Individual Drivers Research Articles

When do traffic reports make traffic better?

We present a simple dynamic traffic model to study whether public information about road conditions increases or decreases travel times. We find that the effect depends on comparing (1) the increase in travel time when a road with a pre-existing delay becomes congested, and (2) the corresponding increase on a road with no pre-existing delay. Traffic reports are helpful when the first value is large relative to the second. Surprisingly, they are less useful when total road capacity is sufficient to potentially accommodate all drivers without congestion. In that case, traffic reports are always harmful in the nonatomic limit as individual drivers become negligible.

Time-series clustering for pattern recognition of speed and heart rate while driving: A magnifying lens on the seconds around harsh events

Driving pattern recognition has been applied for the purposes of driving styles identification and harsh driving events detection. However, the evolution of driving behavior around and especially before such events has not been investigated at a microscopic level. The objective of this research is to reveal existing driving patterns around harsh events at the driving ‘pulse’ level i.e. a few seconds before and after the event. For that purpose, a time-series clustering approach is applied on speed and heart rate metrics of individual drivers using data collected from a large naturalistic driving study. Results show that there are distinct speed patterns before harsh braking, harsh acceleration, and harsh cornering events. A deceleration is identified shortly before most harsh acceleration and cornering events, which possibly indicates reckless behavior, i.e. drivers not dedicating enough time to smoothly brake before cornering, or of a brief ‘decision-making’ moment before the harsh manoeuvre. On the contrary, speed seems to be steady before harsh braking events. Regarding heart rate, the analysis revealed certain patterns only after raw data were cleansed and filtered. These patterns may show increasing, decreasing or variable heart rate trends, which may correspond to different stress patterns of drivers around harsh events. Finally, we introduce the concept of driving pattern consistency, which can reveal the share of individual drivers that follow the same harsh event pattern. It is indicated that more than half of the drivers are not consistent, suggesting that driving patterns around harsh events may be more context-related than driver personality-related.

Evaluating the barriers and drivers of adopting circular economy for improving sustainability in the mining industry

The transition towards a circular economy (CE) has been espoused as a key paradigm for achieving sustainability, but practical adoption has been limited because of the complexity of decisions involving consideration of many factors (both drivers and barriers) from various perspectives. Meanwhile, decision support frameworks that prioritise the main enablers and barriers of CE for the sustainability of the mining industry in emerging and developing economies are inadequate to aid wide-scale implementation. Also, empirical research that integrates well-established theories to capture the relevant set of barriers and drivers of adoption decisions is still scanty within the evolving literature, especially within developing African nations. Using the technology-organisation-environment (T-O-E) theoretical framework, this research develops a decision-support framework of the critical enablers and inhibitors to CE adoption and prioritises the most pertinent ones to achieving sustainability objectives within the specific context of the mining industry in a developing country using the best-worst method (BWM). The most relevant categories within the study context are ranked, as well as the individual drivers and barriers within them. The research outcomes can aid stakeholders of the mining industry in mineral-rich developing countries to understand the relevant drivers and direct resources to address the most pressing barriers to the successful adoption of CE operational practices to reduce ecological degradation, optimise the consumption and extraction of resources within operational activities, and contribute more sustainably to socio-economic development.

Blending Video Therapy and Digital Self-Help for Individuals With Suicidal Ideation: Intervention Design and a Qualitative Study Within the Development Process.

