Multiple Temporal Scales Research Articles

Albedo-Induced Global Warming Impact at Multiple Temporal Scales within an Upper Midwest USA Watershed

Land surface albedo is a significant regulator of climate. Changes in land use worldwide have greatly reshaped landscapes in the recent decades. Deforestation, agricultural development, and urban expansion alter land surface albedo, each with unique influences on shortwave radiative forcing and global warming impact (GWI). Here, we characterize the changes in landscape albedo-induced GWI (GWIΔα) at multiple temporal scales, with a special focus on the seasonal and monthly GWIΔα over a 19-year period for different land cover types in five ecoregions within a watershed in the upper Midwest USA. The results show that land cover changes from the original forest exhibited a net cooling effect, with contributions of annual GWIΔα varying by cover type and ecoregion. Seasonal and monthly variations of the GWIΔα showed unique trends over the 19-year period and contributed differently to the total GWIΔα. Cropland contributed most to cooling the local climate, with seasonal and monthly offsets of 18% and 83%, respectively, of the annual greenhouse gas emissions of maize fields in the same area. Urban areas exhibited both cooling and warming effects. Cropland and urban areas showed significantly different seasonal GWIΔα at some ecoregions. The landscape composition of the five ecoregions could cause different net landscape GWIΔα.

The Multiscale Dynamics of Beat-to-Beat Blood Pressure Fluctuation Mediated the Relationship Between Frailty and Arterial Stiffness in Older Adults.

Beat-to-beat blood pressure (BP) is an important cardiovascular output and regulated by neurophysiological elements over multiple temporal scales. The multiscale dynamics of beat-to-beat BP fluctuation can be characterized by "BP complexity" and has been linked to age-related adverse health outcomes. We here aimed to examine whether BP complexity mediates the association between arterial stiffness and frailty. This cross-sectional study was completed between January and October 2021. A total of 350 older adults completed assessments for frailty, arterial stiffness (ie, average brachial-ankle pulse wave velocity), and beat-to-beat finger BP. The complexity of beat-to-beat systolic blood pressure (SBP) and diastolic blood pressure (DBP) BP series was measured using multiscale entropy. The relationships between frailty, BP complexity, and arterial stiffness were examined using analysis of variance and linear regression models. The effects of BP complexity on the association between arterial stiffness and frailty were examined using mediation analyses. Compared with non-frail, prefrail, and frail groups had significantly elevated lower SBP and DBP complexity (F > 11, p < .001) and greater arterial stiffness (F = 16, p < .001). Greater arterial stiffness was associated with lower BP complexity (β < -0.42, p < .001). Beat-to-beat SBP and DBP complexity mediated the association between arterial stiffness and frailty (indirect effects >0.28), accounting for at least 47% of its total effects on frailty (mediated proportion: SBP: 50%, DBP: 47%). This study demonstrates the association between BP complexity and frailty in older adults, and BP complexity mediates the association between arterial stiffness and frailty, suggesting that this metric would serve as a marker to help characterize important functions in the older adults.

Understanding the impact of temporal scale on human movement analytics

Movement is manifested through a series of patterns at multiple spatial and temporal scales. Movement data today are becoming available at increasingly fine-grained temporal granularity. These observations often represent multiple behavioral modes and complex patterns along the movement path. However, the relationships between the observation scale of movement data and the analysis scales at which movement patterns are captured remain understudied. This article aims at investigating the role of temporal scale in movement data analytics. It takes up an important question of “how do decisions surrounding the scale of movement data and analyses impact our inferences about movement patterns?” Through a set of computational experiments in the context of human movement, we take a systematic look at the impact of varying temporal scales on common movement analytics techniques including trajectory analytics to calculate movement parameters (e.g., speed, path tortuosity), estimation of individual space usage, and interactions analysis to detect potential contacts between multiple mobile individuals.

