Short-term Trends Research Articles

A Case Study of Bright Dairys Acquisition of Israels Tnuva Foods

In recent years, with the development of Chinas economy and the policy encouragement of the Chinese government, more and more Chinese enterprises have participated in cross-border acquisitions and mergers and acquisitions. Studying the cases of cross-border mergers and acquisitions of large Chinese enterprises can help reveal the impact of similar mergers and acquisitions on the short-term stock price trend of enterprises and their financial indicators. This paper chooses Bright Dairy Foods acquisition of Tnuva as the main body of the case study. By constructing the FAMA-French Three-Factor Model, this paper studies the short-term effect the acquisition activity exerted on the stock price of Bright Diary Food. By studying the financial indicators, this paper studies the influence of M&A activities on the profitability and debt-paying ability of Bright Dairy Food. The research shows that Bright Dairys decision to acquire Tnuva aroused widespread concern from shareholders, and shareholders were optimistic about this decision, so the abnormal return of Bright Dairys stock has increased in the short term. In addition, the acquisition of Tnuva has improved the profitability of Bright Dairy but reduced its debt-paying ability.

Stock Price Prediction During the COVID-19 Pandemic Based on the GRU Algorithm

Stock price prediction during the Covid-19 pandemic has emerged as a significant research domain within the financial sector, giving rise to a multitude of neural network-based methods aimed at forecasting stock prices. This paper uses multiple machine learning models and analyzes the stock fund flows to attempt to learn and predict price trends from a dynamic perspective. The data used in this study includes the closing price, change rate, and fund flow of an A-share Pharmaceutical stock in the most recent 1000 trading days. The Mean Squared Error (MSE) is used as the model evaluation metric, and the models MSE can reach 0.013 after training. The predictions generated by the model exhibit a high degree of alignment with the actual price trends, indicating its accuracy in short-term price trend prognostication. These findings substantiate the efficacy of the model for stock price prediction during the pandemic period, thereby contributing to the body of knowledge within the field of financial forecasting.

Effects of point and nonpoint source controls on total phosphorus load trends across the Chesapeake Bay watershed, USA

Reduction of total phosphorus (TP) loads has long been a management focus of Chesapeake Bay restoration, but riverine monitoring stations have shown mixed temporal trends. To better understand the regional patterns and drivers of TP trends across the Bay watershed, we compiled and analyzed TP load data from 90 non-tidal network stations using clustering and random forest (RF) approaches. These stations were categorized into two distinct clusters of short-term (2013–2020) TP load trends, i.e. monotonic increase (n = 35) and monotonic decline (n = 55). RF models were developed to identify likely regional drivers of TP trend clusters. Reductions in point sources and agricultural nonpoint sources (i.e. fertilizer) both contributed to water-quality improvement in our period of analysis, thereby demonstrating the effectiveness of nutrient management and the importance of continuing such efforts. In addition, declining TP trends have a larger chance to occur in carbonate areas but a smaller chance in Coastal Plain areas, with the latter likely reflecting the effect of legacy P. To provide spatially explicit information, TP trend clusters were predicted for the entire watershed at the scale of river segments, which are more directly relevant to watershed planning. Among the 975 river segments, 544 (56%) and 431 (44%) were classified as ‘monotonic increase’ and ‘monotonic decrease’, respectively. Furthermore, these predicted TP trend clusters were paired with our previously published total nitrogen (TN) trend clusters, showing that TP and TN both declined in 185 segments (19%) and neither declined in 337 segments (35%). Broadly speaking, large-scale nutrient reduction efforts are underway in many regions to curb eutrophication. Water-quality responses and drivers may differ among systems, but our work provides important new evidence on the effectiveness of management efforts toward controlling point and nonpoint sources.

