Modeling of territories suitable for the habitat of Allium obliquum L., listed in the Red Book of Tomsk oblast, based on climatic data

Oblique onion – Allium obliquum – is included to the Tomsk Red Data Book with status 0 (probably an extinctspecies). The purpose of this study was to create a model of the territory, according to its climatic parameters, suitable for habitation of A. obliquum, as well as to reveal if the territory of Tomsk oblast as a whole is suitable for the growth of this species.Studies carried out using the MaxEnt algorithm showed that the current climatic conditions of Tomsk oblast as a whole arefavorable for A. obliquum occurence, and the climate change predicted by SCIRO for 2080 will not worsen them significantly.

Climate change impact on the potential geographical distribution of two invading Xylosandrus ambrosia beetles

Xylosandrus compactus and X. crassiusculus are two polyphagous ambrosia beetles originating from Asia and invasive in circumtropical regions worldwide. Both species were recently reported in Italy and further invaded several other European countries in the following years. We used the MaxEnt algorithm to estimate the suitable areas worldwide for both species under the current climate. We also made future projections for years 2050 and 2070 using 11 different General Circulation Models, for 4 Representative Concentration Pathways (2.6, 4.5, 6.0 and 8.5). Our analyses showed that X. compactus has not been reported in all potentially suitable areas yet. Its current distribution in Europe is localised, whereas our results predicted that most of the periphery of the Mediterranean Sea and most of the Atlantic coast of France could be suitable. Outside Europe, our results also predicted Central America, all islands in Southeast Asia and some Oceanian coasts as suitable. Even though our results when modelling its potential distribution under future climates were more variable, the models predicted an increase in suitability poleward and more uncertainty in the circumtropical regions. For X. crassiusculus, the same method only yielded poor results, and the models thus could not be used for predictions. We discuss here these results and propose advice about risk prevention and invasion management of both species.

MaxEnt algorithm applying to moss species distribution analysis

Bioclimatic modeling method MaxEnt is tested on objects occupying micro-habitats on the example ofmosses. Data organizing options and the results are discussed.

Modeling of Conservation Priority Zone for the Helmeted Hornbill (Rhinoplax Vigil) In Silokek Geopark Area, West Sumatra

Helmeted hornbill (Rhinoplax vigil) is a protected wildlife in Indonesia according to enactment no. 5, 1999 about Conservation of Natural Resources and its Ecosystems and Government Regulation no. 9, 1999 about plant and wildlife preservation. Helmeted Hornbill habitats spread in five country regions: Myanmar, Thailand, Malaysia (Malayan Peninsula and Serawak), Brunei, and Indonesia (Sumatra and Borneo). Silokek Geopark which located in Sijunjung Regency, West Sumatra Province, Indonesia is an identified location of Helmeted Hornbill habitat existence. Beside its uniqueness in physics, this bird also have an ecological function as seed dispersal in nature. The utilization of Remote Sensing (RS) technology and and Geographic Information System (GIS) is highly useful in identification the Helmeted Hornbill habitat distribution in this research. Geographic dateset used in this research are Landsat OLI 8 imagery, Shuttle Radar Topographic Model (SRTM), Coordinate points of Helmeted Hornbill existence and location assesment, and other dataset related to administration boundary in Silokek Geopark. This research aims to find conservation priority zone of Helmeted Hornbill in Silokek Geopark. By utilizing Maximun Entropy (MaxEnt) algorithm with finding points and location assessment, we can determine the distribution of Helmeted Hornbill habitat in Silokek Geopark based on habitat likeness. This research produces the model of conservation priority zones in geopark silokek which are distributed in hilly protected forest area and the distributions are concentrated in the center and noth east part of our researc area. This model is highly influenced by forest texture (25.7%), distance of patches (24.3%), and distance of settlement.

Estimating soil organic carbon stocks under commercial forestry using topo-climate variables in KwaZulu-Natal, South Africa

Commercial forests are expanding globally, with great potential to absorb carbon and mitigate climate change. However, whereas the role of natural forests in carbon sequestration has been widely investigated, there is a paucity in the literature on the role of commercial forests in carbon assimilation. Hence, understanding the role of commercial forests in carbon storage is essential for quantifying local, regional or global carbon balances, which is valuable for climate change mitigation. Soil carbon is known to be the largest pool within any forest landscape, and is controlled by a wide range of physical and climatic factors. However, the relationship between soil organic carbon (SOC) and topo-climatic variables controlling its distribution within commercial forests is still poorly understood. Due to the limitations encountered in traditional systems of SOC determination, particularly at large spatial extents, geospatial techniques have recently emerged as a viable alternative for mapping soil properties. Therefore, this study sought to map SOC stocks variability within the commercial forest landscape, using landscape topo-climatic variables. A total of 81 soil samples was analysed for SOC concentrations and 31 topographic and climatic variables were used as predictors to SOC variability. To reduce multicollinearity, these variables were reduced to 11 using stepwise backward elimination and the maximum entropy (Maxent) algorithm was used for regression analysis to determine the relationship between SOC and the selected topo-climatic variables. Good accuracies were obtained for both training (area under the curve = 0.906) and test (area under the curve = 0.885) data sets, and demonstrate the effectiveness of selected topo-climatic variables and the Maxent algorithm in predicting SOC stocks. This study provides a framework for monitoring the status of soil carbon in commercial forest compartments and provides a viable approach for local, national or regional carbon accounting – valuable for climate change mitigation.

