Prospects for biodiversity conservation in the Atlantic Forest: Lessons from aging human-modified landscapes

Recent global assessments have shown the limited coverage of protected areas across tropical biotas, fuelling a growing interest in the potential conservation services provided by anthropogenic landscapes. Here we examine the geographic distribution of biological diversity in the Atlantic Forest of South America, synthesize the most conspicuous forest biodiversity responses to human disturbances, propose further conservation initiatives for this biota, and offer a range of general insights into the prospects of forest species persistence in human-modified tropical forest landscapes worldwide. At the biome scale, the most extensive pre-Columbian habitats across the Atlantic Forest ranged across elevations below 800 masl, which still concentrate most areas within the major centers of species endemism. Unfortunately, up to 88% of the original forest habitat has been lost, mainly across these low to intermediate elevations, whereas protected areas are clearly skewed towards high elevations above 1200 masl. At the landscape scale, most remaining Atlantic Forest cover is embedded within dynamic agro-mosaics including elements such as small forest fragments, early-to-late secondary forest patches and exotic tree mono-cultures. In this sort of aging or long-term modified landscapes, habitat fragmentation appears to effectively drive edge-dominated portions of forest fragments towards an early-successional system, greatly limiting the long-term persistence of forest-obligate and forest-dependent species. However, the extent to which forest habitats approach early-successional systems, thereby threatening the bulk of the Atlantic Forest biodiversity, depends on both past and present landscape configuration. Many elements of human-modified landscapes (e.g. patches of early-secondary forests and tree mono-cultures) may offer excellent conservation opportunities, but they cannot replace the conservation value of protected areas and hitherto unprotected large patches of old-growth forests. Finally, the biodiversity conservation services provided by anthropogenic landscapes across Atlantic Forest and other tropical forest regions can be significantly augmented by coupling biodiversity corridor initiatives with biota-scale attempts to plug existing gaps in the representativeness of protected areas. (C) 2010 Elsevier Ltd. All rights reserved.

Fragmentação florestal: efeitos em múltipla escala sobre a diversidade de aves em remanescentes florestais no noroeste do Estado de São Paulo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Deeply divergent mitochondrial lineages reveal patterns of local endemism in chironomids of the Australian Wet Tropics

The Wet Tropics bioregion of north-eastern Australia has been subject to extensive fluctuations in climate throughout the late Pliocene and Pleistocene. Cycles of rainforest contraction and expansion of dry sclerophyll forest associated with such climatic fluctuations are postulated to have played a major role in driving geographical endemism in terrestrial rainforest taxa. Consequences for the distributions of aquatic organisms, however, are poorly understood.The Australian non-biting midge species Echinocladius martini Cranston (Diptera: Chironomidae), although restricted to cool, well-forested freshwater streams, has been considered to be able to disperse among populations located in isolated rainforest pockets during periods of sclerophyllous forest expansion, potentially limiting the effect of climatic fluctuations on patterns of endemism. In this study, mitochondrial COI and 16S data were analysed for E. martini collected from eight sites spanning theWet Tropics bioregion to assess the scale and extent of phylogeographic structure. Analyses of genetic structure showed several highly divergent cryptic lineages with restricted geographical distributions. Within one of the identified lineages, strong genetic structure implied that dispersal among proximate (<1 km apart) streams was extremely restricted. The results suggest that vicariant processes, most likely due to the systemic drying of the Australian continent during the Plio-Pleistocene, might have fragmented historical E. martini populations and, hence, promoted divergence in allopatry.

The influence of travel time and size of shopping center towards the frequencies of visiting customers in shopping centers in surabaya

Visiting a modern shopping center is becoming vital in our society nowadays. The fast growth of shopping center, transportation system, and modern vehicles has given more choices for consumers in shopping. Although there are many reasons for the consumers in visiting the shopping center, the influence of travel time and size of shopping center are important things to be considered towards the frequencies of visiting customers in shopping centers. A survey to the customers of three major shopping centers in Surabaya has been conducted to evaluate the Ellwood’s model and Huff’s model. A new exponent value N of 0.48 and n of 0.50 has been found from the Ellwood’s model, while a coefficient of 0.267 and an add value of 0.245 have been found from the Huff’s model.

Energy-efficient virtual machine placement in data centers by genetic algorithm

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines in a data center only, but do not consider the energy consumption in communication network in the data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement in order to make the data center more energy-efficient. In this paper, we propose a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both the servers and the communication network in the data center. Experimental results show that the genetic algorithm performs well when tackling test problems of different kinds, and scales up well when the problem size increases.