Digital formats have the potential to enhance accessibility to care for individuals with suicidal ideation. However, digital self-help interventions have faced limitations, including small effect sizes in reducing suicidal ideation, low adherence, and safety concerns. Therefore, we aimed to develop a remote blended cognitive behavioral therapy intervention that specifically targets suicidal ideation by blending video therapy with web-based self-help modules. The objective of this paper is to describe the collaborative development process and the resulting intervention and treatment rationale. First, we compiled intervention components from established treatment manuals designed for people with suicidal ideation or behavior, resulting in the development of 11 drafts of web-based modules. Second, we conducted a qualitative study, involving 5 licensed psychotherapists and 3 lay counselors specialized in individuals with suicidal ideation who reviewed these module drafts. Data were collected using the think-aloud method and semistructured interviews, and a qualitative content analysis was performed. The 4 a priori main categories of interest were blended care for individuals with suicidal ideation, contents of web-based modules, usability of modules, and layout. Subcategories emerged inductively from the interview transcripts. Finally, informed by previous treatment manuals and qualitative findings, we developed the remote blended treatment program. The participants suggested that therapists should thoroughly prepare the web-based therapy with patients to tailor the therapy to each individual's needs. Participants emphasized that the web-based modules should explain concepts in a simple manner, convey empathy and validation, and include reminders for the safety plan. In addition, participants highlighted the need for a simple navigation and layout. Taking these recommendations into account, we developed a fully remote blended cognitive behavioral therapy intervention comprising 12 video therapy sessions and up to 31 web-based modules. The treatment involves collaboratively developing a personalized treatment plan to address individual suicidal drivers. This remote treatment takes advantage of the high accessibility of digital formats while incorporating full sessions with a therapist. In a subsequent pilot trial, we will seek input from individuals with lived experience and therapists to test the feasibility of the treatment.

Long-term orientation and corporate social responsibility in small and medium-sized enterprises: the role of strategy formation mode

PurposeThis study aims to explore the effects of long-term orientation (LTO) and strategy formation mode on corporate social responsibility. While many researchers have investigated how large businesses address corporate social responsibility (CSR), there is little empirical evidence on how small- and medium-sized businesses implement CSR or what individual drivers shape this process.Design/methodology/approachThe paper surveyed 282 small and medium-sized managers from the United Kingdom. The respondents were recruited using platform Prolific Academic.FindingsThe findings reveal that LTO is a prerequisite for developing CSR and shapes strategy formation mode. The findings also suggested that deliberate strategies are positively related to CSR. The results are consistent across different components of LTO (futurity, continuity and perseverance) and CSR types (internal and external).Originality/valueThe results show that all aspects of LTO are relevant for CSR in SMEs. Besides LTO, deliberate strategy formation model is an important factor contributing to CSR. The paper presents as first an empirical contribution to the strategy literature by examining positive relationship between LTO and deliberate strategy formation mode.

Emerging strategies for regeneration of historic urban sites: A systematic literature review

The importance and effectiveness of adaptive reuse as an alternative to the demolition of cultural heritage is well-studied. However, increasing demand in cities to redevelop larger scale urban historic sites requires new strategies to benefit from their inherent values. This paper focuses on strategies of regeneration for historic urban sites. The paper presents a systematic literature review of 45 papers, with case studies geographically concentrated in Europe and Asia, and a focus on residential, commercial, and industrial areas. From the case studies, 46 different regeneration strategies have been deduced. The various strategies are, via content analysis, dissected into a select number of drivers to find commonalities. These key drivers are user participation, top-down, genius loci, sustainability, and conversion. Based on the reflections of the reviewed authors, a performance evaluation assesses the outcomes of these strategies. Further data analysis is conducted at the level of individual drivers as well as their combinations to deduce the most positive-evaluated strategies. Findings show that genius loci contributes to the most positive-evaluated strategies, while top-down driven strategies contribute to the most negative-evaluated strategies. Furthermore, strategies are assessed positively when three or more drivers are considered regardless of their combination. This paper acts as a starting point for further research into the formation of regeneration strategies and into the specific drivers and their combinations or underlying relations. Moreover, it can be of contribution to the practice, to support decision makers and urban planners in developing the appropriate regeneration strategy for historic urban sites.

Differential impacts of road diets on driving behavior among older adults with and without preclinical Alzheimer’s pathology

The driving populace of the United States is aging. The prevalence of Alzheimer disease (AD) will increase in the coming decades. Road diets (that is, the reallocation of one or more lanes of car traffic to other uses) have been proposed as a modification to increase pedestrian safety, particularly for older adults. In contrast, we considered the impacts of road diets on aging drivers, and on those with early pathological accumulation of AD. We observed naturalistic driving data for 60 cognitively normal older drivers (Age Range = 62–87 years, Median = 75 years) driving across three road segments located in the St. Louis metropolitan area, Missouri, United States. We used neuroimaging and lumbar puncture derived biomarker data to determine which of the drivers had preclinical AD. Since previous AD studies identified a variety of changes in driving behavior among older drivers with preclinical AD, we examined driving speed before and after lane repurposing. We found that drivers with preclinical AD drove at lower speeds compared to those without preclinical AD prior to road diet implementation. After lanes were repurposed, there was no statistical difference in the speed between older drivers with and without preclinical AD. We evaluated cognitive performance and found that attentional control had a mediating effect on driver speed, suggesting that an individual’s ability to focus on a specific task and filter out distractions was associated with faster driving. Driving speed after lane repurposing is not mediated by attentional control, suggesting that road diets are impervious to individual driver capacity. We conclude that lane repurposing has potential as an important mobility infrastructure solution that could enhance older driver safety and facilitate aging in place.

