Effects of Biotic and Abiotic Factors on the Community Dynamics in a Mountain Subtropical Forest

Forests ◽

10.3390/f12040427 ◽

2021 ◽

Vol 12 (4) ◽

pp. 427

Author(s):

Tianyang Zhou ◽

Jiaxin Zhang ◽

Yunzhi Qin ◽

Mingxi Jiang ◽

Xiujuan Qiao

Keyword(s):

Climate Change ◽

Ecosystem Services ◽

Forest Dynamics ◽

Community Dynamics ◽

Abiotic Factors ◽

Subtropical Forest ◽

Above Ground Biomass ◽

Short Term ◽

Forest Ecosystem Services ◽

Number Of Individuals

From supporting wood production to mitigating climate change, forest ecosystem services are crucial to the well-being of humans. Understanding the mechanisms that drive forest dynamics can help us infer how to maintain forest ecosystem services and how to improve predictions of forest dynamics under climate change. Despite the growing number of studies exploring above ground biomass (AGB) dynamics, questions of dynamics in biodiversity and in number of individuals still remain unclear. Here, we first explored the patterns of community dynamics in different aspects (i.e., AGB, density and biodiversity) based on short-term (five years) data from a 25-ha permanent plot in a subtropical forest in central China. Second, we examined the relationships between community dynamics and biodiversity and functional traits. Third, we identified the key factors affecting different aspects of community dynamics and quantified their relative contributions. We found that in the short term (five years), net above ground biomass change (ΔAGB) and biodiversity increased, while the number of individuals decreased. Resource-conservation traits enhanced the ΔAGB and reduced the loss in individuals, while the resource-acquisition traits had the opposite effect. Furthermore, the community structure contributed the most to ΔAGB; topographic variables and soil nutrients contributed the most to the number of individuals; demographic process contributed the most to biodiversity. Our results indicate that biotic factors mostly affected the community dynamics of ΔAGB and biodiversity, while the number of individuals was mainly shaped by abiotic factors. Our work highlighted that the factors influencing different aspects of community dynamics vary. Therefore, forest management practices should be formulated according to a specific protective purpose.

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Effect of short-term exposure to particulate air pollution on heart rate variability in normal-weight and obese adults

Environmental Health ◽

10.1186/s12940-021-00707-0 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Luyi Li ◽

Dayu Hu ◽

Wenlou Zhang ◽

Liyan Cui ◽

Xu Jia ◽

...

Keyword(s):

Heart Rate ◽

Air Pollution ◽

Heart Rate Variability ◽

High Frequency ◽

Normal Weight ◽

High Frequency Power ◽

Short Term ◽

Particulate Air Pollution ◽

Short Term Exposure ◽

Frequency Power

Abstract Background The adverse effects of particulate air pollution on heart rate variability (HRV) have been reported. However, it remains unclear whether they differ by the weight status as well as between wake and sleep. Methods A repeated-measure study was conducted in 97 young adults in Beijing, China, and they were classified by body mass index (BMI) as normal-weight (BMI, 18.5–24.0 kg/m2) and obese (BMI ≥ 28.0 kg/m2) groups. Personal exposures to fine particulate matter (PM2.5) and black carbon (BC) were measured with portable exposure monitors, and the ambient PM2.5/BC concentrations were obtained from the fixed monitoring sites near the subjects’ residences. HRV and heart rate (HR) were monitored by 24-h Holter electrocardiography. The study period was divided into waking and sleeping hours according to time-activity diaries. Linear mixed-effects models were used to investigate the effects of PM2.5/BC on HRV and HR in both groups during wake and sleep. Results The effects of short-term exposure to PM2.5/BC on HRV were more pronounced among obese participants. In the normal-weight group, the positive association between personal PM2.5/BC exposure and high-frequency power (HF) as well as the ratio of low-frequency power to high-frequency power (LF/HF) was observed during wakefulness. In the obese group, personal PM2.5/BC exposure was negatively associated with HF but positively associated with LF/HF during wakefulness, whereas it was negatively correlated to total power and standard deviation of all NN intervals (SDNN) during sleep. An interquartile range (IQR) increase in BC at 2-h moving average was associated with 37.64% (95% confidence interval [CI]: 25.03, 51.51%) increases in LF/HF during wakefulness and associated with 6.28% (95% CI: − 17.26, 6.15%) decreases in SDNN during sleep in obese individuals, and the interaction terms between BC and obesity in LF/HF and SDNN were both statistically significant (p < 0.05). The results also suggested that the effects of PM2.5/BC exposure on several HRV indices and HR differed in magnitude or direction between wake and sleep. Conclusions Short-term exposure to PM2.5/BC is associated with HRV and HR, especially in obese individuals. The circadian rhythm of HRV should be considered in future studies when HRV is applied. Graphical abstract

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