Personality testing of large language models: limited temporal stability, but highlighted prosociality.

As large language models (LLMs) continue to gain popularity due to their human-like traits and the intimacy they offer to users, their societal impact inevitably expands. This leads to the rising necessity for comprehensive studies to fully understand LLMs and reveal their potential opportunities, drawbacks and overall societal impact. With that in mind, this research conducted an extensive investigation into seven LLMs, aiming to assess the temporal stability and inter-rater agreement on their responses on personality instruments in two time points. In addition, LLMs' personality profile was analysed and compared with human normative data. The findings revealed varying levels of inter-rater agreement in the LLMs' responses over a short time, with some LLMs showing higher agreement (e.g. Llama3 and GPT-4o) compared with others (e.g. GPT-4 and Gemini). Furthermore, agreement depended on used instruments as well as on domain or trait. This implies the variable robustness in LLMs' ability to reliably simulate stable personality characteristics. In the case of scales which showed at least fair agreement, LLMs displayed mostly a socially desirable profile in both agentic and communal domains, as well as a prosocial personality profile reflected in higher agreeableness and conscientiousness and lower Machiavellianism. Exhibiting temporal stability and coherent responses on personality traits is crucial for AI systems due to their societal impact and AI safety concerns.

The temporal profile of self-prioritization.

Personal relevance exerts a powerful influence on decisional processing, such that arbitrary stimuli associated with the self are classified more rapidly than identical material linked with other people. Notwithstanding numerous demonstrations of this facilitatory effect, it remains unclear whether self-prioritization is a temporally stable outcome of decision-making. Accordingly, using a shape-label matching task in combination with computational modeling, the current experiment investigated this matter. The results were informative. First, regardless of the target of comparison (i.e., friend or stranger), self-prioritization was a persistent product of decision-making across the testing session. Second, a variant of the standard drift diffusion model in which decisional boundaries collapsed gradually over the course of the task best fit the observed data. Third, whereas the efficiency of stimulus processing increased for other-related stimuli during the task, it decreased for self-related material. Collectively, these findings advance understanding of the temporal profile of self-prioritization.

Reshaping the spatiotemporal patterns of temporal stability of forage nutrition quality in alpine grasslands of the Qinghai-Tibet Plateau: Spatial homogeneity, overall decline and localized increases.

There is increasing evidence that forage nutrition quality is becoming more unstable over time due to climate change and/or human activities. However, there are limited numbers of studies at the regional scale exploring the spatiotemporal patterns and driving mechanisms of temporal stability of nutrition quality. Therefore, this study quantified the spatiotemporal patterns of temporal stability of forage nutrition quality in alpine grasslands of the Qinghai-Tibet Plateau under the singular or combined influences of climate change and human activities in 2000-2020. Temporal stability of forage nutrition quality displayed obvious spatiotemporal patterns, with human activities altering the impact of climate change on these spatiotemporal patterns. Under combined effects of climate change and human activities, spatial average values of temporal stability of crude protein (CP), ash (Ash), ether extract (EE), water-soluble carbohydrates (WSC), acid detergent fiber (ADF), and neutral detergent fiber (NDF) decreased by 13.54 %, 7.40 %, 9.02 %, 17.78 %, 9.20 %, and 7.28 % across the whole grasslands, respectively. However, 39.43 %, 45.72 %, 42.98 %, 37.82 %, 42.27 %, and 43.50 % areas showed increasing trends for the temporal stability of CP, Ash, EE, WSC, ADF and NDF, respectively. Climate change predominated 46.15 %, 44.46 %, 44.22 %, 47.32 %, 28.68 %, and 45.31 % of the relative change of temporal stability of CP, Ash, EE, WSC, ADF, and NDF, but human activities had higher influence for 53.82 %, 55.53 %, 55.77 %, 52.55 %, 71.30 %, and 54.68 % of grasslands, respectively. Therefore, the spatial patterns of temporal stability of forage nutrition quality were shifting towards homogeneity, with an overall decrease in temporal stability but localized increases in alpine grasslands of the Qinghai-Tibet Plateau. The effects of climate change and human activities on forage nutrition quality were not always synergistic. The trade-off between nutrition quality and its temporal stability did not always exist, but varied with geographic position.