Characterizing Human–Automated Vehicle Interactions: An Investigation into Car-Following Behavior

Automated vehicles are expected to influence human drivers’ behavior. Accordingly, capturing such changes is critical for planning and operation purposes. With regard to car-following behavior, a key question is whether existing car-following models can replicate these changes in human behavior. Using a data set that was collected from the car-following behavior of human drivers when following automated vehicles, this paper offers a robust methodology based on the concept of dynamic time warping to investigate the critical parameters that can be used to capture changes in human behavior. The results indicate that spacing can best substantiate such changes. Moreover, calibration and validation of the intelligent driver model (IDM) suggest its inability to capture changes in human behavior in response to automated vehicles. Thus, an extension of the IDM that explicitly models stochasticity in the behavior of individual drivers is applied, and the results show such a model can identify a reduction in uncertainty when following an automated vehicle. This finding also has implications for a stochastic extension to other models when analyzing and simulating a mixed-autonomy traffic flow environment.

Decision-making in organizations: should managers use AI?

PurposePrior research has revealed that a large share of managers is reluctant towards the use of artificial intelligence (AI) in decision-making. This aversion can be caused by several factors, including individual drivers. The purpose of this paper is to better understand the extent to which individual factors influence managers’ attitudes towards the use of AI and, based on these findings, to propose solutions for increasing AI adoption.Design/methodology/approachThe paper builds on prior research, especially on the factors driving the adoption of AI in companies. In addition, data was collected by means of 16 expert interviews using a semi-structured interview guideline.FindingsThe study concludes on four groups of individual factors ranked according to their importance: demographics, familiarity, psychology and personality. Moreover, the findings emphasized the importance of communication and training, explainability and transparency and participation in the process to foster the adoption of AI in decision-making.Research limitations/implicationsThe paper identifies four ways to foster AI integration for organizational decision-making as areas for further empirical analysis by business researchers.Practical implicationsThis paper offers four ways to foster AI adoption for organizational decision-making: explaining the benefits and training the more adverse categories, explaining how the algorithms work and being transparent about the shortcomings, striking a good balance between automated and human-made decisions, and involving users in the design process.Social implicationsThe study concludes on four groups of individual factors ranked according to their importance: demographics, familiarity, psychology and personality. Moreover, the findings emphasized the importance of communication and training, explainability and transparency and participation in the process to foster the adoption of AI in decision-making.Originality/valueThis study is one of few to conduct qualitative research into the individual factors driving usage intention among managers; hence, providing more in-depth insights about managers’ attitudes towards algorithmic decision-making. This research could serve as guidance for developers developing algorithms and for managers implementing and using algorithms in organizational decision-making.

Candidate Genetic and Molecular Drivers of Dysregulated Adaptive Immune Responses After Traumatic Brain Injury.

Abstract Neuroinflammation is a significant and modifiable cause of secondary injury after traumatic brain injury (TBI), driven by both central and peripheral immune responses. A substantial proportion of outcome after TBI is genetically mediated, with an estimated heritability effect of around 26%, but because of the comparatively small datasets currently available, the individual drivers of this genetic effect have not been well delineated. A hypothesis-driven approach to analyzing genome-wide association study (GWAS) datasets reduces the burden of multiplicity testing and allows variants with a high prior biological probability of effect to be identified where sample size is insufficient to withstand data-driven approaches. Adaptive immune responses show substantial genetically mediated heterogeneity and are well established as a genetic source of risk for numerous disease states; importantly, HLA class II has been specifically identified as a locus of interest in the largest TBI GWAS study to date, highlighting the importance of genetic variance in adaptive immune responses after TBI. In this review article we identify and discuss adaptive immune system genes that are known to confer strong risk effects for human disease, with the dual intentions of drawing attention to this area of immunobiology, which, despite its importance to the field, remains under-investigated in TBI and presenting high-yield testable hypotheses for application to TBI GWAS datasets.