Urban sprawl facilitates invasions of exotic plants across multiple spatial scales

Exotic plant invasions are considered one of the major threats to biodiversity causing important impacts at the population, community, and ecosystem levels. Understanding the drivers of plant invasions across multiple spatial and temporal scales often requires a landscape approach. The effect of landscape composition on biological invasion has been extensively studied, whereas landscape configuration effects were seldom considered or the analyses were limited to single species. Here, we aimed to analyze how the expansion of urban and agricultural areas can affect exotic species richness (both neophytes and archaeophytes) at three spatial scales, namely regional (scale: 37.5 km2), landscape (scale: 7.1 km2) and local (scale: 100 m2). We considered the possible contribution of urban and agricultural areas both in terms of composition (i.e. habitat cover) and configuration (i.e. shape complexity of patches). First, we found that increasing urbanization coupled with high shape complexity of urban elements were major drivers of both neophyte and archaeophyte invasions across heterogeneous landscapes. In particular, shape complexity seemed to be a key driver of plant invasions at large spatial scale, whereas the type of recipient habitat and urban cover determined the exotic success at the patch level. Second, archaeophytes were also affected by agriculture land use, i.e. agricultural patches shape complexity increased their spread at both regional and landscape scales. High shape complexity of highly disturbed habitats is expected to increase the exchange surface that exotic plant use to spread their propagules across the landscape mosaics. Our findings suggest that urban planning aimed at curbing urban fragmentation by both reducing shape complexity and diffuse urban sprawl might greatly improve the resistance of landscapes to biological invasions.

Modelling Impacts of Environmental Water on Vegetation of a Semi-Arid Floodplain–Lakes System Using 30-Year Landsat Data

River floodplains are among the most dynamic and diverse ecosystems on the planet. They are at risk of degradation due to river regulation and climate change. Environmental water has been delivered to floodplains to maintain environmental health by mimicking natural floods. It is important to understand the long-term effects of environmental water to floodplain vegetation to support its management. This study used Normalized Differences Vegetation index (NDVI) from the 30-year Landsat datasets of the Hattah Lakes floodplain in Australia to investigate the drivers of vegetation dynamics. We developed generalized additive mixed models (GAMM) to model responses of vegetation to environmental water, natural floods, precipitation, temperature, and distance to water across multiple spatial and temporal scales. We found the effect of environmental water on floodplain vegetation to be quite different from that of natural floods in both space and time. Vegetation in most areas of Hattah Lakes will respond to natural floods within one month of flooding, while positive responses to environmental water occur 1 to 3 months after inundation and are more restricted spatially. For environmental water planning, managers need to be aware of these differences. The implementation of new infrastructure to transport or retain environmental water on floodplains needs to be planned carefully, with continuous monitoring of rainfall and natural floods. Whilst environmental floods do not mimic the effect of natural floods, they do provide some positive benefits that can partially offset effects of reduced natural floods.

A network information theoretic framework to characterise muscle synergies in space and time

Objective. Current approaches to muscle synergy extraction rely on linear dimensionality reduction algorithms that make specific assumptions on the underlying signals. However, to capture nonlinear time varying, large-scale but also muscle-specific interactions, a more generalised approach is required. Approach. Here we developed a novel framework for muscle synergy extraction that relaxes model assumptions by using a combination of information and network theory and dimensionality reduction. We first quantify informational dynamics between muscles, time-samples or muscle-time pairings using a novel mutual information formulation. We then model these pairwise interactions as multiplex networks and identify modules representing the network architecture. We employ this modularity criterion as the input parameter for dimensionality reduction, which verifiably extracts the identified modules, and also to characterise salient structures within each module. Main results. This novel framework captures spatial, temporal and spatiotemporal interactions across two benchmark datasets of reaching movements, producing distinct spatial groupings and both tonic and phasic temporal patterns. Readily interpretable muscle synergies spanning multiple spatial and temporal scales were identified, demonstrating significant task dependence, ability to capture trial-to-trial fluctuations and concordance across participants. Furthermore, our framework identifies submodular structures that represent the distributed networks of co-occurring signal interactions across scales. Significance. The capabilities of this framework are illustrated through the concomitant continuity with previous research and novelty of the insights gained. Several previous limitations are circumvented including the extraction of functionally meaningful and multiplexed pairwise muscle couplings under relaxed model assumptions. The extracted synergies provide a holistic view of the movement while important details of task performance are readily interpretable. The identified muscle groupings transcend biomechanical constraints and the temporal patterns reveal characteristics of fundamental motor control mechanisms. We conclude that this framework opens new opportunities for muscle synergy research and can constitute a bridge between existing models and recent network-theoretic endeavours.

Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges

AbstractObserving the environment in the vast regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including National Aeronautics and Space Administration’s Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low‐stature plants and very fine‐scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long‐term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi‐scale spectroscopy, from lab studies to satellites that enable frequent and continuous long‐term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics.

