Temporal Depth Research Articles

MDAR: A Multiscale Features-Based Network for Remotely Measuring Human Heart Rate Utilizing Dual-Branch Architecture and Alternating Frame Shifts in Facial Videos.

Remote photoplethysmography (rPPG) refers to a non-contact technique that measures heart rate through analyzing the subtle signal changes of facial blood flow captured by video sensors. It is widely used in contactless medical monitoring, remote health management, and activity monitoring, providing a more convenient and non-invasive way to monitor heart health. However, factors such as ambient light variations, facial movements, and differences in light absorption and reflection pose challenges to deep learning-based methods. To solve these difficulties, we put forward a measurement network of heart rate based on multiscale features. In this study, we designed and implemented a dual-branch signal processing framework that combines static and dynamic features, proposing a novel and efficient method for feature fusion, enhancing the robustness and reliability of the signal. Furthermore, we proposed an alternate time-shift module to enhance the model's temporal depth. To integrate the features extracted at different scales, we utilized a multiscale feature fusion method, enabling the model to accurately capture subtle changes in blood flow. We conducted cross-validation on three public datasets: UBFC-rPPG, PURE, and MMPD. The results demonstrate that MDAR not only ensures fast inference speed but also significantly improves performance. The two main indicators, MAE and MAPE, achieved improvements of at least 30.6% and 30.2%, respectively, surpassing state-of-the-art methods. These conclusions highlight the potential advantages of MDAR for practical applications.

A geographic history of human genetic ancestry.

Describing the distribution of genetic variation across individuals is a fundamental goal of population genetics. In humans, traditional approaches for describing population genetic variation often rely on discrete genetic ancestry labels, which, despite their utility, can obscure the complex, multi-faceted nature of human genetic history. These labels risk oversimplifying ancestry by ignoring its temporal depth and geographic continuity, and may therefore conflate notions of race, ethnicity, geography, and genetic ancestry. Here, we present a method that capitalizes on the rich genealogical information encoded in genomic tree sequences to infer the geographic locations of the shared ancestors of a sample of sequenced individuals. We use this method to infer the geographic history of genetic ancestry of a set of human genomes sampled from Europe, Asia, and Africa, accurately recovering major population movements on those continents. Our findings demonstrate the importance of defining the spatial-temporal context of genetic ancestry to describing human genetic variation and caution against the oversimplified interpretations of genetic data prevalent in contemporary discussions of race and ancestry.

Utilizing multitemporal indices and spectral bands of Sentinel-2 to enhance land use and land cover classification with random forest and support vector machine

Multitemporal imagery offers a critical advantage by capturing seasonal variations, which are essential for differentiating between land use and land cover (LULC) types. While these types may appear similar when examined at one specific time, they exhibit distinct phenological patterns across different seasons. This temporal depth is crucial for enhancing model accuracy, particularly in heterogeneous landscapes where LULC transitions are frequent and complex. This paper made use of spectral bands and indices of Sentinel-2 from April to September 2020 for LULC classification using two advanced machine learning models: Random forest (RF) and support vector machine (SVM). The spectral indices include the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and modified normalized water index (MNDWI). The dataset was divided into four temporal feature sets: April-May, June-July, August-September, and the entire period from April-September. For each two-month period, the median values of the spectral bands and indices were used. Both models were evaluated based on overall accuracy, F1-score, Kappa coefficient, precision, and recall. Results indicate that incorporating multitemporal features enhanced the performance of the chosen models, with overall accuracy increasing from 82.4% to 94.03% for RF and from 75.4% to 93.54% for SVM. Additionally, the RF algorithm demonstrated higher accuracy than the SVM model across various time periods, with notable improvements in other performance metrics. These improvements also underscore the ability of the models to leverage the rich multitemporal data provided by Sentinel-2 for accurate LULC classification. This study highlights the importance of incorporating the dynamics of features in remote sensing applications to enhance the precision and reliability of LULC classification.

