Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low‐elevation wetlands or mid‐elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy—herbaceous alpine ecotones—were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection. Patrones Espaciales del Éxito Reproductivo de Osos Pardos, Derivados de Modelos Jerárquicos Multi‐Estado 相似文献
The increasing capacity of distributed electricity generation brings new challenges in maintaining a high security and quality of electricity supply. New techniques are required for grid support and power balance. The highest potential for these techniques is to be found on the part of the electricity distribution grid.
This article addresses this potential and presents the EEPOS project’s approach to the automated management of flexible electrical loads in neighborhoods. The management goals are (i) maximum utilization of distributed generation in the local grid, (ii) peak load shaving/congestion management, and (iii) reduction of electricity distribution losses. Contribution to the power balance is considered by applying two-tariff pricing for electricity.
The presented approach to energy management is tested in a hypothetical sensitivity analysis of a distribution feeder with 10 households and 10 photovoltaic (PV) plants with an average daily consumption of electricity of 4.54 kWh per household and a peak PV panel output of 0.38 kW per plant. Energy management shows efficient performance at relatively low capacities of flexible load. At a flexible load capacity of 2.5% (of the average daily electricity consumption), PV generation surplus is compensated by 34–100% depending on solar irradiance. Peak load is reduced by 30% on average. The article also presents the load shifting effect on electricity distribution losses and electricity costs for the grid user. 相似文献