Comparison of ARIMA and ARIMA Error Regression Models: Evidence from the Russian Consumer Price Index

Consumer Price Index (CPI) is regarded as a common approach for measuring inflation. The present study examines the Russia CPI in the context of political and social upheavals, especially under the wars and the COVID-19. Due to the unstable political situation, the inflation rate in Russia sharply grow in 2014 and 2022 which creates two of the largest increase over the past decade after the Crimean war and the Ukrainian war were announced, thus it is crutial to make both long-term and short-term trends prediction. The paper aims to choose the best fitting model to estimate the future value of Russia monthly CPI data, and ultimately provides a suggestion and reference for monetary and fiscal authorities in Russia when making the policies to reduce inflation risk. The study applies the ARIMA (Autoregressive Integrated Moving Average) model to forecast Russia CPI, and uses regression with ARIMA errors to analyze the Russia CPI according to the ARIMA fitted values of the crude oil price in Europe. According to the result, ARIMA (2,3,1) is the best fitting model that can make comparatively sensible prediction for the future values.

Increased Bone Turnover and Decreased BCMA Levels in Patients with Response to Anti-BCMA CAR T Cell Therapy in Relapsed/Refractory Multiple Myeloma

Introduction For most patients (pts), multiple myeloma (MM) remains an incurable disease. Osteolytic lesions (OL) are present in 80% of pts at diagnosis and skeletal-related events are the most common cause for symptomatic relapse. Hence, myeloma bone disease (MBD) is a major cause of morbidity and mortality especially among pts with relapsed/refractory MM (RRMM). B cell maturation antigen (BCMA) targeted chimeric antigen receptor (CAR) T cells have revolutionized treatment of patients with RRMM. While remission rates are high and pts experience long-standing treatment free intervals after CAR T cell treatment, little is known about its effects on MBD and bone turnover. The latter can be determined in plasma by measuring markers of bone formation and resorption. We aimed to analyze the longitudinal development of bone turnover during and after anti-BCMA CAR T cell treatment in RRMM pts. Methods We analyzed 28 consecutive pts with RRMM who underwent treatment with either Idecabtagene-Vicleucel (n=27) or Ciltacabtagen-Autoleucel (n=1) at our center between December 2021 and February 2023. Median age at infusion was 63 (range 39-75) years. 57% of pts were male. All pts were triple-class exposed, 21% had penta-class refractory disease. Status prior to CAR T cell infusion was complete remission (CR, 11%), partial remission (PR, 21%), very good PR (14%), stable disease (4%), or progressive disease (PD, 50%). The impact of CAR T cell treatment on bone turnover was assessed by collecting plasma samples on the day of T cell apheresis, as well as days (d) 30 and 100 after CAR T cell infusion. Samples were analyzed by ELISA to evaluate plasma levels of bone formation marker Osteocalcin (OCN), bone resorption markers Tartrate-resistant acid phosphatase 5b (TRAcP5b) and fibroblast growth factor 23 (FGF23) as well as BCMA as a marker for the targeted myeloma cells. Median levels were compared between subgroups by Wilcoxon tests. Remission status after CAR T cell therapy was first evaluated at d 30 according to International Myeloma Working Group consensus criteria. Median follow-up was 6 months. Results Of the 28 pts treated with anti-BCMA CAR T cells, 64% achieved at least a PR (responders), while 36% had PD shortly (30 d) after. Within 14 d after infusion, 82% developed a cytokine release syndrome (CRS, °I or II), in 30% of which Tocilizumab was applied. Only 1 pt developed immune effector cell-associated neurotoxicity syndrome. In responders, BCMA levels in plasma decreased significantly between apheresis and d 30 ( P<.01) and between apheresis and d 100 ( P<.01), while pts with PD showed no significant change in BCMA levels at the same timepoints ( P=.6 and P=.8). Furthermore, BCMA levels were significantly higher in pts with PD at d 30 ( P<.01) and d 100 ( P=.02), compared to responders (Figure 1). Regarding bone resorption markers, TRAcP5b levels were significantly increased in responders ( P=.01) but not pts with PD ( P=.56) on d 30 after apheresis. Furthermore, we determined a significant increase of intact FGF23 between apheresis and d 30 ( P<.01) and a subsequent significant decrease of intact FGF23 ( P=.02) between d 30 and d 100 in responders. Since FGF23 and interleukin-6 (IL-6) are physiologically dependent, the observed FGF23 dynamics are likely to be an effect of increasing IL-6 in the context of CRS. Aptly, pts who received Tocilizumab because of full-blown CRS had higher intact FGF23 levels at d 30 ( P=.02) compared to pts without Tocilizumab treatment. The bone formation marker OCN showed a significant increase in responders between apheresis and d 100 ( P=.01), as well as between d30 and d100 ( P=.02), while there were no significant changes in OCN levels over the time in pts with PD (Figure 2). Conclusion Pts responding to treatment with anti-BCMA CAR T cells, showed an increased bone turnover shortly after infusion. The increase of bone resorption marker levels appeared to be a short-term trend and could possibly be related to inflammation and high interleukin levels during the first days after infusion. However, we also saw a steady increase of bone formation marker OCN, which remained significant till d 100 in responders. Our data implies, that response to anti-BCMA CAR T cells is accompanied by increasing bone formation. Further analyses are needed to evaluate, whether this treatment can have positive long-term effects on MBD.

