scholarly journals Living in a landscape mosaic: Movement patterns and resource selection of swamp wallabies

Author(s):  
Manuela Fischer ◽  
Julian Di Stefano ◽  
Stephanie Kramer-Schadt ◽  
Pierre Gras ◽  
Milena Stillfried ◽  
...  
2007 ◽  
Vol 34 (2) ◽  
pp. 77 ◽  
Author(s):  
Erik Klop ◽  
Janneke van Goethem ◽  
Hans H. de Iongh

The preference of grazing herbivores to feed on grass regrowth following savanna fires rather than on unburnt grass swards is widely recognised. However, there is little information on which factors govern patterns of resource selection within burnt areas. In this study, we attempted to disentangle the effects of different habitat and grass sward characteristics on the utilisation of post-fire regrowth by nine species of ungulates in a fire-dominated woodland savanna in north Cameroon. We used resource-selection functions based on logistic regression. Overall, the resource-selection functions identified the time elapsed since burning as the most influential parameter in determining probability of use by ungulates, as most species strongly selected swards that were recently burned. This pattern might be related to nutrient levels in the grass sward. In addition, most species selected areas with high grass cover and avoided grass swards with high amounts of dead stem material. This is likely to increase bite mass and, hence, intake rates. The avoidance of high tree cover by some species may suggest selection for open areas with good visibility and, hence, reduced risk of predation. Body mass seemed to have no effect on differential selection of post-fire regrowth, irrespective of feeding style.


1987 ◽  
Vol 19 (2) ◽  
pp. 214-226 ◽  
Author(s):  
William Freedman ◽  
Linda Kent

2021 ◽  
Vol 49 (4) ◽  
pp. 806-816
Author(s):  
Paulo Ávila ◽  
António Pires ◽  
Goran Putnik ◽  
João Bastos ◽  
Maria Cruz-Cunha

The selection of the resources system (SRS) is an important element in the integration/project of Agile/Virtual Enterprises (A/V E) because its performance is dependent of this selection, and even of its creation. However, it remains a difficult matter to solve because is still a very complex and uncertain problem. We propose that using Value Analysis (VA) in the pre-selection of resources phase represents a significant improvement of the SRS process. The current literature fails to formally address the pre-selection phase and none of the resource selection models incorporate the resources value and its consequence in the complexity of the selection process. Whereby, ours developed model with VA constitutes an innovative approach towards greater sustainability in the configuration of A/V E in the context of Industry 4.0, where a massive interconnection among enterprises is expected and consequently the increase of the selection process complexity. After the construction of a demonstrator tool for a set of the problem formulations, this paper verifies by computational results the thesis regarding the benefits of applying VA to the SRS process: VA reduces the complexity of the SRS process, even ensuring that the final system of resources achieve higher quality/value grade.


In the Indian scenario construction industry facing a major problem is cost and time overrun. Effective time performance and cost performance are very important to execute the project in a successful manner by keeping them within the prescribed schedule and cost. Overall cost and duration of construction projects affected by the effective resource selection factor. This paper's objective is to rectify the improper selection of resources by a programming tool. Field survey and codebook study did collect the needed data to feed in the programming tool. The prepared tool gets distributed and making to access by every stakeholder of construction projects. This may result in the selection of construction resources as effectively. The term cost overrun in the resource part will be reduced.


Cloud computing allows users to use resources pay per use model by the help of internet. Users are able to do computation dynamically from different location by using internet resources. The major challenging task in cloud computing is efficient selection of resources for the tasks submitted by users. A number of heuristics and meta-heuristics algorithms are designed by different researchers. The most critical phase is the selection of appropriate resource and its management. The selection of resource include to identify list of authenticated available resources in the cloud for job submission and to choose the best resource. The best resource selection is done by the analysis of several factors like expected time to execute a task by user, access restriction to resources, and expected cost to use resources. In this paper, cloud architecture for resource selection is proposed which combines these factors and make the effective resource selection. In this paper a modified flower pollination algorithm is proposed to migrate the task on efficient virtual machine. The selection of the efficient virtual machine is calculated by the fitness function. By calculating the fitness function, the modified FPA algorithm is used to take the decision regarding VM migration is required to improve the resource efficiency or not. In this paper Virtual machine mapper maps the task as per knowledge base i.e. past history of the virtual machine, task type whether computational or communicational based. The results are compared with the existing meta-heuristic algorithms.


2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 297-297
Author(s):  
Andres Cibils ◽  
Rick Estell ◽  
Alfredo Gonzalez ◽  
Sheri Spiegal ◽  
Martha Anderson ◽  
...  

Abstract Body temperature and movement patterns of Angus Hereford crossbred (AH) vs. Raramuri Criollo (RC) nursing cows were monitored in summer 2016 and 2017. AH and RC cows grazed separately in two adjacent Chihuahuan Desert pastures (1190ha, 1165ha) in a crossover design for 4 weeks each year. Body temperature (BodyT) was monitored at 10 min intervals by placing blank CIDRs containing a temperature logger in 10 cows per breed. Seven to 9 AH and RC cows were also fitted with GPS collars that recorded position and ambient temperature (CollarT) at 10 min intervals. A landscape thermal map (LandT) was developed for habitat analysis. Data were analyzed within four daytime segments: dawn (sunrise to 9AM); pre-noon (9AM to noon); post-noon (noon to 3PM); and dusk (3PM to sunset). ANOVA was used to determine whether BodyT, animal movement, CollarT, and mean LandT position within each day segment were different for AH vs. RC cows. Breed nested within Year*Pasture was treated as the experimental unit. BodyT increased as a day progressed and was higher (P < 0.05) in AH vs. RC during post-noon (38.83 vs. 38.42oC) and dusk (39.22 vs. 38.70oC). Compared to AH counterparts, RC cows traveled farther (4.7 vs. 2.7 km*daytime h-1, P < 0.05), at higher velocities (5.9 vs. 3.5 m*min-1, P < 0.05) and spent more time grazing (5.6 vs. 4.3 daytime h; P < 0.05) and traveling (0.7 vs. 0.3 daytime h; P < 0.05) during all four daytime segments. Largest breed differences were observed during the hottest segments of the day (post-noon and dusk). Increasing CollarT throughout a day was associated with selection of cooler landscape locations (LandT) in both breeds. Apparent lower body heat load in RC cows may reduce constraints on their movement patterns compared to AH cows grazing Chihuahuan Desert rangeland in summer.


Ecosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
Author(s):  
John R. Squires ◽  
Lucretia E. Olson ◽  
Zachary P. Wallace ◽  
Robert J. Oakleaf ◽  
Patricia L. Kennedy

2018 ◽  
Vol 208 ◽  
pp. 49-55 ◽  
Author(s):  
Rafael Arturo Torres-Fajardo ◽  
Pedro Geraldo González-Pech ◽  
Javier Ventura-Cordero ◽  
Guadalupe Isabel Ortíz-Ocampo ◽  
Carlos Alfredo Sandoval-Castro ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document