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Spatial variability of throughfall (i.e. the non-uniform characteristics of throughfall at different canopy positions) and its temporal persistence (i.e. time stability) are related to the quantity and efficiency of soil moisture replenishment, and affect plant competition and community succession dynamics by affecting resource availability. We carried out a meta-analysis with 554 papers (from 2000 to 2022) retrieved from Web of Science and China National Knowledge Infrastructure (CNKI) based on keyword search, quantified and compared the amount, spatial heterogeneity, and temporal stability characteristics of penetrating rain in different climate zones and plant functional types. Our results that throughfall proportion was lower in arid regions (72.0%±13.6%) than humid (75.1%±9.3%) and semi-humid areas (79.9%±10.4%). Cold climates had lower values (74.1%±14.6%) than temperate (74.2%±7.5%) and tropical climates (80.9%±14.6%). Shrubs (68.9%±14.9%) generally had lower throughfall proportion than trees (76.7%±9.1%). Broad-leaved trees (75.2%±11.1%) and conifers (75.1%±9.9%) showed similar throughfall proportions, as did evergreen (76.7%±10.0%) and deciduous species (74.7%±11.9%). Additionally, spatial variability (coefficient of variation) did not significantly differ across rainfall zones, temperature zones, or vegetation types. The spatial distribution of throughfall was relatively stable. Canopy structure was the dominant factor affecting temporal stability of throughfall. However, there was a lack of comparison between typical geographic units (i.e. spatial units with basically consistent geographical environmental conditions) at various temporal scales. Future research should expand upwards to the summary of global spatial scale rules and downwards to the analysis of process based temporal scale mechanisms, to depict the dynamic distribution of penetrating rain and unify observation standards to enhance comparability of different studies, in order to efficiently promote research on canopy penetrating rain and provide ecological and hydrological basis for protecting nature, managing artificial activities, and restoring degraded ecosystems.

Phase-dependent grassland temporal stability is mediated by species and functional group asynchrony: A long-term mowing experiment.

The temporal stability of grasslands plays a key role in the stable provisioning of multiple ecosystem goods and services for humankind. Despite recent progress, our knowledge on how long-term mowing influences ecosystem stability remains unclear. Using a dataset from an 18-year-long mowing experiment with different treatment intensities (no-mowing, mowing once per year, and mowing twice per year) in grasslands of Inner Mongolia, China, we aimed to determine whether and how long-term mowing influenced grassland temporal stability in a temperate steppe. We found mowing decreased ecosystem stability in the early and intermediate periods (1-12 years of treatment), but increased stability in the later period (13-18 years of treatment), indicating responses of ecosystem stability to long-term mowing were phase dependent. Bivariate correlation and structural equation modeling analyses revealed that the degree of asynchrony both at the species and functional group levels, as well as dominant species stability, played key roles in stabilizing the whole community. In addition, portfolio effects rather than diversity made significant contributions to ecosystem stability. Our results suggest the phase-dependent temporal stability of grassland under long-term mowing is mainly mediated by species and functional group asynchrony. This finding provides a new insight for understanding how dryland grassland responds to long-term anthropogenic perturbations.

Determinants of tree population temporal stability in a temperate mixed forest over a gradient of nitrogen addition.

