Seismic Evaluation of Multi-Storey Moment-Resisting Steel Frames With Stiffness Irregularities Using Standard and Advanced Pushover Methods

AbstractThe nonlinear static procedure, based on pushover analysis, has become an important tool to characterize the seismic demand and the performance of structures. The standard pushover procedure is restricted to single-mode response, a valid supposition for symmetrical or low-rise buildings, where the response is dominated by the fundamental vibration mode. The standard pushover procedures become misleading when the response of the structure is influenced by higher vibration modes. This is the case of tall or non-symmetrical buildings. Several pushover procedures, able to take into account the effects of higher vibration modes, have been lately developed to overcome this drawback. This paper presents a comparison between standard, advanced pushover analyses and the exact results obtained by nonlinear time history analysis. The analyses have been conducted on a series of moment-resisting steel frames with stiffness irregularities, with different number of stories, designed according to EC8 and the Romanian Seismic Design Code for Romania's Vrancea Seismic Area

Using Drones to Determine Chimpanzee Absences at the Edge of Their Distribution in Western Tanzania

Effective species conservation management relies on detailed species distribution data. For many species, such as chimpanzees (Pan troglodytes), distribution data are collected during ground surveys. For chimpanzees, such ground surveys usually focus on detection of the nests they build instead of detection of the chimpanzees themselves due to their low density. However, due to the large areas they still occur in, such surveys are very costly to conduct and repeat frequently to monitor populations over time. Species distribution models are more accurate if they include presence as well as absence data. Earlier studies used drones to determine chimpanzee presence using nests. In this study, therefore, we explored the use of drones to determine the absence of chimpanzee nests in areas we flew over on the edge of the chimpanzee distribution in western Tanzania. We conducted 13 flights with a fixed-wing drone and collected 3560 images for which manual inspection took 180 h. Flights were divided into a total of 746 25 m2 plots for which we determined the absence probability of nests. In three flights, we detected nests, in eight, absence was assumed based on a 95% probability criterion, and in two flights, nest absence could not be assumed. Our study indicates that drones can be used to cover relatively large areas to determine the absence of chimpanzees. To fully benefit from the usage of drones to determine the presence and absence of chimpanzees, it is crucial that methods are developed to automate nest detection in images

Spatio-temporal changes in chimpanzee density and abundance in the Greater Mahale Ecosystem, Tanzania

Authors would like to acknowledge the Arcus Foundation, Jane Goodall Institute, United States Agency for International Development (USAID), National Aeronautics and Space Administration (NASA), The Nature Conservancy, and Frankfurt Zoological Society for supporting, facilitating, and funding this work.Species conservation and management require reliable information about animal distribution and population size. Better management actions within a species' range can be achieved by identifying the location and timing of population changes. In the Greater Mahale Ecosystem (GME), western Tanzania, deforestation due to the expansion of human settlements and agriculture, annual burning, and logging are known threats to wildlife. For one of the most charismatic species, the Endangered eastern chimpanzee (Pan troglodytes schweinfurthii), about 75% of the individuals are distributed outside national park boundaries, requiring monitoring and protection efforts over a vast landscape of various protection statuses. These efforts are especially challenging when we lack data on trends in density and population size. To predict spatio-temporal chimpanzee density and abundance across the GME, we employed density surface modelling, fitting a generalised additive model to a ten-year time series data set of nest counts based on line transect surveys. Chimpanzee population declined at an annual rate of 2.41%, including declines of 1.72% in riparian forests (hereafter forests), 2.05% in miombo-woodlands (hereafter woodlands) and 3.45% in non-forests. These population declines were accompanied by ecosystem-wide declines in vegetation types of 1.36% and 0.32% per year for forests and woodlands, respectively; we estimated an annual increase of 1.35% for non-forests. Our model predicted the highest chimpanzee density in forests (0.86 chimpanzees/km2, 95% CI 0.60-1.23; as of 2020), followed by woodlands (0.19, 95% CI 0.12-0.30) and non-forests (0.18, 95% CI 0.10-1.33). Although forests represent only 6% of the landscape, they support nearly a quarter of the chimpanzee population (769 chimpanzees, 95% CI 536-1,103). Woodlands dominate the landscape (71%) and thus support more than a half of the chimpanzee population (2,294; 95% CI 1,420-3,707). The remaining quarter of the landscape is represented by non-forests and supports another quarter of the chimpanzee population (750; 95% CI 408-1,381). Given the pressures on the remaining suitable habitat in Tanzania and the need of chimpanzees to access both forest and woodland vegetation to survive, we urge future management actions to increase resources and expand the efforts to protect critical forest and woodland habitat and promote strategies and policies that more effectively prevent irreversible losses. We suggest that regular monitoring programmes implement a systematic random design to effectively inform and allocate conservation actions and facilitate inter-annual comparisons for trend-monitoring, measuring conservation success and guiding adaptive management.Publisher PDFPeer reviewe

3Rs in Education.

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Using Drones to Determine Chimpanzee Absences at the Edge of Their Distribution in Western Tanzania

Effective species conservation management relies on detailed species distribution data. For many species, such as chimpanzees (Pan troglodytes), distribution data are collected during ground surveys. For chimpanzees, such ground surveys usually focus on detection of the nests they build instead of detection of the chimpanzees themselves due to their low density. However, due to the large areas they still occur in, such surveys are very costly to conduct and repeat frequently to monitor populations over time. Species distribution models are more accurate if they include presence as well as absence data. Earlier studies used drones to determine chimpanzee presence using nests. In this study, therefore, we explored the use of drones to determine the absence of chimpanzee nests in areas we flew over on the edge of the chimpanzee distribution in western Tanzania. We conducted 13 flights with a fixed-wing drone and collected 3560 images for which manual inspection took 180 h. Flights were divided into a total of 746 25 m2 plots for which we determined the absence probability of nests. In three flights, we detected nests, in eight, absence was assumed based on a 95% probability criterion, and in two flights, nest absence could not be assumed. Our study indicates that drones can be used to cover relatively large areas to determine the absence of chimpanzees. To fully benefit from the usage of drones to determine the presence and absence of chimpanzees, it is crucial that methods are developed to automate nest detection in images