Drivers of enhanced evaporative demand in U.S. croplands: Determining relative contribution using constrained input scenarios

AbstractAltered evaporative demand is a global phenomenon observed over recent decades, however, such change has not been attributed explicitly to specific meteorological drivers, hampering consensus on what has caused such change. Here we investigate exactly how much individual drivers have contributed to long‐term grass‐reference evapotranspiration (ETo) change within conterminous United States (CONUS), with an emphasis on agricultural croplands. Using scenarios that constrain individual drivers i.e., air temperatures (T), relative humidity (RH), solar radiation (Rs), and wind speeds (U2) to their climatologies, we determined their relative contribution toward ETo change at monthly and annual scales. Annual ETo increased by 111 mm, or >2 standard deviations (SD) relative to the 1981–2000 baseline, accompanied by strong increase in Rs (2.7 SD), U2 (2.5 SD), T (1.1 SD), and decreased RH (2.3 SD) in regions that account for one‐third of calories produced in the U.S. Annual ETo increase was attributed primarily to T (relative contribution of 36%), followed by Rs (29%), U2 (18%), and RH (17%) with significant spatial and seasonal variability. During agriculturally critical summer months, Rs was the dominant driver with a 40%–50% relative contribution, and other three drivers were roughly equally important. These findings address demand‐side of agricultural water use and imply long‐term change in crop functions and performance, water security, and planning across aridity gradients.

Adoption of Biosecurity Practices in Smallholder Dairy Farms in Ethiopia

Dairy production is an important livelihood source for smallholder dairy farmers who produce the majority of milk consumed and traded in Ethiopia. Dairy production is, however, constrained by livestock diseases that impact farm productivity, food safety, and animal welfare. Biosecurity measures (BSM) include all risk reduction strategies designed to avoid the introduction of pathogenic infections from outside and minimise the spread of diseases within dairy herds. This study used a cross-sectional survey to investigate the adoption of BSM in dairy farms in Addis Ababa and Oromia regions of Ethiopia. Using a questionnaire, scores for adopted external and internal BSM were calculated based on the Ghent’s University Biocheck tool to compare the performance of different farms in Ethiopia. The weighted external biosecurity score was 49.1%, which was below average (below 50% adoption), while the weighted internal biosecurity score was 55.5%. Low adoption of crucial BSM increases the risk of disease introduction into dairy farms and transmission within herds. Adoption of BSM at the farm level was driven by individual, demographic, and socio-economic drivers, including education, farming system, milk value chain, and farming experience among others. Results of this research reveal low adoption of BSM and the imperative to encourage farmers to implement BSM can lead to a reduction in disease pressures and, thus, a reduction in antibiotic use and increased dairy farms productivity, and improved animal health and welfare. Farmers can be encouraged through proactive engagement with veterinarians and extension professionals. Moreover, creating a favourable policy environment can support farmers to adopt and implement BSM, given the known fact that “prevention is better and cheaper than curing diseases.”

Drivers, Motivations, and Barriers in the Creation of Energy Communities: Insights from the City of Segrate, Italy

Energy communities (ECs) are considered significant instruments in the energy transition toward a low-carbon world. Important elements for the creation of ECs are the individual drivers, motivations, and barriers that could stimulate their creation. In this article, we focus on developing an understanding of which aspects favor or slow down the establishment of ECs in the community of Segrate (Italy). From a methodological point of view, the authors present a study based on (i) a preliminary desk analysis, consisting of an extensive and multidisciplinary literature review; (ii) an empirical investigation into the case study of Segrate (a municipality in the Lombardy region, Italy), including energy-related data and geospatial information (i.e., from the census and geographic information system); and (iii) data analysis and the collection of original materials incorporating quantitative and qualitative information (based on online surveys and on-the-spot participatory events) relating to the context. As emerges from the survey, in Segrate (considered a typical European middle-sized city), it is difficult to identify the best physical dimension for ECs: the scale of Segrate’s neighborhoods do not correspond to the EC dimension usually referred to in the literature. In Segrate, the neighborhoods encompass between 4000 and 8000 inhabitants, while existing ECs (with heating systems) cover between 20 and 1200 apartments. Multi-vector ECs are forecastable with 10–20 apartments.

