Short-term Scale Research Articles

Temperature fluctuations in a warmer environment: impacts on microbial plankton.

Warming can cause changes in the structure and functioning of microbial food webs. Experimental studies quantifying such impacts on microbial plankton have tended to consider constant temperature conditions. However, Jensen’s inequality (or the fallacy of the average) recognizes that organism performance under constant conditions is seldom equal to the mean performance under variable conditions, highlighting the need to consider in situ fluctuations over a range of time scales. Here we review some of the available evidence on how warming effects on the abundance, diversity, and metabolism of microbial plankton are altered when temperature fluctuations are considered. We found that fluctuating temperatures may accentuate warming-mediated reductions in phytoplankton evenness and gross photosynthesis while synergistically increasing phytoplankton growth. Also, fluctuating temperatures have been shown to reduce the positive warming effect on cyanobacterial biomass production and recruitment and to reverse a warming effect on cellular nutrient quotas. Other reports have shown that fluctuations in temperature did not alter plankton responses to constant warming. These investigations have mostly focused on a few phytoplankton species (i.e. diatoms and haptophytes) in temperate and marine ecosystems and considered short-term and transient responses. It remains unknown whether the same responses apply to other species and ecosystems and if evolutionary change in thermally varying environments could alter the magnitude and direction of the responses to warming observed over short-term scales. Thus, future research efforts should address the role of fluctuations in environmental drivers. We stress the need to study responses over different biological organization and trophic levels, nutritional modes, temporal scales, and ecosystem types.

Contact Angle Relaxation on Amorphous, Mixed-Phase (Anatase + Rutile), and Anatase TiO2 Films and Its Mechanism.

TiO2 films generally undergo contact angle relaxation in the dark. It has been suggested that carbon contamination and the loss of surface OH generated by UV may be the major causes. However, the mechanisms for the long-lasting hydrophilicity have not been fully understood. Here, we studied contact angle relaxation of amorphous, mixed-phase, and anatase, and a new mechanism is proposed. After UV exposure and oxygen plasma treatment, the films' relaxation was observed over short-term (1 day) and long-term (>30 days) scales with XPS analysis using two quantitative parameters: relative amount and binding energy (B.E.) shifting. One day after plasma treatment, we observed that the donor-acceptor complex (DAC) and Ti-OH peaks of anatase shifted toward lower B.E., while the other films showed no shift or positive B.E. shifting. Interestingly, the relaxation of the amorphous and mixed-phase TiO2 occurred over time despite the large number of total OH groups (IOH/Ibulk > 75%) and DAC (IDAC/Ibulk > 110%), and only the anatase film showed superhydrophilicity (∼10°) for 90 days. Also, the B.E. of all OH peaks increased over time, indicating that polarizable hydroxyls relaxed in the dark. Although the greater binding strength of Ti-OH and DAC on the anatase surface maintains long-lasting hydrophilicity, the loss of polarizable OH causes relaxation on the less-reactive TiO2 films. Carbon contamination can also contribute to the relaxation over time. Taken together, we conclude that the surface energy, polarizable OH, and contaminants are the major factors affecting relaxation; this study gives a full picture of the mechanism integrated over some of the previously reported models.

Living on the edge: spatial response of coyotes (Canis latrans) to wolves (Canis lupus) in the subarctic

Understanding how mesopredators manage the risks associated with apex predators is key to explaining impacts of apex predators on mesopredator populations and patterns of mesopredator space use. Here we examine the spatial response of coyotes (Canis latrans Say, 1823) to risk posed by wolves (Canis lupus Linnaeus, 1758) using data from sympatric individuals fitted with GPS collars in subarctic Alaska, USA, near the northern range limit for coyotes. We show that coyotes do not universally avoid wolves, but instead demonstrate season-specific responses to both wolf proximity and long-term use of the landscape by wolves. Specifically, coyotes switched from avoiding wolves in summer to preferring areas with wolves in winter, and this selection was consistent across short-term and longer term temporal scales. In the summer, coyotes responded less strongly to risk of wolves when in open areas than when in closed vegetation. We also demonstrate that coyotes maintain extremely large territories averaging 291 km2, and experience low annual survival (0.50) with large carnivores being the largest source of mortality. This combination of attraction and avoidance predicated on season and landcover suggests that mesopredators use complex behavioral strategies to mediate the effects of apex predators.

