Predicting impacts of future climate change on the distribution of the widespread selaginellas (Selaginella ciliaris and S. plana) in Southeast Asia

Abstract

Abstract. Pranoto, Rosariastuti R, Prihandoko A. 2018. Short Communication: The utilization and effectiveness test of andisol soil-bioball-Agrobacterium sp. toward heavy metal chrome removal. Biodiversitas 19: 1955-1959. This study was aimed to determine chrome metal adsorption and bioremediation ability using andisol soil, bioball, and Agrobacterium sp. The andisol soil characterization was performed by Fourier Transform Infrared Analyzer (FTIR), X-Ray Diffraction (XRD), and Surface Area Analyzer (SAA) while the cell quantity of Agrobacterium sp. was calculated by hemocytometer method. Determination of adsorption and bioremediation effectiveness was done using several parameters such as effect of pH solution, ratio variation of andisol soil-bio ball-Agrobacterium sp, and contact time. The pH variation was ranged from 1-6, while the composition variation of andisol-bioball-Agrobacterium sp. were 2: 0: 0, 1, 5: 1: 5, 1: 2: 10, 0, 5: 3: 15, and 0: 4: 20 (gr: item: mL), and contact time of 30,60,90,120, and 150 minutes. The result of this research shows that the optimum adsorption and bioremediation of Cr metal at pH 5, while the optimum ratio of andisol soil-bioball-Agrobacterium sp was 1, 5: 1: 5 (gr: item: mL), with the contact time for 120 minutes with Cr decrease percentage of 71.3%. The adsorption isotherm followed Langmuir and Freundlich isotherm.

Prediction of potential climate change impacts on the geographic distribution shift of Casuarina junghuhniana and C. equisetifolia in Southeast Asia

“…A centroid line is computed by averaging the latitude and longitude of the binary input pixels, and subsequently used to represent the central point of the species range and to indicate the direction of change. Subsequently, the study used RCP year 2080 to identify the most pronounced shifts in the geometric distribution centers, as this period is anticipated to be the most affected by climate change (Setyawan et al 2018).…”

“…Several factors can influence the accuracy of habitat distribution predictions, including limited availability of data, microclimate variations, and the effects of anthropogenic activities like land use changes, deforestation, and pollution (Fontúrbel et al 2015). Omission errors can also occur due to incomplete event data used in the models (Setyawan et al 2018). Despite these limitations, this study provides a foundational understanding of how current climate conditions impact the distribution of suitable habitats for C. junghuhniana and C. equisetifolia, and offers predictions for their redistribution under future climate change scenarios.…”

“…Creating an ideal model requires substantial highquality data. Limitations in the availability of ecological and physiological data can impact model accuracy (Setyawan et al 2018). Nevertheless, as climate models improve, there is an opportunity to develop more precise and ideal models.…”

Predicting the current and future distributions of Pinus merkusii in Southeast Asia under climate change

“…As in the research of Padalia et al ( 2014), Maxent will determine the Area Under Curve (AUC) to assess model performance. The AUC value ranges from 0 (lowest) to 1 (highest), with a value of 0 to 0.5 meaning the model is no better than random prediction, between 0.5 to 0.7 means low discrimination, between 0.7 to 0.9 means good discrimination, and a value between 0.9 to 1 means the model is better than random prediction (Setyawan et al 2018;Setyawan et al 2021).…”