Changes in seasonal compound floods in Vietnam revealed by a time-varying dependence structure of extreme rainfall and high surge

Compound floods due to intense rainfall and storm surges in coastal areas have shown an increasing trend in some parts of the world, and many studies suggested a strong link with climate change. Yet, such link has not been fully explored and quantitively assessed. In this paper, we demonstrate the development and application of a nonstationary framework to determining different compound scenarios, where individual drivers and their interactions have altered under climate change. The framework has been applied to one of the most flood-prone areas: the Ho Chi Minh City of Vietnam, to help analyze the present and future compound flood risks in both the dry and wet seasons driven by the joint effect from heavy inland rainfall and high skew surge. Over the period of 1980–2017, the two drivers are found to be significantly correlated in March and April, corresponding to the transition from dry-to-wet seasons. We also find that the commonly-used traditional multivariate statistical models underestimate the flood magnitudes for both the current (represented by 2020) and future (represented by 2050) scenarios, when compared with the results produced by the nonstationary methods. In addition, the results reveal that the dry season is expected to receive more floods triggered by the increased intensity and frequency of rainfall extremes, with the magnitude reaching a similar level to that of the wet season. This is in line with the climate projections under RCP4.5 and 8.5 scenarios although the duration of dry spells is expected to increase and the total annual rainfall to decrease in Vietnam. The simulated flood inundations indicate remarkable increases in flood magnitude and extension, especially at the locations identified as low risk by the stationary models.

Sharing when stranger equals danger: Ridesharing during Covid-19 pandemic

Using data collected from one of the most popular city-to-city ridesharing platforms, we study how individual drivers respond to an increased health hazard. We analyze the impact of the Covid-19 outbreak on the level of mobility and the price of ridesharing. We show a higher level of prices and a larger price dispersion in areas with increased health risk. We exploit daily discontinuities in the reporting of Covid-19 cases to argue that the variation in prices is due to drivers’ perception of health risks. Our results suggest that a decision-maker promoting ridesharing as a solution to intercity travel should account for the impact of the perceived health risks on prices and find ways to ensure access.

The relationship of active consideration of health outcomes and intertemporal preferences to choice process variables and nutrition: evidence from an experiment on food choice

IntroductionUnderstanding drivers of individuals' food choices is critical to be able to address the public health epidemic of obesity-related health problems. A robust literature documents relationships between intertemporal preferences and body mass index (BMI). However, work on cognition differentiates resource investment in decision-making, with processes frequently categorized as habitual (low investment) vs. model-based (high investment).MethodsWe integrate variables related to cognition with intertemporal preferences to examine how discount rates and active consideration of health impacts of foods faced during choice relate to the nutritional quality of foods selected.ResultsOur results show that participants who actively consider health outcomes choose products with significantly higher nutritional quality compared to those who do not consider the health impacts of choices, even while controlling for discount rates. We also find that participants with lower discount rates selected foods with higher nutritional quality compared to high discount-rate participants, corroborating previous literature. Further, we find that participants who actively considered health during the choice process spent significantly more time making choices, while discount rates did not predict differences in decision times, suggesting a role for habitual vs. model-based decisions.DiscussionThese findings suggest that active consideration of the health consequences during food choice is an important predictor of the nutritional quality of choices even while accounting for intertemporal preferences, which may provide new opportunities to intervene in the decision-making process.