Edge Effects on Successional Dynamics of Forest Fragments in the Brazilian Cerrado

Abstract Comparative studies of tree dynamics can be useful for the conservation of forest fragments in the Cerrado. We tested if the successional dynamics in fragments of semideciduous forest (SF) and cerradao (CE) are influenced by edge effects (EE). Two surveys were carried out with plots allocation in both physiognomies, from the edge towards the interior of the fragments. All trees with diameter at breast height ≥ 5cm were measured, identified and classified (ecological groups). Both physiognomies showed high recruitment and mortality rates when compared with other forests in Cerrado. We found an EE on tree density (SF), and on mortality, total basal area and basal area of dead (CE), although do not so notorious on a short-term scale. Probably because EE did not influence the ecological groups and canopy cover. Our results suggest that the forest fragmentation can act as a disturbance agent, causing changes in the tree dynamics of the Cerrado forests.

Comparison of Percutaneous Transluminal Angiography With or Without Catheter-Directed Thrombolysis for Chronic Femoropopliteal Occlusive Disease.

Background:Catheter-directed thrombolysis (CDT) is seldom used for chronic femoropopliteal occlusive disease.Methods:Patients with chronic femoropopliteal occlusive disease enrolled between January, 2011 and April, 2017. Hospitalization expense, limb salvage rate and survival rate were calculated.Results:Twenty-nine patients were treated with CDT (CDT group) and 31 patients without CDT (Non-CDT group).The mean hospitalization expense (5.2 ± 0.5), balloon catheter (1.0 ± 0.2) and stents number (0.8 ± 0.2) in CDT group were significantly less compared to Non-CDT group (P < 0.05). The short-term and long-term effect scales showed similar in both groups. The incidences of perioperative complications (10.3% vs. 19.4%), primary patency and second patency rate, limb salvage rate (14.8% vs. 16.1%) and survival rate were also similar (P > 0.05). Six patients died in each group and only 2 disease related deaths were found in Non-CDT group.Conclusion:CDT is a safe and economic strategy for patients with chronic femoropopliteal occlusive disease, and should be served as blanket treatment for every patient without thrombolytic contradictions or a remedy for failure PTA to achieve a comparable clinical effect.

STHRSCAPE: Developing and validating a collaborative economy based short-term hospitality rental servicescape scale

This study develops a culture-specific servicescape measurement scale for collaborative economy based short-term hospitality rentals (STHRs) from a Chinese customer perspective, as some of the servicescape measures developed from a Western consumer perspective may not be applicable to Chinese consumers due to cultural differences. Dimensions of the servicescape of collaborative economy based STHRs (STHRSCAPE) were identified after text analytics of a total of 31,584 reviews for 6313 SHTRs. These dimensions were tested on Chinese STHRs customers using three sets of data. The findings revealed that the STHRSCAPE scale has two main dimensions, namely, the physical servicescape and the social servicescape. The physical servicescape dimension has 12 subdimensions, while the social servicescape dimension has four subdimensions. The STHRSCAPE scale developed in this study is expected to help hosts and marketers on both international and local STHR platforms to better understand the key aspects of physical and social servicescape dimensions that are valued by Chinese consumers.

Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm

Exploring traffic flow characteristics and predicting its variation patterns are the basis of Intelligent Transportation Systems. The intermittent characteristics and intense fluctuation on short-term scales make it a significant challenge on urban roads. A hybrid model, Genetic Algorithm with Attention-based Long Short-Term Memory (GA-LSTM), combining with spatial–temporal correlation analysis, is proposed in this study to predict traffic volumes on urban roads. The spatial correlation is captured by combining the volume transition matrix estimated from vehicle trajectories and network weight matrix quantified from different detectors. The temporal dependency is explored by the attention mechanism, and we introduce the Genetic Algorithm to optimize it. In the experiment, traffic flow data collected from License Plate Recognition (LPR), is utilized to validate the effectiveness of model. The comparison is conducted with several traditional models to show the superiority of the proposed model with higher accuracy and better stability.

Seasonality in cold coast bluff erosion processes

High water levels in the Laurentian Great Lakes are causing widespread erosion of unlithified coastal bluffs and damaging infrastructure. Measurements of bluff recession along Wisconsin's Lake Michigan coast are mostly limited to estimates generated from comparisons of historical orthophotos with little information about the short-term scales (months to years) that affect many management and hazard-based decisions. Here we investigate the physical factors driving coastal bluff response to toe erosion at seasonal timescales using a multi-method case study of a mixed lithology bluff in Southeastern Wisconsin, an area that undergoes annual freezing. Five drone photogrammetry surveys were conducted from the winter of 2018 through the winter of 2020, and time lapse photography was collected from March 2019 through February 2020. The bluff face volumetric erosion rate between December 2018 and June 2019 was 3800 m3 a−1, which is 300% larger than the erosion rate between June 2019 and November 2019. Statistical analysis of the time lapse photography indicates that high magnitude erosion events are significantly correlated with environmental factors, including freeze-thaw events. These observations suggest that bluff erosion occurs episodically and is dominated by freeze-thaw related mass wasting events during the winter and spring. To investigate the physical processes driving this pattern, we model transient pore pressure response to a frozen bluff face as an input to a 3-D moment-balance slope stability model. Results show that increases in pore pressures up to one meter are possible at the face of the bluff over 5 days, which corresponds to 4–7% reduction in slope stability, indicating that observed increases in upslope bluff erosion during the winter and spring are a result of increased pore pressures, in combination with sediment strength reduction and high vadose zone saturation. As elevated lake levels continue to steepen bluff toes, upslope impacts will likely be temporally clustered during times of freeze-thaw. To accurately predict the seasonality of bluff recession on cold coasts, landscape change models must incorporate transient conditions caused by freeze-thaw events as they appear to be a dominant process.

Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output

Drought forecasting is essential for effectively managing drought-related damage and providing relevant drought information to decision-makers so they can make appropriate decisions in response to drought. Although there have been great efforts in drought-forecasting research, drought forecasting on a short-term scale (up to two weeks) is still difficult. In this research, drought-forecasting models on a short-term scale (8 days) were developed considering the temporal patterns of satellite-based drought indices and numerical model outputs through the synergistic use of convolutional long short term memory (ConvLSTM) and random forest (RF) approaches over a part of East Asia. Two widely used drought indices—Scaled Drought Condition Index (SDCI) and Standardized Precipitation Index (SPI)—were used as target variables. Through the combination of temporal patterns and the upcoming weather conditions (numerical model outputs), the overall performances of drought-forecasting models (ConvLSTM and RF combined) produced competitive results in terms of r (0.90 and 0.93 for validation SDCI and SPI, respectively) and nRMSE (0.11 and 0.08 for validation of SDCI and SPI, respectively). Furthermore, our short-term drought-forecasting model can be effective regardless of drought intensification or alleviation. The proposed drought-forecasting model can be operationally used, providing useful information on upcoming drought conditions with high resolution (0.05°).