Horizontal transmission maintains host specificity and codiversification of symbionts in a brood parasitic host

In host-symbiont systems, interspecific transmissions create opportunities for host switches, potentially leading to cophylogenetic incongruence. In contrast, conspecific transmissions often result in high host specificity and congruent cophylogenies. In most bird-feather mite systems, conspecific transmission is considered dominant, while interspecific transmission is supposedly rare. However, while mites typically maintain high host specificity, incongruent cophylogenies are common. To explain this conundrum, we quantify the magnitude of conspecific vs. interspecific transmission in the brood parasitic shiny cowbird (Molothrus bonariensis). M. bonariensis lacks parental care, allowing the assessment of the role of horizontal transmission alone in maintaining host specificity. We found that despite frequent interspecific interactions via foster parental care, mite species dispersing via conspecific horizontal contacts are three times more likely to colonize M. bonariensis than mites transmitted vertically via foster parents. The results highlight the previously underappreciated rate of transmission via horizontal contacts in maintaining host specificity on a microevolutionary scale. On a macroevolutionary scale, however, host switches were estimated to have occurred as frequently as codivergences. This suggests that macroevolutionary patterns resulting from rare events cannot be easily generalized from short-term evolutionary trends.

Urban Road Traffic Spatiotemporal State Estimation Based on Multivariate Phase Space–LSTM Prediction

The road traffic state is usually analyzed from a temporal and macroscopic perspective; however, traffic flow parameters, such as density and spacing, can explain the evolution of traffic states from the microscopic perspective and the spatial distribution of vehicles in lanes. In this paper, we attempt to take both temporal and spatial characteristics into consideration simultaneously, and a parameter is defined as the traffic spatiotemporal state of urban road sections to represent the operational status of road traffic, using advanced prediction techniques to estimate its short-term trends. An estimation method is constructed for the traffic spatiotemporal state considering travel times, speeds, and queuing situations from temporal and spatial perspectives. Then, based on Takens’ theorem and the single variable phase space, the phase space of multiple traffic parameters is reconstructed and the chaotic characteristics are analyzed. Next, an LSTM prediction model is constructed based on the phase space reconstruction of multiple variables, and the traffic parameters are predicted by empirical analysis. The results show the proposed estimation method has a significantly improved accuracy. Finally, combined with RFID data, the traffic spatiotemporal state of the case section is calculated, which provides a theoretical basis and practical reference for road traffic state evaluations.

