GIS and paleoanthropology: incorporating new approaches from the geospatial sciences in the analysis of primate and human evolution

The ROCEEH Out of Africa Database (ROAD): A large-scale research database serves as an indispensable tool for human evolutionary studies

Large scale databases are critical for helping scientists decipher long-term patterns in human evolution. This paper describes the conception and development of such a research database and illustrates how big data can be harnessed to formulate new ideas about the past. The Role of Culture in Early Expansions of Humans (ROCEEH) is a transdisciplinary research center whose aim is to study the origins of culture and the multifaceted aspects of human expansions across Africa and Eurasia over the last three million years. To support its research, the ROCEEH team developed an online tool named the ROCEEH Out of Africa Database (ROAD) and implemented its web-based applications. ROAD integrates geographical data as well as archaeological, paleoanthropological, paleontological and paleobotanical content within a robust chronological framework. In fact, a unique feature of ROAD is its ability to dynamically link scientific data both spatially and temporally, thereby allowing its reuse in ways that were not originally conceived. The data stem from published sources spanning the last 150 years, including those generated by the research team. Descriptions of these data rely on the development of a standardized vocabulary and profit from online explanations of each table and attribute. By synthesizing legacy data, ROAD facilitates the reuse of heritage data in novel ways. Database queries yield structured information in a variety of interoperable formats. By visualizing data on maps, users can explore this vast dataset and develop their own theories. By downloading data, users can conduct further quantitative analyses, for example with Geographic Information Systems, modeling programs and artificial intelligence. In this paper, we demonstrate the innovative nature of ROAD and show how it helps scientists studying human evolution to access datasets from different fields, thereby connecting the social and natural sciences. Because it permits the reuse of "old" data in new ways, ROAD is now an indispensable tool for researchers of human evolution and paleogeography.

Drainage Ditch Berm Delineation Using Lidar Data: A Case Study of Waseca County, Minnesota

Within a drainage system, drainage ditches are designed to improve existing natural drainage. Although drainage ditches are mostly engineered, they can also be part of natural watercourses. For environmental sustainability, in many places there are guidelines to establish vegetative buffer strips along the boundary of drainage ditches. In this landscape planning study, a geospatial modeling framework was established to identify these drainage system landforms and the boundary that separates these landforms from their surrounding areas across Waseca County in south-central Minnesota. By employing almost 2000 GPS spot elevation measurements from five ditch systems and one-meter Light Detection and Ranging (LiDAR) derived digital elevation model (DEM) data, the drainage ditch berm polygons were delineated. Eight low light angle hillshade rasters at 45-degree azimuth intervals were used to construct the model. These hillshade rasters were combined to form a composite raster so that the effect of multiple azimuths can be captured during ditch berm delineation. The GPS points identified as the top of the berm were used to extract cell values from the combined hillshade. These cell values were modeled further using statistical distribution graphs. The statistical model derived +0.5 and +1 standard deviation values (cell values 812 and 827, respectively) of the combined hillshade raster were utilized to obtain complete berm polygons. In this semi-automated method, between 67.30% to 79.80% of ditch berm lengths were mapped with an average error that is less than the resolution of the DEM. Demarcation of these boundaries are important for local governments in Minnesota and throughout the world, as it could help guide land–water management and aid sustainable agriculture.

Potential utility of reflectance spectroscopy in understanding the paleoecology and depositional history of different fossils

The potential of reflectance spectroscopy to infer the paleoecological and depositional evolution of different micro and macro invertebrate fossils has been evaluated by analyzing their reflectance spectra within the spectral domain of 350–2500 nm using the FIELDSPEC3 spectroradiometer. Mineralogical information derived from the rapid and non-destructive spectral analysis has been substantiated using concurrent mineralogical data from conventional geochemical analyses. The diagnostic Fe-crystal field effect induced spectral features are identified on the representative spectra of different benthic foraminifera. These spectral features are resulted due to the incorporation of Fe during the biomineralization process. These features are absent in planktic foraminifera. The encrustation of Fe-oxides is inferred to be responsible for imprinting the Fe-crystal field feature in the spectra of micro and macrofossils at 900–1200 nm. Vibrational spectral features of the Al–OH bond are also identified. Both of these features are an indicator of post-depositional diagenetic history. The presence of Al and Fe in macrofossil shells is also believed to be related to ecological conditions as these elements are biogenically incorporated during shell formation. This study reveals the value of reflectance spectroscopy to infer ecological behavior and post-depositional environment of different organisms.

Combining legacy data with new drone and DGPS mapping to identify the provenance of Plio-Pleistocene fossils from Bolt's Farm, Cradle of Humankind (South Africa)