Habitat complexity and lifetime predation risk influence mesopredator survival in a multi-predator system

Variability in habitat selection can lead to differences in fitness; however limited research exists on how habitat selection of mid-ranking predators can influence population-level processes in multi-predator systems. For mid-ranking, or mesopredators, differences in habitat use might have strong demographic effects because mesopredators need to simultaneously avoid apex predators and acquire prey. We studied spatially-explicit survival of cheetahs (Acinonyx jubatus) in the Mun-Ya-Wana Conservancy, South Africa, to test hypotheses related to spatial influences of predation risk, prey availability, and vegetation complexity, on mesopredator survival. For each monitored cheetah, we estimated lion encounter risk, prey density, and vegetation complexity within their home range, on short-term (seasonal) and long-term (lifetime) scales and estimated survival based on these covariates. Survival was lowest for adult cheetahs and cubs in areas with high vegetation complexity on both seasonal and lifetime scales. Additionally, cub survival was negatively related to the long-term risk of encountering a lion. We suggest that complex habitats are only beneficial to mesopredators when they are able to effectively find and hunt prey, and show that spatial drivers of survival for mesopredators can vary temporally. Collectively, our research illustrates that individual variation in mesopredator habitat use can scale-up and have population-level effects.

Effects of applying platelet-rich plasma during arthroscopic rotator cuff repair: a systematic review and meta-analysis of randomised controlled trials

Because of its healing properties, platelet-rich plasma (PRP) has been applied to the bone–tendon interface during arthroscopic rotator cuff repair to improve surgical outcomes. However, its effects remain ambiguous. Therefore, we conducted this systematic review and meta-analysis to assess the effects of PRP on retear rate and functional outcomes. Randomised control trials were identified and extracted. Data collection was completed on 15 February 2020. The results are expressed as the risk ratio (RR) for the categorical variables and weighted mean difference for the continuous variables, with 95% confidence intervals (CIs). Analyses were performed using RevMan 5.3 software. Seven randomised controlled trials published from 2013 to 2018, with 541 patients in total, were included. The results revealed a significant decrease in retear rate [RR 0.38, 95% CI (0.22, 0.68), P = 0.0009). Furthermore, a significant improvement was observed regarding short-term Constant score [mean difference = 3.28, 95% CI (1.46, 5.11), P = 0.0004), short-term University of California at Los Angeles activity score [mean difference = 1.60, 95% CI (0.79, 2.42), P = 0.0001], and short-term visual analogue scale score [mean difference = − 0.14, 95% CI (− 0.23, − 0.05), P = 0.002]. This systematic review indicates the efficacy of PRP when applied to the bone–tendon interface during arthroscopic rotator cuff repair.

The impacts of algae biological pump effect on the occurrence, source apportionment and toxicity of SPM-bound PAHs in lake environment

The algae biological pump (ABP) effect for hydrophobic organic contaminants in deep oligotrophic lakes and oceans has been well studied. Suspended particulate matter (SPM) plays a connective role in ABP processes. However, little is known about the impacts of ABP effect on the occurrence, source apportionment and toxicity of SPM-bound polycyclic aromatic hydrocarbons (PAHs) in a typically shallow eutrophic lake under strong anthropogenic emissions of PAHs. In this study, we study this gap knowledge on the eutrophic Lake Chaohu, China. SPM-bound PAHs in Lake Chaohu were controlled by anthropogenic emissions in all seasons. Apparent ABP effect only occurred in spring and summer in lake area. Algae blooms in spring and summer significantly increased 46.5% ± 7.9% (mean ± standard deviation) and 19.8% ± 2.4% of Σ21 SPM-bound PAHs, and greatly enhanced their toxicity (1.98 ± 0.46 times in spring and 32.9% ± 4.2% in summer). Therefore, there need more attentions focusing on the coupling effect of persistent toxic substances such as PAHs and harmful algae blooms in aquatic environment for sustainable development. The apparent ABP effect had little influence on their source apportionment. However, it may cause a regime shift for the source apportionment on a short-term scale. Further study could pay more attentions on in-depth and short-term studies on ABP effect.

