Temporal Dynamics Research Articles

Better early than late: the temporal dynamics of pointing cues during cross-situational word learning

Abstract Learning the meaning of a word is a difficult task due to the variety of possible referents present in the environment. Visual cues such as gestures frequently accompany speech and have the potential to reduce referential uncertainty and promote learning, but the dynamics of pointing cues and speech integration are not yet known. If word learning is influenced by when, as well as whether, a learner is directed correctly to a target, then this would suggest temporal integration of visual and speech information can affect the strength of association of word–referent mappings. Across two pre-registered studies, we tested the conditions under which pointing cues promote learning. In a cross-situational word learning paradigm, we showed that the benefit of a pointing cue was greatest when the cue preceded the speech label, rather than following the label (Study 1). In an eye-tracking study (Study 2), the early cue advantage was due to participants’ attention being directed to the referent during label utterance, and this advantage was apparent even at initial exposures of word–referent pairs. Pointing cues promote time-coupled integration of visual and auditory information that aids encoding of word–referent pairs, demonstrating the cognitive benefits of pointing cues occurring prior to speech.

Critical needs to close monitoring gaps in pan-tropical wetland CH4 emissions

Abstract Global wetlands are the largest and most uncertain natural source of atmospheric methane (CH4). The FLUXNET-CH4 synthesis initiative has established a global network of flux tower infrastructure, offering valuable data products and fostering a dedicated community for the measurement and analysis of methane flux data. Existing studies using the FLUXNET-CH4 Community Product v1.0 have provided invaluable insights into the drivers of ecosystem-to-regional spatial patterns and daily-to-decadal temporal dynamics in temperate, boreal, and Arctic climate regions. However, as the wetland CH4 monitoring network grows, there is a critical knowledge gap about where new monitoring infrastructure ought to be located to improve understanding of the global wetland CH4 budget. Here we address this gap with a spatial representativeness analysis at existing and hypothetical observation sites, using 16 process-based wetland biogeochemistry models and machine learning. We find that, in addition to eddy covariance monitoring sites, existing chamber sites are important complements, especially over high latitudes and the tropics. Furthermore, expanding the current monitoring network for wetland CH4 emissions should prioritize, first, tropical and second, sub-tropical semi-arid wetland regions. Considering those new hypothetical wetland sites from tropical and semi-arid climate zones could significantly improve global estimates of wetland CH4 emissions and reduce bias by 79% (from 76 to 16 TgCH4 y−1), compared with using solely existing monitoring networks. Our study thus demonstrates an approach for long-term strategic expansion of flux observations.

Supplemental Material for Closeness Regulation in Couples: The Temporal Dynamics and Dissociations of Closeness Frustration and Closeness Surfeit

Supplemental Material for Closeness Regulation in Couples: The Temporal Dynamics and Dissociations of Closeness Frustration and Closeness Surfeit

Temporal enrichment of comammox Nitrospira and Ca. Nitrosocosmicus in a coastal plastisphere.

Plastic marine debris is known to harbor a unique microbiome (termed the "plastisphere") that can be important in marine biogeochemical cycles. However, the temporal dynamics in the plastisphere and their implications for marine biogeochemistry remain poorly understood. Here, we characterized the temporal dynamics of nitrifying communities in the plastisphere of plastic ropes exposed to a mangrove intertidal zone. The 39-month colonization experiment revealed that the relative abundances of Nitrospira and Candidatus Nitrosocosmicus representatives increased over time according to 16S rRNA gene amplicon sequencing analysis. The relative abundances of amoA genes in metagenomes implied that comammox Nitrospira were the dominant ammonia oxidizers in the plastisphere, and their dominance increased over time. The relative abundances of two metagenome-assembled genomes of comammox Nitrospira also increased with time and positively correlated with extracellular polymeric substances content of the plastisphere but negatively correlated with NH4+ concentration in seawater, indicating the long-term succession of these two parameters significantly influenced the ammonia-oxidizing community in the coastal plastisphere. At the end of the colonization experiment, the plastisphere exhibited high nitrification activity, leading to the release of N2O (2.52ng N2O N g-1) in a 3-day nitrification experiment. The predicted relative contribution of comammox Nitrospira to N2O production (17.9%) was higher than that of ammonia-oxidizing bacteria (4.8%) but lower than that of ammonia-oxidizing archaea (21.4%). These results provide evidence that from a long-term perspective, some coastal plastispheres will become dominated by comammox Nitrospira and thereby act as hotspots of ammonia oxidation and N2O production.

Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning.

Different brain systems have been hypothesized to subserve multiple "experts" that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture differences between automaticity vs. deliberation. However, shifts in strategy cannot be captured by a static MoA. To investigate such dynamics, we present the mixture-of-agents hidden Markov model (MoA-HMM), which simultaneously learns inferred action values from a set of agents and the temporal dynamics of underlying "hidden" states that capture shifts in agent contributions over time. Applying this model to a multi-step, reward-guided task in rats reveals a progression of within-session strategies: a shift from initial MB exploration to MB exploitation, and finally to reduced engagement. The inferred states predict changes in both response time and OFC neural encoding during the task, suggesting that these states are capturing real shifts in dynamics.

Temporal dynamics of alpha and beta diversity of lake algae: Opposing patterns and influencing factors over the past 200 years

Abstract Better understanding of the responses of algal biodiversity to multiple pressures, such as climate warming and eutrophication, is a key issue in aquatic ecology. Alpha and beta diversity may have various patterns over temporal scales, especially in the Anthropocene, when external pressures became more multifaceted. However, the limited availability of historical data hampers the exploration of algal biodiversity through time. Recently, sediment DNA has emerged as a potential tool for elucidating temporal patterns in algal communities. Here, we used sediment DNA to reconstruct temporal turnover and diversity of algal communities in four remote lakes in northern China over the past 200 years. Furthermore, to distinguish the contributions of possible influencing environmental factors, we conducted structural equation modelling. Our results revealed that algal communities have experienced rapid shifts since the Anthropocene, characterized by increased alpha diversity and decreased temporal beta diversity. Warmer climate and eutrophication were associated with changes in alpha diversity, while temporal environmental variation was associated with temporal beta diversity. This study revealed opposing patterns in alpha and beta diversity for algal communities, possibly caused by warming, eutrophication and lower temporal environmental variation, respectively. While climatic factors played a major role in remote lakes with a natural environment, lakes that are more human impacted may be more structured by nutrient‐related factors. Under climate warming and intensified human activities, remote lakes may encounter complex pressures in the near future. Our findings offer valuable insights into patterns in aquatic biodiversity and possible factors underlying multiple pressures.

Simultaneous Probing of Electron Density and Temperature Dynamics Inside Air Plasma with Intense Terahertz Pulses

AbstractAir plasma induced by ultrafast laser pulses is an extraordinary source of electromagnetic waves, emitting microwave, terahertz (THz) radiation, and cavityless lasing in the near‐infrared and visible ranges. The temporal dynamics of the electron density have been revealed by optical pump‐probe techniques, while the evolution of the electron temperature remains elusive due to a lack of suitable methods. Here, it is demonstrated that the intense THz‐field‐enhanced fluorescence emission from the excited molecules of nitrogen is a novel tool that allows to explore the complex dynamics of the plasma density and electron temperature simultaneously. Two relaxation times of electrons in air plasma are observed and interpreted as a competition between the excitation of a triplet state by laser or THz‐field‐heated electrons and the dissociative recombination of nitrogen molecular ions. Based on the theoretical simulations, the tens of picoseconds relaxation process is attributed to the ultrafast temperature decrease, while the longer relaxation in the range of hundreds of picoseconds is ascribed to the decay of electron density. The temporal relaxation of both the electron density and temperature revealed by applying an intense THz field provides further insights into the laser‐air plasma interaction and will benefit the engineering of this exceptional source.

Temporal Dynamics of Pain: Examining the Relationship Between Intramuscular Injection Duration and Pain Perception: A Crossover Randomized Controlled Trial.