Analysis of Economic Indicators in Zhanjiang City during Epidemic Period Based on Remote Sensing Light Index

GDP (Gross Domestic Product) reflects the economic power and market scale of a city, offering an important reference for political decision-making and national development. The luminous remote sensing plays an important role in exploring the city economic development, analyzing and forecasting urban GDP. The luminous remote sensing is a new method to evaluate and display macro issues of economic and social development. This article analyzes the correlation between the lighting index and the economic index GDP, and constructs the best linear regression model of the economic index of Zhanjiang, considering the impact of the Covid-19. Based on the regression slope of night light intensity before and after the Covid-19, this article analyzes the economic trend of Zhanjiang during the epidemic period. The results show that there is a significant linear correlation between the night light index and the socio-economic index, and the regression slope of the light can reflect the short-term economic trend and evaluate the city-level economic indicators well.

Short-term stock price trend prediction with imaging high frequency limit order book data

Predicting price movements over a short period is a challenging problem in high-frequency trading. Deep learning methods have recently been used to forecast short-term prices via limit order book (LOB) data. In this paper, we propose a framework to convert LOB data into a series of standard images in 2D matrices and predict the mid-price movements via an image-based convolutional neural network (CNN). The empirical study shows that the image-based CNN model outperforms other traditional machine learning and deep learning methods based on raw LOB data. Our findings suggest that the additional information implicit in LOB images contributes to short-term price forecasting.

Multiple time scales investor sentiment impact the stock market index fluctuation: From margin trading business perspective

The stock market is viewed as a complex dynamic system, and investor sentiment has an important impact on index fluctuation. This study constructs investor sentiment from margin trading business perspective and investigates its impact on the Chinese stock market index fluctuation in multiple time scales. First, we utilize 12 indicators and two-stage PCA to construct a composite investor sentiment index of the margin trading business(ISMT). Second, based on TEI@I complex system theory, we use the VMD algorithm to decompose and reconstruct the ISMT, Shanghai Securities Composite Index(SSEC), and Shenzhen Securities Component Index(SZI), and obtain multiple time scale measurement sequences that reflect short-term, medium-term, and long-term fluctuation of each index, respectively. We provide evidence that the ISMT has an asymmetric impact on stock market index fluctuations. Specifically, for long-term trend, the ISMT has a significant positive impact on the SSEC, and a significant negative impact on the SZI. For medium and short-term trends, the ISMT has a significant positive impact on both the fluctuations of SSEC and SZI, and the impact degree on SSEC is greater than SZI. We also find that the impact degree of ISMT on SSEC and SZI decreases from short to long-term trend. In addition, we measure the fluctuation periodicity of ISMT in multiple time scales based on Fast Fourier Transformation, investigate the impact result during the COVID-19 pandemic, discuss the impact of ISMT on the other nine indexes commonly used in the Chinese stock market, and evaluate the predictive power of ISMT for 11 stock market index returns. This paper takes a new perspective and technology to investor sentiment research, and the results enrich relevant financial theories. The findings are crucial for investor decision-making and financial department regulation.

Hybrid analytic network process (ANP)-Entropy model, time series analysis for predicting nitrate and fluoride in groundwater and cumulative health risk assessment

The study focused on groundwater evaluation of the trans-Himalayan region using a novel concept of Hybrid Analytic Network Process (ANP) - entropy and Holt's exponential smoothing model for short-term prediction of groundwater quality. Hierarchy cluster analysis of groundwater was performed to identify the sources and spatial distribution of water quality. The PCA coupled with multivariate analysis dictated a high correlation of various ions i.e., NO3−, K+, F−, Cl−, SO42−, PO42−, and Na+ with the lithology of the area and helped in identification of various polluting sources i.e., excessive use of fertilizers, wastewater discharges and agrochemicals. The hybrid ANP-entropy technique offered a holistic approach to provide a more accurate and complete understanding of groundwater quality and revealed that groundwater quality in 21.43% (in winter) and 42.86% (in summer) of the locations fall into the poor category. The non-carcinogenic health risk analysis revealed that fluoride poses a major potential health risk followed by nitrate, primarily via the oral pathway while it was negligible through the dermal pathway. The total hazard index was observed highest in children (1.99) followed by infants (1.64). 95th percentile of the probable risk obtained through Monte Carlo simulation also dictated highest risk to infants than child. The predicted values of the Holt's exponential smoothing model for the year 2021 and 2022 was varied within ±5 % of the observed values. The proposed model effectively treats uncertainties in groundwater quality assessment and considers average and incremental increase both hence, can be used for short-term prediction and trend analysis of groundwater quality.