Evaluation of gridded precipitation datasets over international basins and large lakes

Reliable precipitation estimates are a crucial component for hydrologic modeling and hydro-climate applications. However, watersheds that extend across international boundaries or those that contain large bodies of water pose particular challenges to the acquisition of consistent and accurate precipitation estimates. The North American Great Lakes basin is characterized by both of these features, which has led to long-standing challenges to water budget analysis and hydrologic prediction. In order to provide optimal conditions for hydrologic model calibration, retrospective analyses, and real-time forecasting, this study comprehensively evaluates four gridded datasets over the Great Lakes basin, including the Analysis of Record for Calibration (AORC), Canadian Precipitation Analysis (CaPA), Multi-sensor Precipitation Estimate (MPE), and a merged CaPA-MPE. These products are analyzed at multiple spatial (overland, overlake, sub-basin, country) and temporal (daily, monthly, annual) scales using station observations and a statistical water balance model. In comparison with gauge observations from the Global Historical Climatology Network Daily (GHCN-D), gridded datasets generally agree with ground observations, however the international border clearly delineates a decrease in gridded precipitation accuracy over the Canadian portion of the basin. Analysis reveals that rank in gridded precipitation accuracy differs for overland and overlake regions, and between colder and warmer months. Overall, the AORC has the lowest variance compared to gauge observations and has greater performance over temporal and spatial scales. While CaPA and AORC may better capture atmospheric dynamics between land and lake regions, comparison with a statistical water balance model suggests that AORC and MPE provide the best estimates of monthly overlake precipitation.

Landscapes of forgetting and structural silence in the American Southeast

AbstractArchaeological studies of memory have shown how the past is continually resurrected through selective practices of remembrance. Strategies of social amnesia have received less attention, however. The US southeast provides a useful vantage point for exploring how landscape and the built environment legislated acts of forgetting at multiple spatial and temporal scales. Whereas platform mounds and plazas mediated tensions between remembrance and erasure within communities prior to the arrival of Europeans, later colonial encroachments on Indigenous lands led to the increasing importance of these features as anchors of memory. This shifting, topological record embodied heterogenous, disjunctive framings of temporality.

Global holiday datasets for understanding seasonal human mobility and population dynamics

Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010–2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements.

A method for identifying critical time windows of maternal air pollution exposures associated with low birth weight in offspring using massive geographic data.

Associations between maternal exposures to air pollutants and low birth weight (LBW) in offspring varied when different exposure windows were considered. Methods used in previous studies lacked flexibility in delineating exposure windows and did not consider time periods before conception, which may restrict the discoveries of critical exposure windows. This study introduces a novel method to identify critical windows of maternal air pollution exposures associated with LBW in offspring using massive georeferenced data. Through a case-control study based on birth data (94,106 LBW cases and 376,424 controls) and air quality monitoring data (367 chemicals) in Texas during 1996-2008, this study used the average ambient concentration measured by the monitoring site closest to the residence location of a mother during a time window as the maternal exposure to a specific chemical during that exposure window. Binary logistic regression was utilized to estimate air pollutant-LBW associations in different exposure windows. The odds ratios (ORs) were adjusted for child's sex, gestational weeks, maternal age, race/ethnicity, and education. The adjusted ORs were plotted against the exposure window series of different sizes for each chemical, aiming at interactively visualizing and exploring the critical exposure windows across multiple temporal scales. This study identifies ten chemicals and seventeen corresponding critical exposure windows where strong air pollutant-LBW associations are detected. The ten identified chemicals are benzaldehyde, sum of Photochemical Assessment Monitoring Stations (PAMS) target compounds, n-undecane, m-tolualdehyde, organic carbon fraction 2 (OC2), ethylene dibromide, valeraldehyde, propionaldehyde, 4-methyl-1-pentene, and zirconium. Nine critical exposure windows involving six chemicals start more than fivemonths prior to conception, seven windows involving five chemicals commence in the second and/or third trimester of pregnancy, and the remaining one window is located in other time periods. The novel method reveals a number of critical time windows of maternal exposure to ten chemicals that are positively associated with LBW in offspring. These ten chemicals were identified as LBW risk factors for the first time. Additional studies with more data are needed to validate the results in the future.

