A comprehensive test of the Locally-Adaptive Model of Archaeological Potential (LAMAP) W. Christopher Carleton a, ⁎, Kong F. Cheong b , Dan Savage c , Jack Barry c , James Conolly c , Gyles Iannone c a Human Evolutionary Studies Program and Department of Archaeology, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia, V5A 1S6, Canada b Department of Anthropology, American University, 4400 Massachusetts Ave NW, Hamilton Hall, WA, District of Columbia 20016-8003, United States c Department of Anthropology, Trent University, 2140 East Bank Drive, Life & Health Sciences, DNA Building, Module “C”, Peterborough, Ontario K9J 7B8, Canada abstract article info Article history: Received 18 June 2016 Received in revised form 9 November 2016 Accepted 15 November 2016 Available online xxxx Extensive archaeological surveys are critical for understanding past human-landscape interaction, but they are frequently impeded by access difficulties, rugged terrain, or obscurant vegetation. These challenges can make ex- tensive surveys prohibitively costly and time-consuming. Consequently, many archaeologists are interested in predictive techniques—i.e., methods that can estimate the potential for a given region to contain archaeological remains. Predictive techniques can reduce the costs of extensive surveys by allowing archaeologists to focus on the regions with the greatest archaeological potential. A few years ago, our research team developed a new tech- nique called the Locally-Adaptive Model of Archaeological Potential (LAMAP) and used it to enhance our under- standing of the relationship between the Classic Maya centre of Minanha, its surrounding landscape, and nearby Maya centres (Carleton et al. 2012). However, when we introduced the method its efficacy had yet to be compre- hensively tested. Recently, we tested its efficacy using a combination of ground-truth survey and remote sensing of Classic Maya sites in west-central Belize. The test involved identifying previously unrecorded archaeological resources and comparing their locations to the LAMAP prediction and to a random model that acted as a null hy- pothesis. Our results indicate that the model performs very well. The high-potential areas of the study region contained three times more archaeological sites than low potential areas, a statistically significant result com- pared to our null model. Our findings indicate that the LAMAP is a useful new archaeological prediction tool and, as a corollary, that the hypothesis of human land-use behaviour underpinning it might accurately reflect the behaviour of the Classic Maya. © 2016 Elsevier Ltd. All rights reserved. Keywords: Classic Maya GIS Predictive modeling LiDAR Survey Human-landscape interaction 1. Introduction Human-landscape interaction is a foundational element of human ecology. It has defined and constrained where people could live, how they could feed themselves, how they could communicate or move about, and the natural resources that they had access to, affecting our bi- ological and cultural evolution. Thus, finding patterns in past human- landscape interaction can illuminate important aspects of human histo- ry, including evolutionary processes, migratory events, economic and political structures, and even past human cognition (e.g., Alexakis et al., 2011; Bevan and Conolly, 2002; Conolly and Lake, 2006; Kanter, 2008; Llobera, 2003; Rua, 2009; Swanson, 2003; Verhagen and Whitley, 2011). To see these patterns we often have to use extensive ar- chaeological surveys because they provide the necessary data at a large enough scale to observe correlations between human activity and land- scape features or traits. They can reveal, for example, patterns in the proximity of settlements to various natural resources or differences among settlements in terms of ecological conditions, from which we can infer past landscape use. Large-scale surveys can be challenging, however, because of difficult access, rugged terrain, dense vegetation, and sediment accumulation. These challenges can make traditional pe- destrian surveys prohibitively time consuming and expensive. And since archaeological projects are commonly constrained by limited re- sources, complete surveys of large areas are not always possible limiting our view of large-scale patterns in past landscape use. The challenges involved in large-scale landscape survey can be par- tially overcome with predictive modeling. Predictive models are tools for estimating the potential of a given parcel of land to contain archae- ological material on the basis of a set of empirically observable relation- ships between site locations and a set of potential predictor variables (Verhagen and Whitley, 2011). In the last few decades, several predic- tive modeling techniques have been published in the academic litera- ture, but rarely have they been rigorously tested after their initial publication. Here we report the results of a robust test of a new Journal of Archaeological Science: Reports 11 (2017) 59–68 ⁎ Corresponding author. E-mail addresses: w.ccarleton@gmail.com (W.C. Carleton), kong.cheong@american.edu (K.F. Cheong), danielsavage@trentu.ca (D. Savage), jack.barry29@gmail.com (J. Barry), jamesconolly@trentu.ca (J. Conolly), giannone@trentu.ca (G. Iannone). http://dx.doi.org/10.1016/j.jasrep.2016.11.027 2352-409X/© 2016 Elsevier Ltd. All rights reserved. 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