Daily to weekly impacts of mixing and biological activity on carbonate dynamics in a large river-dominated shelf

Large eutrophic river plumes can lead to hypoxic near-bottom water during summer. However, how the carbonate system in this stratified water column varies at a daily-to-weekly scale is still unclear. At the end of the first severe El Niño Southern Oscillation event in the 21st century during 2015/2016, high temperature, high salinity water was observed in the middle of the Pearl River plume on the northern South China Sea shelf over 6 d (July 24–29, 2016). We deployed a sensor pack (conductivity, temperature, pressure, and dissolved oxygen [DO]) along the water column each hour and took discrete samples, including total alkalinity and dissolved inorganic carbon every 3 h, to calculate pH. We observe a pH reduction rate of 0.011 pH unit·d−1 and an oxygen consumption rate of 4.4 μmol kg−1·d−1 in the near-bottom water. The temporal variations in calculated net community production rate and excess DO (measured DO - saturated DO) implies the switch in the dominance of net respiration to net photosynthesis in the near-surface water during this mixing event. We suggest that both net photosynthesis and net respiration were in the water with oversaturated DO on a short-term scale. The pH reduction and oxygen consumption rates in this study could help to estimate the level of coastal acidification and hypoxia better.

Optimized Two-Time Scale Robust Dispatching Method for the Multi-Terminal Soft Open Point in Unbalanced Active Distribution Networks

To alleviate the impact of output uncertainty of renewable energy sources (RESs) in unbalanced active distribution networks (ADNs), this paper proposed a two-time scale robust optimization method for the multi-terminal soft open point (SOP). In the long-term scale, the operation points of the SOP were optimized by semidefinite programming (SDP) model to minimize system loss and mitigate voltage unbalance. An improved iterative cutting plane (ICP) method was proposed to strengthen the exactness of the SDP rank-one relaxation. In the short-term scale, V 2- P and V 2- Q droop control mode was implemented in the SOP to better respond to the output uncertainty of RESs. Then a slope robust optimization model was established based on long-term operation points and short-term forecast data to improve system robust security. To coordinate these two time scales, a co-optimization re-dispatch strategy was proposed in the short-term model so that the robust safety margin could be further expanded in extreme conditions. Case studies show that the proposed method can fully utilize the flexible power flow regulation ability of the multi-terminal SOP to reduce system loss, mitigate voltage unbalance and guarantee system robust security in the presence of RES output uncertainty.

Short-Term Spatio-Temporal Drought Forecasting Using Random Forests Model at New South Wales, Australia

Droughts can cause significant damage to agriculture and water resources, leading to severe economic losses and loss of life. One of the most important aspect is to develop effective tools to forecast drought events that could be helpful in mitigation strategies. The understanding of droughts has become more challenging because of the effect of climate change, urbanization and water management; therefore, the present study aims to forecast droughts by determining an appropriate index and analyzing its changes, using climate variables. The work was conducted in three different phases, first being the determination of Standard Precipitation Evaporation Index (SPEI), using global climatic dataset of Climate Research Unit (CRU) from 1901–2018. The indices are calculated at different monthly intervals which could depict short-term or long-term changes, and the index value represents different drought classes, ranging from extremely dry to extremely wet. However, the present study was focused only on forecasting at short-term scales for New South Wales (NSW) region of Australia and was conducted at two different time scales, one month and three months. The second phase involved dividing the data into three sample sizes, training (1901–2010), testing (2011–2015) and validation (2016–2018). Finally, a machine learning approach, Random Forest (RF), was used to train and test the data, using various climatic variables, e.g., rainfall, potential evapotranspiration, cloud cover, vapor pressure and temperature (maximum, minimum and mean). The final phase was to analyze the performance of the model based on statistical metrics and drought classes. Regarding this, the performance of the testing period was conducted by using statistical metrics, Coefficient of Determination (R2) and Root-Mean-Square-Error (RMSE) method. The performance of the model showed a considerably higher value of R2 for both the time scales. However, statistical metrics analyzes the variation between the predicted and observed index values, and it does not consider the drought classes. Therefore, the variation in predicted and observed SPEI values were analyzed based on different drought classes, which were validated by using the Receiver Operating Characteristic (ROC)-based Area under the Curve (AUC) approach. The results reveal that the classification of drought classes during the validation period had an AUC of 0.82 for SPEI 1 case and 0.84 for SPEI 3 case. The study depicts that the Random Forest model can perform both regression and classification analysis for drought studies in NSW. The work also suggests that the performance of any model for drought forecasting should not be limited only through statistical metrics, but also by examining the variation in terms of drought characteristics.

