Prioritization of Effective Factors on Zataria multiflora Habitat Suitability and its Spatial Modeling

2019 ◽  
pp. 411-427 ◽  
Author(s):  
Mohsen Edalat ◽  
Enayat Jahangiri ◽  
Emran Dastras ◽  
Hamid Reza Pourghasemi
Keyword(s):  
Spatial Modeling ◽  
Habitat Suitability ◽  
Zataria Multiflora ◽  
Effective Factors

scholarly journals Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2049 ◽  
Author(s):  
Abdolrahman Rahimian Boogar ◽  
Hassan Salehi ◽  
Hamid Reza Pourghasemi ◽  
Thomas Blaschke
Keyword(s):  
Machine Learning ◽  
Maximum Entropy ◽  
Spatial Modeling ◽  
Habitat Suitability ◽  
Natural Habitat ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Fars Province ◽  
Conditioning Factors ◽  
Learning Techniques

Support vector machine (SVM) and maximum entropy (MaxEnt) machine learning techniques are well suited to model the habitat suitability of species. In this study, SVM and MaxEnt models were developed to predict the habitat suitability of Juniperus spp. in the Southern Zagros Mountains of Iran. In recent decades, drought extension and climate alteration have led to extensive changes in the geographical occurrence of this species and its growth and regeneration are extremely limited in this area. This study evaluated the habitat suitability of Juniperus through spatial modeling and predicts appropriate regions for future cultivation and resource conservation. We modeled the natural habitat of Juniperus for an area of 700 ha in Sepidan Area in the Fars province using (1) data regarding the presence of the species (295 samples) collected through field surveys and GPS, (2) habitat soil information and indices derived from 60 soil samples collected in the study area, and (3) climatic and topographic datasets collected from various sources. In total, 15 conditioning factors were used for this spatial modeling approach. Receiver operator characteristic (ROC) curves were applied to estimate the accuracy of the habitat suitability models produced by the SVM and MaxEnt techniques. Results indicated logical and similar area under the curve (AUC)-ROC values for the SVM (0.735) and MaxEnt (0.728) models. Both the SVM and MaxEnt methods revealed a significant relationship between the Juniperus spp. distribution and conditioning factors. Environmental factors played a vital role in evaluating the presence of Juniperus sp. as Max and Min temperatures and annual mean rainfall were the three most important factors for habitat suitability in the study area. Finally, an area with high and very high suitability for the future cultivation of Juniperus sp. and for landscape conservation was suggested based on the SVM model.

Testing the relevance of using spatial modeling to predict foraging habitat suitability around bat maternity: A case study in Mediterranean landscape

2015 ◽  
Vol 192 ◽  
pp. 120-129 ◽  
Author(s):  
Jocelyn Fonderflick ◽  
Clémentine Azam ◽  
Clarisse Brochier ◽  
Emmanuel Cosson ◽  
Delphine Quékenborn
Keyword(s):  
Spatial Modeling ◽  
Habitat Suitability ◽  
Foraging Habitat ◽  
Mediterranean Landscape

scholarly journals Pemodelan Spasial Kesesuaian Habitat Elang Jawa (Nisaetus bartelsi) di Taman Nasional Bromo Tengger Semeru (Spatial Modeling of Javan Hawk-Eagle (Nisaetus bartelsi) Habitat Suitability in Bromo Tengger Semeru National Park)

2021 ◽  
Vol 9 (1) ◽  
pp. 179
Author(s):  
Nirmala Ayu Aryanti ◽  
Tander Scila Serata Dwi Susilo ◽  
Ari Nadya Ningtyas ◽  
Mahmuddin Rahmadana
Keyword(s):  
Maximum Entropy ◽  
Spatial Modeling ◽  
Habitat Suitability ◽  
National Park ◽  
Ecological Niche Modeling ◽  
Spatial Models ◽  
Habitat Suitability Model ◽  
Conservation Area ◽  
Auc Value ◽  
Point Data

Bromo Tengger Semeru National Park (TNBTS) is a conservation area as the habitat of endemic species in Java Island, such as the Javan hawk-eagle (Nisaetus bartelsi). One of the spatial models of habitat is the Ecological Niche Modeling (ENM) approach. This study aimed to determine habitat suitability for the Javan hawk-eagle in TNBTS. The research was conducted from September 2019 to January 2020. The habitat suitability model used the present coordinate point data and the Javan hawk-eagle habitat environment variables. The data were then analyzed to build a Javan hawk-eagle habitat suitability model using the Maximum Entropy (MaxEnt) algorithm. The results showed three models of habitat suitability categories, i.e.: high of 15,131.18 ha (30%), medium 11,216.61 ha (22%), and low 23,298.41 ha (48%). The evaluation of the Javan hawk-eagle habitat suitability model in TNBTS has an excellent model accuracy with an AUC value of 0.97 and a standard deviation of 0.93.Keywords: endemic, habitat, Javan hawk-eagle, maximum entropy, spatial modeling

Habitat suitability for Proboscis Monkey (Nasalis larvatus Wurmb, 1781) in the mangrove forest of Kutai National Park, East Kalimantan

