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Aim Temporal dynamics of biodiversity along tropical elevational gradients are unknown. We studied seasonal changes of Lepidoptera biodiversity along the only complete forest elevational gradient in the Afrotropics. We focused on shifts of species richness patterns, seasonal turnover of communities and seasonal shifts of species’ elevational ranges, the latter often serving as an indicator of the global change effects on mountain ecosystems. Location Mount Cameroon, Cameroon. Taxon Butterflies and moths (Lepidoptera). Methods We quantitatively sampled nine groups of Lepidoptera by bait‐trapping (16,800 trap‐days) and light‐catching (126 nights) at seven elevations evenly distributed along the elevational gradient from sea level (30 m a.s.l.) to timberline (2,200 m a.s.l.). Sampling was repeated in three seasons. Results Altogether, 42,936 specimens of 1,099 species were recorded. A mid‐elevation peak of species richness was detected for all groups but Eupterotidae. This peak shifted seasonally for five groups, most of them ascending during the dry season. Seasonal shifts of species’ elevational ranges were mostly responsible for these diversity pattern shifts along elevation: we found general upward shifts in fruit‐feeding butterflies, fruit‐feeding moths and Lymantriinae from beginning to end of the dry season. Contrarily, Arctiinae shifted upwards during the wet season. The average seasonal shifts of elevational ranges often exceeded 100 m and were even several times higher for numerous species. Main conclusions We report seasonal uphill and downhill shifts of several lepidopteran groups. The reported shifts can be driven by both delay in weather seasonality and shifts in resource availability, causing phenological delay of adult hatching and/or adult migrations. Such shifts may lead to misinterpretations of diversity patterns along elevation if seasonality is ignored. More importantly, considering the surprising extent of seasonal elevational shifts of species, we encourage taking account of such natural temporal dynamics while investigating the global climate change impact on communities of Lepidoptera in tropical mountains.

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.

Precise regulation of macrophage fate is crucial for effective management of inflammation. However, conventional biochemical strategies often suffer from limitations in safety and efficiency, necessitating the development of more effective and controllable alternatives. In this study, we develop an ultrasound‐engineered cell culture device capable of delivering finely tuned ultrasonic mechanical stimulation and demonstrate for the first time that ultrasound can remotely and dynamically modulate macrophage phenotypic fate. Our results demonstrate that ultrasound stimulation not only induces flexible transitions between macrophage subtypes but also exhibits superior immunomodulatory performance in induction efficiency, dynamic responsiveness, and spatio‐temporal controllability compared to classical biochemical methods. Transcriptome sequencing reveals that ultrasound directionally polarizes M2a macrophages via activation of the integrin αXβ2/TGF‐β/c‐Fos/IL‐10 pathway. Based on the programmable dynamic control of macrophage phenotype, we propose “sequential ultrasound therapy” and combine it with metformin hydrogel with controlled‐release function to construct a physicochemically coordinated “inflammatory switch and angiogenesis promotion” therapeutic platform, exhibiting superior inflammatory repair effects than single treatment for in vivo diabetic wounds and myocardial infarction models. Overall, this study not only advances the mechanistic understanding of ultrasound in tissue repair but also proposes a remote, non‐invasive ultrasound immunotherapy paradigm with clinical translation potential. This study presents an innovative therapeutic strategy that integrates sequential ultrasound therapy with an injectable oxidized dextran‐metformin (ODE‐Met) hydrogel to dynamically regulate macrophage polarization and promote angiogenesis for inflamed tissue repair. A custom‐designed ultrasound culture device enables precise control of macrophage phenotype transitions, with mechanotransduction driving M2a polarization through the integrin αXβ2/TGF‐β/c‐Fos/IL‐10 axis. Meanwhile, the injectable ODE‐Met hydrogel ensured sustained metformin release and enhanced pro‐angiogenic activity. The combined approach achieves significant therapeutic efficacy in both diabetic wound and myocardial infarction models, highlighting its broad applicability and translational potential.

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.