Two New Russula Species (Fungi) from Dry Dipterocarp Forest in Thailand Suggest Niche Specialization to This Habitat Type

Dry dipterocarp forests are among the most common habitat types in Thailand. Russulaceae are known as common ectomycorrhizal symbionts of Dipterocarpaceae trees in this type of habitat. The present study aims to identify collections of Russula subsection Amoeninae Buyck from dry dipterocarp forests in Thailand. A multi-locus phylogenetic analysis placed Thai Amoeninae collections in two novel lineages, and they are described here as Russula bellissima sp. nov. and R. luteonana sp. nov. The closest identified relatives of both species were sequestrate species suggesting that they may belong to drought-adapted lineages. The analysis of publicly available ITS sequences in R. subsect. Amoeninae did not confirm evidence of any of the new species occurring in other Asian regions, indicating that dry dipterocarp forests might harbor a novel community of ectomycorrhizal fungi. Macromorphological characters are variable and are not totally reliable for distinguishing the new species from other previously described Asian Amoeninae species. Both new species are defined by a combination of differentiated micromorphological characteristics in spore ornamentation, hymenial cystidia and hyphal terminations in the pileipellis. The new Amoeninae species may correspond to some Russula species collected for consumption in Thailand, and the detailed description of the new species can be used for better identification of edible species and food safety in the region.

Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms

Climate change and global warming have serious adverse impacts on tropical forests. In particular, climate change may induce changes in leaf phenology. However, in tropical dry forests where tree diversity is high, species responses to climate change differ. The objective of this research is to analyze the impact of climate variability on the leaf phenology in Thailand’s tropical forests. Machine learning approaches were applied to model how leaf phenology in dry dipterocarp forest in Thailand responds to climate variability and El Niño. First, we used a Self-Organizing Map (SOM) to cluster mature leaf phenology at the species level. Then, leaf phenology patterns in each group along with litterfall phenology and climate data were analyzed according to their response time. After that, a Long Short-Term Memory neural network (LSTM) was used to create model to predict leaf phenology in dry dipterocarp forest. The SOM-based clustering was able to classify 92.24% of the individual trees. The result of mapping the clustering data with lag time analysis revealed that each cluster has a different lag time depending on the timing and amount of rainfall. Incorporating the time lags improved the performance of the litterfall prediction model, reducing the average root mean square percent error (RMSPE) from 14.35% to 12.06%. This study should help researchers understand how each species responds to climate change. The litterfall prediction model will be useful for managing dry dipterocarp forest especially with regards to forest fires.

Restoration of Water Storage Potential in a Degraded Dry Dipterocarp Forest with Enrichment Planting of Three Needle Pine (Pinus kesiya Royle ex Gordon), Northern Thailand

The research assessed water storage in a dry dipterocarp forest (DDF) with enriched 34-year-old pine planting and the role of pine. Plant surveys were carried out using 10, 40×40 m2 plots, and data were obtained by measuring tree stem girths and heights. Plant features, biomass, and stored water amounts were measured. Fresh plant samples of abundant species were taken one time per month from January to December 2018. Three soil pits were made in three plots, and soil samples along 100 cm depth were taken on the same days of collecting plant samples for studying fied capacity, water content and water amount. The DDF was divided into three stands based on the most dominant tree species; Shorea obtusa, Dipterocarpus tuberculatus, and Dipterocarpus obtusifolius. The forest was composed of 86 species with biomass at 101.62 Mg/ha and contained an average water amount of 88.01 m3/ha. The water amount in biomass varied with sampling times from 58.74 to 111.83 m3/ha. The average MWHC of 100 cm soil was estimated to be 5,113.74 m3/ha. The water amount in soil also varied with sampling times from 3,651.50 to 4,481.06 m3/ha. As a result, the total water amount in plant biomass and soil (ecosystem) of the DDF varied in a range from 3,735.0 to 4,558.67 m3/ha. The pine contributed to 30.87 m3/ha (35.07% of the total) and could increase by 64.92% the water storage potential of the forest, and thus these results support the concept of pine enrichment planting in the poor DDF.

