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Supplementary Methods The following Zenodo repository contains all the necessary material to reproduce the results reported in the text: https://zenodo.org/doi/10.5281/zenodo.10061466. At a high level, the file resistance-resilience.RProj can be opened within RStudio to access and run the entire workflow. 1. Contents The Supplementary Information is organised into six main folders: data - radiocarbon date tables for 16 regions. scripts - R scripts for running Bayesian MCMC models, statistical modelling of results, and producing outputs. fits & output - the results of running the above scripts. figures & supplement – figures and tables produced for the main text and for the Extended Data. 2. Data Raw data for the MCMC analysis can be found in the data folder, comprising 18 tables (.csv format) of archaeological radiocarbon dates with accompanying metadata. 3. Analysis Bayesian MCMC Code for performing Bayesian Markov Chain Monte Carlo analysis on aggregated radiocarbon data (mcmc.R). Please note that, given the long processing time and memory requirements for each MCMC fit, the script contains code to reproduce a single example: Southeastern Norway. This is one of the smaller datasets (617 dates), and takes approximately ~6 hours to complete on an Intel(R) Core(TM) i5-9600 CPU @ 3.10GHz with 16 GB of DDR3 RAM. However, any of the 18 radiocarbon datasets can be substituted in this script and the parameters altered per Table S1 to obtain posteriors for any case study. The output folder contains the full results of the Bayesian MCMC analysis: MCMC diagnostics, parameters, posterior checks, and resistance-resilience metrics collected on each fit, including traceplots, Rhat, and ESS checks. Resistance-resilience metrics Code for the resmet() function is also contained in the mcmc.R file. resmet() is an adaptation of Edinborough et al.'s post-hoc statistical test for demographic events in written and oral history (https://doi.org/10.1073/pnas.1713012114). The inspiration for this function - p2pTest() in rcarbon - is for use with objects of class ‘SpdModelTest’. This function extends the principle to ‘spdppc’ objects. Following Riris and De Souza (ref. 12), Nimmo et al. (ref. 52), Cantarello et al. (ref. 53), and Van Meerbeek et al. (ref. 11), this will perform post-hoc tests for resistance and resilience on marks of an ‘spdppc’ object over all periods where SPDs are below growth model expectations ('downturns'). These two metrics are defined as the ability to absorb disturbances and "bounce back" following disturbances, respectively. They are normalised relative to the value of the SPD at the start of the interval of interest and fully described in the Methods section of the main text. The function outputs a data frame containing the value of both metrics, as well as the duration, end- and start-times of downturns, and the time to SPD minimum, all in calendar years Before Present. Parameter 'LD' (short for lag/duration) is the Time to SPD minimum normalised by the downturn duration - which we term 'Pace' in the main text. Raw results on individual posterior predictive checks can be found in the mcmc_metrics subfolder. resistance-resilience_metrics.csv contains the compiled, cleaned, and annotated dataset used in statistical modelling. Statistical Modelling Code for performing linear mixed-effect modelling on resistance-resilience metrics is contained in the statisticalmodelling.R file. It generates fitted models and diagnostics from the file resistance-resilience_metrics.csv. 4. Display items Figures and tables for the main paper text and the Materials & Methods can be found in the relevant sub-folders. The plotting.R script produces Figures 2-3 and Figures S1-7. Supplementary references 53. Nimmo, D.G., R. MacNally, S.C. Cunningham, A. Haslem, A.F. Bennett. Vive la résistance: reviving resistance for 21st century conservation. TREE 30, 516-23 (2015). https://doi.org/10.1016/j.tree.2015.07.008 54. Cantarello, E., A.C. Newton, P.A. Martin, P.M. Evans, A. Gosal, M.S. Lucash. Quantifying resilience of multiple ecosystem services and biodiversity in a temperate forest landscape. Ecol. Evol. 7, 9661-75. https://doi.org/10.1002/ece3.3491

The interest in testing different survey methods within the framework of the IPAAST project was motivated by three objectives: identifying evidence of rural life in the hinterland of the Roman colony of Augusta Emerita, with special attention to forms of resilience and diversification of agrarian activities beyond the areas of highest productivity of the alluvial plain of the Guadiana River. assessing the composition and depth of soils in the area in order to evaluate the representativeness of surface finds and perform a regression analysis to assess the potential distribution of arable lands in the area from Roman times to the present. combining geophysical methods commonly used in precision agriculture and archaeology in order to evaluate their interoperability and the complementary information they can provide. Attention was focused on a sector of the estate where two elements were coincident 1) preliminary evidence suggested the estate’s highest concentration of archaeological finds. 2) land plots within the estate used for grazing where LIFE Adapt experiments were undertaken. This area encompassed approximately. 6.5 ha. 2 geophysical methods for the exploration were used: Magnetic survey: a 2 sensors gradiometer system was used (Grad602 Bartington). Data sets: RC_MAG 01 Vector limits of survey area. 02 Vector point file of vertices of survey area. 03 Grid composite. 04 Raster interpolation of magnetic data. Electromagnetic induction with a EM38Mk2 by Geonics. Data sets: RC_EMI01. 01 Vector limits of survey area 02 Vector file of point data (raw data) 03 Raster interpolation of quad-phase (conductivity) 0,5m 04 Raster interpolation of quad-phase (conductivity) 1m 05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m 06 Raster interpolation of in-phase (magnetic susceptibility) 1m RC_EMI02. 01 Vector limits of survey area 02 Vector file of point data 03 Raster interpolation of quad-phase (conductivity) 0,5m 04 Raster interpolation of quad-phase (conductivity) 1m 05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m 06 Raster interpolation of in-phase (magnetic susceptibility) 1m

