Analysis of deciduous tree species dynamics after a severe ice storm using SORTIE model simulations

Ice storms are frequent natural disturbance events in hardwood forests of eastern Canada and the United States, but their effects on forest dynamics are not well understood. Our objectives were to characterize short- and long-term tree species dynamics after a severe ice storm, and to assess the influence of spatial distribution of trees on these dynamics. SORTIE, a spatially explicit individual tree-based forest model, was used to simulate the effects of a severe ice storm on 300 years old stands. Crown radius was reduced and tree mortality was increased for a 5-year period following the ice storm disturbance. To investigate the influence of the spatial distribution of trees, we repeated the same experiment in a uniformly distributed stand where we systematically assigned coordinates of all trees, saplings and seedlings before the ice storm was modeled. Our results showed that six types of dynamics can be adopted by a species following an ice storm and that spatial distribution of trees influenced the species responses. In summary, we found that a combination of factors, namely, species density and spatial distribution, shade tolerance, growth rate, extent of canopy openness and canopy loss resulting from the ice storm, determine how tree species respond to ice storm disturbance

A Multi-instance Multi-label Learning Framework of Image Retrieval

Part 7: MultimediaInternational audienceBecause multi-instance and multi-label learning can effectively deal with the problem of ambiguity when processing images. A multi-instance and multi-label learning method based on Content Based Image Retrieve ( CBIR) is proposed in this paper, and the image processing stage we use in image retrieval process is multi-instance and multi-label. We correspond the instances with category labels by using a package which contains the color and texture features of the image area. According to the user to select an image to generate positive sample packs and anti-packages, using multi-instance learning algorithms to learn, using the image retrieval and relevance feedback, the experimental results show that the algorithm is better than the other three algorithms to retrieve results and its retrieval efficiency is higher. According to the user to select an image to generate positive sample packs and anti-packages, using multi- instance learning algorithms to learn, using the image retrieval and relevance feedback. Compared with several algorithms, the experimental results show that the performance of our algorithm is better and its retrieval efficiency is higher

Identifying non-independent anthropogenic risks using a behavioral individual-based model

Anthropogenic disturbances contribute to an animal\u27s perception of and responses to the predation risk of its environment. Because an animal rarely encounters threatening stimuli in isolation, multiple disturbances can act in non-independent ways to shape an animal\u27s landscape of fear, making it challenging to isolate their effects for effective and targeted management. We present extensions to an existing behavioral agent-based model (ABM) to use as an inverse modeling approach to test, in a scenario-sensitivity analysis, whether threatened Alberta boreal caribou (Rangifer tarandus caribou) differentially respond to industrial features (linear features, forest cutblocks, wellsites) and their attributes: presence, density, harvest age, and wellsite activity status. The spatially explicit ABM encapsulates predation risk, heterogeneous resource distribution, and species-specific energetic requirements, and successfully recreates the general behavioral mechanisms driving habitat selection. To create various industry-driven, predation-risk landscape scenarios for the sensitivity analysis, we allowed caribou agents to differentially perceive and respond to industrial features and their attributes. To identify which industry had the greatest relative influence on caribou habitat use and spatial distribution, simulated caribou movement patterns from each of the scenarios were compared with those of actual caribou from the study area, using a pattern-oriented, multi-response optimization approach. Results revealed caribou have incorporated forestry- and oil and gas features into their landscape of fear that distinctly affect their spatial and energetic responses. The presence of roads, pipelines and seismic lines, and, to a minor extent, high-density cutblocks and active wellsites, all contributed to explaining caribou behavioral responses. Our findings also indicated that both industries produced interaction effects, jointly impacting caribou spatial and energetic patterns, as no one feature could adequately explain anti-predator movement responses. We demonstrate that behavior-based ABMs can be applied to understanding, assessing, and isolating non-consumptive anthropogenic impacts, in support of wildlife management