Biophysical and Socioeconomic Factors Associated with Forest Transitions at Multiple Spatial and Temporal Scales

Forest transitions (FT) occur when socioeconomic development leads to a shift from net deforestation to reforestation; these dynamics have been observed in multiple countries across the globe, including the island of Puerto Rico in the Caribbean. Starting in the 1950s, Puerto Rico transitioned from an agrarian to a manufacturing and service economy reliant on food imports, leading to extensive reforestation. In recent years, however, net reforestation has leveled off. Here we examine the drivers of forest transition in Puerto Rico from 1977 to 2000 at two subnational, nested spatial scales (municipality and barrio) and over two time periods (1977-1991 and 1991-2000). This study builds on previous work by considering the social and biophysical factors that influence both reforestation and deforestation at multiple spatial and temporal scales. By doing so within one analysis, this study offers a comprehensive understanding of the relative importance of various social and biophysical factors for forest transitions and the scales at which they are manifest. Biophysical factors considered in these analyses included slope, soil quality, and land-cover in the surrounding landscape. We also considered per capita income, population density, and the extent of protected areas as potential factors associated with forest change. Our results show that, in the 1977-1991 period, biophysical factors that exhibit variation at municipality scales (~100 km²) were more important predictors of forest change than socioeconomic factors. In this period, forest dynamics were driven primarily by abandonment of less productive, steep agricultural land in the western, central part of the island. These factors had less predictive power at the smaller barrio scale (~10 km²) relative to the larger municipality scale during this time period. The relative importance of socioeconomic variables for deforestation, however, increased over time as development pressures on available land increased. From 1991-2000, changes in forest cover reflected influences from multiple factors, including increasing population densities, land development pressure from suburbanization, and the presence of protected areas. In contrast to the 1977-1991 period, drivers of deforestation and reforestation over this second interval were similar for the two spatial scales of analyses. Generally, our results suggest that although broader socioeconomic changes in a given region may drive the demand for land, biophysical factors ultimately mediate where development occurs. Although economic development may initially result in reforestation due to rural to urban migration and the abandonment of agricultural lands, increased economic development may lead to deforestation through increased suburbanization pressures

High-yield oil palm expansion spares land at the expense of forests in the Peruvian Amazon

High-yield agriculture potentially reduces pressure on forests by requiring less land to increase production. Using satellite and field data, we assessed the area deforested by industrial-scale high-yield oil palm expansion in the Peruvian Amazon from 2000 to 2010, finding that 72% of new plantations expanded into forested areas. In a focus area in the Ucayali region, we assessed deforestation for high- and smallholder low-yield oil palm plantations. Low-yield plantations accounted for most expansion overall (80%), but only 30% of their expansion involved forest conversion, contrasting with 75% for high-yield expansion. High-yield expansion minimized the total area required to achieve production but counter-intuitively at higher expense to forests than low-yield plantations. The results show that high-yield agriculture is an important but insufficient strategy to reduce pressure on forests. We suggest that high-yield agriculture can be effective in sparing forests only if coupled with incentives for agricultural expansion into already cleared lands

Reinforcement learning in eye movement: Modeling the influences of top-down and bottom-up processes

Understanding and reproducing complex human oculomotor behaviors using computational models is a challenging task. In this paper, two studies are presented, which focus on the development and evaluation of a computational model to show the influences of cyclic top-down and bottom-up processes on eye movements. To explain these processes, reinforcement learning was used to control eye movements. The first study showed that, in a picture-viewing task, different policies obtained from different picture-viewing conditions produced different types of eye movement patterns. In another visual search task, the second study illustrated that feedback information from each saccadic eye movement could be used to update the model's eye movement policy, generating different patterns in the following saccade. These two studies demonstrate the value of an integrated reinforcement learning model in explaining both top-down and bottom-up processes of eye movements within one computational model.close7

