Abstract:Tree seedlings planted in abandoned agricultural fields interact with herb communities through competition, tolerance, and facilitation. In addition, they are subject to herbivory by small mammals, deer or invertebrates. To increase the success of forest restoration in abandoned fields and reduce management costs, we should determine which species are tolerant to or facilitated by herbaceous vegetation and those which require protection from competition and predation. Eight native tree species were planted in plots covered by herbaceous vegetation, plots where herbaceous vegetation was removed, and plots where seedlings were surrounded by an organic mulch mat. Half of the seedlings were protected against small mammal damage. Results showed that two non-pioneer and moderately shade-tolerant species (yellow birch and red oak) were inhibited by herbaceous vegetation. Birch species were particularly affected by small mammal predation. No effects of predation or herbaceous competition were observed for conifer species. Rather, herbaceous vegetation had a positive effect on the survival and the height growth of tamarack (Larix laricina). None of the tested herb communities had a stronger competitive effect on tree growth than another. Restoration of abandoned fields using multi-tree species should be designed at the seedling scale rather than at the site scale to account for different tree responses to predation and competition as well as variable site conditions. An approach resembling precision agriculture is proposed to lower costs and any potential negative impact of more intensive vegetation management interventions.Keywords: tree plantation; abandoned agricultural field; predation; competition; tolerance; facilitation; precision restoration
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In several of the cases listed here, the game's developers released the source code expressly to prevent their work from becoming abandonware. Such source code is often released under varying (free and non-free, commercial and non-commercial) software licenses to the games' communities or the public; artwork and data are often released under a different license than the source code, as the copyright situation is different or more complicated. The source code may be pushed by the developers to public repositories (e.g. SourceForge or GitHub), or given to selected game community members, or sold with the game, or become available by other means. The game may be written in an interpreted language such as BASIC or Python, and distributed as raw source code without being compiled; early software was often distributed in text form, as in the book BASIC Computer Games. In some cases when a game's source code is not available by other means, the game's community "reconstructs" source code from compiled binary files through time-demanding reverse engineering techniques.
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I am a geologist currently use Mapinfo 8.5, which is barely an improvement on Mapinfo 4.5, and now Mapinfo is somewhere around MI15 or so and I have no intention of buying yet more upgrades for a relatively clunky cartographic/GIS system. I am thinking of migrating to QGIS. A key issue in geology is the third dimension and having symbols (e.g. dip and strike of strata) so as to image 3D on a 2D map. Discover was written to accompany Mapinfo a good time ago to do this but again one pays through the nose to buy it and its upgrades. Is there any freeware add-on to QGIS that will do what Discover does?
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In the title with rudimentary pixel graphics, participants become gorillas and chase each other through the forest or play paintball. Crossplay allows Quest and SteamVR users to play together.
Global Adjustments Added Turbine Day and Turbine Night maps
Optimized GPU and CPU performance for Quest players
Improved shipping containers models on Cargo, Tanker, Abandoned and Quarantine
Fixed several locations where spectators and drones could fly outside the map
Fixed several exploits where players could see through some walls
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Added more movement freedom for Casters/flying spectators in most scenes
Fixed several issues that allowed AI to walk through solid objects
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