Nitrogen (N) deposition is a significant threat to the functioning of forests and negatively impacts the delivery of forest goods and services. Contemporary management approaches seek to adapt forests to such N-deposition stressors, but to date how plant populations in natural forests respond to N deposition and what factors determine the contrasting responses among populations are still unclear. Here, we investigated the impact of N-addition (control: 0 kg ha-1 yr-1; low: 25 kg ha-1 yr-1; medium: 50 kg ha-1 yr-1; high: 75 kg ha-1 ha yr-1) on tree population temporal stability and how initial tree size, mycorrhizal type, and leaf N content (LNC; as a surrogate for functional trait composition) mediate tree population responses to N-addition in a Korean pine and mixed broadleaved dominated temperate forest in northern China. We quantified tree species population temporal stability as the ratio of mean to standard deviation of the year-by-year stem increments recorded in individual trees from 2015 to 2022 experimental period. The results showed different temporal stabilities of tree species among four N-addition levels, with the highest population stability observed within the high N-addition plots. Furthermore, initial tree size had significantly (p < 0.001) positive effects on population temporal stability. The effect of LNC and initial tree size were also contingent on the level of N applied. Specifically, increase in tree population LNC reduced population temporal stability in all plots where N was added. Our results imply that retention of large-sized trees and species with resource-conservative strategies (e.g., low LNC) could enhance forest stability under N deposition.

Two-Year Study on the Intra-Individual Dynamics of Gut Microbiota and Short-Chain Fatty Acids Profiles in Healthy Adults.

While the gut microbiome has been intensively investigated for more than twenty years already, its role in various disorders remains to be unraveled. At the same time, questions about what changes in the gut microbiota can be considered as normal or pathological and whether communities are able to recover after exposure to negative factors (diseases, medications, environmental factors) are still unclear. Here, we describe changes in the gut microbiota composition and the content of short-chain fatty acids in adult healthy volunteers (n = 15) over a 24 month-period. Intraindividual variability in gut microbial composition was 40%, whereas the short chain fatty acids profile remained relatively stable (2-year variability 20%, inter-individual 26%). The changes tend to accumulate over time. Nevertheless, both short-term and long-term changes in the gut microbiome composition were significantly smaller within individuals than interindividual differences (two-year interindividual variability was 75%). Seasonal changes in gut microbiota were found more often in autumn and spring involving the content of minor representatives (less than 1.5% of the community in average) in the phyla Actinobacteriota, Firmicutes and Proteobacteria.

Investigating the impact of temporal instability in smart roadway retrofitting on terrain-related crash injury severity.

The advancement of intelligent road systems in developing countries poses unique challenges in identifying risk factors and implementing safety strategies. The variability of factors affecting crash injury severity leads to different risks across levels of roadway smartness, especially in hazardous terrains, complicating the adaptation of smart technologies. Therefore, this study investigates the temporal instability of factors affecting injury severities in crashes across various terrains, with a focus on the evolution of road smartness. Crash data from selected complex terrain regions in Shaanxi Province during smart road adaptation were used, and categorized into periods before, during, and after smart road implementations. A series of mixed logit models were employed to account for unobserved heterogeneity in mean and variance, and likelihood ratio tests were conducted to assess the spatio-temporal instability of model parameters across different topographic settings and smart processes. Moreover, a comparison between partially constrained and unconstrained temporal modeling approaches was made. The findings reveal significant differences in injury severity determinants across terrain conditions as roadway intelligence progressed. On the other hand, certain factors like pavement damage, truck and pedestrian involvement were identified that had relatively stable effects on crash injury severities. Out-of-sample predictions further emphasize the need for modeling across terrain and roadway development stages. These insights are crucial for developing tailored safety measures for smart road retrofitting in different terrain conditions, thereby supporting the transition towards smarter road systems in developing regions.

Temporal asynchrony of plant and soil biota determines ecosystem multifunctional stability.

