The role of human hunting behavior versus climate change in the mass extinction of megafauna during the Late Quaternary is much debated. To move beyond monocausal arguments, we treat human–megafauna–environment relationships as social–ecological systems from a complex adaptive systems perspective, to create an agent-based model that tests how human hunting may interact with environmental stress and animal life history to affect the probability of extinction. Using the extinction of Syncerus antiquus in South Africa at 12–10 ka as a loose inspirational case study, we parameterized a set of experiments to identify cross-feedbacks among environmental dynamics, prey life history, and human hunting pressure that affect extinction probability in a non-linear way. An important anthropogenic boundary condition emerges when hunting strategies interrupt prey animal breeding cycles. This effect is amplified in patchy, highly seasonal environments to increase the chances of extinction. This modeling approach to human behavior and biodiversity loss helps us understand how these types of cross-feedback effects and boundary conditions emerge as system components interact and change. We argue that this approach can help translate archaeological data and insight about past extinction for use in understanding and combating the current mass extinction crisis.

In the desert of southeastern California, the geological and archaeological remnants of a once massive lake, Lake Cahuilla, are still visible. One of the most distinctive features marking Lake Cahuilla's relic shorelines is a series of rock fish trap features that, in some cases, stretch across thousands of square meters. These fish traps are severely understudied, and systematic archaeological survey can help scientists reconstruct the dynamic human-environmental history of the region. The large number of fish traps along with the rocky desert terrain, however, make traditional pedestrian archaeological surveys both difficult and inefficient. We used unmanned aerial vehicle (UAV) technology along with traditional archaeological methods to conduct surveys and identify patterning in the shapes, orientations, and frequencies of fish traps. Our study demonstrates the potential of emerging archaeological field technology to better understand the nature of human-environmental ecodynamics through time and space.

The data from older archaeological surveys are incredibly important resources, often containing our only information about sites that have been destroyed or that are now inaccessible. These surveys occurred before the advent of GPS technology, however, so their spatial accuracy is often uncertain. Many types of locational errors accumulate in such “legacy” datasets, so using them in modern GIS-based spatial analyses is frequently problematic. Many of the sources of error can be identified and quantified, however, and systematic and random errors (derived mainly from Cartesian, rounding, and human error) can largely be mitigated by scanning the original field maps, georectifying the maps to trusted imagery, and then digitizing sites directly. The remaining “mislocation” errors derive from difficulty identifying locations in the field. The original survey notes may contain clues about mislocation error, but it is impossible to mitigate these errors without re-recording site locations with more accurate survey instruments. Instead, I advocate the use of GIS-based models to estimate the influence of specific surveying practices on site location accuracy. These models can provide a standardized, quantifiable measure of mislocation error in a legacy dataset, which can help guide its use in modern GIS analyses that require accurate site locations.

Archaeology has an opportunity to offer major contributions to our understanding of the long-term interactions of humans and the environment. To do so, we must elucidate dynamic socioecological processes that generally operate at regional scales. However, the archaeological record is sparse, discontinuous, and static. Recent advances in computational modeling provide the potential for creating experimental laboratories where dynamic processes can be simulated and their results compared against the archaeological record. Coupling computational modeling with the empirical record in this way can increase the rigor of our explanations while making more transparent the concepts on which they are based. We offer an example of such an experimental laboratory to study the long-term effects of varying landuse practices by subsistence farmers on landscapes, and compare the results with the Levantine Neolithic archaeological record. Different combinations of intensive and shifting cultivation, ovicaprid grazing, and settlement size are modeled for the Wadi Ziqlab drainage of northern Jordan. The results offer insight into conditions under which previously successful (and sustainable) landuse practices can pass an imperceptible threshold and lead to undesirable landscape consequences. This may also help explain long-term social, economic, and settlement changes in the Neolithic of Southwest Asia.