Multi-step and multi-task learning to predict quality-related variables in wastewater treatment processes

In wastewater treatment processes, lack of hardware sensors together with unacceptable dynamics, strong nonlinearity and large time delay often leads to a large number of key variables that are difficult to measure online accurately and timely, then frustrate safe operations of the processes. To accurately and timely capture the short-term behavior changes and trend development of critical variables, a novel neural network based soft-sensing model is proposed to take full use of multi-task learning, direct multi-step prediction strategy and evolutionary algorithm to formulate a novel multi-task multi-step evolution (MTMSE) neural network. Firstly, single-output MTMSE (SO-MTMSE) neural network is used to realize the dynamic monitoring of a single variable. Moreover, by considering the spatiotemporal interaction among the data, the model is extended to multi-output MTMSE (MO-MTMSE) neural network to simultaneously realize multi-step prediction of multiple variables, thus providing a desired reference for optimizing the wastewater treatment processes. Finally, the proposed model is applied to the benchmark simulation model 2 (BSM2) and a full-scale wastewater treatment plant in Shenzhen. And the results show that the proposed dynamic soft sensor model outperforms the standard methods, such as autoregressive moving average model (ARMA) and multiple output gaussian process regression (MGPR).

Epidemic trends and predictive analysis of other infectious diarrhea in Jiangxi Province, 2017-2022

Objective: To analyze epidemic trends of other infectious diarrhea in Jiangxi Province from 2017 to 2022, and explore the application of autoregressive integrated moving average (ARIMA) model in the prediction of the incidence of other infectious diarrhea in Jiangxi Province, providing reference for the prediction and prevention and control of other infectious diarrhea. Methods: To conduct a descriptive epidemiological analysis of other infectious diarrhea cases in Jiangxi Province from 2017 to 2022, and establish an ARIMA model to predict the number of other infectious diarrhea cases in 2023. Results: From 2017 to 2022, Jiangxi Province reported 204 842 cases of other infectious diarrhea. The annual average reported incidence rate was 74.32/100 000. The cases were reported in each age group with obvious seasonal characteristics of the main peak. There were two peak periods of incidence in winter and spring (from January to March) and in summer and autumn (from July to September) and the peak value was higher in winter and spring. All parameters of the model ARIMA (0,1,2)(2,1,0)12 and ARIMA (1,0,0)(2,1,0)12 were statistically significant (P<0.05), and the minimum values of Bayesian information criterion were 13.83 and 9.12, respectively. The residual series were all white noise (P>0.05); The predicted value of the model is in good agreement with the actual value, and the predicted trend is consistent with the actual trend. The model has a good prediction effect. Conclusions: The other infectious diarrhea occurred in 2017-2022 was still the first case of notifiable disease in Jiangxi Province. The prevention and control situation cannot be ignored. Disease monitoring and health education for families of children under 3 years of age and scattered children among key populations for prevention and control should be strengthened during the epidemic season. The ARIMA model can be used for short-term prediction and trend analysis of other infectious diarrhea outbreaks in Jiangxi Province.

A novel short-term radio flux trend prediction model based on deep learning

A novel short-term radio flux trend prediction model based on deep learning

Effects of Closures and Openings on Public Health in the Time of COVID-19: A Cross-Country and Temporal Trend Analysis

Many countries mandated social distancing measures during the COVID-19 pandemic of 2020 to 2022 that variously included opening hours restrictions on hospitality and retail, economy-wide closures, and additional international border controls. We analyzed whether more restrictive (hereafter, closures) or less restrictive (hereafter, openings) social distancing measures changed the short-term trends in the number of COVID-19 cases, hospitalizations, and ICU patients in Australia, Canada, and the United Kingdom. Our analysis uses a “before-and-after” trend analysis (decremental/incremental and growth/decay trends) to compare the trends of epidemic indicators before and after each closure or opening event. Results show that, in general, and for these three countries: (a) closures resulted in reduced trend growth in adverse COVID-19 public health outcomes and (b) openings resulted in increased trend growth for the three selected measures of public health.