Weather, pollution and biotic factors drive net forest - atmosphere exchange of CO2 at different temporal scales in a temperate-zone mixed forest

Understanding the drivers of net ecosystem exchange of carbon (NEE) between forests and the atmosphere is crucial for the prediction of future global carbon dynamics. We therefore analyzed the long-term (1999–2014) ecosystem carbon fluxes of a mixed coniferous/deciduous forest (Brasschaat forest) in the Campine ecoregion of Belgium. The carbon uptake of this forest increased over the 16-year study period. By consecutively performing time series decomposition and the statistical technique of random forests, the correlative strength between multiple meteorological drivers, tropospheric pollutants and biotic indices with NEE was quantified at different time scales: i.e., long-term, seasonal and weekly, and separately for day- and nighttime NEE fluxes. The drivers that were most correlated with the trend in carbon sink capacity were the increasing atmospheric CO2 level and soil recovery from acidification. The radiation-saturated carbon uptake increased remarkably and explained much of the long-term variability of daytime NEE. When the long-term and seasonal variation were extracted the remaining weekly variation in daytime NEE was most strongly correlated with variation in the incoming radiation and cloudiness, and to a lesser extent by variation in vapor pressure deficit. In contrast to daytime NEE, nighttime NEE did not show a steady trend over time, but fluctuated, peaking in 1999 and in 2011. The long-term variability in nighttime NEE was most strongly correlated with the groundwater table depth. Air temperature was highly correlated to the seasonal as well as to the remaining weekly variation, i.e., after removal of the long-term and seasonal variability, in nighttime NEE. Biotic drivers (e.g., quantum yield and radiation-saturated carbon uptake) explained less of the variation in NEE on a seasonal and short-term scale, but were more important at the long term.

Transmitter-Oriented Dual-Mode SWIPT With Deep-Learning-Based Adaptive Mode Switching for IoT Sensor Networks

In this article, we propose a dual-mode simultaneous wireless information and power transfer (SWIPT) system with a deep-learning-based adaptive mode switching (MS) algorithm to exploit both advantages of single-tone and multitone SWIPT. For self-powering of low-energy Internet-of-Things (IoT) devices, a duty-cycling operation is used with nonlinear energy harvesting. For this, we employ a new energy-assisted single-tone modulation which simplifies the receiver structure for information decoding. Considering the symbol-error rate performance, we formulate an adaptive MS problem to maximize the achievable rate under the energy-causality constraint by adjusting the MS threshold. To relieve the computational burden of the receiver, we introduce asymmetric processing for adaptive MS, for which the transmitter adapts the communication mode based on the feedback from the receiver. We invoke deep learning for adaptive MS at the transmitter that iteratively updates the MS threshold in a long-term scale via deep long short-term memory (LSTM) recurrent neural network (RNN) while deciding on the communication mode and modulation index in a short-term scale. We demonstrate the achievable rate improvement under an energy-neutral operation while providing interesting insights into designing the adaptive MS algorithm for the dual-mode SWIPT system.