SNP-based phylogenomic inference in Holarctic ground squirrels (Urocitellus)

Resolution of rapid evolutionary radiatons requires harvesting maximal signal from phylogenomic datasets. However, studies of non-model clades often target conserved loci that are characterized by reduced information content, which can negatively affect gene tree precision and species tree accuracy. Single nucleotide polymorphism (SNP)-based methods are an underutilized but potentially valuable tool for estimating phylogeny and divergence times because they do not rely on resolved gene trees, allowing information from many or all variant loci to be leveraged in species tree reconstruction. We evaluated the utility of SNP-based methods in resolving phylogeny of Holarctic ground squirrels (Urocitellus), a radiation that has been difficult to disentangle, even in prior phylogenomic studies. We inferred phylogeny from a dataset of >3,000 ultraconserved element loci (UCEs) using two methods (SNAPP, SVDquartets) and compared our results with a new mitogenome phylogeny. We also systematically evaluated how phasing of UCEs improves per-locus information content, inference of topology, and other parameters within each of these SNP-based methods. Phasing improved topological resolution and branch length estimation at shallow levels (within species complexes), but less so at deeper levels, likely reflecting true uncertainty due to ancestral polymorphisms segregating in rapidly diverging lineages. We resolved key clades in Urocitellus and present targeted opportunities for future phylogenomic inquiry. Our results also extend the roadmap for use of SNPs to address vertebrate radiations and inform comparative analyses at multiple temporal scales.

Multi-Objective Optimization Models to Design a Responsive Built Environment: A Synthetic Review

A synthetic review of the application of multi-objective optimization models to the design of climate-responsive buildings and neighbourhoods is carried out. The review focused on the software utilized during both simulation and optimization stages, as well as on the objective functions and the design variables. The hereby work aims at identifying knowledge gaps and future trends in the research field of automation in the design of buildings. Around 140 scientific journal articles, published between 2014 and 2021, were selected from Scopus and Web of Science databases. A three-step selection process was applied to refine the search terms and to discard works investigating mechanical, structural, and seismic topics. Meta-analysis of the results highlighted that multi-objective optimization models are widely exploited for (i) enhancing building’s energy efficiency, (ii) improving thermal and (iii) visual comfort, minimizing (iv) life-cycle costs, and (v) emissions. Reviewed workflows demonstrated to be suitable for exploring different design alternatives for building envelope, systems layout, and occupancy patterns. Nonetheless, there are still some aspects that need to be further enhanced to fully enable their potential such as the ability to operate at multiple temporal and spatial scales and the possibility of exploring strategies based on sector coupling to improve a building’s energy efficiency.

An Operational Methodology for Validating Satellite-Based Snow Albedo Measurements Using a UAV

Snow albedo is highly variable over multiple temporal and spatial scales. This variability is more pronounced in areas that experience seasonal snowpack. Satellite retrievals, physically based models and parameterizations for snow albedo all require ground-based measurements for calibration, initialization, and validation. Ground measurements are generally made using upward and downward-facing pyranometers at opportunistically located weather stations that are sparsely distributed, particularly in mountainous regions. These station-based measurements cannot capture the spatial variability of albedo across the land surface. Uncrewed Aerial Vehicles (UAVs) equipped with upward and downward-facing pyranometers provide near-surface measurements of broadband albedo that are spatially distributed across landscapes, offering improvements over in-situ sensors. At the hillslope to watershed scale albedo measurements from UAVs taken over heterogeneous terrain are a function of the spatial variability in albedo and topography within the downward-facing sensor’s field-of-view (FOV). In this research we propose methods for topographic correction of UAV snow albedo measurements and comparison to gridded satellite albedo products. These methods account for the variability of surface topography and albedo within the sensor FOV, sensor tilt, and the angular response of pyranometers. We applied the proposed methodologies to UAV snow albedo measurements collected over an alpine meadow in southwest Montana, United States (45.23°, −111.28°). Sensitivity analyses were conducted to determine the effect of altering the processing FOV (PFOV) for both topographic corrections and comparison to coincident Landsat 8-derived albedo measurements. Validation from ground-based albedo measurements showed the topographic correction to reduce albedo measurement error considerably over mildly sloping terrain. Our sensitivity analyses demonstrated that outcomes from the topographic correction and satellite comparison are highly dependent on the specified PFOV. Based on field observations and analyses of UAV albedo measurements made at different altitudes, we provide guidelines for strategizing future UAV albedo surveys. This research presents considerable advances in the standardization of UAV-based albedo measurement. We establish the foundation for future research to utilize this platform to collect near-surface validation measurements over heterogeneous terrain with high accuracy and consistency.