Background Effective pain management is crucial for optimizing the patient experience and satisfaction with care. Many factors affect the intensity of injection pain, and one of these factors is the duration of the injection. This study aimed to evaluate the impact of intramuscular injection duration on perceived pain intensity in adults. Methods Fifty participants received injections according to a predetermined research protocol. Each participant served as their own control group. The intervention group was administered the drug over 40 seconds. The control group was given the same drug on the other side dorsogluteal region the following day, according to the unit's routine, in 10 seconds. Pain intensity was measured using the Wong-Baker Faces Pain Rating Scale and the Numerical Rating Scale. Statistical analysis was performed using SPSS (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp), including Wilcoxon and correlation analyses, with a significance level set at p < 0.05. Results The Wilcoxon test revealed a significant decrease in pain scores for the intervention group (p < 0.05), indicating that longer injection durations were associated with lower pain intensity. Significant correlations were found between hospital phobia and the use of pain relief measures, with pain perception (p < 0.05). Conclusion This research establishes a correlation between extended durations of intramuscular injections and pain intensity. These findings have implications for pain management during medical procedures and can contribute to optimizing patient comfort.

Metaproteogenomics resolution of a high-CO2 aquifer community reveals a complex cellular adaptation of groundwater Gracilibacteria to a host-dependent lifestyle

BackgroundBacteria of the candidate phyla radiation (CPR), constituting about 25% of the bacterial biodiversity, are characterized by small cell size and patchy genomes without complete key metabolic pathways, suggesting a symbiotic lifestyle. Gracilibacteria (BD1-5), which are part of the CPR branch, possess alternate coded genomes and have not yet been cultivated. The lifestyle of Gracilibacteria, their temporal dynamics, and activity in natural ecosystems, particularly in groundwater, has remained largely unexplored. Here, we aimed to investigate Gracilibacteria activity in situ and to discern their lifestyle based on expressed genes, using the metaproteogenome of Gracilibacteria as a function of time in the cold-water geyser Wallender Born in the Volcanic Eifel region in Germany.ResultsWe coupled genome-resolved metagenomics and metaproteomics to investigate a cold-water geyser microbial community enriched in Gracilibacteria across a 12-day time-series. Groundwater was collected and sequentially filtered to fraction CPR and other bacteria. Based on 725 Gbps of metagenomic data, 1129 different ribosomal protein S3 marker genes, and 751 high-quality genomes (123 population genomes after dereplication), we identified dominant bacteria belonging to Gallionellales and Gracilibacteria along with keystone microbes, which were low in genomic abundance but substantially contributing to proteomic abundance. Seven high-quality Gracilibacteria genomes showed typical limitations, such as limited amino acid or nucleotide synthesis, in their central metabolism but no co-occurrence with potential hosts. The genomes of these Gracilibacteria were encoded for a high number of proteins involved in cell to cell interaction, supporting the previously surmised host-dependent lifestyle, e.g., type IV and type II secretion system subunits, transporters, and features related to cell motility, which were also detected on protein level.ConclusionsWe here identified microbial keystone taxa in a high-CO2 aquifer, and revealed microbial dynamics of Gracilibacteria. Although Gracilibacteria in this ecosystem did not appear to target specific organisms in this ecosystem due to lack of co-occurrence despite enrichment on 0.2-µm filter fraction, we provide proteomic evidence for the complex machinery behind the host-dependent lifestyle of groundwater Gracilibacteria.38kgAxaMce3Yn9ghud_S2pVideo

Prospective study on the impact of different antithrombotic therapies on subclinical leaflet thickening and its temporal dynamics in transcatheter aortic valves—The NOTION-4 trial

