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Comparative Legacies of Human Land Use in the Brazilian Atlantic Forest

Lead Research Organisation: Bournemouth University
Department Name: Faculty of Science and Technology

Abstract

Tropical forests have long been considered 'pristine' barriers to past human presence, with industrial-scale clearance supposedly confirming that sustainable futures for humans in these habitats is impractical. However, a wealth of recent archaeological and anthropological research, applying some of the latest surveying, modelling, and biomolecular techniques is showing the long history of human occupation and management of these biodiverse habitats. Detailed local and regional studies of this tropical cultural heritage is now acknowledged as being critical for the development of more effective policy and conservation efforts in tropical forests the world over. Nevertheless, comparative information on the long-term human history of tropical forests at the centennial scale and above remains limited by isolated environmental, historical, and archaeological literatures. Consequently, a lack of cross- and inter-disciplinary knowledge on the legacies of different forms of anthropogenic impact hinders the humanities' ability to contribute meaningfully to conservation and restoration efforts, an area that, in theory, they are ideally positioned to contribute.

The project aims to understand how long-term human land use and population change have impacted the Brazilian Atlantic Forest, one of the most threatened tropical forests around the world in the 21st century. Focusing on changes to its ecology, and associated earth systems, that were caused by human activity, it will capture the timing and extent of environmental impacts between three focal periods. These represent major transitions in subsistence practices and demographic regimes: Pre-Columbian (before AD1500), Colonial (AD1500-1700), and Early Industrial (AD1700-1808). The project will produce a novel combination of demographic information, settlement data, and multi-proxy palaeoecological records to create the first deep time perspective on how humans have shaped the Atlantic Forest at a biome scale. The comparative analysis of specialised archaeological, historical, and ecological domain knowledge will produce long-term land use data and a 'usable past' for climate science, ecosystem restoration, and policy goals that aim to protect this crucial, but threatened, environment into the future.

To address these issues, the project will synthesise multifaceted "palaeocultural" and palaeoecological data to compare how different land use regimes have impacted the Atlantic Forest of southern and central Brazil over the long term. The novelty of this research lies in the creation and integration of archaeological and historical data that can inform ongoing efforts to incorporate past human activity into conservation policy and climate models. Accounting for pre-1800 human activity is the next logical step in their development yet major methodological challenges remain, making accurate, deep-time models of land use history more important than ever. The approaches pioneered by this project target two prominent problems in this arena: first, the inability to systematically compare prehistoric and historic land use and population data, and second, the difficulty of directly linking environmental and vegetational change to drivers such as past land use. The proposed approach is transferable to other settings with similar sources of palaeo-cultural and -ecological data, which will enable crucial comparisons of the long-term human impacts across the global tropics to take place. Beyond estimates of environmental footprints, the project will also provide a greater understanding of synergies and trade-offs between land use intensity and biodiversity in the Atlantic Forest, as well as a greater understanding of the thresholds that define the Anthropocene in the Neotropics.
 
Description The scale of past human impacts on vegetation is of widespread interest to archaeologists. In the Atlantic Forest of southern Brazil (Portuguese: Mata Atlântica), previous work has identified Native American inhabitation as influential to the extent of forest cover, with legacies of ancient impact that remain detectable in modern forests. Previously published investigations into the ecological history of the southern Atlantic Forest have either. This study measures the impact of Pre-Columbian settlement and associated land use on the modern-day distribution of several key plant species across the entire southern Atlantic Forest. We use species distribution models (SDMs) using Indigenous archaeological site locations (Tupiguarani and southern proto-Je archaeological cultues) and
modern plant species occurrence data (30 unique species) in a comparative framework to investigate Indigenous influence on the likelihood of occurrence of culturally significant or medicinal plant species.
Exploitation Route The methods are cross-applicable to any setting where Maxent modelling may be used. Results will be of interest to Indigenous groups seeking to reconnect with ancient land use practices.
Sectors Agriculture

Food and Drink

Communities and Social Services/Policy

Environment

Culture

Heritage

Museums and Collections

 
Title Continuous and reclassified model predictions showing relative likelihood of occurrence for Amerindian groups and plant species in the southern Atlantic Forest, Brazil. 
Description Continuous and reclassified model predictions showing relative likelihood of occurrence for Amerindian groups and plant species in the southern Atlantic Forest, Brazil. 
Type Of Material Database/Collection of data 
Year Produced 2025 
Provided To Others? Yes  
Impact Free and open access to research outputs and workflows. 
URL https://zenodo.org/doi/10.5281/zenodo.14955744
 