Canopy Solar-Induced Chlorophyll Fluorescence and Its Link to Transpiration in a Temperate Evergreen Needleleaf Forest during the Fall Transition

Northern hemisphere evergreen needleleaf forest (ENF) contributes a significant fraction of global water exchange but regional transpiration (T) observation in ENF ecosystems is still challenging. Traditional remote sensing techniques and terrestrial biosphere models reproduce the transpiration seasonality with difficulty, and with large uncertainties. Solar-induced chlorophyll fluorescence (SIF) emission from vegetation correlates to photosynthesis at multiple spatial and temporal scales. However, how SIF links to transpiration of evergreen forest during seasonal transition is unclear. Here, we explored the relationship between canopy SIF and T retrieved from ground observation towers in ENF. We also examined the role of meteorological and soil factors on the relationship between SIF and T. A slow decrease of SIF and T with a fast reduction in photosynthetically active radiation (PAR), air temperature, vapor pressure deficit (VPD), soil temperature and soil water content (SWC) were found in the ENF during the fall transition. The correlation between SIF and T at hourly and daily scales varied significantly among different months (Pearson correlation coefficient = 0.29–0.68, p &lt; 0.01). SIF and T were significantly linearly correlated at hourly (R2 = 0.53, p &lt; 0.001) and daily (R2 = 0.67, p &lt; 0.001) timescales in the October. Air temperature and PAR were the major moderating factors for the relationship between SIF and T in the fall transition. Soil water content (SWC) influenced the SIF-T relationship at an hourly scale. Soil temperature and VPD’s effect on the SIF-T relationship was evident at a daily scale. This study can help extend the possibility of constraining ecosystem T by SIF at an unprecedented spatiotemporal resolution during season transitions.

Multi-spatiotemporal heterogeneous legacy effects of climate on terrestrial vegetation dynamics in China

ABSTRACT Investigating vegetation–climate interactions is critical for understanding behavioral patterns of terrestrial ecosystems and formulating food security strategies. However, the multi-spatiotemporal legacy effects of climate on terrestrial vegetation remain unclear. In this study, we examined the dynamic trends of vegetation distribution and climatic factors at multiple temporal and spatial scales. Moreover, using cross-wavelet transform, wavelet coherence transform, and partial correlation analysis, a paradigm framework was established to determine the multi-spatiotemporal legacy effect of climate on vegetation in China in 2000–2019, as well as the response of different vegetation types. The results indicate a significant greening trend in China, accompanied by a warming and wetting pattern over the past 20 years. The phase difference of the wavelet coherence transform in the time-frequency domain revealed remarkable legacy effects and regional variations, indicating a complicated relationship between vegetation and climate. Meanwhile, different vegetation types exhibited heterogeneous responses of legacy effect of precipitation and temperature on multi-spatiotemporal scales; moreover, the lag time in spring was shorter than that in summer and autumn. The average legacy effect on different vegetation types was approximately 1–2 months. Therefore, the heterogeneity of the legacy effects is a complicated process of dynamic variation, which can be summarized as the comprehensive characteristics of vegetation response to climate with regional discrepancy, ecosystem category, and multi-temporal scale. These findings advance our understanding regarding the preference of vegetation to hydrothermal conditions across biomes and ecosystems and provide a future framework for elucidating the dynamic response of vegetation to other more complex factors in this warmer world. Furthermore, our results emphasize that multi-spatiotemporal legacy effects should be incorporated into the vegetation–climate interaction model and the formulation of macro-environmental management policies.

Evaluation of GPM-IMERG rainfall estimates at multiple temporal and spatial scales over Greece

Accurate precipitation data in space and time are among the most important variables for analyzing, modelling and forecasting many hydrometeorological applications and hazards. For countries like Greece, which have a complex terrain and great spatial variability of precipitation, satellite precipitation products (SPPs) are an alternative source of precipitation information that could complement the sparse rain gauge network. The objective of this study is to evaluate the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B Final-run product over Greece for a 5-year period (January 2015 to December 2019) using as reference measurements from 263 rain gauges. The evaluation of IMERG is performed at multiple temporal (annual, monthly, daily) and spatial (country, region, grid) scales by employing several statistical and graphical methods. The rainfall detection capability of IMERG is examined with categorical verification metrics and the accuracy of the estimated rainfall amounts with continuous verification metrics. In addition, the performance of IMERG under different rainfall intensities, elevations and seasons is examined.On the monthly and annual scale evaluation the intercomparison between the calibrated (IMERGcal) and uncalibrated (IMERGuncal) IMERG estimates revealed the superiority of IMERGcal, but on the same time it highlighted the weakness of the GPCC gauge-based calibration product over Greece. The monthly IMERGcal estimates could reproduce the annual cycle of precipitation with a slight overestimation of monthly precipitation (RBIAS = 5.1%). On a daily scale IMERGcal performed well on detecting rainfall events (POD = 0.77), but it also produced many false alarm events (FAR = 0.40) which are primarily found over the islands of Aegean and Ionian Sea and along the coastline of mainland Greece. The assessment of daily rainfall intensities by IMERGcal showed a moderate correlation across Greece (0.49), with overestimation of light and moderate precipitation events (0.4–20 mm/day) and underestimation of heavy rainfall events (>20 mm/day). On a regional scale, IMERGcal tends to underestimate the daily rainfall over the rainfall-prone western part of Greece because in addition to the smoothing of high rainfall values by the GPCC calibration, satellite sensors fail to detect the magnitude of orographic rainfall. The complete opposite situation was found over the Aegean and Ionian Sea and many areas of the coastline, with great overestimation of rainfall. The results of our study highlight the potentials and limitations of IMERG rainfall estimates over Greece and they can be very helpful for future users of IMERG in Greece.