Drivers of winegrowers' decision on land use abandonment based on exploratory spatial data analysis and multilevel models

The frequency of producers opting to abandon agricultural land has become increasingly, highlighting the significance of this phenomenon due to its environmental, landscape, and socio-economic impacts. The decisions of producers to abandon or maintain/improve their farms depend on individual and contextual factors. The aims of this research are twofold. Firstly, to evaluate the influence of the neighbours on the winegrowers' decisions, using spatial analysis. Secondly, to clarify the specific importance of each of the individual and contextual drivers in farmers' decisions to improve their farms, to keep them unchanged or to abandon them, using multilevel models. The results obtained for the case study of vineyards in Spain, reveal a strong agglomeration phenomenon in farmers' decisions indicating that producers make land use decisions influenced by what their neighbours do. A multilevel analysis identifies that individual factors are determinant and that the influence of contextual factors is conditioned by the innovation process at farm level. Individual drivers, such as size, innovation, Protected Designations of Origin and irrigation influence vineyard area, with irrigation having the greatest overall influence, and is expected to be decisive in climate change projections. The Protected Designations of Origin are driving forces that dynamize the territory and achieve productive concentrations, encouraging winegrowers to replant, but they are not enough to halt abandonment. The elements that slow down the abandonment of plots are irrigation and the combination of innovation and context variables, mainly the combination of modernised plots in the municipalities with trading options.

Taking AI risks seriously: a new assessment model for the AI Act

The EU Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address this, we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This integrated model enables the estimation of AI risk magnitude by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We illustrate this model using large language models (LLMs) as an example.

Modelling speed reduction behaviour on variable speed limit-controlled highways considering surrounding traffic pressure: A random parameters duration modelling approach

Variable speed limits are frequently used to improve traffic safety and harmonise traffic flow. This study investigates how, and to what extent, drivers reduce their speed upon passing a variable speed limit sign. We specifically consider the impact on braking behaviour due to the systematic inclusion of different social pressures exerted by surrounding traffic. This social pressure is the natural result of having two vehicle cohorts created by a change in the variable speed limit (the new speed limit being higher than the original). The cohort with the higher speed limit overtakes vehicles with the lower speed limit, instigating a specific passing rate on drivers in the lower speed cohort. A driving simulator study is employed to obtain individual driver data whilst being able to systematically change the social pressure applied. A sample comprising sixty-seven participants conducted multiple randomised drives, with varying passing rates from as low as 90 veh/h to as high as 360 veh/h. The speed reduction behaviour of the participants is modelled using a random parameter duration modelling approach. Both the panel nature of the data and unobserved heterogeneity are captured through a correlated grouped random parameters with heterogeneity-in-the-mean model. The random parameters are predicated on the different passing rate scenarios, allowing drivers to take shorter or longer to reduce their speeds compared to the reference passing rate. It is shown that the extent of social pressure impacts braking behaviour and therefore affects safety measures, which is a function of the magnitude of the speed limit change. In addition, an extensive decision tree analysis is conducted to understand differential braking behaviour. Results reveal that, on average, female drivers take a shorter time to reduce their speed under a high passing rate but longer in a low passing rate scenario compared to males. Similarly, young drivers are found to take longer to reduce their speeds in a high passing rate scenario compared to other age groups. Our main findings indicate that the within-cohort safety is lowest under low passing rates due to comparatively larger speed differences between drivers. Yet, under a high passing rate, we observe an increase in violation of the speed limit by the lower speed limit vehicles (but less within cohort speed differences). Whilst normally this would be an undesired effect across cohorts, this violation is argued to lead to increased safety due to the smaller discrepancy in speed.

Reconstructing groundwater and lake level histories in Northern Wisconsin: isolation of groundwater’s influence on tree rings from climatic and environmental drivers

Tree rings can reveal long-term environmental dynamics and drivers of tree growth. However, individual ecological drivers of tree growth need to be disentangled from the effects of other co-occurring environmental and climatic conditions in tree rings to examine the histories of stand- to landscape-level ecological processes. Here, we integrate ecohydrological theory of groundwater–tree interactions with dendrochronological approaches and develop a new framework to isolate water-level effects on tree rings from climate induced variability in tree ring growth. Our results indicate that changing depth to groundwater within 1–2.3 m of the land surface exerts a substantial influence on red pine growth and this influence can be quantified and used to reconstruct long-term groundwater and lake level histories from tree ring patterns in Northern Wisconsin. This research suggests a substantial influence of groundwater on tree growth with implications for improving the mechanistic understanding of climate-induced tree mortality and reduce uncertainty in forest productivity models. Further, this is a transferable approach to isolate and reconstruct strong environmental drivers of tree growth that co-occur with other environmental signals.