scholarly journals Sparse Radiocarbon Data Confound Culture-Climate Links in Late Pre-Columbian Amazonia

Quaternary ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 33
Author(s):  
Philip Riris

It has recently been argued that pre-Columbian societies in the greater Amazon basin during the Late Holocene were subject to “adaptive cycling”. In this model, cultures practicing “intensive” land use practices, such as raised field agriculture, were vulnerable to perturbations in hydroclimate, whereas “extensive” land use patterns, such as polyculture agroforestry, are viewed as more resilient to climate change. On the basis of radiocarbon data, the relative rise and fall of late pre-Columbian cultures and their inferred patterns of land use in six regions are highlighted to exemplify this model. This paper re-examines the radiocarbon evidence marshalled in favour of adaptive cycling, demonstrating that alleged temporal patterning in these data are overwhelmingly likely due to a combination of sampling effects, lack of statistical controls, and unacknowledged uncertainties that are inherent to radiocarbon dating. The outcome of this combination of factors seriously limits the possibility of cross-referencing archaeological data with palaeo-ecological and -climatological data without controlling for these effects, undermining the central archaeological pillar in support of adaptive cycling in Amazonia. This paper illustrates examples of such mitigation measures and provides the code to replicate them. Suggestions for how to overcome the serious limitations identified in the Late Holocene radiocarbon record of Amazonia are presented in the context of ongoing debates on inferring climatic causation in archaeological and historical datasets.

Author(s):  
Matt Grove

This chapter aims to summarize the results of recent research producing estimates of hominin range areas, population sizes, and land use patterns based on archaeological data. Estimates of such variables are essential to any geographic or demographic discussion of human evolution, yet at present no generally applicable quantitative method is available to link them to the often abundant data of the archaeological record. Such data offer a unique window onto the patterns of adaptation characterizing prehistoric human populations, and developing a generic method to describe trajectories of change will allow researchers to compare range areas, population sizes and land use patterns between different regions and periods from throughout the vast spatio-temporal range of human evolution. The discussion gives particular emphasis to estimating a trajectory of group size through time from shortly after 2 million years ago until approximately 14,000 years ago.


1993 ◽  
Vol 14 (1) ◽  
pp. 25-42 ◽  
Author(s):  
Jordan E. Kerber

Selecting an effective archaeological survey takes careful consideration given the interaction of several variables, such as the survey's goals, nature of the data base, and budget constraints. This article provides justification for a “siteless survey” using evidence from a project on Potowomut Neck in Rhode Island whose objective was not to locate sites but to examine the distribution and density of prehistoric remains to test an hypothesis related to land use patterns. The survey strategy, random walk, was chosen because it possessed the advantages of probabilistic testing, as well as the ease of locating sample units. The results were within the limits of statistical validity and were found unable to reject the hypothesis. “Siteless survey” may be successfully applied in similar contexts where the distribution and density of materials, as opposed to ambiguously defined sites, are sought as evidence of land use patterns, in particular, and human adaptation, in general.


2021 ◽  
Vol 13 (4) ◽  
pp. 631
Author(s):  
Kyle D. Woodward ◽  
Narcisa G. Pricope ◽  
Forrest R. Stevens ◽  
Andrea E. Gaughan ◽  
Nicholas E. Kolarik ◽  
...  

Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas.


2013 ◽  
Vol 35 (1) ◽  
pp. 48-70 ◽  
Author(s):  
Andrea Sarzynski ◽  
George Galster ◽  
Lisa Stack

1996 ◽  
Vol 18 (1) ◽  
pp. 87-93 ◽  
Author(s):  
P. Sainsbury ◽  
R. Hussey ◽  
J. Ashton ◽  
B. Andrews

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