Transcatheter aortic valve replacement (TAVR) has become the standard-of-care treatment for a majority of patients with severe, symptomatic aortic stenosis. The post-procedural anti-thrombotic therapeutic management is still a topic of debate and could affect the incidence of HALT, a phenomenon which can be assessed by four-dimensional computed tomography (4DCT). The NOTION-4 trial is a randomized controlled trial comprising TAVR patients with no indication for oral anticoagulant (OAC) therapy, comparing lifelong single anti-platelet therapy (standard arm) versus early 3-month direct oral anticoagulant (DOAC) therapy followed by single anti-plateletet therapy (experimental arm). The incidence of HALT and clinical endpoints will be evaluated in both groups at 3 months, 1 year and 5 years after randomization. The primary endpoint is the number of patients with at least one bioprosthetic aortic valve leaflet with HALT as assessed by cardiac 4DCT imaging at 1 year. The trial is powered for superiority testing and started enrollment in 2021. In total, 324 patients will be included. The last patient is expected to be enrolled by the end of 2024 and the primary endpoint is to be presented in 2026. The NOTION-4 trial aims to study whether an early 3-month DOAC therapy after TAVR can result in a sustained lower incidence of HALT in transcatheter aortic valves. This trial holds the potential to give valuable insights into whether early OAC therapy should be integrated in future guidelines for post-TAVR anti-thrombotic therapeutic management. NOTION-4, ClinicalTrials.gov ID NCT06449469, https://clinicaltrials.gov/study/NCT06449469.

The temporal dynamics of perceived stress and depression in college students: An ecological momentary assessment.

Previous studies have implied that stress is a risk factor for depression, but relatively little is known about how healthy individuals' stress dynamically affects depression as a mood in daily life. Therefore, an ecological momentary assessment study was conducted among 141 college students to test the temporal dynamic effect of daily perceived stress on depression and the underlying mediating and moderating role of rumination. Perceived stress, state rumination, and depression were measured using self-compiled questionnaire three times a day over 12 days. Trait rumination was measured with the Nolen-Hoeksema Ruminative Response Scale. Hierarchical linear models with HLM 7.0 were adopted to examine the mediation and moderation effects. In the mediation model, the greater the daily perceived stress at time t, the higher the state rumination at time t + 1, and state rumination at t + 1 positively predicted college students' depression at t + 2. In the moderation model, trait rumination significantly facilitated perceived stress-induced depression. These results verified that daily perceived stress could affect college students' depression directly or indirectly through the critical mediating mechanism of state rumination, and this effect would be exacerbated with a higher level of trait rumination.

Breeding season temporal and spatial trends in continental-scale migration of the monarch butterfly.

The monarch butterfly (Danaus plexippus) is a vagile species that undertakes an annual, multi-generational migration across North America. The abundance of this species at both eastern and western overwintering sites in Central Mexico and California indicates a population decline. Success of continental-scale conservation programs for a migratory species depends on providing, maintaining, and protecting habitats at appropriate temporal and spatial scales. Here, dynamics of monarch continental-scale migration and gene flow were obtained by combined stable isotope, morphological, and genetic analyses. These analyses were applied to temporal monarch samples collected from May to September during 2016-2021 at locations in Iowa, USA and spatial collections from Pennsylvania, Delaware, Iowa, Ohio, Nevada, Idaho, Hawaii, 3 Australian locations during July and August 2016, and Texas in April 2021. Evidence of seasonal multi-generational migration was obtained through δ2H analyses of spatial collections, which was corroborated by decreased wing hue (a morphological marker for non-migratory individuals). In Iowa, 10-15% of monarchs represented migrants from southern areas throughout the breeding season and 6% were migrants from the North in midsummer. Limited sequence variation detected across the mitochondrial genome impacted the capability to detect significant population genetic variation in our North American samples. However, 2 novel substitutions were identified and predicted to be fixed among Australia samples, contributing to intercontinental differentiation from counterparts in North America. Our assessment of temporal and spatial population dynamics across the North American monarch breeding range provides insight into continental-scale migration and previously undetected mitochondrial DNA variation among globally distributed populations.