Demographic Development of Russian Cities According to Population Censuses of 1897–2020

The purpose of the study was to analyze the population dynamics and gender structure of Russian cities from 1897 to 2020. The presented time period made it possible to study both short-term and long-term trends in the development of cities of various categories, based on data from official population censuses. A key aspect of the study was to assess the dynamics of population growth and changes in the gender balance in the largest cities of the country. The main attention was paid to the dynamics of population changes in cities of various categories according to the classification of the Ministry of Construction of the Russian Federation. The study made it possible to draw a conclusion about a significant transformation of the settlement system of the Russian Federation during the analyzed period. The most noticeable trend is towards an increase in the number of largest cities, which indicates the processes of urbanization and concentration of the population in the large administrative and economic centers of the country. At the same time, there is a recovery in the number of small towns, especially with a population of up to 10 thousand people, which may be due to the development of infrastructure and improvement of living conditions in small settlements. This study examines quantitative and percentage changes in the population in the context of cities of various categories, analyzes the gender composition of the population in the largest cities of Russia for various periods, and assesses the rate of population growth or decline over different time periods. The study seeks to contribute to the understanding of urbanization processes and demographic changes in Russia, and can serve as a basis for developing strategies for sustainable urban development and planning socio-economic policies at local and regional levels.

Variation Trend Prediction of Dam Displacement in the Short-Term Using a Hybrid Model Based on Clustering Methods

The deformation behavior of a dam can comprehensively reflect its structural state. By comparing the actual response with model predictions, dam deformation prediction models can detect anomalies for effective advance warning. Most existing dam deformation prediction models are implemented within a single-step prediction framework; the single-time-step output of these models cannot represent the variation trend in the dam deformation, which may contain important information on dam evolution during the prediction period. Compared with the single value prediction, predicting the tendency of dam deformation in the short term can better interpret the dam’s structural health status. Aiming to capture the short-term variation trends of dam deformation, a multi-step displacement prediction model of concrete dams is proposed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, the k-harmonic means (KHM) algorithm, and the error minimized extreme learning machine (EM-ELM) algorithm. The model can be divided into three stages: (1) The CEEMDAN algorithm is adopted to decompose dam displacement series into different signals according to their timing characteristics. Moreover, the sample entropy (SE) method is used to remove the noise contained in the decomposed signals. (2) The KHM clustering algorithm is employed to cluster the denoised data with similar characteristics. Furthermore, the sparrow search algorithm (SSA) is utilized to optimize the KHM algorithm to avoid the local optimal problem. (3) A multi-step prediction model to capture the short-term variation of dam displacement is established based on the clustered data. Engineering examples show that the model has good prediction performance and strong robustness, demonstrating the feasibility of applying the proposed model to the multi-step forecasting of dam displacement.

Forced and internal components of observed Arctic sea-ice changes

Abstract. The Arctic sea-ice cover is strongly influenced by internal variability on decadal timescales, affecting both short-term trends and the timing of the first ice-free summer. Several mechanisms of variability have been proposed, but how these mechanisms manifest both spatially and temporally remains unclear. The relative contribution of internal variability to observed Arctic sea-ice changes also remains poorly quantified. Here, we use a novel technique called low-frequency component analysis to identify the dominant patterns of winter and summer decadal Arctic sea-ice variability in the satellite record. The identified patterns account for most of the observed regional sea-ice variability and trends, and they thus help to disentangle the role of forced and internal sea-ice changes over the satellite record. In particular, we identify a mode of decadal ocean–atmosphere–sea-ice variability, characterized by an anomalous atmospheric circulation over the central Arctic, that accounts for approximately 30 % of the accelerated decline in pan-Arctic summer sea-ice area between 2000 and 2012 but accounts for at most 10 % of the decline since 1979. For winter sea ice, we find that internal variability has dominated decadal trends in the Bering Sea but has contributed less to trends in the Barents and Kara seas. These results, which detail the first purely observation-based estimate of the contribution of internal variability to Arctic sea-ice trends, suggest a lower estimate of the contribution from internal variability than most model-based assessments.

