Disturbance as a driver of trait assembly in liana communities in a semi-deciduous Atlantic Forest

Villagra, Mariana; Trentini, Carolina Paola; Francescantonio, Débora di; Eleutério, Ana Alice; Foletto, Fernando; Montti, Lía; Campanello, Paula I.

doi:10.1007/s11258-021-01142-4

Cited by 3 publications

(2 citation statements)

References 84 publications

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“…The birth of Hadoop and HBase technology has laid an important technical foundation for solving the problem of efficient storage and rapid indexing of ecological big data. However, native Hadoop cannot handle the problem of small files well, and native HBase only supports primary indexing by default [6]. There are two main problems in the above platforms: (1) In terms of storage, native Hadoop does not solve the storage problem of massive small images and does not partition massive data.…”

Section: Introductionmentioning

confidence: 99%

Data Management Platform of Forest Ecological Station Based on Internet of Things and Big Data Sensor

Sun

2022

Journal of Sensors

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In order to solve the problems of low data storage efficiency and poor retrieval performance in forest ecological station, a method of a forest ecological station data management platform based on Internet of Things and big data sensor is proposed. The framework method designs the prepartition algorithm to ensure the uniform distribution of data in the cluster. According to the characteristics of ecological data, the RowKey is scientifically designed to realize the rapid retrieval of ecological data. The Elasticsearch index fragment placement strategy based on index data and server performance evaluation is designed, and the packaging and merging strategy based on data site and time correlation is proposed to improve the storage efficiency. The results are as follows: when the scale of structured data is 108, the retrieval time of the system is 1.045 s, which is 3.99 times faster than that of the original HBase. When the scale of unstructured data is 107, the packaging small image strategy based on data site and time correlation is 1.15 times higher than that of the sequence file and 1.79 times higher than that of the original HBase. In the case of 104 concurrent users, the number of queries per second after optimization is 1.88 times higher than the original, the throughput per second is 1.74 times higher than that before optimization, and the system response time is 69.5% lower than that before optimization. The results show that the proposed scheme has significantly improved the performance in the aspects of cluster load balancing and massive structured and unstructured data retrieval efficiency and system throughput, and provide the necessary theoretical basis and technical implementation for the storage and management of forest ecological data.

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Section: Introductionmentioning

confidence: 99%

Data Management Platform of Forest Ecological Station Based on Internet of Things and Big Data Sensor

Sun

2022

Journal of Sensors

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“…Stem-twiners were reported to be more abundant in disturbed tropical forests than undisturbed forests (Addo-Fordjour and Rahmad, 2015), which show that forest disturbances may somewhat facilitate this climbing mechanism. Likewise, Villagra et al (2021) reported scramblers to be abundant in disturbed forests associated with high specific leaf area (SLA) and it is directly linked with rapid growth rate (Coley, 1988). In our study sites deciduous species dominate the forest stand.…”

Section: Changes In Liana Basal Area Biomass and Carbon Stocksmentioning

confidence: 59%

Increasing liana biomass and carbon stocks in tropical dry evergreen forests of southern India

et al. 2022

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Tropical forests act as a great carbon reservoir covering about 30% of the global carbon content, however, structural alteration of these forests caused by forest disturbances adversely affects the carbon cycle. One such structural change happening in these tropical forests is the increasing dominance of lianas (woody climbers). Among various tropical forest types, lianas are an integral constituent of the tropical dry evergreen forests (TDEFs) found in peninsular India. A re-inventory of lianas was carried out to observe temporal changes in basal area and carbon stock in two 1-ha permanent plots of two disturbed tropical dry evergreen forest sites (TDEF; Oorani -OR and Puthupet - PP) over a 19-year interval (2001-2020). The total basal area in OR and PP increased respectively by 2.26 m2 ha-1 and 0.93 m2 ha-1. The total biomass and the carbon stock in OR and PP increased by 82% and 51% respectively. The dominant species Strychnos lenticellata showed an increase in its basal area by three-fold in OR, whereas, in PP, a marginal increase of 4% was observed. The lower diameter class (1-6 cm) showed an increase in basal area in OR and PP by 101% and 16% respectively. The mid-diameter class (6-11 cm) was the top contributor of the total biomass/carbon in both OR and PP in the latest re-inventory (2020). The present results show that lianas, although known to negatively affect the forest biomass/carbon stock, play an important role in carbon sequestration, thus providing insights into their ecological importance which will certainly be useful in proposing strategies for the conservation of this forest type dominated by lianas.

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Leaf nutrient-based processes of conservatism and convergence regulate the vertical stratification of plant growth forms during subtropical forest degradation

Bai,

Yang,

et al. 2024

Ecological Indicators

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Disturbance as a driver of trait assembly in liana communities in a semi-deciduous Atlantic Forest

Cited by 3 publications

References 84 publications

Data Management Platform of Forest Ecological Station Based on Internet of Things and Big Data Sensor

Data Management Platform of Forest Ecological Station Based on Internet of Things and Big Data Sensor

Increasing liana biomass and carbon stocks in tropical dry evergreen forests of southern India

Leaf nutrient-based processes of conservatism and convergence regulate the vertical stratification of plant growth forms during subtropical forest degradation

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