An Artificial Intelligence-Based Platform for Medical Diagnosis

Medical diagnosis using artificial intelligence models uses methods of information collection via the Internet of Things (IoT) for the categorization of diseases. This may be attributed to a number different issues, such as inefficient auxiliary frameworks, high expenses associated with acquiring datasets, or difficulties encountered while constructing classifiers. The present paper analyses the recent developments in AI for medical diagnosis. The research uses a Deep Convolutional Neural Network (DCNN) to categorise medical conditions. In this paper, a model for categorising medical diagnosis has been introduced by making it easier for the network to adapt to new medical data. This article estimates the degree to which the classification falls into the right medical diagnosis category and separates the training sample into normal, critical, and suggestion samples using a dynamic threshold to solve the diagnostic issue. The recommended strategy categorises input to help the convolutional neural network learn more. This research modifies the convolutional neural network design to accommodate for input variety and data evolution temporal dynamics. This adjustment is made with the goal of achieving the five physiological data properties of bio signals (heart rate, blood pressure, EEG, ECG, and oxygen level). Because of the realised CNN optimisation algorithm model, the prediction effect has been increased, and the accuracy rate has been found to be 92.8% in medical diagnosis, which is a reasonably good performance in machine learning algorithms.

The temporal dynamics of visual attention.

Researchers have long debated how humans select relevant objects amid physically salient distractions. An increasingly popular view holds that the key to avoiding distractions lies in suppressing the attentional priority of a salient distractor. However, the precise mechanisms of distractor suppression remain elusive. Because the computation of attentional priority is a time-dependent process, distractor suppression must be understood within these temporal dynamics. In four experiments, we tracked the temporal dynamics of visual attention using a novel forced-response method, by which participants were required to express their latent attentional priority at varying processing times via saccades. We show that attention could be biased either toward or away from a salient distractor depending on the timing of observation, with these temporal dynamics varying substantially across experiments. These dynamics were explained by a computational model assuming the distractor and target priority signals arrive asynchronously in time and with different influences on saccadic behavior. The model suggests that distractor signal suppression can be achieved via a "slow" mechanism in which the distractor priority signal dictates saccadic behavior until a late-arriving priority signal overrides it, or a "fast" mechanism which directly suppresses the distractor priority signal's behavioral expression. The two mechanisms are temporally dissociable and can work collaboratively, resulting in time-dependent patterns of attentional allocation. The current work underscores the importance of considering the temporal dynamics of visual attention and provides a computational architecture for understanding the mechanisms of distractor suppression. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Temporal Analysis of Riparian Buffer Zones and Demographic Trends in the Coastal Districts of Kastamonu

Aim of study: This study investigates the temporal changes of forest areas within and outside the Riparian Buffer Zones (RBZs) and examines their correlation with demographic trends in the coastal districts of Kastamonu from 1987, 2000, and 2016 to assess how population dynamics influence forest cover. Area of study: The study was conducted in four coastal districts in northern Kastamonu province, Türkiye, covering approximately 1000 square kilometers and focusing on 153 forest villages. Material and methods: Geographic Information Systems and remote sensing data delineated and analyzed land cover and population changes. The study used Landsat imagery and population data to explore spatial and temporal dynamics within the RBZs Main results: The study found a significant increase in forest area from 1987 to 2000 and a subsequent decrease from 2000 to 2016, with these changes showing a weak but significant negative correlation with the declining local population. Research highlihts: The findings suggest that declining rural populations may reduce anthropogenic pressures on forests and pose challenges to effective forest management and conservation. The research highlights the need for sustainable strategies integrating demographic change with forest conservation efforts to maintain ecosystem services and biodiversity.

Effect of Initial Inoculum on the Temporal and Spatial Dynamics of Wheat Blast Under Field Conditions in Bolivia.

Epidemiological studies to better understand wheat blast (WB) spatial and temporal patterns were conducted in three field environments in Bolivia between 2019 and 2020. The temporal dynamics of wheat leaf blast (WLB) and spike blast (WSB) were best described by the logistic model compared with the Gompertz and exponential models. The nonlinear logistic infection rates were higher under defined inoculation in experiments two and three than under undefined inoculation in experiment one, and they were also higher for WSB than for WLB. The onset of WLB began with a spatial clustering pattern according to autocorrelation analysis and Moran's index values, with higher severity and earlier onset for defined than for undefined inoculation until the last sampling time. The WSB onset did not start with a spatial clustering pattern; instead, it was detected later until the last sampling date across experiments, with higher severity and earlier onset for defined than for undefined inoculation. Maximum severity (Kmax) was 1.0 for WSB and less than 1.0 for WLB. Aggregation of WLB and WSB was higher for defined than for undefined inoculation. The directionality of hotspot development was similar for both WLB and WSB, mainly occurring concentrically for defined inoculation. Our results show no evidence of synchronized development but suggest a temporal and spatial progression of disease symptoms on wheat leaves and spikes. Thus, we recommend that monitoring and management of WB should be considered during early growth stages of wheat planted in areas of high risk.