The role of plant biodiversity in stabilizing ecosystem multifunctionality has been extensively studied; however, the impact of soil biota biodiversity on ecosystem multifunctional stability, particularly under multiple environmental changes, remains unexplored. By conducting an experiment with environmental changes (adding water and nitrogen to a long-term grazing experiment) and an experiment without environmental changes (an undisturbed site) in semi-arid grasslands, our research revealed that environmental changes-induced changes in temporal stability of both above- and belowground multifunctionality were mainly impacted by plant and soil biota asynchrony, rather than by species diversity. Furthermore, changes in temporal stability of above- and belowground multifunctionality, under both experiments with and without environmental changes, were mainly associated with plant and soil biota asynchrony, respectively, suggesting that the temporal asynchrony of plant and soil biota has independent and non-substitutable effects on multifunctional stability. Our findings emphasize the importance of considering both above- and belowground biodiversity or functions when evaluating the stabilizing effects of biodiversity on ecosystem functions.

Temporal stability of optimism and pessimism (LOT-R) over 6 years in the general population.

Objective: The aims of this study were to examine changes in habitual optimism over a six-year period and to analyze the relationship between changes in optimism and changes in other quality of life-related variables. Method: A randomly selected community sample of the German adult general population (N = 4,965) was surveyed twice, with a time interval of 6.04 years. Results: During the course of the 6 years, the mean score of the LOT-R total scale improved (effect size d = 0.11). The temporal stability in terms of the test-retest correlation was r = 0.61 for the total sample. There were only marginal gender differences in this temporal stability, however, the stability in the oldest age group ≥70 years (r = 0.50) was lower than the stability of the other age groups. The cross-sectional correlations showed clear relationships between optimism on the one hand and quality of life, life satisfaction, social support, and low levels of anxiety and physical complaints on the other. The corresponding longitudinal correlations between changes in optimism and changes in the other variables were less pronounced, but in the same direction. Conclusion: The study confirmed the applicability of the LOT-R in longitudinal studies. In samples with participants of 70 years and above, the limited stability in the optimism assessments needs to be considered in clinical practice and epidemiologic research.

Beyond the surface: Exploring the temporally stable factors influencing injury severities in large-truck crashes using mixed logit models.

An analysis of crash data spanning four years (January 1, 2015, to December 31, 2018) from the State of Washington is conducted to investigate factors influencing injury severity outcomes in large truck-involved crashes. The study utilizes a mixed logit model that accounts for unobserved heterogeneity to capture the variation influenced by other variables. Transferability and temporal stability across the years are assessed using the likelihood ratio test. A wide range of attributes, including driver characteristics, vehicle features, crash-related attributes, roadway conditions, environmental factors, and temporal elements, are considered. Despite a significant temporal instability warranted by the likelihood ratio test across the years, twenty-one parameters consistently exhibit stable effects on injury severity over the years of which thirteen are new. The identified stable parameters included over speeding, following too closely, falling asleep, missing/ faulty airbags, head-on collisions, crashes involving two or more than three vehicles, rear-end collisions, lane width, low-light conditions, sag curves, New Jersey barriers, snowy weather, and morning hours. The temporally stable factors affecting injury severities in large truck crashes are crucial in developing the needed to address these crashes. The findings of this study offer valuable insights for researchers, stakeholders in the trucking industry, and policymakers, empowering them to develop targeted policies that not only improve traffic safety but also alleviate associated economic losses.

Internal consistency and temporal stability of the Community Assessment of Psychic Experiences (CAPE): A reliability generalization meta-analysis.

Psychotic experiences (PE) are prevalent in general and clinical populations and can increase the risk for mental disorders in young people. The Community Assessment of Psychic Experiences (CAPE) is a widely used measure to assess PE in different populations and settings. However, the current knowledge on their overall reliability is limited. We examined the reliability of the CAPE-42 and later versions, testing the role of age, sex, test scores, and clinical status as moderators. A systematic search was conducted on the Scopus, Web of Science, PubMed, EBSCOhost, ProQuest, and GoogleScholar databases. Internal consistency and temporal stability indices were examined through reliability generalization meta-analysis (RGMA). Moderators were tested through meta-regression analysis. From a pool of 1,015 records, 90 independent samples were extracted from 71 studies. Four versions showed quantitative evidence for inclusion: CAPE-42, CAPE-20, CAPE-P15, and CAPE-P8. Internal consistency indices were good (α/ω≈.725-0.917). Temporal stability was only analyzed for the CAPE-P15, yielding a moderate but not-significant effect (r=0.672). The evidence for temporal stability is scant due to the limited literature, and definitive conclusions cannot be drawn. Further evidence on other potential moderators such as adverse experiences or psychosocial functioning is required.

