2026-10-11T18:46:18Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33865256 2026-09-16T17:24:12Z category_1 category_24 category_272 category_39 category_45 category_734 category_61 portal_5 item_type_3 month_year_09_2026

<p>Spearman rank correlation matrix.</p> Luca Alessandri (7860995) Biophysics Evolutionary Biology Environmental Sciences not elsewhere classified Ecology Sociology Biological Sciences not elsewhere classified Developmental Biology xlink "> understanding standing archaeological interpretations normalized split frequencies historically contingent choices explainable ai techniques evaluate predictor importance elevation progressively dominate capturing structural roles 725 bce ). 2100 &# 8211 early iron age territorially structured landscapes rmcaiii phase signals explainable machine learning tracing settlement dynamics investigate settlement dynamics final bronze age early bronze age random forest machine environmentally constrained pseudo coherent diachronic transformation atium vetus </ random forest early phases primarily structured model phase learning framework latium vetus l </ transition culminates term drivers study shows specific patterns rmca phases results reveal quantitative backbone like relationships increasingly saturated hydrological accessibility hierarchically organised functionally diversified expose non exploratory tools environmental logic defensible locations central italy approach demonstrates absence points <div><p>Understanding the long-term drivers of settlement location requires disentangling ecological constraints from historically contingent choices. Here I apply a Random Forest machine-learning framework, coupled with explainable AI techniques, to investigate settlement dynamics in Latium vetus (central Italy) from the Early Bronze Age to the early Iron Age (c. 2100–725 BCE). Using 108 securely dated sites and a set of environmentally constrained pseudo-absence points, I model phase-specific patterns and evaluate predictor importance through both normalized split frequencies and SHAP values, capturing structural roles as well as the magnitude and direction of effects. The results reveal a coherent diachronic transformation in the environmental logic of settlement. Early phases are primarily structured by hydrological accessibility, whereas from MBA3 onwards topographic configuration and elevation progressively dominate, marking a shift towards morphologically distinctive and defensible locations. This transition culminates in highly canalised settlement signatures during the Final Bronze Age and RMCA phases, before the RMCAIII phase signals a landscape that is increasingly saturated, hierarchically organised and functionally diversified, despite overall political stability. While the reliance on pseudo-absences and uneven sample sizes constrain predictive robustness, the approach demonstrates the value of Random Forest and SHAP as exploratory tools to expose non-linear, threshold-like relationships and to formalise long-standing archaeological interpretations. The study shows how explainable machine learning can provide a quantitative backbone for reconstructing the emergence, consolidation and transformation of territorially structured landscapes in protohistoric central Italy.</p></div> 2026-09-16T17:40:04Z Dataset Dataset 10.1371/journal.pone.0357054.s012 https://figshare.com/articles/dataset/_p_Spearman_rank_correlation_matrix_p_/33865256 CC BY 4.0