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Phenological shifts across plant species is a powerful indicator to quantify the effects of climate change. Today, mobile applications with automated species identification open new possibilities for phenological monitoring across space and time. Here, we introduce an innovative spatio‐temporal machine learning methodology that harnesses such crowd‐sourced data to quantify phenological dynamics across taxa, space and time. Our algorithm links individual phenological responses across thousands of species and geographical locations, using a similarity measure. The analysis draws on nearly ten million plant observations collected through the AI‐based plant identification app Flora Incognita in Germany from 2018 to 2021. Our method quantifies changes in synchronisation across the annual cycle. During the growing season, synchronised behaviour can be encoded by a few characteristic macrophenological patterns. Nonlinear spatio‐temporal changes of these patterns can be efficiently quantified using a data compressibility measure. Outside the growing season, the phenological synchronisation diminishes introducing noise into the patterns. Despite biases and uncertainties associated with crowd‐sourced data, for example due to human data collection behaviour, our study demonstrates the feasibility of deriving meaningful indicators for monitoring plant macrophenology from individual plant observations. As crowd‐sourced databases continue to expand, our approach holds promise to study climate‐induced phenological shifts and feedback loops.

Smallholder farmers in East and West Hararghe zones, Ethiopia frequently face problems of climate extremes. Knowledge of past and projected climate change and variability at local and regional scales can help develop adaptation measures. A study was therefore conducted to investigate the spatio-temporal dynamics of rainfall and temperature in the past (1988–2017) and projected periods of 2030 and 2050 under two Representative Concentration Pathways (RCP4.5 and RCP8.5) at selected stations in East and West Hararghe zones, Ethiopia. To detect the trends and magnitude of change Mann–Kendall test and Sen’s slope estimator were employed, respectively. The result of the study indicated that for the last three decades annual and seasonal and monthly rainfall showed high variability but the changes are not statistically significant. On the other hand, the minimum temperature of the ‘Belg’ season showed a significant (p < 0.05) increment. The mean annual minimum temperature is projected to increase by 0.34 °C and 2.52 °C for 2030, and 0.41 °C and 4.15 °C for 2050 under RCP4.5 and RCP8.5, respectively. Additionally, the mean maximum temperature is projected to change by −0.02 °C and 1.14 °C for 2030, and 0.54 °C and 1.87 °C for 2050 under RCP4.5 and RCP 8.5, respectively. Annual rainfall amount is also projected to increase by 2.5% and 29% for 2030, and 12% and 32% for 2050 under RCP4.5 and RCP 8.5, respectively. Hence, it is concluded that there was an increasing trend in the Belg season minimum temperature. A significant increasing trend in rainfall and temperature are projected compared to the baseline period for most of the districts studied. This implies a need to design climate-smart crop and livestock production strategies, as well as an early warning system to counter the drastic effects of climate change and variability on agricultural production and farmers’ livelihood in the region.

Language control processes allow for the flexible manipulation and access to context‐appropriate verbal representations. Functional magnetic resonance imaging (fMRI) studies have localized the brain regions involved in language control processes usually by comparing high vs. low lexical–semantic control conditions during verbal tasks. Yet, the spectro‐temporal dynamics of associated brain processes remain unexplored, preventing a proper understanding of the neural bases of language control mechanisms. To do so, we recorded functional brain activity using magnetoencephalography (MEG) and fMRI, while 30 healthy participants performed a silent verb generation (VGEN) and a picture naming (PN) task upon confrontation with pictures requiring low or high lexical–semantic control processes. fMRI confirmed the association between stronger language control processes and increased left inferior frontal gyrus (IFG) perfusion, while MEG revealed these controlled mechanisms to be associated with a specific sequence of early (< 500 ms) and late (> 500 ms) beta‐band (de)synchronization processes within fronto‐temporo‐parietal areas. Particularly, beta‐band modulations of event‐related (de)synchronization mechanisms were first observed in the right IFG, followed by bilateral IFG and temporo‐parietal brain regions. Altogether, these results suggest that beyond a specific recruitment of inferior frontal brain regions, language control mechanisms rely on a complex temporal sequence of beta‐band oscillatory mechanisms over antero‐posterior areas. The study aimed at investigating the oscillatory brain dynamics underlying language control processes, a mechanism that critically allows the flexible access to context‐relevant representations during language production tasks. We showed that language control processes rely on a complex sequence of beta‐band brain synchronization processes encompassing fronto‐temporo‐parietal brain regions, with the prefrontal areas being particularly involved at early stages (< 500 ms).