Mowing increased community stability in semiarid grasslands more than either fencing or grazing.

A substantial body of empirical evidence suggests that anthropogenic disturbance can affect the structure and function of grassland ecosystems. Despite this, few studies have elucidated the mechanisms through which grazing and mowing, the two most widespread land management practices, affect the stability of natural grassland communities. In this study, we draw upon 9 years of field data from natural grasslands in northern China to investigate the effects of gazing and mowing on community stability, specifically focusing on community aboveground net primary productivity (ANPP) and dominance, which are two major biodiversity mechanisms known to characterize community fluctuations. We found that both grazing and mowing reduced ANPP in comparison to areas enclosed by fencing. Grazing reduced community stability by increasing the likelihood of single-species dominance and decreasing the relative proportion of nondominant species. In contrast, mowing reduced the productivity of the dominant species but increased the productivity of nondominant species. As a consequence, mowing improved the overall community stability by increasing the stability of nondominant species. Our study provides novel insight into understanding of the relationship between community species fluctuation-stability, with implications for ecological research and ecosystem management in natural grasslands.

Hair cortisol stability after 5-year storage: Insights from a sample of 17-year-old adolescents.

Background: Hair has become an increasingly valuable medium to investigate the association between chronic stress, stable differences in systemic cortisol secretion and later health. Assessing cortisol in hair has many advantages, notably its non-invasive and retrospective nature, the need for a single biospecimen and convenient storage until analysis. However, few studies offered empirical evidence documenting the long-term temporal stability of hair cortisol concentration (HCC) prior to analysis, especially in humans. Yet, knowing how long hair samples can be stored without compromising the accuracy of cortisol measurement is of crucial importance when planning data collection and analysis. This study examined the stability of HCC in hair samples assayed twice, five years apart. Methods: We randomly selected from a larger distribution of HCC measured in 17-year-old participants 39 hair samples to be reanalyzed five years later, under the same general conditions. Samples were assayed in duplicate using a luminescence immunoassay and compared with the original HCC using the Lin's concordance correlation coefficient (CCC), Bland-Altman plot analysis and Wilcoxon rank test. Results: Findings indicated a good concordance and temporal stability between the two samples assayed five years apart (CCC [95% confidence interval] = 0.84 [0.72-0.91]), although a small decrease in HCC was noted 5 years later (8.4% reduction, p = 0.001). Conclusion: Our study confirms that hair samples, when stored at room temperature and away from sunlight, can be assayed for at least five years without risking a loss of precision in HCC measurement.

Temporal stability of tongue microbiota in older patients - A pilot study.

Background/purpose: Healthy states of human microbiota depend on a stable community of symbiotic microbes irrespective of external challenges from the environment. Thus, long-term stability of the oral microbiota is of importance, particularly for older patient populations. Materials and methods: We used next-generation sequencing (NGS) to examine the tongue microbiota of 18 individuals receiving long-term care over a 10-month period. Results: Beta diversity analysis demonstrated temporal stability of the tongue microbiota, as microbial compositions from all time points were indistinguishable from each other (P = 0.0887). However, significant individual variation in microbial composition (P = 0.0001) was observed, underscoring the presence of a unique microbial profile for each patient. Conclusion: The temporal dynamics of tongue microbiota exhibit long-term stability, providing diagnostic implications for oral diseases within older patient populations.

An investigation of heterogeneous impact, temporal stability, and aggregate shift in factors affecting the driver injury severity in single-vehicle rollover crashes.