Title Data and scripts for the paper 'Frequent disturbances enhance the resilience of past human populations' 
Description 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 
Type Of Material Database/Collection of data 
Year Produced 2024 
Provided To Others? Yes  
URL https://zenodo.org/doi/10.5281/zenodo.10061466
 
Title Data and scripts for the paper 'Frequent disturbances enhance the resilience of past human populations' 
Description 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 
Type Of Material Database/Collection of data 
Year Produced 2024 
Provided To Others? Yes  
URL https://zenodo.org/doi/10.5281/zenodo.10818811
 
Title Electronic Supplementary Information for "The biogeography of population resilience in lowland South America" chapter 
Description This repository holds the code and data for reproducing the analysis in the chapter "The biogeography of population resilience in lowland South America", forthcoming in the Oxford Handbook of Resilience in Climate History. It also contains supplementary information on the statistical modelling undertaken as part of the aforementioned work. The code comprises five principal sections, plus setup: Setup & data loading Data processing and display Radiocarbon analysis Statistical modelling Output Supplementary output The code features an extended version of the p2pPerm function that was first introduced in Riris and De Souza (2021), here called the resmet (resilience metrics) function. In addition, the code is accompanied by three datasets: A table containing archaeological radiocarbon dates from lowland tropical South America A shapefile of South American ecoregions, original data available here A table of domesticated Neotropical plants resolved in Amazonian palaeoecological records, after Iriarte et al. (2020) Briefly, the georeferenced radiocarbon data used in the paper have been compiled from a wide range of sources, including Goldberg et al. (2016), Riris & Arroyo-Kalin (2019), Napolitano et al. (2019) Arroyo-Kalin & Riris (2021), De Souza & Riris (2021), and Bird et al. (2022). These sources have been extensively cross-checked for duplicate lab codes, variation in site naming conventions, and reported locations, in order to minimise errors arising from these variables. It does not purport to be error-free, although it is adequate for the current analysis. As well as its use in the production of Figure 1, the ecoregions shapefile has been intersected with the radiocarbon date locations to append this information to rhdata.csv. An extended description and rationale for its use can be found in the main text. For the convenience of the end-user, an additional file containing the main results (metrics_regular.csv) is included. The data contained in this table is the subject of section 3 of the code, Statistical Modelling. It forms the basis of the discussion in the chapter. Data cleaning was carried out manually on the raw output of the resmet function to remove false positives from the table. These "events" are either: a) statistically significant downturns present in periods where, logically, no humans should be present, e.g. in the Greater Antilles before ~6000 cal BP, or: b) downturns where there are no minima in the summed probability distributions of calibrated radiocarbon dates, returning nonsensical resilience metrics. Removing these data rows introduces errors to the variable Cumulative, which counts the cumulative number of downturns detected by the permTest function in rcarbon. The file version of the output in this repository should be considered authoritative for present purposes, as these counting errors in Cumulative have been manually fixed too. References Arroyo-Kalin, M. and Riris, P. 2021. Did pre-Columbian populations of the Amazonian biome reach carrying capacity during the Late Holocene? Phil. Trans. R. Soc. B 376: 20190715 http://doi.org/10.1098/rstb.2019.0715 Bird, D., Miranda, L., Vander Linden, M., Robinson, E., Bocinsky, R.K., Nicholson, C., Capriles, J.M., Finley, J.B., Gayo, E.M., Gil, A. and d'Alpoim Guedes, J. 2022. p3k14c, a synthetic global database of archaeological radiocarbon dates. Scientific Data 9: 1-19. https://doi.org/10.1038/s41597-022-01118-7 De Souza, J.G. & Riris, P. 2021. Delayed demographic transition following the adoption of cultivated plants in the eastern La Plata Basin and Atlantic coast, South America. Journal of Archaeological Science. 125: 105293. https://doi.org/10.1016/j.jas.2020.105293 Goldberg, A., Mychajliw, A.M. and Hadly, E.A. 2016. Post-invasion demography of prehistoric humans in South America. Nature 532: 232-235. https://doi.org/10.1038/nature17176 Iriarte, J., Elliott, S., Maezumi, S.Y., Alves, D., Gonda, R., Robinson, M., de Souza, J.G., Watling, J. and Handley, J. 2020. The origins of Amazonian landscapes: Plant cultivation, domestication and the spread of food production in tropical South America. Quaternary Science Reviews 248: 106582. https://doi.org/10.1016/j.quascirev.2020.106582 Napolitano MF, DiNapoli RJ, Stone JH, Levin MJ, Jew NP, Lane BG, O'Connor JT, Fitzpatrick SM. 2019. Reevaluating human colonization of the Caribbean using chronometric hygiene and Bayesian modeling. Science Advances. 5: eaar7806. https://doi.org/10.1126/sciadv.aar7806 Riris, P. and Arroyo-Kalin, M. 2019. Widespread population decline in South America correlates with mid-Holocene climate change. Scientific Reports 9: 6850. https://doi.org/10.1038/s41598-019-43086-w Riris, P. and De Souza, J.G. 2021. Formal tests for resistance-resilience in archaeological time series. Frontiers in Ecology and Evolution, 9. https://doi.org/10.3389/fevo.2021.740629 
Type Of Material Database/Collection of data 
Year Produced 2024 
Provided To Others? Yes  
Impact Access to a large database of Latin American radiocarbon dates, and workflows for their analysis and processing. 
URL https://github.com/philriris/resilience-handbook-chapter
 