A queuing network model for eye movement

Eye movement is a basic human behavior that offers a valuable means to explore human cognitive processes. This article introduces two modeling studies of eye movement. First, random menu search was modeled using a queueing network approach and second, a reinforcement learning algorithm was used to generate various eye movement patterns. Random menu search is a task component involved in many human-machine interfaces and has been modeled with several cognitive models including ACT-R and EPIC. Based upon review of empirical data in menu search, and strengths and limitations of existing models, this article proposes a queueing network model, which has been successfully applied in some other task domains (e.g., response time, driver performance). The queueing network model for random menu search was implemented and evaluated through model simulation. In contrast to existing models that rely on four task-specific strategies to account for data, the queueing network model accounted for the same data using only one strategy already employed in cognitive modeling. To extend this parsimonious, “minimal task strategy ” modeling approach, Q-Learning, one of the reinforcement learning methods, was adopted to generate different patterns in eye movement. The same strategy from random menu search was used to generate eye movement, and the simulated eye movements were qualitatively compared to the human eye movement. I

Investigation of driver performance with night vision and pedestrian detection systems, Part 2: Queueing network performance modeling

This paper introduces a queueing network-based computational model to explain driver performance in a pedestrian-detection task assisted with night-vision-enhancement systems. The computational cognitive model simulated the pedestrian-detection task using images displayed by two night-vision systems as input stimuli. The system equipped with a far-infrared (FIR) sensor generated less-cluttered images than the system equipped with a near-infrared (NIR) sensor. Using a reinforcement learning process, the model developed eye-movement strategies for each night-vision system. The differences in eye-movement strategies generated different eye-movement behaviors, in accord with the empirical findings.close9

Investigation of driver performance with night vision and pedestrian detection systems, Part 1: Empirical study on visual cluster and search behavior

This paper describes two studies in which two nightvision enhancement systems were examined to compare nighttime driver performance in pedestrian detection. In the first study, the levels of clutter in the images displayed by the two types of night-vision enhancement systems were measured objectively and subjectively. The subjective ratings of clutter changed as a power function of the objective measure of clutter intensity. In the second study, the effect of clutter on glance behavior during pedestrian detection was examined in a driving simulator. Night-vision images with less clutter required shorter search times and fewer glances to detect the pedestrian, but the duration of each glance remained relatively constant.close16181

Forecasting the Exchange Rates of CHF vs USD Using Neural networks

In this paper, an experimental research based on a neural network forecasting methodology is discussed. The exchange rates between Swiss Franc and American Dollar are predicted a reasonable out-of-sample profits achieved in the experiment. The results show that a simple backpropagation network with efficient learning and a simple set of technical indicators as inputs serves well as a predictive model for a six month forecasting period. The paper discusses several issues on the frequency of sampling, choice of network architecture, forecasting periods, and measures for evaluating the model's predictive power. After presenting the experimental results, a disscusion on future research concludes the paper. Forecasting the Exchange Rates of CHF vs USD Using Neural networks. Abstract In this paper, an experimental research based on a neural network forecasting methodology is discussed. The exchange rates between Swiss Franc and American Dollar are predicted a reasonable out-of-sample profi..

Perceptional and socio-demographic factors associated with household drinking water management strategies in rural Puerto Rico.

Identifying which factors influence household water management can help policy makers target interventions to improve drinking water quality for communities that may not receive adequate water quality at the tap. We assessed which perceptional and socio-demographic factors are associated with household drinking water management strategies in rural Puerto Rico. Specifically, we examined which factors were associated with household decisions to boil or filter tap water before drinking, or to obtain drinking water from multiple sources. We find that households differ in their management strategies depending on the institution that distributes water (i.e. government PRASA vs community-managed non-PRASA), perceptions of institutional efficacy, and perceptions of water quality. Specifically, households in PRASA communities are more likely to boil and filter their tap water due to perceptions of low water quality. Households in non-PRASA communities are more likely to procure water from multiple sources due to perceptions of institutional inefficacy. Based on informal discussions with community members, we suggest that water quality may be improved if PRASA systems improve the taste and odor of tap water, possibly by allowing for dechlorination prior to distribution, and if non-PRASA systems reduce the turbidity of water at the tap, possibly by increasing the degree of chlorination and filtering prior to distribution. Future studies should examine objective water quality standards to identify whether current management strategies are effective at improving water quality prior to consumption