Aim Sea otters (Enhydra lutris) are an apex predator of the nearshore marine community and nearly went extinct at the turn of the 20th century. Reintroductions and legal protection allowed sea otters to re‐colonize much of their former range. Our objective was to chronicle the colonization of this apex predator in Glacier Bay, Alaska, to help understand the mechanisms that governed their successful colonization. Location Glacier Bay is a tidewater glacier fjord in southeastern Alaska that was entirely covered by glaciers in the mid‐18th century. Since then, it has endured the fastest tidewater glacier retreat in recorded history. Methods We collected and analysed several data sets, spanning 20 years, to document the spatio‐temporal dynamics of an apex predator expanding into an area where they were formerly absent. We used novel quantitative tools to model the occupancy, abundance and colonization dynamics of sea otters, while accounting for uncertainty in the data collection process, the ecological process and model parameters. Results Twenty years after sea otters were first observed colonizing Glacier Bay, they became one of the most abundant and widely distributed marine mammal. The population grew exponentially at a rate of 20% per year. They colonized Glacier Bay at a maximum rate of 6 km per year, with faster colonization rates occurring early in the colonization process. During colonization, sea otters selected shallow areas, close to shore, with a steep bottom slope, and a relatively simple shoreline complexity index. Main conclusions The growth and expansion of sea otters in Glacier Bay demonstrate how legal protection and translocation of apex predators can facilitate their successful establishment into a community in which they were formerly absent. The success of sea otters was, in part, a consequence of habitat that was left largely unperturbed by humans for the past 250 years. Further, sea otters and other marine predators, whose distribution is limited by ice, have the potential to expand in distribution and abundance, reshaping future marine communities in the wake of deglaciation and global loss of sea ice.

Coastal wetlands represent one of the most critical ecosystem types worldwide, and offer a diverse array of vital ecosystem services. Large‐scale and rapid invasion of Spartina alterniflora (S. alterniflora) has imposed significant impacts on coastal wetland ecosystems and biodiversity globally. Tracking dynamic trajectories of S. alterniflora and the rhythmic changes of its vegetation functional traits is important to understand the invasion mechanism. This study proposed a novel time series adaptive threshold segmentation method (NSATS) to extract S. alterniflora. Specifically, for the San Francisco Bay (SFB) of the U.S., the extraction was carried out from 1995 to 2004, while for three representative bays along the coastal zone of China, the extraction was conducted from 2011 to 2020, respectively, and to explore their spatio‐temporal distribution patterns. We developed an innovative CCD‐HCM method to track the historical growth trajectories of S. alterniflora, and evaluated their growth dynamics. Finally, we explored the phenological rhythm of S. alterniflora functional traits, and clarified the response mechanisms of its vegetation functional traits to hydro‐meteorological factors. NSATS showed high accuracy (0.82–0.96) in identifying S. alterniflora. Its invasion was faster in China's bays, with rapid expansion in 2011–2020, especially in YRD. SFB remained stable, with minor changes. Functional traits showed earlier SOS and longer LOS with latitude decrease. Air and water temperatures influence S. alterniflora traits differently across bays. These findings aid in monitoring and controlling its invasion. Plain Language Summary The rapid expansion of S. alterniflora has severely degraded wetlands and destroyed native biodiversity. Therefore, the control of invasive S. alterniflora is a key global issue. Understanding the spatio‐temporal pattern, growth trajectory and phenological rhythm of S. alterniflora is of great significance to reveal the invasion and control of S. alterniflora. Functional traits (chlorophyll content, canopy biomass, water content) are important indicators of vegetation growth, and are the key to understanding the response of coastal vegetation to hydroclimatic change. This study proposed the NSATS method to extract S. alterniflora in four Chinese bays (2011–2020) and the San Francisco Bay (1995–2004) and analyzed their spatio‐temporal patterns. We used the CCD‐HCM method to track growth trajectories and examined the phenological rhythm of functional traits, clarifying their response to hydro‐meteorological factors. NSATS showed high accuracy (0.82–0.96). China's bays saw faster S. alterniflora expansion than SFB, with rapid growth in the Yellow River Delta (YRD) from 2011 to 2015. Air and water temperatures significantly influenced functional traits. These findings aid in monitoring and preventing S. alterniflora invasion. Key Points China's bay expansion was more intense than that of San Francisco Bay (SFB) From land to sea, the growing season (LOS) of S. alterniflora traits shortens, with SFB having a longer LOS than the Yellow River Delta Air and water temperature significantly affect functional traits. Precipitation, salinity, and temperature are key drivers in China's bays

Random number generators in digital information systems make use of physical entropy sources such as electronic and photonic noise to add unpredictability to deterministically generated pseudo-random sequences 1 , 2 . However, there is a large gap between the generation rates achieved with existing physical sources and the high data rates of many computation and communication systems; this is a fundamental weakness of these systems. Here we show that good quality random bit sequences can be generated at very fast bit rates using physical chaos in semiconductor lasers. Streams of bits that pass standard statistical tests for randomness have been generated at rates of up to 1.7 Gbps by sampling the fluctuating optical output of two chaotic lasers. This rate is an order of magnitude faster than that of previously reported devices for physical random bit generators with verified randomness. This means that the performance of random number generators can be greatly improved by using chaotic laser devices as physical entropy sources. Random-number generators are important in digital information systems. However, the speed at which current sources operate is much slower than the typical data rates used in communication and computing. Chaos in semiconductor lasers might help to bridge the gap.

