In the mid-1950s Western Desert of Australia, Aboriginal populations were in decline as families left for ration depots, cattle stations, and mission settlements. In the context of reduced population density, an ideal free-distribution model predicts landscape use should contract to the most productive habitats, and people should avoid areas that show more signs of extensive prior use. However, ecological or social facilitation due to Allee effects (positive density dependence) would predict that the intensity of past habitat use should correlate positively with habitat use. We analyzed fire footprints and fire mosaics from the accumulation of several years of landscape use visible on a 35,300-km² mosaic of aerial photographs covering much of contemporary Indigenous Martu Native Title Lands imaged between May and August 1953. Structural equation modeling revealed that, consistent with an Allee ideal free distribution, there was a positive relationship between the extent of fire mosaics and the intensity of recent use, and this was consistent across habitats regardless of their quality. Fire mosaics build up in regions with low cost of access to water, high intrinsic food availability, and good access to trade opportunities; these mosaics (constrained by water access during the winter) then draw people back in subsequent years or seasons, largely independent of intrinsic habitat quality. Our results suggest that the positive feedback effects of landscape burning can substantially change the way people value landscapes, affecting mobility and settlement by increasing sedentism and local population density. Significance Models of human habitat choice and landscape use assume that people have negative effects on resource availability, which causes them to avoid regions that are already occupied or that show signs of extensive past use in favor of regions of higher quality. We show that when people engage in activities that increase resource productivity, like burning, there is the potential for these improvements to change habitat preferences in favor of places that have been previously modified and occupied by people. This process changes the way we think about intensification (and the origins of broad-spectrum economies), which may arise not from the negative effects of people on resources, but from the positive (and often unintentional) feedbacks between people and their environments.

This paper provides the first comprehensive sourcing analysis of the tin ingots carried by the well-known Late Bronze Age shipwreck found off the Turkish coast at Uluburun (ca. 1320 BCE). Using lead isotope, trace element, and tin isotope analyses, this study demonstrates that ores from Central Asia (Uzbekistan and Tajikistan) were used to produce one-third of the Uluburun tin ingots. The remaining two-thirds were derived from the Taurus Mountains of Turkey, namely, from stream tin and residual low-grade mineralization remaining after extensive exploitation in the Early Bronze Age. The results of our metallurgical analysis, along with archaeological and textual data, illustrate that a culturally diverse, multiregional, and multivector system underpinned Eurasian tin exchange during the Late Bronze Age. The demonstrable scale of this connectivity reveals a vast and disparate network that relied as much on the participation of small regional communities as on supposedly hegemonic institutions of large, centralized states.

Biological data are frequently nonlinear, heteroscedastic and conditionally dependent, and often researchers deal with missing data. To account for characteristics common in biological data in one algorithm, we developed the mixed cumulative probit (MCP), a novel latent trait model that is a formal generalization of the cumulative probit model usually used in transition analysis. Specifically, the MCP accommodates heteroscedasticity, mixtures of ordinal and continuous variables, missing values, conditional dependence and alternative specifications of the mean response and noise response. Cross-validation selects the best model parameters (mean response and the noise response for simple models, as well as conditional dependence for multivariate models), and the Kullback–Leibler divergence evaluates information gain during posterior inference to quantify mis-specified models (conditionally dependent versus conditionally independent). Two continuous and four ordinal skeletal and dental variables collected from 1296 individuals (aged birth to 22 years) from the Subadult Virtual Anthropology Database are used to introduce and demonstrate the algorithm. In addition to describing the features of the MCP, we provide material to help fit novel datasets using the MCP. The flexible, general formulation with model selection provides a process to robustly identify the modelling assumptions that are best suited for the data at hand.

The mixed cumulative probit (MCP), a new, flexible algorithm that accommodates a variety of mean and shape parameters in univariate models and conditional dependence/independence in multivariate models, was used to develop subadult age estimation models. Sixty-two variables were collected on computed tomography (CT) images of 1317 individuals (537 females and 780 males) aged between birth and 21 years from the United States sample in the Subadult Virtual Anthropology Database (SVAD). Long bone measurements (n = 18), stages of epiphyseal fusion and ossification (n = 28), and stages of dental development of permanent teeth (n = 16) were used in univariate, multivariate, and mixed models and compared using test mean log posterior (TMNLP), root mean squared error (RMSE), and percent accuracy on an independent test sample. Out of the six possible parameter combinations, all combinations were accounted for at least once in the data and conditionally dependent models outperformed the conditionally independent models. Overall, multivariate models exhibited smaller TMNLP and RMSE, and an overall greater stability in the age estimations compared to univariate models across all ages and independent of indicator type. Pre-optimized subadult age estimation models are freely available for immediate application through MCP-S-Age, a graphical user interface.