Prospect and sustainability prediction of China's new energy vehicles sales considering temporal and spatial dimensions

The development prospect and sustainability of new energy vehicles (NEVs) are facing numerous challenges under the coupling influence of various factors, which has become a major strategic issue in the automotive industry research within China. Establishing a prediction method to forecast the development trends of NEVs under the complex conditions is of great significance. This paper comprehensively addresses the relevant factors influencing the sales of NEVs over temporal depth and spatial breadth, including random white noise and lag effect resulting from lag operators of influencing factors. A multivariate forecasting approach utilizing the ARIMA model and its optimized model, alongside the GBDT model were adopted to forecast NEV sales in China. The findings demonstrate that: 1) NEVs have exhibited a trend of rapid growth, but are still susceptible to random interference and lag effects caused by influencing factors. Key technology breakthroughs, accessibility of charging infrastructure, and national policy support remain as primary drivers of NEVs development; 2) The ARIM–IO–LSTM model achieved high prediction accuracy with a goodness-of-fit R2 exceeding 90%, outperforming the ARIMA and ARIMA-IO models by 23.586% and 20.790%, respectively, while the ARIMA-IO model improved accuracy by 5.126% compared to ARIMA. The method of model combination and outlier incorporation have proved to be practical solutions for strengthening model accuracy. 3) In the next 3–5 years, the NEV industry will enter a stage characterized by steady progress with an upward trend. Adapting the intensity, scope, and direction of policy support, coupled with achieving technological breakthroughs and enhancing infrastructure construction, are essential for sustaining industrial development and attaining desired objectives.

Temporal Depth & Directionality: Competitive Advantage for Sustainable Family Enterprises

Firms seeking to embed sustainability into their core business strategy face inherent tensions of managing conflicting goals of performing on economic, social, and environmental dimensions, in the short- and long-term. Hahn et al., (2014) argue that managers that view these tensions as a paradox may consider radical strategies but end up adopting prudent strategies because they are unable to implement workable solutions due to higher awareness of uncertainty and risks. We argue that subjective interpretation of temporal depth and directionality influences the adoption of sustainable development strategies. Firms with long-term orientation that adopt an overarching vision, structures to incorporate diverse perspectives, and guardrails to prevent tunnel vision, are better positioned to balance financial and non-financial goals. Multi-generational family enterprises with aspirations for transgenerational continuity draw selectively upon their historic past to prepare for success in distant future. When focused on balancing wealth creation for their families and communities, they have the potential to unleash creativity and innovation for a sustainable competitive advantage.

Understanding arrival contexts of local refugee reception using a ‘phase space’ perspective

AbstractMigration is an inherently spatial phenomenon as it depicts the processes and effects of humans' movement from one place to another. Recent debates in geographical migration research highlight the need to adequately understand how the distinct nature of space and place shape migration and arrival processes. Taking up this call, this contribution proposes to bring together more‐than‐relational approaches to space in geography and the ‘local turn’ in research on migration and integration. Drawing on the empirical example of refugee reception and integration in the city of Rostock in Germany, the contribution explores how space, it's persisting and changing elements, impacts migration flows and constrains or supports migrants' arrival processes. The analysis works along three aspects of more‐than‐relational approaches to space, namely the role of persistent spatial aspects, such as the topography or national borders, and their interplay with relational space; the impact of spatially confined administrative units and regional embeddedness; and the temporal depth of space. The contribution closes by proposing two points how a more‐than‐relational approach can enhance the conceptualisation of space in geographical migration studies: (1) the mutual constitution of relational and non‐relational aspects of space into a specific space shaping migration flows through their interplay, and (2) the openness of a ‘phase space’ approach towards future developments admitting that the inner logic of a place can develop and change.

AI Patent Approvals in Service Firms, Patent Radicalness, and Stock Market Reaction

Artificial intelligence (AI)-driven automation is of growing interest in the service sector. Using practice theory in service innovation and recombinant uncertainty frameworks, we ask whether AI patent approval for service firms is received positively by the stock market and whether patent radicalness strengthens or exacerbates the stock market reaction. We draw on 650 service industry firms from the years 1976 to 2019 with 133,813 non-AI patents and AI patents, including 7,543 (AI machine learning), 33,804 (AI hardware), and 53,419 (AI planning/control). The results show that the stock market reaction is positive for machine learning AI patents, and increasing radicalness strengthens the positive relationship; however, the reaction is negative to AI-related planning and control patents and increasing radicalness exacerbates the negative reaction. In addition, stock market reaction is insignificant to AI-related hardware patents and increasing radicalness does not influence this relationship. The findings are robust to excluding large firms representing a significant portion of the AI patents. With increasing radicalness, the stock market reaction to machine learning patents is more positive for low temporal depth and exacerbates with higher patent pedigree. The findings have implications for AI patenting among firms in the service sector.