Multitemporal Variability in the Morphodynamics of a River-Estuarine Island

Lobo, F.C.; Peixoto, H.J.C.; Borba, T.A.; Monteiro, S.M., and Rollnic, M., 2020. Multitemporal variability in a river-estuarine island. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 865–869. Coconut Creek (Florida), ISSN 0749-0208.Cotijuba Island, in Northern Brazil (1°14.249′S, 48°32.806′W), has typical characteristics of Amazon lowlands, such as low and smooth topography and the presence of cliffs surrounding its river-estuarine beaches. The beach environment on this island has morphological variations determined by hydrodynamics of the Para River estuary (mesotides and mean annual outflow of 104 m3s-1), climatic seasonality in the region, and anthropogenic changes. Thus, it was investigated how coastal morphological changes occur on the beaches of Cotijuba Island at different temporal scales, considering regional seasonality. The polygons methodology was used based on Landsat satellite images taken in 1987, 1998, 2008, and 2017, and on in situ measurements of morphological variations of the beaches in 2018 using topographic profiles taken with a digital level GEOMAX ZDL 700, covering 8.7 km of beaches that are bathed by the Para River estuary. Cotijuba Island seems to have little or no influence from long-term climatic conditions, as the ENSO phenomena. Although climatic influence is evident at short-term temporal scales, it is worth emphasizing that the physiographic features of the beaches, such as the presence of perennial and temporary channels, and of rocky outcrops, also affect morphodynamic changes on the beaches, especially at a small temporal scale.

Reactive and pre-emptive spatial cohesion in a social primate

Spatial cohesion in group-living animals is assumed as a risk-sensitive characteristic. Few studies have explicitly investigated this assumption or asked whether risk-related changes in spatial cohesion operate over short-term or long-term scales. We explored whether two groups of wild samango monkeys, Cercopithecus albogularis schwarzi, would adjust cohesion in reaction to naturally occurring risk from eagles and intergroup encounters using the number of conspecific neighbours as our response. Data on these directly observed encounters were used to assess reactive responses to immediate events. GPS-recorded locations of these encounters allowed us to create relative risk landscapes to investigate whether these groups might pre-emptively increase cohesion in high risk locations, in the absence of a direct threat. Multimodel inference was used to compare support for candidate models representing biological hypotheses. We found support for changes in cohesion in reaction to immediate intergroup conflict in both study groups. In contrast, only eagle risk apparently elicited a pre-emptive response. These results suggest that spatial cohesion is risk sensitive, but that responses differ between types of risk and between groups.

Zooplankton community modulated by spatial and tidal changes in the Bahía Blanca Estuary, Argentina

The variability of the zooplankton community over a spatial and a tidal scale, the relationship between physico-chemical variables, and the abundance of zooplankton were studied in the temperate and turbid Bahía Blanca Estuary (Southwestern Atlantic, Argentina). Samples were taken by pumps during 12-h tidal cycles, at 3-h intervals, from two depths and three sites across the main channel in the inner and middle estuary zones. The zooplankton was dominated by the copepods Acartia tonsa and Euterpina acutifrons, and larvae of the invasive Pacific oyster Crassostrea gigas. The physico-chemical variables that most influenced the zooplankton community were salinity, temperature, suspended particulate matter, and chlorophyll a Most taxa showed higher abundances in the inner estuary, which corresponds to a turbid and shallow area. The abundance of A. tonsa was higher in the inner estuary while that of E. acutifrons was higher in the middle estuary. This spatial distribution is consistent with the tidal distribution of these species as in the inner estuary the abundance of A. tonsa peaked at ebb tide and that of E. acutifrons peaked at flood tide. Significant differences in the zooplankton community structure both across the channel and between depths were detected by multivariate analyses, but no clear patterns were found in the abundance of each taxon by a species-level analysis. The results provide insight into the patterns of zooplankton distribution and abundance in a temperate, turbid, and human-impacted coastal ecosystem, considering different spatial and short-term scales. These results will be useful to design efficient sampling programmes in highly dynamic environments.