Evidence from Escalera al Cielo: Abandonment of a Terminal Classic Puuc Maya Hill Complex in Yucatán, Mexico

This is a postprint (author's final draft) version of an article published in Journal of Field Arhcaeology in 2012. The final version of this article may be found at http://dx.doi.org/10.1179/0093469012Z.00000000025 (login may be required). The version made available in OpenBU was supplied by the author.Excavations at the hilltop site of Escalera al Cielo, located in the Puuc Maya region of Yucatán, Mexico, have uncovered evidence of a planned abandonment at the end of the Terminal Classic period (A.D. 800–950). Six buildings investigated among three residential groups contain rich floor assemblages similar to those known from only a few rapidly abandoned sites in the Maya area. Through an analysis of de facto refuse—most of which was recovered in locations of storage and provisional discard—and midden refuse, this paper illustrates how the assemblages represent an example of household-level abandonment with anticipated return. We also consider Escalera al Cielo in light of our present understanding of the political and environmental history of the Puuc region during the late 9th century A.D

The Inq13 POOC::A Participatory Experiment in Open, Collaborative Teaching and Learning.

This article offers a broad analysis of a POOC (“Participatory Open Online Course”) offered through the Graduate Center, CUNY in 2013. The large collaborative team of instructors, librarians, educational technologists, videographers, students, and project leaders reflects on the goals, aims, successes, and challenges of the experimental learning project. The graduate course, which sought to explore issues of participatory research, inequality and engaged uses of digital technology with and through the New York City neighborhood of East Harlem, set forth a unique model of connected learning that stands in contrast to the popular MOOC (Massive Open Online Course) model

Uncertainty and unwillingness to receive a COVID-19 vaccine in adults residing in Puerto Rico: Assessment of perceptions, attitudes, and behaviors.

Background: Widespread vaccination against COVID-19 is essential to control the pandemic. Estimates of unwillingness and uncertainty toward COVID-19 vaccination and factors associated with it remain unclear among adults in Puerto Rico (PR). Objective: To examine factors associated with uncertainty and unwillingness of COVID-19 vaccination among adults in PR. Methods: The Health Belief Model was used to develop an online survey. Analyses included adjusted logistic regressions (aOR). A total of 1,911 adults completed the survey from December 2020 to February 2021. Results: Overall, 76.2% were females, 33.7% were aged 50 or older, and 82.7% reported an intent to get vaccinated. Individuals who did not perceive that their chances of getting COVID-19 were high (aOR = 2.94; 95%CI = 2.24–3.86), that getting COVID-19 was not a possibility for them (aOR = 2.86; 95%CI = 2.19–3.74), or unafraid of getting COVID-19 (aOR = 3.80; 95%CI = 2.76–5.23) had higher odds of uncertainty and unwillingness to get vaccinated against COVID-19. Participants who perceived that COVID-19 complications were not serious also had higher odds of uncertainty and unwillingness (aOR = 7.50; 95%CI = 3.94–14.3), whereas those who did not perceive that they would get very sick with COVID-19 had 89% increased odds. Those who agreed that they would only take the vaccine if many individuals took it had higher odds of uncertainty and unwillingness (aOR = 3.32; 95%CI = 2.49–4.43). The most reported reasons for uncertainty and unwillingness toward COVID-19 vaccination were vaccine safety (63.8%), efficacy (49.4%), and novelty (45.5%). Discussion: Although COVID-19 vaccination intent was high, the study highlights concern over vaccine safety and efficacy that should be addressed by public health campaigns and interventions to enhance vaccine uptake

Landscape-scale consequences of differential tree mortality from catastrophic wind disturbance in the Amazon.

Wind disturbance can create large forest blowdowns, which greatly reduces live biomass and adds uncertainty to the strength of the Amazon carbon sink. Observational studies from within the central Amazon have quantified blowdown size and estimated total mortality but have not determined which trees are most likely to die from a catastrophic wind disturbance. Also, the impact of spatial dependence upon tree mortality from wind disturbance has seldom been quantified, which is important because wind disturbance often kills clusters of trees due to large treefalls killing surrounding neighbors. We examine (1) the causes of differential mortality between adult trees from a 300-ha blowdown event in the Peruvian region of the northwestern Amazon, (2) how accounting for spatial dependence affects mortality predictions, and (3) how incorporating both differential mortality and spatial dependence affect the landscape level estimation of necromass produced from the blowdown. Standard regression and spatial regression models were used to estimate how stem diameter, wood density, elevation, and a satellite-derived disturbance metric influenced the probability of tree death from the blowdown event. The model parameters regarding tree characteristics, topography, and spatial autocorrelation of the field data were then used to determine the consequences of non-random mortality for landscape production of necromass through a simulation model. Tree mortality was highly non-random within the blowdown, where tree mortality rates were highest for trees that were large, had low wood density, and were located at high elevation. Of the differential mortality models, the non-spatial models overpredicted necromass, whereas the spatial model slightly underpredicted necromass. When parameterized from the same field data, the spatial regression model with differential mortality estimated only 7.5% more dead trees across the entire blowdown than the random mortality model, yet it estimated 51% greater necromass. We suggest that predictions of forest carbon loss from wind disturbance are sensitive to not only the underlying spatial dependence of observations, but also the biological differences between individuals that promote differential levels of mortality

Landscape-scale consequences of differential tree mortality from catastrophic wind disturbance in the Amazon

Wind disturbance can create large forest blowdowns, which greatly reduces live biomass and adds uncertainty to the strength of the Amazon carbon sink. Observational studies from within the central Amazon have quantified blowdown size and estimated total mortality but have not determined which trees are most likely to die from a catastrophic wind disturbance. Also, the impact of spatial dependence upon tree mortality from wind disturbance has seldom been quantified, which is important because wind disturbance often kills clusters of trees due to large treefalls killing surrounding neighbors. We examine (1) the causes of differential mortality between adult trees from a 300-ha blowdown event in the Peruvian region of the northwestern Amazon, (2) how accounting for spatial dependence affects mortality predictions, and (3) how incorporating both differential mortality and spatial dependence affect the landscape level estimation of necromass produced from the blowdown. Standard regression and spatial regression models were used to estimate how stem diameter, wood density, elevation, and a satellite-derived disturbance metric influenced the probability of tree death from the blowdown event. The model parameters regarding tree characteristics, topography, and spatial autocorrelation of the field data were then used to determine the consequences of non-random mortality for landscape production of necromass through a simulation model. Tree mortality was highly non-random within the blowdown, where tree mortality rates were highest for trees that were large, had low wood density, and were located at high elevation. Of the differential mortality models, the non-spatial models overpredicted necromass, whereas the spatial model slightly underpredicted necromass. When parameterized from the same field data, the spatial regression model with differential mortality estimated only 7.5% more dead trees across the entire blowdown than the random mortality model, yet it estimated 51% greater necromass. We suggest that predictions of forest carbon loss from wind disturbance are sensitive to not only the underlying spatial dependence of observations, but also the biological differences between individuals that promote differential levels of mortality. © 2016 by the Ecological Society of America