The Multi-temporal and Multi-dimensional Global Urban Centre Database to Delineate and Analyse World Cities

Monitoring sustainable urban development requires comparable geospatial information on cities across several thematic domains. Here we present the first global database combining such information with city extents. The Global Human Settlement Urban Centre Database (GHS-UCDB) is produced by geospatial data integration to characterise more than 10,000 urban centres worldwide. The database is multi-dimensional and multi-temporal, containing 28 variables across five domains and having multitemporal attributes for one or more epochs when the UC are delineated (1975-1990-2000-2015). Delineation of urban centres for the year 2015 is performed via a logic of grid cell population density, population size, and grid cell contiguity defined by the Degree of Urbanisation method. Each of the urban centres has 160 attributes, including a validation assessment. The novel aspects of this database concern the thematic richness and temporal depth of the variables (across geography, socio-economic, environmental, disaster risk reduction, and sustainable development domains) and the type of geo-information provided (location and extent), featuring an overall consistency that allows comparative analyses across locations and time.

A Carta de Património Arqueológico do PDM do Porto Possibilidades e limites de uma cartografia da memória e mutabilidade da cidade. Parte II – Para além do Centro Histórico

This article’s second part concludes the presentation of the archaeological safeguarding areas included on the Municipal Master Plan of Porto. Aside from the multiple and dense vestiges known at the Historic Centre, the municipal territory holds a variety of remains referring to different inhabiting strategies over time. Recognizing the cultural signifi cance of this diversity can help revitalize the memory of the city, understand its transformations, and explore its temporal depth as a resource for urban and social development

Interpreting a virtual reconstruction from different levels of detail: 3D modeling approaches combined with a phenomenological exploratory study

Despite offering new possibilities for the education, interpretation, preservation, and management of cultural assets, virtual heritage still demands more consistent theoretical-practical debates. Under the scope of developing historical buildings' virtual models, the level of detail (LOD) is defined as a parameter that directly affects workflows, requiring time, investments, and specialized professionals. This work investigates how different LOD configurations influence the construction and interpretation of three-dimensional models of historic buildings. To this end, five 3D models with different LODs of a historic building in Brazil were set in an exploratory study that addressed users' perceptions when experimenting with them from a phenomenological perspective. The goal was to identify which elements and LOD configurations of the model were preponderant. The experiment was performed through an online workshop, where first-year architecture students could virtually experience the 3D models created, comparing them to a movie of the existing building. Participants were divided into five groups, one for each proposed LOD, and described their experiences through reports that were then analyzed, considering a list of themes emerging from their speeches. The findings underwent statistical analysis to understand how different themes and videos were linked. The experiment findings showed that (1) the 3D models with higher LODs led to more comprehensive comments on the architecture of the building and (2) elements such as the building's surroundings, the humanization of the scenery, and the temporal depth of the 3D models should be considered in the virtual reconstruction process.

Chihuahuan Desert Shrine Caves: Refining Chronologies of Religious Iconography and Social Histories for the Jornada and Mimbres Mogollon Regions of the North American Southwest

AbstractThis article presents radiocarbon dates on 29 perishable objects deposited in shrine caves in the Jornada and Mimbres Mogollon regions of far west Texas and southern New Mexico. The dated objects include tablita fragments, effigies, prayer sticks, hafted projectile point foreshafts, and flat curved sticks. Analysis of the dates reveals three significant trends: a particular set of Indigenous ritual practices involving shrine caves in the North American Southwest was of extraordinary temporal depth and continuity; the meanings and material culture associated with shrine caves changed through time; and a signature iconographic expression of Jornada and Mimbres origin cosmologies, the Goggle-eye or “Tlaloc” entity, is older than previously understood. The dating of shrine caves and iconographic motifs provides new insights on early eras of religious expression in the southern Southwest, clarifying both the nature and time depth of foundational cosmologies and providing a deep time perspective for interpretations of how such cosmologies and their material and iconographic expressions changed through time.