Single-vehicle rollover crashes have been acknowledged as a predominant highway crash type resulting in serious casualties. To investigate the heterogeneous impact of factors determining different injury severity levels in single-vehicle rollover crashes, the random parameters logit model with unobserved heterogeneity in means and variances was employed in this paper. A five-year dataset on single-vehicle rollover crashes, gathered in California from January 1, 2013, to December 31, 2017, was utilized. Driver injury severities that were determined to be outcome variables include no injury, minor injury, and severe injury. Characteristics pertaining to the crash, driver, temporal, vehicle, roadway, and environment were acknowledged as potential determinants. The results showed that the gender indicator specified to minor injury was consistently identified as a significant random parameter in four years' models and the joint five-year model, excluding the 2016 crash model where the night indicator associated with no injury was observed to produce the random effect. Additionally, two series of likelihood ratio tests were conducted to assess the year-to-year and aggregate-to-component temporal stability of model estimation results. Marginal effects of explanatory variables were also calculated and compared to analyze the temporal stability and interpret the results. The findings revealed an overall temporal instability of model specifications across individual years, while there is no significant aggregate-to-component variation. Injury severities were observed to be stably affected by several variables, including improper turn indicator, under the influence of alcohol indicator, old driver indicator, seatbelt indicator, insurance indicator, and airbag indicator. Furthermore, the year-to-year and aggregate-to-component shift was quantified and characterized by calculating the differences in probabilities between within-sample observations and out-of-sample predictions. The overall results imply that continuing to expand and refine the model to incorporate more comprehensive datasets can result in more robust and stable injury severity prediction, thus benefiting in mitigating the associated driver injury severity.

Evaluating helmet-wearing of single-vehicle overspeeding motorcycle crashes: Insights from temporal instability in parsimonious pooled framework.

OBJECTIVE: A lower helmet-wearing rate and overspeeding in Pakistan are critical risk behaviors of motorcyclists, causing severe injuries. To explore the differences in the determinants affecting the injury severities among helmeted and non-helmeted motorcyclists in motorcycle crashes caused by overspeeding behavior, single-vehicle motorcycle crash data in Rawalpindi city for 2017-2019 is collected. Considering three possible crash injury severity outcomes of motorcyclists: fatal injury, severe injury and minor injury, the rider, roadway, environmental, and temporal characteristics are estimated. METHODS: To provide a mathematically simpler framework, the current study introduces parsimonious pooled random parameters logit models. Then, the standard pooled random parameters logit models without considering temporal effects are also simulated for comparison. By comparing the goodness of fit measure and estimation results, the parsimonious pooled random parameters logit model is suitable for capturing the temporal instability. Then, the non-transferability among helmeted and non-helmeted overspeeding motorcycle crashes is illustrated by likelihood ratio tests and out-of-sample prediction, and two types of models provide robust results. The marginal effects are also calculated. RESULTS: Several variables, such as age, cloudy and weekday indicators illustrate temporal instability. Moreover, several variables are observed to only show significance in non-helmeted models, showing non-transferability across helmeted and non-helmeted models. CONCLUSIONS: More educational campaigns, regulation and enforcement, and management countermeasures should be organized for non-helmeted motorcyclists and overspeeding behavior. Such findings also provide research reference for the risk-compensating behavior and self-selected group issues under overspeeding riding considering the usage of helmets.

Plant diversity and community age stabilize ecosystem multifunctionality.