Multi-scale temporal variation of marine femtoplankton and picophytoplankton: the role of size and environment

Femtoplankton and picophytoplankton organisms exert a major role in the balance between producers and consumers and are responsible for a large part of net primary production in the ocean. However, despite their ecological importance, the magnitude and drivers of their temporal dynamics remain largely unexplored. To address this significant knowledge gap, we performed weekly sampling over ten months in a wind-driven coastal upwelling area in the subtropical South Atlantic Ocean. We combined this intensive feldwork with multi-color fow cytometry and statistical modeling to investigate the temporal changes of both femto- and picophytoplankton at multiple temporal scales. We found that femtoplanktonic organisms (including virus-like particles) responded faster (i.e., without a temporal lag) to environmental changes, mainly related to chlorophyll-a (chl-a) and phaeopigment variations. On the other hand, picophytoplanktonic organisms showed a slower response to environmental changes, with positive responses to variation in pH and NH4 concentrations after a one-week lag. Overall, our results demonstrate that the speed of response of planktonic organisms to environmental changes may be dependent on their size, which highlights the importance of environmental variables and biological interactions as drivers of their temporal dynamics.

Ecological and Coevolutionary Dynamics in Modern Markets Yield Nonstationarity in Market Efficiencies

The U.S. stock market is one of the largest and most complex marketplaces in the global financial system. Over the past several decades, this market has evolved at multiple structural and temporal scales. New exchanges became active, and others stopped trading, regulations have been introduced and adapted, and technological innovations have pushed the pace of trading activity to blistering speeds. These developments have supported the growth of a rich machine‐trading ecology that leads to qualitative differences in trading behavior at human and machine time scales. We conduct a longitudinal analysis of comprehensive market data to quantify nonstationary dynamics throughout this system. We quantify the relationship between fluctuations in the number of active trading venues and realized opportunity costs experienced by market participants. We find that information asymmetries, in the form of quote dislocations, predict market‐wide volatility indicators. Lastly, we uncover multiple micro‐to‐macro level pathways, including those exhibiting evidence of self‐organized criticality.

Vertical distribution patterns of ichthyoplankton in temperate waters of New Zealand

Multifactorial sampling designs were used to determine the vertical distribution of ichthyoplankton at multiple temporal and spatial scales in New Zealand. Hypotheses concerning the vertical distribution of fish larvae were tested in the following: depth strata, surface, near-surface, mid-depth and deep, and near the substratum. The consistency of abundance patterns was examined at three sites separated by 2–20 km over 2 months. We also tested for differences in shallow water columns of two depths (20 and 40 m) and both day and night. Although peak abundance of total larval fish was found at upper and lower strata, regardless of total depth of the water column, consistent taxa specific patterns of vertical distribution were also found. Some taxa were most abundant at the surface (e.g. mullids, hemiramphids, and kyphosids), whereas others were found at multiple depths below the surface and throughout the water column, regardless of site, time or depth of water column (e.g. carangids, engraulids, clupeids, scombrids, sparids and pleuronectids). Some taxa were most abundant in shallow water columns (e.g. mullids, tripterygiids and gobiids). Rank abundance by depth stratum for non-surface dwelling species varied among sites and times. Diel vertical movements were detected, some taxa (e.g. clupeids, scombrids and bothids) that were most abundant at the surface at night whereas for other taxa this pattern was more variable (e.g. carangids). We conclude that diel depth-related patterns in shallow water columns will influence interactions among taxa and the importance of different transport mechanisms for larval transport.