Decomposing Habitat Suitability Across the Forager to Farmer Transition

Vernon, Kenneth Blake; Yaworsky, Peter M.; Spangler, Jerry D.; Brewer, Simon; Codding, Brian F.

doi:10.1080/14614103.2020.1746880

Cited by 13 publications

(10 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The machine learning methods are better for prediction, but they lack transparency and standard statistical inference methods [77]. Using the methods showcased here, we can address local and regional research questions pertaining to changing prehistoric land use patterns, such as the transition to agriculture and agriculture's effects on prehistoric population distributions on the GSENM and how farmers of the arid southwest responded to volatile climatic conditions and the subsistence-settlement dynamics that ultimately led to decreased population and a return to foraging subsistence patterns [22,23].…”

Section: Discussionmentioning

confidence: 99%

“…On the local level, our research creates a structured data set of known archaeological sites and models the distribution and land use patterns of farmers in an arid landscape. Using the modeling framework here, we can address questions about the local transition to agriculture and agriculture's effects on prehistoric population distributions on the GSENM [22]. On the regional scale, we can begin to address how arid farmers of the southwest responded to volatile climatic conditions and the subsistence-settlement dynamics that ultimately led to decreased population and a return to foraging subsistence patterns [23].…”

Section: Plos Onementioning

confidence: 99%

See 1 more Smart Citation

Advancing predictive modeling in archaeology: An evaluation of regression and machine learning methods on the Grand Staircase-Escalante National Monument

Yaworsky

Vernon

Spangler³

et al. 2020

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Predictive models are central to both archaeological research and cultural resource management. Yet, archaeological applications of predictive models are often insufficient due to small training data sets, inadequate statistical techniques, and a lack of theoretical insight to explain the responses of past land use to predictor variables. Here we address these critiques and evaluate the predictive power of four statistical approaches widely used in ecological modeling-generalized linear models, generalized additive models, maximum entropy, and random forests-to predict the locations of Formative Period (2100-650 BP) archaeological sites in the Grand Staircase-Escalante National Monument. We assess each modeling approach using a threshold-independent measure, the area under the curve (AUC), and threshold-dependent measures, like the true skill statistic. We find that the majority of the modeling approaches struggle with archaeological datasets due to the frequent lack of true-absence locations, which violates model assumptions of generalized linear models, generalized additive models, and random forests, as well as measures of their predictive power (AUC). Maximum entropy is the only method tested here which is capable of utilizing pseudo-absence points (inferred absence data based on known presence data) and controlling for a non-representative sampling of the landscape, thus making maximum entropy the best modeling approach for common archaeological data when the goal is prediction. Regression-based approaches may be more applicable when prediction is not the goal, given their grounding in well-established statistical theory. Random forests, while the most powerful, is not applicable to archaeological data except in the rare case where trueabsence data exist. Our results have significant implications for the application of predictive models by archaeologists for research and conservation purposes and highlight the importance of understanding model assumptions.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Advancing predictive modeling in archaeology: An evaluation of regression and machine learning methods on the Grand Staircase-Escalante National Monument

Yaworsky

Vernon

Spangler³

et al. 2020

PLoS ONE

Self Cite

View full text Add to dashboard Cite

show abstract

“…Local resource abundance is important for determining where in a given landscape humans decide to live. The availability of water, soil, and plants offers the conditions and resources for both grazing and farming [55,56]. In addition, human beings alter their environment to boost the abundance and enhance the diversity of local varieties [57,58].…”

Section: Floristic Compositionmentioning

confidence: 99%

Archeological Sites and Relict Landscapes as Refuge for Biodiversity: Case Study of Alexandria City, Egypt

et al. 2022

View full text Add to dashboard Cite

The role of heritage sites as a shelter for biodiversity is overlooked. Eight archeological sites representing different landscapes in Alexandria City were surveyed, from which 59 stands were sampled between April 2019 and March 2021. The archeological sites and the relictual landscapes are geographically dispersed and are arranged here from west to east, representing the full range of environmental variation within the study area. The selection of stands in each site was based on the area and the variability within the habitats, the physiography, and the levels of disturbance. A composite soil sample was collected from each site. Two-way indicator species analysis (TWINSPAN) and detrended correspondence analysis (DECORANA) were carried out to identify the plant communities in the study area. The recorded taxa, their national geographical distribution, life forms, habitats, chorological types, and vegetation groups are listed. A total of 221 specific taxa, 172 native and 49 alien non-native species (representing some 10.3% of the whole range of Egyptian flora), belonging to 150 genera and 44 families, are reported in the present study. Only two endemic species were recorded in the studied urban habitats. The phytosociological analysis of the sites showed differences among vegetation types found in the archeological sites as a function of the varying degrees of enthronization. A significant effect of archeological site and relictual landscape on species diversity was observed as indicated using the richness, Shannon’s and Simpson’s indices. Flat plains are substantially more diverse than any of the other habitats in the present study, followed by the habitat of rocky ridge slope. The present study found evidence of an ecological legacy that persists today within the semi-arid climatic ecosystem of Alexandria City. The study highlights the urgent need for measures to maintain cultural landscapes while considering the conservation of biodiversity within the archeological sites. It is hoped that the outcomes of the current study can provide guidance on the potential integration of biodiversity conservation in planning the management of archeological sites.

show abstract

“…That is, land-use patterns in the region should not be assumed to be wholly structured by these environmental variables. Perceptions of agricultural suitability may also vary depending on subsistence practices or sociocultural contexts and can themselves be altered through landscape interventions and modifications [73,74]. Given the marked topographic diversity of the region, we expect then that the analysis of elevation and slope should reveal differing and not necessarily overlapping land-use patterns between the past and present.…”

Section: Predictor Variablesmentioning

confidence: 99%

Linking Past and Present Land-Use Histories in Southern Amazonas, Peru

et al. 2021

View full text Add to dashboard Cite

This paper analyzes remotely sensed data sources to evaluate land-use history within the Peruvian department of Amazonas and demonstrates the utility of comparing present and past land-use patterns using continuous datasets, as a complement to the often dispersed and discrete data produced by archaeological and paleoecological field studies. We characterize the distribution of ancient (ca. AD 1–1550) terracing based on data drawn from high-resolution satellite imagery and compare it to patterns of deforestation between 2001 and 2019, based on time-series Landsat data. We find that the patterns reflected in these two datasets are statistically different, indicating a distinctive shift in land-use, which we link to the history of Inka and Spanish colonialism and Indigenous depopulation in the 15th through 17th centuries AD as well as the growth of road infrastructure and economic change in the recent past. While there is a statistically significant relationship between areas of ancient terracing and modern-day patterns of deforestation, this relationship ultimately explains little (6%) of the total pattern of modern forest loss, indicating that ancient land-use patterns do not seem to be structuring modern-day trajectories of land-use. Together, these results shed light on the long-term history of land-use in Amazonas and their enduring legacies in the present.

show abstract

Decomposing Habitat Suitability Across the Forager to Farmer Transition

Cited by 13 publications

References 58 publications

Advancing predictive modeling in archaeology: An evaluation of regression and machine learning methods on the Grand Staircase-Escalante National Monument

Advancing predictive modeling in archaeology: An evaluation of regression and machine learning methods on the Grand Staircase-Escalante National Monument

Archeological Sites and Relict Landscapes as Refuge for Biodiversity: Case Study of Alexandria City, Egypt

Linking Past and Present Land-Use Histories in Southern Amazonas, Peru

Contact Info

Product

Resources

About