Hierarchical generative modelling for autonomous robots

Humans generate intricate whole-body motions by planning, executing and combining individual limb movements. We investigated this fundamental aspect of motor control and approached the problem of autonomous task completion by hierarchical generative modelling with multi-level planning, emulating the deep temporal architecture of human motor control. We explored the temporal depth of nested timescales, where successive levels of a forward or generative model unfold, for example, object delivery requires both global planning and local coordination of limb movements. This separation of temporal scales suggests the advantage of hierarchically organizing the global planning and local control of individual limbs. We validated our proposed formulation extensively through physics simulation. Using a hierarchical generative model, we showcase that an embodied artificial intelligence system, a humanoid robot, can autonomously complete a complex task requiring a holistic use of locomotion, manipulation and grasping: the robot adeptly retrieves and transports a box, opens and walks through a door, kicks a football and exhibits robust performance even in the presence of body damage and ground irregularities. Our findings demonstrated the efficacy and feasibility of human-inspired motor control for an embodied artificial intelligence robot, highlighting the viability of the formulized hierarchical architecture for achieving autonomous completion of challenging goal-directed tasks.

Bayesian regression versus machine learning for rapid age estimation of archaeological features identified with lidar at Angkor

Lidar (light-detection and ranging) has revolutionized archaeology. We are now able to produce high-resolution maps of archaeological surface features over vast areas, allowing us to see ancient land-use and anthropogenic landscape modification at previously un-imagined scales. In the tropics, this has enabled documentation of previously archaeologically unrecorded cities in various tropical regions, igniting scientific and popular interest in ancient tropical urbanism. An emerging challenge, however, is to add temporal depth to this torrent of new spatial data because traditional archaeological investigations are time consuming and inherently destructive. So far, we are aware of only one attempt to apply statistics and machine learning to remotely-sensed data in order to add time-depth to spatial data. Using temples at the well-known massive urban complex of Angkor in Cambodia as a case study, a predictive model was developed combining standard regression with novel machine learning methods to estimate temple foundation dates for undated Angkorian temples identified with remote sensing, including lidar. The model’s predictions were used to produce an historical population curve for Angkor and study urban expansion at this important ancient tropical urban centre. The approach, however, has certain limitations. Importantly, its handling of uncertainties leaves room for improvement, and like many machine learning approaches it is opaque regarding which predictor variables are most relevant. Here we describe a new study in which we investigated an alternative Bayesian regression approach applied to the same case study. We compare the two models in terms of their inner workings, results, and interpretive utility. We also use an updated database of Angkorian temples as the training dataset, allowing us to produce the most current estimate for temple foundations and historic spatiotemporal urban growth patterns at Angkor. Our results demonstrate that, in principle, predictive statistical and machine learning methods could be used to rapidly add chronological information to large lidar datasets and a Bayesian paradigm makes it possible to incorporate important uncertainties—especially chronological—into modelled temporal estimates.

Ancestral commons: the deep-time emergence of Bronze Age pastoral mobility

During the third millennium BC, new types of anthropogenic landscape emerged across northern Europe: heathlands and pasture. These open landscapes afforded mobile pastoralism and the arena for a new funerary practice: barrow building. Here, the authors define this entanglement of people, animals and landscapes as a literal and figurative ‘ancestral commons’. Focusing on western Jutland, they combine palaeoecological and archaeological evidence to characterise the form and temporal depth of the co-emergent links between pastoralism, barrows and mobility. Conceptualising the ancestral commons as a deep-time entanglement, characterised by rhythms of physical and metaphorical movement, reveals a landscape that afforded shared understanding of the ancestral past and a foundation for the subsequent Nordic Bronze Age.

Vegetation dynamics in Dhofar, Oman, from the Late Holocene to present inferred from rock hyrax middens

AbstractArid regions are especially vulnerable to climate change and land use. More than one-third of Earth's population relies on these ecosystems. Modern observations lack the temporal depth to determine vegetation responses to climate and human activity, but paleoecological and archaeological records can be used to investigate these relationships. Decreasing rainfall across the Late Holocene provides a case study for vegetation response to changing hydroclimate. Rock hyrax (Procavia capensis) middens preserve paleoenvironmental indicators in arid environments where traditional archives are unavailable. Pollen from modern middens collected in Dhofar, Oman, demonstrates the reliability of this archive. Pollen, stable isotope (δ13C, δ15N), and microcharcoal data from fossil middens reveal changes in vegetation, relative moisture, and fire from 4000 cal yr BP to the present. Trees limited to moister areas (e.g., Terminalia) today existed farther inland at ~3100 cal yr BP. After ~2900 cal yr BP, taxa with more xeric affiliations (e.g., Senegalia) had increased. Coprophilous fungal spores (Sporormiella) and grazing indicator pollen revealed an amplified signal of domesticate grazing at ~1000 cal yr BP. This indicates that trees associated with semiarid environments were maintained in the interior desert during ~3000–4000 yr of decreasing rainfall and that impacts of human activity intensified after the transition to a drier environment.