2016 ◽  
Vol 6 (1) ◽  
pp. 12-25
Author(s):  
SUWARTO SUWARTO ◽  
LILIK BUDI PRASETYO ◽  
AGUS PRIYONO KARTONO
Keyword(s):  
Principal Component Analysis ◽  
Spatial Modeling ◽  
Habitat Suitability ◽  
Mangrove Forest ◽  
National Park ◽  
Principal Component ◽  
Component Analysis ◽  
Proboscis Monkey ◽  
Nasalis Larvatus ◽  
East Kalimantan

Suwarto, Prasetyo LB, Kartono AP. 2016. Habitat suitability for Proboscis Monkey (Nasalis larvatus Wurmb, 1781) in the mangrove forest of Kutai National Park, East Kalimantan. Bonorowo Wetlands 6: 12-25. This study aims to identify the factors determining that influence the suitability proboscis monkey (Nasalis larvatus Wurmb, 1781) in the mangrove habitat Kutai National Park through spatial modeling. Habitat suitability was analyzed using Principal Component Analysis (PCA) and linear regression were integrated with geographical information systems. Principal Component Analysis is a technique to construct new variables that are linear combinations of the original variables by reducing the variables used. The presence of groups of proboscis monkey marked with GPS. Satellite images from Landsat 8 path 116 row 60 processed digitally to generate proboscis vegetation distribution and Normalization Difference Vegetation Index, Variable distance from roads, distance from settlements, the distance from the fishpond, and the distance from the source of water is obtained from the analysis euclidean distance of Indonesia Earth Appearance map. Spatial modeling using the coordinates of the encounter group proboscis as the dependent variable and the predictor variables used in the regression model is the distance from the road, the distance from the settlement, the distance from the pond, the distance from the source of water, the distance of Avicennia, distance from Bruguiera, distance from Rhizophora, distance from Sonneratia, and LAI (Leaf Area Index). The overall area of the study area was used to build the model is 7 343.88 hectares. The results habitat suitability modeling proboscis monkey in the mangroves of TNK showed that only 99.50 hectares or 1.35% have high compatibility, the suitability being has a total area of 384.58 hectares or 18.85%, whereas an area of 5 859.81 hectares or 79.79% low suitability. The results of models have explained that the distribution of the proboscis monkey habitat suitability is influenced by factors of disturbance.

Potential anti-diabetic and anti-oxidant activity of essential oil of Zataria multiflora leaves

Planta Medica ◽  
2008 ◽  
Vol 74 (09) ◽  
Author(s):  
YY Kamrani ◽  
M Amanlou ◽  
A Yazdanyar ◽  
A AdliMoghaddam ◽  
SN Ebrahimi
Keyword(s):  
Essential Oil ◽  
Zataria Multiflora ◽  
Anti Oxidant

Predicting bycatch hotspots based on suitable habitat derived from fishery-independent data

2020 ◽  
Vol 641 ◽  
pp. 159-175
Author(s):  
J Runnebaum ◽  
KR Tanaka ◽  
L Guan ◽  
J Cao ◽  
L O’Brien ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Gulf Of Maine ◽  
Homarus Americanus ◽  
Management Tool ◽  
American Lobster ◽  
Suitable Habitat ◽  
Climate Conditions ◽  
Suitability Index

Bycatch remains a global problem in managing sustainable fisheries. A critical aspect of management is understanding the timing and spatial extent of bycatch. Fisheries management often relies on observed bycatch data, which are not always available due to a lack of reporting or observer coverage. Alternatively, analyzing the overlap in suitable habitat for the target and non-target species can provide a spatial management tool to understand where bycatch interactions are likely to occur. Potential bycatch hotspots based on suitable habitat were predicted for cusk Brosme brosme incidentally caught in the Gulf of Maine American lobster Homarus americanus fishery. Data from multiple fisheries-independent surveys were combined in a delta-generalized linear mixed model to generate spatially explicit density estimates for use in an independent habitat suitability index. The habitat suitability indices for American lobster and cusk were then compared to predict potential bycatch hotspot locations. Suitable habitat for American lobster has increased between 1980 and 2013 while suitable habitat for cusk decreased throughout most of the Gulf of Maine, except for Georges Basin and the Great South Channel. The proportion of overlap in suitable habitat varied interannually but decreased slightly in the spring and remained relatively stable in the fall over the time series. As Gulf of Maine temperatures continue to increase, the interactions between American lobster and cusk are predicted to decline as cusk habitat continues to constrict. This framework can contribute to fisheries managers’ understanding of changes in habitat overlap as climate conditions continue to change and alter where bycatch interactions could occur.

Using Spatial Modeling to Improve Wetland Inventories in Great Smoky Mountains National Park

2019 ◽  
Vol 39 (4) ◽  
pp. 482
Author(s):  
Alix A. Pfennigwerth ◽  
Joshua Albritton ◽  
Troy Evans
Keyword(s):  
Spatial Modeling ◽  
National Park ◽  
Great Smoky Mountains ◽  
Smoky Mountains
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