Drop in Rain Forest Productivity Could Speed Future Climate Change

As temperatures rise, tropical forests will become more stressed and photosynthesize less.

Forecasting Amazon Rain-Forest Deforestation Using a Hybrid Machine Learning Model

The present work aims to carry out an analysis of the Amazon rain-forest deforestation, which can be analyzed from actual data and predicted by means of artificial intelligence algorithms. A hybrid machine learning model was implemented, using a dataset consisting of 760 Brazilian Amazon municipalities, with static data, namely geographical, forest, and watershed, among others, together with a time series data of annual deforestation area for the last 20 years (1999–2019). The designed learning model combines dense neural networks for the static variables and a recurrent Long Short Term Memory neural network for the temporal data. Many iterations were performed on augmented data, testing different configurations of the regression model, for adjusting the model hyper-parameters, and generating a battery of tests to obtain the optimal model, achieving a R-squared score of 87.82%. The final regression model predicts the increase in annual deforestation area (square kilometers), for a decade, from 2020 to 2030, predicting that deforestation will reach 1 million square kilometers by 2030, accounting for around 15% compared with the present 1%, of the between 5.5 and 6.7 millions of square kilometers of the rain-forest. The obtained results will help to understand the impact of man’s footprint on the Amazon rain-forest.

Biogeographical variation on craniomandibular morphology in pallas’s squirrel \(\textit{Callosciurus erythraeus}\) (Pallas, 1779) (Rodentia: Sciuridae) in Vietnam

Different forest vegetations provide herbivorous small mammals with different resources, forcing adaptation since food habits depend on available resources. We expect differences in vegetation to be reflected in the size and shape of the skull and mandible as a result of potentially different feeding resources. Therefore, we analyzed the craniomandibular characteristics of Pallas’s squirrel (Callosciurus erythraeus) in Vietnam. This species commonly occurs in different vegetations in Vietnam, making it a good model for examining morphological adaptation to vegetation type. We analyzed morphologically the skulls and mandibles of 156 specimens collected from 31 localities in Vietnam from 1960 to the present. Principal component analysis showed that females occurring in the tropical lowland evergreen rain forest were clearly separated from those in other vegetations.

A New Species of Phyllocladiferous Phyllanthus (Phyllanthaceae) from the Atlantic Rain Forest

Abstract— A new species of Phyllanthus with phylloclades endemic to the State of Bahia is described here. Phyllanthus dracaenoides can be recognized for its peculiar habit that resembles a species of Dracaena, a very unusual height of up to 7 m, the plagiotropic phylloclades spirally arranged, and its vermiform cincinni. Notes on its habitat, taxonomic affinity, conservation status, geographic distribution, illustrations, and photographs are provided.

Orchid Identification and Collection Techniques in the Kembang Kuning Resort Area, Rinjani Mount National Park (RMNP)

The Rinjani Mount National Park (RMNP) area is one part of the tropical rain forest in the West Nusa Tenggara region. Orchid is one of the flora that has a high bioprospective in this area. The spread of natural orchids can continue to grow and there are still many that have not been identified. This study aims to train specific techniques in collecting and identifying natural orchids at The Resort of Kembang Kuning, Rinjani Mount National Park. The collection of orchids was carried out using the roaming method, while the identification of orchid species was carried out using a species determination technique through observation of morphological and anatomical descriptions. The out comes of this study are an increase in participants' appreciation, knowledge and skills in collecting and identifying species of natural orchids in the Kembang Kuning area of Rinjani ​​Mount National Park. Based on the observations obtained 9 species (7 species were identified to the species level and 2 species were identified to the genus level) from 6 genera of natural orchids in area of The Kembang Kuning Resort, The Rinjani Mount National Park (RMNP).

Effect of Climate Change on CO2 Flux in Temperate Grassland, Subtropical Artificial Coniferous Forest and Tropical Rain Forest Ecosystems

The interactions between CO2 flux, an important component of ecosystem carbon flux, and climate change vary significantly among different ecosystems. In this research, the inter-annual variation characteristics of ecosystem respiration (RE), gross ecosystem exchange (GEE), and net ecosystem exchange (NEE) were explored in the temperate grassland (TG) of Xilinhot (2004–2010), the subtropical artificial coniferous forest (SACF) of Qianyanzhou (2003–2010), and the tropical rain forest (TRF) of Xishuangbanna (2003–2010). The main factors of climate change affecting ecosystem CO2 flux were identified by redundancy analysis, and exponential models and temperature indicators were constructed to consider the relationship between climate change and CO2 flux. Every year from 2003 to 2010, RE and GEE first increased and then decreased, and NEE showed no significant change pattern. TG was a carbon source, whereas SACF and TRF were carbon sinks. The influence of air temperature on RE and GEE was greater than that of soil temperature, but the influence of soil moisture on RE and GEE was greater than that of air moisture. Compared with moisture and photosynthetically active radiation, temperature had the greatest impact on CO2 flux and the exponential model had the best fitting effect. In TG and SACF, the average temperature was the most influential factor, and in TRF, the accumulated temperature was the most influential factor. These results provide theoretical support for mitigating and managing climate change and provide references for achieving carbon neutrality.