Phase-Amplitude Coupling Localizes Pathologic Brain with Aid of Behavioral Staging in Sleep.

Low frequency brain rhythms facilitate communication across large spatial regions in the brain and high frequency rhythms are thought to signify local processing among nearby assemblies. A heavily investigated mode by which these low frequency and high frequency phenomenon interact is phase-amplitude coupling (PAC). This phenomenon has recently shown promise as a novel electrophysiologic biomarker, in a number of neurologic diseases including human epilepsy. In 17 medically refractory epilepsy patients undergoing phase-2 monitoring for the evaluation of surgical resection and in whom temporal depth electrodes were implanted, we investigated the electrophysiologic relationships of PAC in epileptogenic (seizure onset zone or SOZ) and non-epileptogenic tissue (non-SOZ). That this biomarker can differentiate seizure onset zone from non-seizure onset zone has been established with ictal and pre-ictal data, but less so with interictal data. Here we show that this biomarker can differentiate SOZ from non-SOZ interictally and is also a function of interictal epileptiform discharges. We also show a differential level of PAC in slow-wave-sleep relative to NREM1-2 and awake states. Lastly, we show AUROC evaluation of the localization of SOZ is optimal when utilizing beta or alpha phase onto high-gamma or ripple band. The results suggest an elevated PAC may reflect an electrophysiology-based biomarker for abnormal/epileptogenic brain regions.

Amazonian Kichwa of the Curaray River: Kinship and History in the Western Amazon

Studies of contemporary Amazonian societies have always been in dialogue with the theoretical concerns of the anthropology of the day. Whether these themes are environmentally, philosophically, or politically founded (or, less often, economically founded), their sharpest focus is a single village. Rarely have anthropologists deliberately aimed to provide regional-level studies, incorporating people and societies that have related to each other, and continue to do so, over long periods of time. The connections across a region include marriage, friendship and ritual relations, and political alliances, as well as raiding, fighting, and enslavement. The success of this book is to have provided such a study in the Ecuadorean Amazon, for Pastaza Province, encompassing the Kichwa ethnolinguistic group of Sápara, Canelos Runa, Achuar, Napo Runa, Gaes, and Andoas peoples and, more recently, some Waorani kindreds.The main part addresses the kinship relations and the qualities of the memorized landscape of contemporary regional society in the twentieth and twenty-first centuries. The six chapters cover various perspectives concerning how regional society is made and remembered. Indigenous concepts of kinship are key: the llacta (the residential cluster), the ayllu (the extended kin group covering different environments), and the runa (self-identified collectivity). Mary-Elizabeth Reeve builds on the proposal that kinship is practically based in the sense that living and working together forms closeness. To maintain long-distance ties between kin means visiting regularly, exchanging food and knowledge, and participating in festivals, all of which creates a “mutuality of being,” following Marshall Sahlins (p. 147). These relations are reinforced with more formal ones of ritual alliance and friendship, such as godparenthood, and more recently shared political ambitions across ethnic groupings. Movement and travel along rivers and forest paths through settlements and towns (called purina in Kichwa) are continuous elements of each chapter. These social relations are supported by regionally shared forms of healing by shamans and women's skills concerning the making of ceramic pots and the quality of their designs. The attachment to the landscape, and territory, is constituted by stories and myths, which circulate among kinsfolk on a regional level.While each chapter is clearly part of a whole, each could stand on its own in terms of topic and treatment. The writing mixes concise theoretical contextualization and regional comparison with vignettes, particular episodes that took place during the author's fieldwork experience. These vignettes effectively deepen the argument and provide a fine-grain word picture of individuals and their lives. This change of focus makes for compelling and attractive reading and will ensure that the book appeals to a variety of audiences. It also matches a core notion at work in this volume—namely, scale and the levels at which regional society works.Scale is essentially a spatial concept from geography and has been used by social theorists to refer to the way that actors assemble and organize sociopolitical relations across spaces. The analytical significance of scale is to provide flexibility in spatial and temporal terms for the configuration of these relations. Here, kinship is the most important of these scales as it enables Reeve to look at the micro level and scale out to the regional level. Also considered from this perspective in the concluding chapter are Indigenous political organizations in Ecuador and the ways in which their alliances have moved from the regional to the national level. Furthermore, what scale lends the argument in this book is temporal depth. Based on historical understanding of regional society over the last 400 years, Reeve knows that the reach and power of regional society is in flux. Today the dynamics of regional society have been curtailed by the border closure with Peru in the 1940s and incursions by non-Indigenous people, but in the past Amazonian Kichwa societies were larger and stronger, supported by ethnogenesis after colonial-era epidemics. Yet Reeve was only able to understand how this historical regional society was internally made by doing fieldwork in Pastaza and observing the way in which kinship connected people across the region. This fieldwork started in the early 1980s and has intermittently continued to the present day, providing a remarkable series of ethnographic insights that have been enriched by archival documentation (analysis of which Reeve has published elsewhere). This historically informed ethnography is a major achievement. It is hoped that the book will stimulate further such approaches in the future.