Supplementary information

These are the models and CAR scores presented in reported in R. Kyle Bocinsky, Johnathan Rush, Keith W. Kintigh, and Timothy A. Kohler. Exploration and exploitation in the macrohistory of the prehispanic Pueblo Southwest. Science Advances, 2:e1501532. These files are R data sets. See the paleocar package for information on how to extract model uncertainty and other data from these data files.

Data and code version submitted to the journal. The dataset included here provides a collection of archaeological sites from three areas in modern Iraq (Haditha dam, Mosul dam, Hamrin dam). In addition, this repository provides reproducible analyses in the form R scripts, JavaScript code for Google Earth Engine and QGIS models.

Near Eastern Neolithic farmers introduced several species of domestic plants and animals as they dispersed into Europe. Dogs were the only domestic species present in both Europe and the Near East prior to the Neolithic. Here, we assessed whether early Near Eastern dogs possessed a unique mitochondrial lineage that differentiated them from Mesolithic European populations. We then analysed mitochondrial DNA sequences from 99 ancient European and Near-Eastern dogs spanning the Upper Palaeolithic to the Bronze Age to assess if incoming farmers brought Near Eastern dogs with them, or instead primarily adopted indigenous European dogs after they arrived. Our results show that European pre-Neolithic dogs all possessed the mitochondrial haplogroup C, and that the Neolithic and Post-Neolithic dogs associated with farmers from Southeastern Europe mainly possessed haplogroup D. Thus, the appearance of haplogroup D most likely resulted from the dissemination of dogs from the Near East into Europe. In Western and Northern Europe, the turnover is incomplete and C haplogroup persists well into the Chalcolithic at least. These results suggest that dogs were an integral component of the Neolithic farming package and a mitochondrial lineage associated with the Near East was introduced into Europe alongside pigs, cows, sheep, and goats. It got diluted into the native dog population when reaching the Western and Northern margins of Europe. modern and ancient dog mt sequence (HVR1)all-sequence file.fst

Here we present a systematic survey of works published in international journals in 2001–2022, with the aim of providing an annotated bibliography on the theme and collect quantitative information about each case study. Data collected allowed to analyze the geographic distribution of LiDAR-based studies, the specifics of acquisitions, the topography and vegetation cover of each study area, the characteristics of the material culture detected, major goals and integrated techniques.

First release of the Views of Leuven Collection Dataset. Full dataset of the Views of Leuven Collection which includes images and maps of Leuven from the 16th-20th centuries.

These data were collected as part of a case study for the ipaast project. The aim of the survey was to produce datasets interoperable for applications in archaeology and precision agriculture. The OptRx® Crop Sensors (AgLeader Technology, Ames, IO, USA) measure the reflectance in the 630–685 nm (red), 695–750 nm (RE red edge) and 760–850 nm (NIR—Near InfraRed) wavebands. Using those wavebands, NDVI and NDRE indexes are calculated. NDVI and NDRE are vegetative indexes obtained from the red, red-edge and NIR wavebands with formulas 1 and 2: NDVI = NIR−REDNIR+RED ; NDRE= NIR−RENIR+RE The two index values range from -1 (bare ground or water) to 1 (highly vigorous vegetation). To collect data, the sensor was mounted on a ground vehicle, a Kubota B2420 tractor. The sensor was paired with a GNNS receiver, GPS 6500 from AgLeader Technology (Ames, IO, USA). The instrumentation was coupled with the hardware and the rough book (Panasonic ToughPad FG-Z1, Panasonic Core. It was possible to install the sensor facing the ground using a metal bracket positioned on the front of the tractor. The sensor was positioned 1.15 m from the ground, emitting a rectangular footprint of 1.14 m in length and 20cm in width. The data were collected every 30 cm in alternate rows. 12 rows in total were analysed, covering a surface of 1.07 ha. Data were processed on QGIS. First, the data was interpolated with the Inverse Distance Weighting (IDW) function. The function was set up with a distance coefficient P of 4, with 40 rows and 98 columns. A Gaussian filter with a standard deviation value of 2 and a range of research of 3 was subsequently applied to create a representative raster.