A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a source of potential biomarkers for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address non-stationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices of interest without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of major depressive disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD.

Investigating the role of auditory cues in modulating motor timing: insights from EEG and deep learning.

Research on action-based timing has shed light on the temporal dynamics of sensorimotor coordination. This study investigates the neural mechanisms underlying action-based timing, particularly during finger-tapping tasks involving synchronized and syncopated patterns. Twelve healthy participants completed a continuation task, alternating between tapping in time with an auditory metronome (pacing) and continuing without it (continuation). Electroencephalography data were collected to explore how neural activity changes across these coordination modes and phases. We applied deep learning methods to classify single-trial electroencephalography data and predict behavioral timing conditions. Results showed significant classification accuracy for distinguishing between pacing and continuation phases, particularly during the presence of auditory cues, emphasizing the role of auditory input in motor timing. However, when auditory components were removed from the electroencephalography data, the differentiation between phases became inconclusive. Mean accuracy asynchrony, a measure of timing error, emerged as a superior predictor of performance variability compared to inter-response interval. These findings highlight the importance of auditory cues in modulating motor timing behaviors and present the challenges of isolating motor activation in the absence of auditory stimuli. Our study offers new insights into the neural dynamics of motor timing and demonstrates the utility of deep learning in analyzing single-trial electroencephalography data.

Accurately uncovering the regional ecological restoration priorities: A multidimensional approach to eco-dynamic sustainable management

Accurate identification, projection and dynamic evaluation of ecological degradation priorities are important prerequisites for realizing ecological restoration and territorial protection. However, the complexity of ecological systems and environmental diversity often limit the effectiveness of single-model evaluation approaches. Utilizing Badong County as a case study, this paper integrated the InVEST-Circuit-PLUS model to comprehensively analyze the eco-dynamic sustainability and restoration needs. Through performing projection of land cover, construction of ecological source areas, calculation of ecological resistance characteristics and extraction of ecological elements, we were able to identify priority ecological restoration areas over a twenty-year period (2007–2027). The results revealed cyclical land cover transformations from forest to cropland and back to forest, with an overall trend of forest cover expansion. While the county's ecological quality showed a slight decline, the location and extent of high-value ecological source areas were mapped in detail. By integrating ecosystem service value assessments, driver factor analyses, and the development of a "One Belt, Two Axes, Three Sources and Four Zones" ecological management framework, we were able to uncover the nuanced spatial and temporal dynamics shaping the regional ecology. These findings provide an evidence-based foundation to guide targeted ecological restoration and sustainable land use planning, which has verified the feasibility of the framework.

Temporal dynamics in vertical leaf angles can confound vegetation indices widely used in Earth observations

Earth observation data is key for monitoring vegetation dynamics across temporal and spatial scales. The most widely used method to estimate vegetation properties from Earth observation data is vegetation indices. However, temporal dynamics in vertical leaf angles can strongly alter reflectance signals and, hence, vegetation indices. Here, we derive leaf angles from plant photographs to simulate the effect on vegetation indices with radiative transfer models. We show that leaf angle dynamics systematically confound widely applied vegetation indices. Moreover, we demonstrate that these effects are not random but tightly linked to abiotic environmental conditions. These systematic effects of vertical leaf angles have implications for monitoring plant properties, biodiversity, and ecosystem functional properties. We discuss the related challenges and opportunities to assess spatio-temporal vegetation dynamics with remote sensing and vegetation indices.