Title Electronic Supplementary Material for: The biogeography of population resilience in lowland South America 
Description ## Electronic Supplementary Information for "The biogeography of population resilience in lowland South America" chapter This repository holds the code and data for reproducing the analysis in the chapter "The biogeography of population resilience in lowland South America", forthcoming in the Oxford Handbook of Resilience in Climate History. It also contains supplementary information on the statistical modelling undertaken as part of the aforementioned work.  The code comprises five principal sections, plus setup: 0. Setup & data loading1. Data processing and display2. Radiocarbon analysis3. Statistical modelling4. Output5. Supplementary output The code features an extended version of the p2pPerm function: (https://github.com/philriris/p2pPerm) that was first introduced in Riris and De Souza (2021) (https://doi.org/10.3389/fevo.2021.740629), here called the `resmet` (RESilience METrics) function.  In addition, the code is accompanied by three datasets: - A table containing archaeological radiocarbon dates from lowland tropical South America - A shapefile of South American ecoregions, original data available here: http://ecologicalregions.info/data/sa/- A table of domesticated Neotropical plants resolved in Amazonian palaeoecological records, after Iriarte et al. (2020)(https://doi.org/10.1016/j.quascirev.2020.106582) Briefly, the georeferenced radiocarbon data used in the paper have been compiled from a wide range of sources, including Goldberg et al. (2016), Riris & Arroyo-Kalin (2019), Napolitano et al. (2019) Arroyo-Kalin & Riris (2021), De Souza & Riris (2021), and Bird et al. (2022). These sources have been extensively cross-checked for duplicate lab codes, variation in site naming conventions, and reported locations, in order to minimise errors arising from these variables. It does not purport to be error-free, although it is adequate for the current analysis.  As well as its use in the production of Figure 1, the ecoregions shapefile has been intersected with the radiocarbon date locations to append this information to `rhdata.csv`. An extended description and rationale for its use can be found in the main text.  For the convenience of the end-user, an additional file containing the main results (metrics_regular.csv) is included. The data contained in this table is the subject of section 3 of the code, Statistical Modelling. It forms the basis of the discussion in the chapter.  Data cleaning was carried out manually on the raw output of the `resmet` function to remove false positives from the table. These "events" are either: a) statistically significant downturns present in periods where, logically, no humans should be present, e.g. in the Greater Antilles before ~6000 cal BP, or: b) downturns where there are no minima in the summed probability distributions of calibrated radiocarbon dates, returning nonsensical resilience metrics. Removing these data rows introduces errors to the variable Cumulative, which counts the cumulative number of downturns detected by the `permTest` function in `rcarbon`. The file version of the output in this repository should be considered authoritative for present purposes, as these counting errors in Cumulative have been manually fixed too.  ### References - Arroyo-Kalin, M. and Riris, P. 2021. Did pre-Columbian populations of the Amazonian biome reach carrying capacity during the Late Holocene? *Phil. Trans. R. Soc. B* 376: 20190715 http://doi.org/10.1098/rstb.2019.0715 - Bird, D., Miranda, L., Vander Linden, M., Robinson, E., Bocinsky, R.K., Nicholson, C., Capriles, J.M., Finley, J.B., Gayo, E.M., Gil, A. and d'Alpoim Guedes, J. 2022. p3k14c, a synthetic global database of archaeological radiocarbon dates. *Scientific Data* 9: 1-19. https://doi.org/10.1038/s41597-022-01118-7 - De Souza, J.G. & Riris, P. 2021. Delayed demographic transition following the adoption of cultivated plants in the eastern La Plata Basin and Atlantic coast, South America. *Journal of Archaeological Science*. 125: 105293. https://doi.org/10.1016/j.jas.2020.105293 - Goldberg, A., Mychajliw, A.M. and Hadly, E.A. 2016. Post-invasion demography of prehistoric humans in South America. *Nature* 532: 232-235. https://doi.org/10.1038/nature17176  - Iriarte, J., Elliott, S., Maezumi, S.Y., Alves, D., Gonda, R., Robinson, M., de Souza, J.G., Watling, J. and Handley, J. 2020. The origins of Amazonian landscapes: Plant cultivation, domestication and the spread of food production in tropical South America. _Quaternary Science Reviews_ 248: 106582. https://doi.org/10.1016/j.quascirev.2020.106582  - Napolitano MF, DiNapoli RJ, Stone JH, Levin MJ, Jew NP, Lane BG, O'Connor JT, Fitzpatrick SM. 2019. Reevaluating human colonization of the Caribbean using chronometric hygiene and Bayesian modeling. _Science Advances_. 5: eaar7806. https://doi.org/10.1126/sciadv.aar7806 - Riris, P. and Arroyo-Kalin, M. 2019. Widespread population decline in South America correlates with mid-Holocene climate change. *Scientific Reports* 9: 6850. https://doi.org/10.1038/s41598-019-43086-w - Riris, P. and De Souza, J.G. 2021. Formal tests for resistance-resilience in archaeological time series. _Frontiers in Ecology and Evolution_, 9. https://doi.org/10.3389/fevo.2021.740629 
Type Of Material Database/Collection of data 
Year Produced 2024 
Provided To Others? Yes  
URL https://zenodo.org/doi/10.5281/zenodo.11108601
 