Assessing Terrestrial Water Storage Variations in Southern Spain Using Rainfall Estimates and GRACE Data

This paper investigates the relationship between rainfall, groundwater and Gravity Recovery and Climate Experiment (GRACE) data to generate regional-scale estimates of terrestrial water storage variations in the Andalucía region of southern Spain. These estimates can provide information on groundwater depletion (caused by periods of low rainfall or droughts) and groundwater recovery. The spatial distribution of groundwater bodies in southern Spain is complex and current in situ groundwater monitoring methods are deficient, particularly in terms of obtaining representative samples and in implementing and maintaining groundwater monitoring networks. The alternative approach proposed here is to investigate the relationship between precipitation time series and changes in the terrestrial water storage estimated from GRACE observations. The results were validated against the estimated fluctuation in regional groundwater. The maximum correlation between the mean groundwater level and the GRACE observations is 0.69 and this occurs at a lag of one month because the variation in gravity is immediate, but rainfall water requires around one month to travel across the vadose zone before it reaches the groundwater table. Using graphical methods of accumulated deviations from the mean, we show that, in general, groundwater storage follows the smooth, multi-year trends of terrestrial water storage but with less short-term trends; the same is true of rainfall, for which the local trends are more pronounced. There is hysteresis-like behaviour in the variations in terrestrial water storage and in the variations of groundwater. In practical terms, this study shows that, despite the abnormal dryness of the Iberian Peninsula during the 2004–2010 drought, the depleted groundwater storage in Andalucía recovered almost to its pre-drought level by 2016. In addition, groundwater storage and terrestrial water storage show very similar trends but with a delay in the groundwater trend.

Value of combining transect counts and telemetry data to determine short-term population trends in a globally threatened species.

To evaluate conservation interventions, it is necessary to obtain reliable population trends for short (<10 years) time scales. Telemetry can be used to estimate short-term survival rates and is a common tool for assessing population trends, but it has limitations and can be biased toward specific behavioral traits of tagged individuals. Encounter rates calculated from transects can be useful for assessing changes across multiple species, but they can have large confidence intervals and be affected by variations in survey conditions. The decline of African vultures has been well-documented, but understanding of recent trends is lacking. To examine population trends, we used survival estimates from telemetry data collected over 6 years (primarily for white-backed vultures [Gyps africanus]) and transect counts conducted over 8 years (for 7 scavenging raptors) in 3 large protected areas in Tanzania. Population trends were estimated using survival analysis combined with the Leslie Lefkovitch matrix model from the telemetry data and using Bayesian mixed effects generalized linear regression models from the transect data. Both methods showed significant declines for white-backed vultures in Ruaha and Nyerere National Parks. Only telemetry estimates suggested significant declines in Katavi National Park. Encounter rates calculated from transects also showed declines in Nyerere National Park for lappet-faced vultures (38% annual declines) and Bateleurs (18%) and in Ruaha National Park for white-headed vultures (Trigonoceps occipitalis) (19%). Mortality rates recorded and inferred from telemetry suggested that poisoning is prevalent. However, only 6 mortalities of the 26 presumed mortalities were confirmed to be caused by poisoning, highlighting the challenges of determining the cause of death when working across large landscapes. Despite declines, our data provide evidence that southern Tanzania has higher current encounter rates of African vultures than elsewhere in East Africa. Preventing further declines will depend greatly on mitigating poisoning. Based on our results, we suggest that the use of multiple techniques improves understanding of population trends over the short term.