Developing Transdisciplinary Approaches to Sustainability Challenges: The Need to Model Socio-Environmental Systems in the Longue Durée

Human beings are an active component of every terrestrial ecosystem on Earth. Although our local impact on the evolution of these ecosystems has been undeniable and extensively documented, it remains unclear precisely how our activities are altering them, in part because ecosystems are dynamic systems structured by complex, non-linear feedback processes and cascading effects. We argue that it is only by studying human–environment interactions over timescales that greatly exceed the lifespan of any individual human (i.e., the deep past or longue durée), we can hope to fully understand such processes and their implications. In this article, we identify some of the key challenges faced in integrating long-term datasets with those of other areas of sustainability science, and suggest some useful ways forward. Specifically, we (a) highlight the potential of the historical sciences for sustainability science, (b) stress the need to integrate theoretical frameworks wherein humans are seen as inherently entangled with the environment, and (c) propose formal computational modelling as the ideal platform to overcome the challenges of transdisciplinary work across large, and multiple, geographical and temporal scales. Our goal is to provide a manifesto for an integrated scientific approach to the study of socio-ecological systems over the long term

Developing Transdisciplinary Approaches to Sustainability Challenges: The Need to Model Socio-Environmental Systems in the Longue Durée

Human beings are an active component of every terrestrial ecosystem on Earth. Although our local impact on the evolution of these ecosystems has been undeniable and extensively documented, it remains unclear precisely how our activities are altering them, in part because ecosystems are dynamic systems structured by complex, non-linear feedback processes and cascading effects. We argue that it is only by studying human–environment interactions over timescales that greatly exceed the lifespan of any individual human (i.e., the deep past or longue durée), we can hope to fully understand such processes and their implications. In this article, we identify some of the key challenges faced in integrating long-term datasets with those of other areas of sustainability science, and suggest some useful ways forward. Specifically, we (a) highlight the potential of the historical sciences for sustainability science, (b) stress the need to integrate theoretical frameworks wherein humans are seen as inherently entangled with the environment, and (c) propose formal computational modelling as the ideal platform to overcome the challenges of transdisciplinary work across large, and multiple, geographical and temporal scales. Our goal is to provide a manifesto for an integrated scientific approach to the study of socio-ecological systems over the long term

A Bayesian alternative for Aoristic analyses in archaeology

Aoristic analysis is often used to handle chronological uncertainties of datasets where scientific dates (e.g. 14C, OSL, etc.) are unavailable, and observations are described by association to archaeological periods or phases. While several advances have been made over the last two decades, the basic principle of this approach remains fundamentally the same. Temporal windows of analyses are first divided into regularly sized time-blocks, and probability weight is assigned to each of these for every observation. Weights are then aggregated by time-block, and the resulting vector of summed probabilities is interpreted as a curve representing changes in the intensity over time of a particular phenomenon. This paper reviews the basic principles and assumptions of aoristic analyses in archaeology, highlighting several issues with its application and interpretation, advocating for a Bayesian alternative implemented via baorista, a new package written in R statistical computing language. The robustness of the proposed solution is evaluated through a series of experiments based on simulated datasets, which showcase key advantages over aoristic analysis. Two specific solutions are considered: a parametric approach where data are fitted to specific growth models and a non-parametric approach that allows for the visualisation of the changing frequencies of events, accounting for sampling error and the peculiarities of archaeological periodisation

The Arrival of Siberian Ancestry Connecting the Eastern Baltic to Uralic Speakers further East

In this study, we compare the genetic ancestry of individuals from two as yet genetically unstudied cultural traditions in Estonia in the context of available modern and ancient datasets: 15 from the Late Bronze Age stone-cist graves (1200-400 BC) (EstBA) and 6 from the Pre-Roman Iron Age tarand cemeteries (800/500 BC-50 AD) (EstIA). We also included 5 Pre-Roman to Roman Iron Age Ingrian (500 BC-450 AD) (IngIA) and 7 Middle Age Estonian (1200-1600 AD) (EstMA) individuals to build a dataset for studying the demographic history of the northern parts of the Eastern Baltic from the earliest layer of Mesolithic to modern times. Our findings are consistent with EstBA receiving gene flow from regions with strong Western hunter-gatherer (WHG) affinities and EstIA from populations related to modern Siberians. The latter inference is in accordance with Y chromosome (chrY) distributions in present day populations of the Eastern Baltic, as well as patterns of autosomal variation in the majority of the westernmost Uralic speakers [1-5]. This ancestry reached the coasts of the Baltic Sea no later than the mid-first millennium BC; i.e., in the same time window as the diversification of west Uralic (Finnic) languages [6]. Furthermore, phenotypic traits often associated with modern Northern Europeans, like light eyes, hair, and skin, as well as lactose tolerance, can be traced back to the Bronze Age in the Eastern Baltic. VIDEO ABSTRACT.status: publishe

The Arrival of Siberian Ancestry Connecting the Eastern Baltic to Uralic Speakers further East.

In this study, we compare the genetic ancestry of individuals from two as yet genetically unstudied cultural traditions in Estonia in the context of available modern and ancient datasets: 15 from the Late Bronze Age stone-cist graves (1200-400 BC) (EstBA) and 6 from the Pre-Roman Iron Age tarand cemeteries (800/500 BC-50 AD) (EstIA). We also included 5 Pre-Roman to Roman Iron Age Ingrian (500 BC-450 AD) (IngIA) and 7 Middle Age Estonian (1200-1600 AD) (EstMA) individuals to build a dataset for studying the demographic history of the northern parts of the Eastern Baltic from the earliest layer of Mesolithic to modern times. Our findings are consistent with EstBA receiving gene flow from regions with strong Western hunter-gatherer (WHG) affinities and EstIA from populations related to modern Siberians. The latter inference is in accordance with Y chromosome (chrY) distributions in present day populations of the Eastern Baltic, as well as patterns of autosomal variation in the majority of the westernmost Uralic speakers [1-5]. This ancestry reached the coasts of the Baltic Sea no later than the mid-first millennium BC; i.e., in the same time window as the diversification of west Uralic (Finnic) languages [6]. Furthermore, phenotypic traits often associated with modern Northern Europeans, like light eyes, hair, and skin, as well as lactose tolerance, can be traced back to the Bronze Age in the Eastern Baltic. VIDEO ABSTRACT