Estimating the economic value of absorbing and storing carbon in dipterocarp forests in Ia Pa district, Gia Lai province

Dry dipterocarp forest is one of the ecosystems that features variety of rare plants and animals. The forest in Ia Pa district, Gia Lai province is declining owing to illegal logging and encroachment of forest land for cultivation. Forest degradation can reduce CO2 sequestration, contributing to increasing greenhouse gas emission and global warming. The study was carried out in the 4 quadrats (each 2,500 m2 = 50 × 50 m) in dry dipterocarp forest of Ia Pa district. In each quadrat, 5 sub-quadrats (each 25 m2 = 5 × 5 m) were set up, one at a central point and four at four corners of the quadrat, respectively. Based on the relationship between carbon stock and above-ground biomass, we found that CO2 accumulation in dry dipterocarp forest was 105.6 tons/ha corresponding to the economic value of 12,299,760 VND/ha. Thus, it is necessary to improve forest quality towards sustainable management of dry dipterocarp forest ecosystem and increase the economic value of the forest with respect to environmental services.

Estimating Leopard Cat Prionailurus bengalensis Kerr, 1792 (Carnivora: Felidae) density in a degraded tropical forest fragment in northeastern Thailand

The Leopard Cat Prionailurus bengalensis is thought to be Asia’s most abundant wild cat. Yet, the species’ status is poorly known due to a lack of rigorous population estimates. Based on the few studies available, Leopard Cats appear to be more abundant in degraded forests, potentially due to increased prey availability. We conducted camera trap surveys, rodent live-trapping, and spatially-explicit capture-recapture analyses to estimate the density of Leopard Cats within a degraded tropical forest fragment (148km2) in northeastern Thailand. A total effort of 12,615 camera trap nights across 65km2 of trapping area resulted in at least 25 uniquely identified individuals. Average rodent biomass (the main prey of Leopard Cats) was highest in the dry evergreen forest (469.0g/ha), followed by dry dipterocarp forest (287.5g/ha) and reforested areas (174.2g/ha). Accordingly, Leopard Cat densities were highest in the dry evergreen forest with 21.42 individuals/100km2, followed by the reforested areas with 7.9 individuals/100km2. Only two detections came from the dry dipterocarp forest despite both an extensive survey effort (4,069 trap nights) and available prey. Although the dipterocarp supported the second highest average rodent biomass, it lacked a key prey species, Maxomys surifer, possibly explaining low encounter rates in that habitat. Our results provide important baseline information concerning the population status of Leopard Cat in southeastern Asia. Further, our findings corroborate with other studies that found a tolerance among Leopard Cats for degraded forests, highlighting the potential for forest fragments to serve as long-term conservation areas for the species.

Contribution of Root Respiration to Soil Respiration during Rainy Season in Dry Dipterocarp Forest, Northern Thailand

Soil respiration (Rs) plays a key role in regulating the terrestrial carbon cycle. The nature of this role is determined by the different responses of root respiration (Rr) and microbial respiration (Rm) to environmental factors such as precipitation, soil moisture and temperature. Understanding these responses is fundamental to improving our predictions of climate change impacts on carbon cycling processes. In this study, the ratio of root respiration to soil respiration (Rr/Rs) was studied to improve our understanding of soil CO2emissions. The study aimed to improve our knowledge of Rr in relation to rainy season soil environmental factors in a dry dipterocarp forest in northern Thailand. With values of Rrranging from 41.04-61.97 mgCO2m-2h-1, with a Rr/Rsratio from 23-48%,the results suggest that soil moisture was a main driver for emitted CO2from Rr while soil temperature was only weakly related with Rr during the rainy season. However, longer-term studies are needed, including measurements of root biomass to improve accuracy and understanding of the dynamics of root respiration and their linkages with CO2emissions.