It is well known that biodiversity positively affects ecosystem functioning, leading to enhanced ecosystem stability. However, this knowledge is mainly based on analyses using single ecosystem functions, while studies focusing on the stability of ecosystem multifunctionality (EMF) are rare. Taking advantage of a long-term grassland biodiversity experiment, we studied the effect of plant diversity (1-60 species) on EMF over 5 years, its temporal stability, as well as multifunctional resistance and resilience to a 2-year drought event. Using split-plot treatments, we further tested whether a shared history of plants and soil influences the studied relationships. We calculated EMF based on functions related to plants and higher-trophic levels. Plant diversity enhanced EMF in all studied years, and this effect strengthened over the study period. Moreover, plant diversity increased the temporal stability of EMF and fostered resistance to reoccurring drought events. Old plant communities with shared plant and soil history showed a stronger plant diversity-multifunctionality relationship and higher temporal stability of EMF than younger communities without shared histories. Our results highlight the importance of old and biodiverse plant communities for EMF and its stability to extreme climate events in a world increasingly threatened by global change.

Motion state-dependent motor learning based on explicit visual feedback has limited spatiotemporal properties compared with adaptation to physical perturbations.

We recently showed that subjects can learn motion state-dependent changes to motor output (temporal force patterns) based on explicit visual feedback of the equivalent force field (i.e., without the physical perturbation). Here, we examined the spatiotemporal properties of this learning compared with learning based on physical perturbations. There were two human subject groups and two experimental paradigms. One group (n = 40) experienced physical perturbations (i.e., a velocity-dependent force field, vFF), whereas the second (n = 40) was given explicit visual feedback (EVF) of the force-velocity relationship. In the latter, subjects moved in force channels and we provided visual feedback of the lateral force exerted during the movement, as well as the required force pattern based on movement velocity. In the first paradigm (spatial generalization), following vFF or EVF training, generalization of learning was tested by requiring subjects to move to 14 untrained target locations (0° to ±135° around the trained location). In the second paradigm (temporal stability), following training, we examined the decay of learning over eight delay periods (0 to 90 s). Results showed that learning based on EVF did not generalize to untrained directions, whereas the generalization for the vFF was significant for targets ≤ 45° away. In addition, the decay of learning for the EVF group was significantly faster than the FF group (a time constant of 2.72 ± 1.74 s vs. 12.53 ± 11.83 s). Collectively, our results suggest that recalibrating motor output based on explicit motion state information, in contrast to physical disturbances, uses learning mechanisms with limited spatiotemporal properties.NEW & NOTEWORTHY Adjustment of motor output based on limb motion state information can be achieved based on explicit information or from physical perturbations. Here, we investigated the spatiotemporal characteristics of short-term motor learning to determine the properties of the respective learning mechanisms. Our results suggest that adjustments based on physical perturbations are more temporally stable and applied over a greater spatial range than the learning based on explicit visual feedback, suggesting largely separate learning mechanisms.

Climate change-related biodiversity fluctuations and composition changes in an old-growth subtropical forest: A 26-yr study.

The detection and attribution of biodiversity change is of great scientific interest and central to policy effects aimed at meeting biodiversity targets. Yet, how such a diverse climate scenarios influence forest biodiversity and composition dynamics remains unclear, particularly in high diversity systems of subtropical forests. Here we used data collected from the permanent sample plot spanning 26 years in an old-growth subtropical forest. Combining various climatic events (extreme drought, subsequent drought, warming, and windstorm), we analyzed long-term dynamics in multiple metrics: richness, turnover, density, abundance, reordering and stability. We did not observe consistent and directional trends in species richness under various climatic scenarios. Still, drought and windstorm events either reduced species gains or increased species loss, ultimately increased species turnover. Tree density increased significantly over time as a result of rapid increase in smaller individuals due to mortality in larger trees. Climate events caused rapid changes in dominant populations due to a handful of species undergoing strong increases or declines in abundance over time simultaneously. Species abundance composition underwent significant changes, particularly in the presence of drought and windstorm events. High variance ratio and species synchrony weaken community stability under various climate stress. Our study demonstrates that all processes underlying forest community composition changes often occur simultaneously and are equally affected by climate events, necessitating a holistic approach to quantifying community changes. By recognizing the interconnected nature of these processes, future research should accelerate comprehensive understanding and predicting of how forest vegetation responds to global climate change.