Bolt’s Farm is a Plio-Pleistocene fossil site located within the southwestern corner of the UNESCO Hominid Fossil Sites of South Africa World Heritage Site. The site is a complex of active caves and more than 20 palaeokarst deposits or pits, many of which were exposed through the action of lime mining in the early 20th century. The pits represent heavily eroded cave systems, and as such associating the palaeocave sediments within and between the pits is difficult, especially as little geochronological data exists. These pits and the associated lime miner’s rubble were first explored by palaeoanthropologists in the late 1930s, but as yet no hominin material has been recovered. The first systematic mapping was undertaken by Frank Peabody as part of the University of California Africa Expedition (UCAE) in 1947–1948. A redrawn version of the map was not published until 1991 by Basil Cooke and this has subsequently been used and modified by recent researchers. Renewed work in the 2000s used Cooke’s map to try and relocate the original fossil deposits. However, Peabody’s map does not include all the pits and caves, and thus in some cases this was successful, while in others previously sampled pits were inadvertently given new names. This was compounded by the fact that new fossil bearing deposits were discovered in this new phase, causing confusion in associating the 1940s fossils with the deposits from which they originated; as well as associating them with the recently excavated material. To address this, we have used a Geographic Information System (GIS) to compare Peabody’s original map with subsequently published maps. This highlighted transcription errors between maps, most notably the location of Pit 23, an important palaeontological deposit given the recovery of well-preserved primate crania (Parapapio, Cercopithecoides) and partial skeletons of the extinct felid Dinofelis. We conducted the first drone and Differential Global Positioning System (DGPS) survey of Bolt’s Farm. Using legacy data, high-resolution aerial imagery, accurate DGPS survey and GIS, we relocate the original fossil deposits and propose a definitive and transparent naming strategy for Bolt’s Farm, based on the original UCAE Pit numbers. We provide datum points and a new comprehensive, georectified map to facilitate spatially accurate fossil collection for all future work. Additionally, we have collated recently published faunal data with historic fossil data to evaluate the biochronological potential of the various deposits. This suggests that the palaeocave deposits in different pits formed at different times with the occurrence of Equus in some pits implying ages of <2.3 Ma, whereas more primitive suids (Metridiochoerus) hint at a terminal Pliocene age for other deposits. This study highlights that Bolt’s Farm contains rare South African terminal Pliocene fossil deposits and creates a framework for future studies of the deposits and previously excavated material.

Where to Dig for Fossils: Combining Climate-Envelope, Taphonomy and Discovery Models

Fossils represent invaluable data to reconstruct the past history of life, yet fossil-rich sites are often rare and difficult to find. The traditional fossil-hunting approach focuses on small areas and has not yet taken advantage of modelling techniques commonly used in ecology to account for an organism's past distributions. We propose a new method to assist finding fossils at continental scales based on modelling the past distribution of species, the geological suitability of fossil preservation and the likelihood of fossil discovery in the field, and apply it to several genera of Australian megafauna that went extinct in the Late Quaternary. Our models predicted higher fossil potentials for independent sites than for randomly selected locations (mean Kolmogorov-Smirnov statistic = 0.66). We demonstrate the utility of accounting for the distribution history of fossil taxa when trying to find the most suitable areas to look for fossils. For some genera, the probability of finding fossils based on simple climate-envelope models was higher than the probability based on models incorporating current conditions associated with fossil preservation and discovery as predictors. However, combining the outputs from climate-envelope, preservation, and discovery models resulted in the most accurate predictions of potential fossil sites at a continental scale. We proposed potential areas to discover new fossils of Diprotodon, Zygomaturus, Protemnodon, Thylacoleo, and Genyornis, and provide guidelines on how to apply our approach to assist fossil hunting in other continents and geological settings.

Digital data collection in paleoanthropology

Understanding patterns of human evolution across space and time requires synthesizing data collected by independent research teams, and this effort is part of a larger trend to develop cyber infrastructure and e-science initiatives. At present, paleoanthropology cannot easily answer basic questions about the total number of fossils and artifacts that have been discovered, or exactly how those items were collected. In this paper, we examine the methodological challenges to data integration, with the hope that mitigating the technical obstacles will further promote data sharing. At a minimum, data integration efforts must document what data exist and how the data were collected (discovery), after which we can begin standardizing data collection practices with the aim of achieving combined analyses (synthesis). This paper outlines a digital data collection system for paleoanthropology. We review the relevant data management principles for a general audience and supplement this with technical details drawn from over 15 years of paleontological and archeological field experience in Africa and Europe. The system outlined here emphasizes free open-source software (FOSS) solutions that work on multiple computer platforms; it builds on recent advances in open-source geospatial software and mobile computing.

Walking back the cat: Unsupervised classification as an aid in "remote" fossil prospecting

Counterintelligence analysts use a technique called "walking back the cat'' to reveal "moles" or others passing on disinformation in which they compare what they now know as fact against what their agents or informers had told them to expect about certain persons or events. Thus, "walking back the cat" is a perfect metaphor for working backwards; that is, retracing the complex development of an event and examining the "run up" to it in order to gain useful insights about how that event unfolded. Perhaps paleoanthropology can profit from such an approach.

Let your fingers do the walking: A simple spectral signature model for "remote" fossil prospecting

Even with the most meticulous planning, and utilizing the most experienced fossil-hunters, fossil prospecting in remote and/or extensive areas can be time-consuming, expensive, logistically challenging, and often hit or miss. While nothing can predict or guarantee with 100% assurance that fossils will be found in any particular location, any procedures or techniques that might increase the odds of success would be a major benefit to the field. Here we describe, and test, one such technique that we feel has great potential for increasing the probability of finding fossiliferous sediments - a relatively simple spectral signature model using the spatial analysis and image classification functions of ArcGIS(®)10 that creates interactive thematic land cover maps that can be used for "remote" fossil prospecting. Our test case is the extensive Eocene sediments of the Uinta Basin, Utah - a fossil prospecting area encompassing ∼1200 square kilometers. Using Landsat 7 ETM+ satellite imagery, we "trained" the spatial analysis and image classification algorithms using the spectral signatures of known fossil localities discovered in the Uinta Basin prior to 2005 and then created interactive probability models highlighting other regions in the Basin having a high probability of containing fossiliferous sediments based on their spectral signatures. A fortuitous "post-hoc" validation of our model presented itself. Our model identified several paleontological "hotspots", regions that, while not producing any fossil localities prior to 2005, had high probabilities of being fossiliferous based on the similarities of their spectral signatures to those of previously known fossil localities. Subsequent fieldwork found fossils in all the regions predicted by the model.