Es nuestra tradición: the archaeological implications of an ethnography on a modern ballgame in Oaxaca, Mexico

AbstractPre-hispanic ballgames have an extensive temporal depth and geographical breadth across Mesoamerica, with over 1,500 ball courts recorded on 1,200 archaeological sites in Mexico alone. It is likely that ballgames played critical but variable roles in how communities related to each other. Most interpretations emphasize ballgames as cosmological rituals and legitimation practices exclusive to elites, perhaps often overlooking the more mundane sociopolitical processes and reasons why they carried such critical meaning for people of all classes and statuses. Ethnographic research on modern ballgames played by Indigenous and mestizo communities today can helpfully provide some insights or maybe deeper understandings into ancient ballgame practices and their relation to Mesoamerican communities. While modern games are not isomorphic with the ancient games, the duration of these traditions underscores their continuing importance and their relativity to current research. In this article I present the results of an ethnographic study of the modern ballgame pelota mixteca de hule (Mixtec rubber ballgame) in the southern Mexican state of Oaxaca. Considering the results from the ethnographic data, I then discuss an archaeological case study in the Nejapa Valley of southeastern Oaxaca where numerous ballcourts were recently documented over the past decade.

The Australopithecus assemblage from Sterkfontein Member 4 (South Africa) and the concept of variation in palaeontology.

Interpreting morphological variation within the early hominin fossil record is particularly challenging. Apart from the fact that there is no absolute threshold for defining species boundaries in palaeontology, the degree of variation related to sexual dimorphism, temporal depth, geographic variation or ontogeny is difficult to appreciate in a fossil taxon mainly represented by fragmentary specimens, and such variation could easily be conflated with taxonomic diversity. One of the most emblematic examples in paleoanthropology is the Australopithecus assemblage from the Sterkfontein Caves in South Africa. Whereas some studies support the presence of multiple Australopithecus species at Sterkfontein, others explore alternative hypotheses to explain the morphological variation within the hominin assemblage. In this review, I briefly summarize the ongoing debates surrounding the interpretation of morphological variation at Sterkfontein Member 4 before exploring two promising avenues that would deserve specific attention in the future, that is, temporal depth and nonhuman primate diversity.

Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling

The establishment of high-throughput sequencing technologies and subsequent large-scale genomic datasets has flourished across fields of fundamental biological sciences. The introduction of genomic resources in fisheries management has been proposed from multiple angles, ranging from an accurate re-definition of geographical limitations of stocks and connectivity, identification of fine-scale stock structure linked to locally adapted sub-populations, or even the integration with individual-based biophysical models to explore life history strategies. While those clearly enhance our perception of patterns at the light of a spatial scale, temporal depth and consequently forecasting ability might be compromised as an analytical trade-off. Here, we present a framework to reinforce our understanding of stock dynamics by adding also a temporal point of view. We propose to integrate genomic information on temporal projections of species distributions computed by Species Distribution Models (SDMs). SDMs have the potential to project the current and future distribution ranges of a given species from relevant environmental predictors. These projections serve as tools to inform about range expansions and contractions of fish stocks and suggest either suitable locations or local extirpations that may arise in the future. However, SDMs assume that the whole population respond homogenously to the range of environmental conditions. Here, we conceptualize a framework that leverages a conventional Bayesian joint-SDM approach with the incorporation of genomic data. We propose that introducing genomic information at the basis of a joint-SDM will explore the range of suitable habitats where stocks could thrive in the future as a function of their current evolutionary potential.