Geographic Layers as Landscape Drivers for the Marco Polo Argali Habitat in the Southeastern Pamir Mountains of Tajikistan

Salas, Eric Ariel L.; Valdez, Raúl; Boykin, Kenneth G.

doi:10.3390/ijgi4042094

Cited by 6 publications

(12 citation statements)

References 20 publications

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“…The Pamir region is known as the “roof of the world” with wide and grassy valley floors, and meandering rivers and streams (Breu & Hurni, ). In the study area, elevations in the northern region range from 4,500 to 7,300 m, while those in the southern region range from 3,300 to 5,200 m above mean sea level (Salas, Valdez, & Boykin, ). The January mean temperature in the eastern Pamirs varies from −15 to −20°C with extreme seasonal temperature variations (WHC ).…”

Section: Methodsmentioning

confidence: 99%

“…We derived the green vegetation cover and sparse vegetation cover from the Global Land Cover 2000 project (Tateishi, Zhu, & Sato, 2000) and used them to represent forage abundance. Previous studies considered vegetation distribution as an important predictor in modeling the argali habitat (Salas et al, 2015;Singh et al, 2009). For escape terrain, we created a continuous distance around polygon patches with slopes ≥ 30° (Smith, Flinders, & Winn, 1991;Turner, Douglas, Hallum, Krausman, & Ramey, 2004).…”

Section: Environmental Variablesmentioning

confidence: 99%

“…Likewise, the vegetation cover was found by our models to be an important contributor to the predicted habitat of Marco Polo argali. Salas et al (2015) showed that the availability of forage and the distribution of argali are strongly correlated. Previous studies also reported cases where the normalized difference vegetation index (NDVI) (Guyot & Gu, 1994), used as an index of forage abundance (NDVI > 0.4) (Singh et al, 2009), was linked to the distribution of large herbivores (Pettorelli, 2014).…”

Section: Present and Future Projectionsmentioning

confidence: 99%

See 2 more Smart Citations

Habitat assessment of Marco Polo sheep (Ovis ammon polii) in Eastern Tajikistan: Modeling the effects of climate change

Salas

Valdez

Michel³

et al. 2018

Ecology and Evolution

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Identifying the factors predicting the high‐elevation suitable habitats of Central Asian argali wild sheep and how these suitable habitats are affected by the changing climate regimes could help address conservation and management efforts and identify future critical habitat for the species in eastern Tajikistan. This study used environmental niche models (ENMs) to map and compare potential present and future distributions of suitable environmental conditions for Marco Polo argali. Argali occurrence points were collected during field surveys conducted from 2009 to 2016. Our models showed that terrain ruggedness and annual mean temperature had strong correlations on argali distribution. We then used two greenhouse gas concentration trajectories (RCP 4.5 and RCP 8.5) for two future time periods (2050 and 2070) to model the impacts of climate change on Marco Polo argali habitat. Results indicated a decline of suitable habitat with majority of losses observed at lower elevations (3,300–4,300 m). Models that considered all variables (climatic and nonclimatic) predicted losses of present suitable areas of 60.6% (6,928 km2) and 63.2% (7,219 km2) by 2050 and 2070, respectively. Results also showed averaged habitat gains of 46.2% (6,106 km2) at much higher elevations (4,500–6,900 m) and that elevational shifts of habitat use could occur in the future. Our results could provide information for conservation planning for this near threatened species in the region.

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Section: Methodsmentioning

confidence: 99%

Section: Environmental Variablesmentioning

confidence: 99%

Section: Present and Future Projectionsmentioning

confidence: 99%

See 1 more Smart Citation

Habitat assessment of Marco Polo sheep (Ovis ammon polii) in Eastern Tajikistan: Modeling the effects of climate change

Salas

Valdez

Michel³

et al. 2018

Ecology and Evolution

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“…We refer the reader to Salas et al [43] for additional details of the field data collection. Sparse vegetation on dry upland locations consisted mainly of Poa sp.…”

Section: Field Data and Class Selectionmentioning

confidence: 99%

“…Texture allowed the extraction of image structures that are not detectable by spectral characteristics alone [43,49]. The use of all eight texture features (e.g., [21]) in the RF classification, may not be advisable for future studies.…”

Section: Importance Of Texture Featuresmentioning

confidence: 99%

Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs

2016

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Abstract:We tested the Moment Distance Index (MDI) in combination with texture features for the summer vegetation mapping in the eastern Pamir Mountains, Tajikistan using the 2014 Landsat OLI (Operational Land Imager) image. The five major classes identified were sparse vegetation, medium-dense vegetation, dense vegetation, barren land, and water bodies. By utilizing object features in a random forest (RF) classifier, the overall classification accuracy of the land cover maps were 92% using a set of variables including texture features and MDI, and 84% using a set of variables including texture but without MDI. A decrease of the Kappa statistics, from 0.89 to 0.79, was observed when MDI was removed from the set of predictor variables. McNemar's test showed that the increase in the classification accuracy due to the addition of MDI was statistically significant (p < 0.05). The proposed method provides an effective way of discriminating sparse vegetation from barren land in an arid environment, such as the Pamir Mountains.

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Summer and winter habitat suitability of Marco Polo argali in southeastern Tajikistan: A modeling approach

Salas

Valdez

Michel³

2017

Heliyon

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We modeled summer and winter habitat suitability of Marco Polo argali in the Pamir Mountains in southeastern Tajikistan using these statistical algorithms: Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, and Multivariate Adaptive Regression Splines. Using sheep occurrence data collected from 2009 to 2015 and a set of selected habitat predictors, we produced summer and winter habitat suitability maps and determined the important habitat suitability predictors for both seasons. Our results demonstrated that argali selected proximity to riparian areas and greenness as the two most relevant variables for summer, and the degree of slope (gentler slopes between 0° to 20°) and Landsat temperature band for winter. The terrain roughness was also among the most important variables in summer and winter models. Aspect was only significant for winter habitat, with argali preferring south-facing mountain slopes. We evaluated various measures of model performance such as the Area Under the Curve (AUC) and the True Skill Statistic (TSS). Comparing the five algorithms, the AUC scored highest for Boosted Regression Tree in summer (AUC = 0.94) and winter model runs (AUC = 0.94). In contrast, Random Forest underperformed in both model runs.

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Geographic Layers as Landscape Drivers for the Marco Polo Argali Habitat in the Southeastern Pamir Mountains of Tajikistan

Cited by 6 publications

References 20 publications

Habitat assessment of Marco Polo sheep (Ovis ammon polii) in Eastern Tajikistan: Modeling the effects of climate change

Habitat assessment of Marco Polo sheep (Ovis ammon polii) in Eastern Tajikistan: Modeling the effects of climate change

Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs

Summer and winter habitat suitability of Marco Polo argali in southeastern Tajikistan: A modeling approach

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