Title Electronic Supplementary Material for: The biogeography of population resilience in lowland South America 
Description ## Electronic Supplementary Information for "The biogeography of population resilience in lowland South America" chapter This repository holds the code and data for reproducing the analysis in the chapter "The biogeography of population resilience in lowland South America", forthcoming in the Oxford Handbook of Resilience in Climate History. It also contains supplementary information on the statistical modelling undertaken as part of the aforementioned work.  The code comprises five principal sections, plus setup: 0. Setup & data loading1. Data processing and display2. Radiocarbon analysis3. Statistical modelling4. Output5. Supplementary output The code features an extended version of the p2pPerm function: (https://github.com/philriris/p2pPerm) that was first introduced in Riris and De Souza (2021) (https://doi.org/10.3389/fevo.2021.740629), here called the `resmet` (RESilience METrics) function.  In addition, the code is accompanied by three datasets: - A table containing archaeological radiocarbon dates from lowland tropical South America - A shapefile of South American ecoregions, original data available here: http://ecologicalregions.info/data/sa/- A table of domesticated Neotropical plants resolved in Amazonian palaeoecological records, after Iriarte et al. (2020)(https://doi.org/10.1016/j.quascirev.2020.106582) Briefly, the georeferenced radiocarbon data used in the paper have been compiled from a wide range of sources, including Goldberg et al. (2016), Riris & Arroyo-Kalin (2019), Napolitano et al. (2019) Arroyo-Kalin & Riris (2021), De Souza & Riris (2021), and Bird et al. (2022). These sources have been extensively cross-checked for duplicate lab codes, variation in site naming conventions, and reported locations, in order to minimise errors arising from these variables. It does not purport to be error-free, although it is adequate for the current analysis.  As well as its use in the production of Figure 1, the ecoregions shapefile has been intersected with the radiocarbon date locations to append this information to `rhdata.csv`. An extended description and rationale for its use can be found in the main text.  For the convenience of the end-user, an additional file containing the main results (metrics_regular.csv) is included. The data contained in this table is the subject of section 3 of the code, Statistical Modelling. It forms the basis of the discussion in the chapter.  Data cleaning was carried out manually on the raw output of the `resmet` function to remove false positives from the table. These "events" are either: a) statistically significant downturns present in periods where, logically, no humans should be present, e.g. in the Greater Antilles before ~6000 cal BP, or: b) downturns where there are no minima in the summed probability distributions of calibrated radiocarbon dates, returning nonsensical resilience metrics. Removing these data rows introduces errors to the variable Cumulative, which counts the cumulative number of downturns detected by the `permTest` function in `rcarbon`. The file version of the output in this repository should be considered authoritative for present purposes, as these counting errors in Cumulative have been manually fixed too.  ### References - Arroyo-Kalin, M. and Riris, P. 2021. Did pre-Columbian populations of the Amazonian biome reach carrying capacity during the Late Holocene? *Phil. Trans. R. Soc. B* 376: 20190715 http://doi.org/10.1098/rstb.2019.0715 - Bird, D., Miranda, L., Vander Linden, M., Robinson, E., Bocinsky, R.K., Nicholson, C., Capriles, J.M., Finley, J.B., Gayo, E.M., Gil, A. and d'Alpoim Guedes, J. 2022. p3k14c, a synthetic global database of archaeological radiocarbon dates. *Scientific Data* 9: 1-19. https://doi.org/10.1038/s41597-022-01118-7 - De Souza, J.G. & Riris, P. 2021. Delayed demographic transition following the adoption of cultivated plants in the eastern La Plata Basin and Atlantic coast, South America. *Journal of Archaeological Science*. 125: 105293. https://doi.org/10.1016/j.jas.2020.105293 - Goldberg, A., Mychajliw, A.M. and Hadly, E.A. 2016. Post-invasion demography of prehistoric humans in South America. *Nature* 532: 232-235. https://doi.org/10.1038/nature17176  - Iriarte, J., Elliott, S., Maezumi, S.Y., Alves, D., Gonda, R., Robinson, M., de Souza, J.G., Watling, J. and Handley, J. 2020. The origins of Amazonian landscapes: Plant cultivation, domestication and the spread of food production in tropical South America. _Quaternary Science Reviews_ 248: 106582. https://doi.org/10.1016/j.quascirev.2020.106582  - Napolitano MF, DiNapoli RJ, Stone JH, Levin MJ, Jew NP, Lane BG, O'Connor JT, Fitzpatrick SM. 2019. Reevaluating human colonization of the Caribbean using chronometric hygiene and Bayesian modeling. _Science Advances_. 5: eaar7806. https://doi.org/10.1126/sciadv.aar7806 - Riris, P. and Arroyo-Kalin, M. 2019. Widespread population decline in South America correlates with mid-Holocene climate change. *Scientific Reports* 9: 6850. https://doi.org/10.1038/s41598-019-43086-w - Riris, P. and De Souza, J.G. 2021. Formal tests for resistance-resilience in archaeological time series. _Frontiers in Ecology and Evolution_, 9. https://doi.org/10.3389/fevo.2021.740629 
Type Of Material Database/Collection of data 
Year Produced 2024 
Provided To Others? Yes  
URL https://zenodo.org/doi/10.5281/zenodo.11108600
 
Description Collaboration to acquire novel sedimentary records of human-environmental interaction in a highland/lowland ecotone of the Brazilian Atlantic Forest 
Organisation Regional Community University of Chapeco
Country Brazil 
Sector Academic/University 
PI Contribution Project Co-Investigator Prof Dr Hermann Behling and doctoral candidate Antonia Reinhardt are carrying out primary data collection in southern Brazil as part of the data generation proposed by the project. The collection of 3-4 new cores in late February 2024 will substantially augment the scope and dimensionality of the project's primary outputs and improve the anticipated outcomes for Ms Reinhardt's doctoral thesis.
Collaborator Contribution Collaborators Jefferson Radaeski & Mirian Carbonera at Unochapecó will host and provide logistical support, as well as their direct support in the field and with consumables for the fieldwork to be undertaken. Their time is given as an in-kind contribution.
Impact No outputs currently; fieldwork will commence in late February 2024. It is a multi-disciplinary collaboration between archaeology and tropical palaeoecology.
Start Year 2023