MODELING SPECIES DISTRIBUTION OF SHOREA GUISO (BLANCO) BLUME AND PARASHOREA MALAANONAN (BLANCO) MERR IN MOUNT MAKILING FOREST RESERVE USING MAXENT

Abstract

Abstract. Climate change is regarded as one of the most significant drivers of biodiversity loss and altered forest ecosystems. This study aimed to model the current species distribution of two dipterocarp species in Mount Makiling Forest Reserve as well as the future distribution under different climate emission scenarios and global climate models. A machine-learning algorithm based on the principle of maximum entropy (Maxent) was used to generate the potential distributions of two dipterocarp species – Shorea guiso and Parashorea malaanonan. The species occurrence records of these species and sets of bioclimatic and physical variables were used in Maxent to predict the current and future distribution of these dipterocarp species. The variables were initially reduced and selected using Principal Component Analysis (PCA). Moreover, two global climate models (GCMs) and climate emission scenarios (RCP4.5 and RCP8.5) projected to 2050 and 2070 were utilized in the study. The Maxent models predict that suitable areas for P. malaanonan will decline by 2050 and 2070 under RCP4.5 and RCP 8.5. On the other hand, S. guiso was found to benefit from future climate with increasing suitable areas. The findings of this study will provide initial understanding on how climate change affects the distribution of threatened species such as dipterocarps. It can also be used to aid decision-making process to better conserve the potential habitat of these species in current and future climate scenarios.

Leaf Traits of Parashorea malaanonan Along Elevational Gradient in Mount Makiling Forest Reserve, Philippines

“…Furthermore, the species can be found in lowland Dipterocarp forests up to 1,000 masl (Randi et al 2019) and in lowland tropical rainforests at altitudes up to 1,300 masl in the Philippines (Abasolo et al 2009). The species has been identified as one of the dominant dipterocarp tree species, with an importance value of 8.878, in the Molawin-Dampalit Subwatershed of the MMFR (Castillo et al 2018;Tumaneng et al 2019). Moreover, a high species diversity index (3.52) in the Molawin-Dampalit Subwatershed was also reported, indicating that despite the presence of many plant competitors, P. malaanonan was able to dominate the area and adapt to the environment (Castillo et al 2018).…”

“…In the case of P. malaanonan, because it is a slowgrowing species, it prefers a resource-conservative strategy in a resource-limited environment (Newberry et al 2011;Ouédraogo et al 2013). P. malaanonan is classified as a species belonging to the family of slow traits (Tumaneng et al 2019), which could influence its behavior in a different environment. It is partly related to the species' resource-conservative strategy, in which, due to the limited resources available at higher elevations, they require specialized traits that can conserve resources (e.g., water and nutrients) for their growth and survival, as opposed to low elevation, which is more resource-rich in comparison to high elevation (Bai et al 2015;Reich 2014;Umaña and Swenson 2019).…”

“…An ecologically and economically important tree species worthy of a study on leaf trait variation along elevation gradients is Parashorea malaanonan, a member species of the timber family Dipterocarpaceae. While threats to the species have been controlled hence, classified as least concern (Randi et al 2019), however, it is projected to face a decline in its suitable habitat due to the impacts of climate change on tree species in the tropics (Tumaneng et al 2019). P. malaanonan traits vary along the elevational gradient based on the growth-limitation hypothesis and plant economics spectrum (Reich 2014).…”

Evaluating the species distribution patterns of the genus Saurauia Willd. in the Philippines using geospatial analysis

“…In the Philippines, numerous studies have examined the distribution of other floral species using GIS-assisted methodologies and related technologies such as the Musa balbisiana (Rabara et al 2020), sago palm (Santillan and Makinano-Santillan 2016), Shorea guiso (Blanco) Blume and Parashorea malaanonan (Blanco) Merr. (Tumaneng et al 2019), among others.…”

Modeling the risk of illegal forest activity and its distribution in the southeastern region of the Sierra Madre Mountain Range, Philippines

“…An advantage of the MaxEnt approach is that it only requires presence data for an item of interest, while other approaches require both presence and absence data to identify spatial patterns. MaxEnt models substitute absence data with randomly generated background data, which is particularly useful for predicting the spatial distribution of a species due to the difficulties associated with obtaining absence data (Elith & Leathwick 2009, Torres et al 2016, Muscarella et al 2017, Zhang et al 2018, Tumaneng et al 2019. We then used the results to recommend improvements in the patrol strategy for the region.…”