Modeling and Prediction of Habitat Suitability for Ferula gummosa Medicinal Plant in a Mountainous Area

The influence of climate change on the future distribution of two Thymus species in Iran: MaxEnt model-based prediction

Within a few decades, the species habitat was reshaped at an alarming rate followed by climate change, leading to mass extinction, especially for sensitive species. Species distribution models (SDMs), which estimate both present and future species distribution, have been extensively developed to investigate the impacts of climate change on species distribution and assess habitat suitability. In the West Asia essential oils of T. daenensis and T. kotschyanus include high amounts of thymol and carvacrol and are commonly used as herbal tea, spice, flavoring agents and medicinal plants. Therefore, this study aimed to model these Thymus species in Iran using the MaxEnt model under two representative concentration pathways (RCP 4.5 and RCP 8.5) for the years 2050 and 2070. The findings revealed that the mean temperature of the warmest quarter (bio10) was the most significant variable affecting the distribution of T. daenensis. In the case of T. kotschyanus, slope percentage was the primary influencing factor. The MaxEnt modeling also demonstrated excellent performance, as indicated by all the Area Under the Curve (AUC) values exceeding 0.9. Moreover, based on the projections, the two mentioned species are expected to undergo negative area changes in the coming years. These results can serve as a valuable achievement for developing adaptive management strategies aimed at enhancing protection and sustainable utilization in the context of global climate change.

Habitat potential modelling and the effect of climate change on the current and future distribution of three Thymus species in Iran using MaxEnt

Over the course of a few decades, climate change has caused a rapid and alarming reshaping of species habitats, resulting in mass extinction, particularly among sensitive species. In order to investigate the effects of climate change on species distribution and assess habitat suitability, researchers have developed species distribution models (SDMs) that estimate present and future species distribution. In West Asia, thyme species such as T. fedtschenkoi, T. pubescens, and T. transcaucasicus are rich in thymol and carvacrol, and are commonly used as herbal tea, spice, flavoring agents, and medicinal plants. This study aims to model the distribution of these Thymus species in Iran using the MaxEnt model under two representative concentration pathways (RCP 4.5 and RCP 8.5) for the years 2050 and 2070. The objective is to identify the crucial bioclimatic (n = 5), edaphic (n = 1), and topographic (n = 3) variables that influence their distribution and predict how their distribution might change under various climate scenarios. The findings reveal that the most significant variable affecting T. fedtschenkoi and T. pubescens is altitude, while soil organic carbon content is the primary factor influencing the distribution of T. transcaucasicus. The MaxEnt modeling demonstrates excellent performance, as indicated by all the area under the curve (AUC) values exceeding 0.9. Based on the projections, it is expected that these three thyme species will experience negative area changes in the coming years. These results can serve as a valuable tool for developing adaptive management strategies aimed at enhancing protection and sustainable utilization in the context of global climate change. Special attention should be given to conserving T. fedtschenkoi, T. pubescens, and T. transcaucasicus due to their significant habitat loss in the future.

Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques.. [DOI: 10.21203/rs.3.rs-2441475/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Spatial locational modeling techniques are increasingly used in species distribution modeling. However, the implemented techniques differ in their modeling performance. In this study, we tested the predictive accuracy of three algorithms, namely "random forest (RF)," "support vector machine (SVM)," and "boosted regression trees (BRT)" to prepare habitat suitability mapping of an invasive species, Alhagi maurorum, and its potential biological control agent, Aceria alhagi. Location of this study was in Fars Province, southwest of Iran. The spatial distributions of the species were forecasted using GPS devices and GIS software. The probability values of occurrence were then checked using three algorithms. The predictive accuracy of the machine learning (ML) techniques was assessed by computing the “area under the curve (AUC)” of the “receiver-operating characteristic” plot. When the Aceria alhagi was modeled, the AUC values of RF, BRT and SVM were 0.89, 0.81, and 0.79, respectively. However, in habitat suitability models (HSMs) of Alhagi maurorum the AUC values of RF, BRT and SVM were 0.89, 0.80, and 0.73, respectively. The RF model provided significantly more accurate predictions than other algorithms. The importance of factors on the growth and development of Alhagi maurorum and Aceria alhagi was also determined using the partial least squares (PLS) algorithm, and the most crucial factors were the road and slope. Habitat suitability modeling based on algorithms may significantly increase the accuracy of species distribution forecasts, and thus it shows considerable promise for different conservation biological and biogeographical applications.

Predicting the Geographical Distribution Shift of Medicinal Plants in South Africa Due to Climate Change

There has been a recent rise in the number of medicinal plant users in Southern Africa, with approximately a million users reported to utilize these plants for various health conditions. Unfortunately, some of these plants are reportedly endangered and facing extinction due to harvesting pressure. In addition, climate change is likely to negatively affect the geographical distribution of these medicinal plants. In the current study, future greenhouse gas emission scenarios of the representative concentration pathways, RCP2.6 and RCP8.5, for future projections to 2050 and 2080 were used to simulate the effect of climate change on three medicinal plants’ (Aloe ferox, Bowiea volubilis, and Dioscorea elephantipes) distribution in South Africa. We studied these plant species as the International Union for Conservation of Nature stated that A. ferox is currently of least concern in South Africa, while B. volubilis and D. elephantipes are categorised as declining and vulnerable, respectively. Specifically, we utilised a species distribution model (i.e., the maximum entropy: MaxEnt) to investigate the effect of climate change on the future spatial distribution of medicinal plants in South Africa. In 2050 and 2080, under both RCP scenarios, the suitable habitat of the studied plant species will reduce in the country’s northern parts. Specifically, the habitat for D. elephantipes will totally disappear in the country’s northern parts. However, there will be slight additions of suitable habitats for the species in the country’s southern parts. Model validation indicated that the area under curve (AUC) for A. ferox was 0.924 ± 0.004, while for B. volubilis and D. elephantipes it was 0.884 ± 0.050 and 0.944 ± 0.030, respectively. Using the results from this study, there is a need for the long-term in situ and ex situ conservation of these medicinal plants. The results of the present study could guide the development of effective and efficient policies and strategies for managing and conserving medicinal plants in South Africa.

Identifying Key Environmental Factors for Paulownia coreana Habitats: Implementing National On-Site Survey and Machine Learning Algorithms

Monitoring and preserving natural habitats has become an essential activity in many countries today. As a native tree species in Korea, Paulownia coreana has periodically been surveyed in national ecological surveys and was identified as an important target for conservation as well as habitat monitoring and management. This study explores habitat suitability models (HSMs) for Paulownia coreana in conjunction with national ecological survey data and various environmental factors. Together with environmental variables, the national ecological survey data were run through machine learning algorithms such as Artificial Neural Network and Decision Tree & Rules, which were used to identify the impact of individual variables and create HSMs for Paulownia coreana, respectively. Unlike other studies, which used remote sensing data to create HSMs, this study employed periodical on-site survey data for enhanced validity. Moreover, localized environmental resources such as topography, soil, and rainfall were taken into account to project habitat suitability. Among the environment variables used, the study identified critical attributes that affect the habitat conditions of Paulownia coreana. Therefore, the habitat suitability modelling methods employed in this study could play key roles in planning, monitoring, and managing plants species in regional and national levels. Furthermore, it could shed light on existing challenges and future research needs.