The generation of random bit sequences based on non-deterministic physical mechanisms is of paramount importance for cryptography and secure communications. High data rates also require extremely fast generation rates and robustness to external perturbations. Physical generators based on stochastic noise sources have been limited in bandwidth to ∼100 Mbit s −1 generation rates. We present a physical random bit generator, based on a chaotic semiconductor laser, having time-delayed self-feedback, which operates reliably at rates up to 300 Gbit s −1 . The method uses a high derivative of the digitized chaotic laser intensity and generates the random sequence by retaining a number of the least significant bits of the high derivative value. The method is insensitive to laser operational parameters and eliminates the necessity for all external constraints such as incommensurate sampling rates and laser external cavity round trip time. The randomness of long bit strings is verified by standard statistical tests. The generation of random bit sequences at a data rate of up to 300 Gbit s −1 — a rate many orders of magnitude faster than previously achieved — is realized by exploiting the output of a chaotic semiconductor laser. The randomness of the generated bits is verified by standard statistical tests.

In an environment with changing availability and quality of host plants, phytophagous insects are under selection pressure to find quality hosts. They need to maximize their fitness by locating suitable plants and avoiding unsuitable ones. Thus, they have evolved a finely tuned sensory system, for detection of host cues, and a nervous system, capable of integrating inputs from sensory neurons with a high level of spatio-temporal resolution. Insect responses to cues are not fixed but depend on the context in which they are perceived, the physiological state of the insect, and prior learning experiences. However, there are examples of insects making ‘mistakes’ and being attracted to poor quality hosts. While insects have evolved ways of finding hosts, plants have been under selection pressure to do precisely the opposite and evade detection or defend themselves when attacked. Once on the plant, insect-associated molecules may trigger or suppress defence depending on whether the plant or the insect is ahead in evolutionary terms. Plant volatile emission is influenced by defence responses induced by insect feeding or oviposition which can attract natural enemies but repel herbivores. Conversely, plant reproductive fitness is increased by attraction of pollinators. Interactions can be altered by other organisms associated with the plant such as other insects, plant pathogens, or mycorrhizal fungi. Plant phenotype is plastic and can be changed by epigenetic factors in adaptation to periods of biotic stress. Space and time play crucial roles in influencing the outcome of interactions between insects and plants.

Processes that drive plant invasions play out across multiple spatial and temporal scales. Understanding individual steps along the introduction-naturalization-invasion continuum and its drivers is crucial for management. This review, targeting the broad audience of invasion scientists, field ecologists and land managers, summarizes the state-of-the-art and potential of remote sensing (RS) in plant invasion science and management. It identifies challenges and research gaps, discusses the discrepancies between technology, science and practice, and suggests ways of addressing some of these issues. Mapping, modelling and predicting invasion processes across scales is a major challenge since they are dynamic and highly complex. Integration of RS data collected at different spatial and temporal scales (“rocking” across scales) has the potential to elucidate the dynamics of invasions and to reveal its drivers, thereby improving the efficiency of control measures. Increasing spatial/temporal resolution of imagery from satellites and drones has much potential to (i) precisely identify even less conspicuous invasive species; (ii) map invasion dynamics; and (iii) provide information on environmental variables and landscape structure at scales fine enough to capture underlying ecological processes. Until now, RS research has focussed primarily on spatio-temporal patterns of plant invasions. Other more challenging topics, such as early monitoring, and revealing the invasion mechanisms and impacts have received less attention. Despite the power of RS technology and recent developments, large discrepancies remain between possibilities and actual implications in research and practical management of invasions. Although recent technological advances, such as powerful algorithms, cloud solutions, and data streams from citizen science, might overcome some limitations, the mutual dialog among field ecologists, managers, invasion scientists and RS specialists remains crucial; our review contributes to such communication.

Dispersal as well as population growth is a key demographic process that determines population dynamics. However, determining the effects of environmental covariates on dispersal from spatial‐temporal abundance proxy data is challenging owing to the complexity of model specification for directional dispersal permeability and the extremely high computational loads for numerical integration. In this paper, we present a case study estimating how environmental covariates affect the dispersal of Japanese sika deer by developing a spatially explicit state‐space matrix model coupled with an improved numerical integration technique (Markov chain Monte Carlo with particle filters). In particular, we explored the environmental drivers of inhomogeneous range expansion, characteristic of animals with short dispersal. Our model framework successfully reproduced the complex population dynamics of sika deer, including rapid changes in densely populated areas and distribution fronts within a decade. Furthermore, our results revealed that the inhomogeneous range expansion of sika deer seemed to be primarily caused by the dispersal process (i.e., movement barriers in fragmented forests) rather than population growth. Our state‐space matrix model enables the inference of population dynamics for a broad range of organisms, even those with low dispersal ability, in heterogeneous landscapes, and could address many pressing issues in conservation biology and ecosystem management. We present a case study estimating how environmental elements affect the dispersal of Japanese sika deer by developing a spatially explicit state‐space matrix model coupled with an improved numerical integration technique. Our results revealed that the inhomogeneous range expansion of sika deer was primarily caused by the dispersal process (i.e., movement barriers in fragmented forests) rather than population growth.