Energy efficient virtual machine placement in data centers

Electricity cost has become a major expense for running data centers and server consolidation using virtualization technology has been used as an important technology to improve the energy efficiency of data centers. In this research, a genetic algorithm and a simulation-annealing algorithm are proposed for the static virtual machine placement problem that considers the energy consumption in both the servers and the communication network, and a trading algorithm is proposed for dynamic virtual machine placement. Experimental results have shown that the proposed methods are more energy efficient than existing solutions.

A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.

An instrument to assess the obesogenic environment of child care centers

OBJECTIVES To describe protocol and interobserver agreements of an instrument to evaluate nutrition and physical activity environments at child care. METHODS Interobserver data were collected from 9 child care centers, through direct observation and document review (17 observer pairs). RESULTS Mean agreement between observer pairs was 87.26% and 79.29% for the observation and document review, respectively. Items with lower agreement were primarily staff behavior, counting across the day/week, and policy classifications. CONCLUSIONS Although some revisions are required, the interobserver agreement for the environment and policy assessment and observation (EPAO instrument) appears to be quite good for assessing the nutrition and physical activity environment of child care centers.

A random key genetic algorithm for live migration of multiple virtual machines in data centers

Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

Profiling : an application assignment approach for green data centers

In the past few years, there has been a steady increase in the attention, importance and focus of green initiatives related to data centers. While various energy aware measures have been developed for data centers, the requirement of improving the performance efficiency of application assignment at the same time has yet to be fulfilled. For instance, many energy aware measures applied to data centers maintain a trade-off between energy consumption and Quality of Service (QoS). To address this problem, this paper presents a novel concept of profiling to facilitate offline optimization for a deterministic application assignment to virtual machines. Then, a profile-based model is established for obtaining near-optimal allocations of applications to virtual machines with consideration of three major objectives: energy cost, CPU utilization efficiency and application completion time. From this model, a profile-based and scalable matching algorithm is developed to solve the profile-based model. The assignment efficiency of our algorithm is then compared with that of the Hungarian algorithm, which does not scale well though giving the optimal solution.

Community environment education centers : Facilitating community created ecoscapes

This study focuses on designing a community environment education center (CEEC) for Chillingham, as a hub for community transition to sustainability, redressing social fragmentation, youth unemployment, a high eco-footprint and economic rural decline due to globalisation. The ecologically sustainable development framework was delivered by integrating environment education and community development through project-based experiential learning. The development of Chillingham Community Centre involved case study research and incorporated participatory design charrettes, transformative learning, eco-positive development and community-public-private partnerships. This process evolved from community strategic planning in a small rural village buffering world heritage rainforests impacted by a rapidly expanding urban conurbation on Australia’s east coast. This community space encompasses socio-environmental flows connecting people to each other and the ecoscape to grow natural capital, community cohesion and empower eco-governance. Modelling passive solar design, on-site renewable energy/water/nutrient cycling, community garden/market and environment education programs sowed the seeds for a green local economy, demonstrating community capacity to participate in transition to sustainability. A small rural community can demonstrate to other communities that a CEEC enables people to meet their socio-environmental and economic needs locally and sustainably. The ecologically sustainable solution is holistic, all settlements need to be richly biodiverse, locally specific and globally wise.

Using genetic algorithm in profile-based assignment of applications to virtual machines for greener data centers

The increase in data center dependent services has made energy optimization of data centers one of the most exigent challenges in today's Information Age. The necessity of green and energy-efficient measures is very high for reducing carbon footprint and exorbitant energy costs. However, inefficient application management of data centers results in high energy consumption and low resource utilization efficiency. Unfortunately, in most cases, deploying an energy-efficient application management solution inevitably degrades the resource utilization efficiency of the data centers. To address this problem, a Penalty-based Genetic Algorithm (GA) is presented in this paper to solve a defined profile-based application assignment problem whilst maintaining a trade-off between the power consumption performance and resource utilization performance. Case studies show that the penalty-based GA is highly scalable and provides 16% to 32% better solutions than a greedy algorithm.

A penalty-based genetic algorithm for the migration cost-aware virtual machine placement problem in cloud data centers

In the past few years, the virtual machine (VM) placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used in today's cloud data centers as they do not consider the migration cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the migration cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm (GA) for the VM placement problem that considers the migration cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.