POTENTIAL HABITAT MODELING OF WATER HYACINTH IN LAKES OF NEPAL USING MAXENT ALGORITHM

Invasive Alien Plant Species are spreading outside of their natural geographic range. Water hyacinth (Eichhornia crassipes) is one of the most widely and rapidly spreading invasive species throughout the tropical and subtropical regions of Nepal. In the last decade, water hyacinth has become a chronic problem in many major lakes of Nepal which have affected the habitat aquatic plants and animals. Our study focuses on potential habitat modeling of Water hyacinth over the major lakes of Nepal using Maxent algorithm. Primary data used for modeling were 19 bioclimatic variables and Shuttle Radar Topography Model (SRTM) Digital Elevation Model (DEM). After preparation of the species distribution model, major lakes of Nepal were overlaid over the model to prepare potential invasive map. The performance and accuracy of potential habitat distribution model was evaluated using parameter Area under the Receiver Operating Characteristic Curve (AUC) which was within the range of 0.9–1. Validation of the model was done for the year 2015 with precision and recall, overall accuracy and F-measure and its values are 93% and 85%, 87% and 89% respectively. The model prepared for 2030 and 2050 shows the most suitable habitat for water hyacinth is in province 2 of Nepal and the moderately suitable habitat for this species is plain area of Province 4, 7 and 5. Similarly, the area of potential habitat has been increasing from current scenario to 2030 and 2050. From the potential invasion map, it can be observed that lakes in the Terai and Churey regions have the high risk of invasion of water hyacinth.

Niche Modeling of Economically Important Mahanarva (Hemiptera, Cercopidae) Species in South and Central America: Are Brazilian Spittlebug Sugarcane Pests Potential Invaders of South and Central America?

Mahanarva fimbriolata, Mahanarva spectabilis, Mahanarva liturata, and Mahanarva posticata (Hemiptera: Cercopidae) are known pests in South American sugarcane and pasture plantations. They cause phytotoxicity by feeding directly from plant sap, greatly decreasing their production. In this work, we applied Species Distribution Modeling using the Maxent algorithm to analyze these four spittlebug species possible occurrence in South and Central America. Therefore, current and future bioclimatic variables, as well as elevation and other agricultural variables, were used within RStudio. Future climatic variables were differentiated between the years 2050 and 2070 with several representative concentration pathways. Overall, the species showed various suitable habitats in different countries of South and Central America. Nevertheless, when compared with future climate analysis, the number of suitable habitats is declining due to climate change. Elevation, isothermality, and different precipitation variables were mainly responsible for the results. We were able to analyze that spittlebug populations are not limited by temperature, but rather by other abiotic factors, such as precipitation.

Modeling Potential Habitat for Amblyomma Tick Species in California

The Amblyomma genus of ticks comprises species that are aggressive human biters and vectors of pathogens. Numerous species in the genus are undergoing rapid range expansion. Amblyomma ticks have occasionally been introduced into California, but as yet, no established populations have been reported in the state. Because California has high ecological diversity and is a transport hub for potentially parasitized humans and animals, the risk of future Amblyomma establishment may be high. We used ecological niche modeling to predict areas in California suitable for four tick species that pose high risk to humans: Amblyomma americanum, Amblyomma maculatum, Amblyomma cajennense and Amblyomma mixtum. We collected presence data in the Americas for each species from the published literature and online databases. Twenty-three climatic and ecological variables were used in a MaxEnt algorithm to predict the distribution of each species. The minimum temperature of the coldest month was an important predictor for all four species due to high mortality of Amblyomma at low temperatures. Areas in California appear to be ecologically suitable for A. americanum, A. maculatum, and A. cajennense, but not A. mixtum. These findings could inform targeted surveillance prior to an invasion event, to allow mitigation actions to be quickly implemented.

Environmental niche forecasts of globally threatened yellow-throated bulbul, Pycnonotus xantholaemus for conservation prospects in the Deccan peninsula, India

Yellow-throated Bulbul, Pycnonotus xantholaemus is an endemic, rare and threatened species with discontinuous distribution across Deccan peninsula. IUCN has enlisted surveys for newer populations as one of the conservation measures. We used maxent algorithm to generate environmental niche model for further surveys. We looked into climatic envelope at occurrence points and contrast it with background climatic envelope. We compared the model for current scenario and future scenario to assess change in extent of predicted niche over time. We used six variables: climatic, topographical and vegetation layers, and a final set of 102 verified presence locations to generate the model. Topographic ruggedness index and precipitation of wettest month (Bio13) were the strong predictors for Yellow-throated Bulbul niche. Model predicts highly discontinuous and small fragments totalling 7% area of peninsular India as suitable niche. Only 10 % of predicted niche falls within India’s protected area network. Loss of habitat due to granite quarrying and anthropogenic pressure will be a bigger threat than climate change.