Repository logo

Browsing by Author "Evangelista, Paul H., author"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Assessing habitat quality of the mountain nyala Tragelaphus buxtoni in the Bale Mountains, Ethiopia
    (Colorado State University. Libraries, 2012) Evangelista, Paul H., author; Norman, John, III, author; Swartzinki, Paul, author; Young, Nicholas E., author; Current Zoology, publisher
    Populations of the endangered mountain nyala Tragelaphus buxtoni are significantly threatened by the loss of critical habitat. Population estimates are tentative, and information on the species' distribution and available habitat is required for formulating immediate management and conservation strategies. To support management decisions and conservation priorities, we integrated information from a number of small-scale observational studies, interviews and reports from multiple sources to define habitat parameters and create a habitat quality model for mountain nyala in the Bale Mountains. For our analysis, we used the FunConn model, an expertise-based model that considers spatial relationships (i.e., patch size, distance) between the species and vegetation type, topography and disturbance to create a habitat quality surface. The habitat quality model showed that approximately 18,610 km2 (82.7% of our study area) is unsuitable or poor habitat for the mountain nyala, while 2,857 km2 (12.7%) and 1,026 km2 (4.6%) was ranked as good or optimal habitat, respectively. Our results not only reflected human induced habitat degradation, but also revealed an extensive area of intact habitat on the remote slopes of the Bale Mountain's southern and southeastern escarpments. This study provides an example of the roles that expert knowledge can still play in modern geospatial modeling of wildlife habitat. New geospatial tools, such as the FunConn model, are readily available to wildlife managers and allow them to perform spatial analyses with minimal software, data and training requirements. This approach may be especially useful for species that are obscure to science or when field surveys are not practical.
  • Thumbnail Image
    ItemUnknown
    Mapping Tamarix: new techniques for field measurements, spatial modeling and remote sensing
    (Colorado State University. Libraries, 2009) Evangelista, Paul H., author; Romme, William, advisor; Stohlgren, Thomas, advisor
    Native riparian ecosystems throughout the southwestern United States are being altered by the rapid invasion of Tamarix species, commonly known as tamarisk. The effects that tamarisk has on ecosystem processes have been poorly quantified largely due to inadequate survey methods. I tested new approaches for field measurements, spatial models and remote sensing to improve our ability measure and to map tamarisk occurrence, and provide new methods that will assist in management and control efforts. Examining allometric relationships between basal cover and height measurements collected in the field, I was able to produce several models to accurately estimate aboveground biomass. The best two models were explained 97% of the variance (R 2 = 0.97). Next, I tested five commonly used predictive spatial models to identify which methods performed best for tamarisk using different types of data collected in the field. Most spatial models performed well for tamarisk, with logistic regression performing best with an Area Under the receiver-operating characteristic Curve (AUC) of 0.89 and overall accuracy of 85%. The results of this study also suggested that models may not perform equally with different invasive species, and that results may be influenced by species traits and their interaction with environmental factors. Lastly, I tested several approaches to improve the ability to remotely sense tamarisk occurrence. Using Landsat7 ETM+ satellite scenes and derived vegetation indices for six different months of the growing season, I examined their ability to detect tamarisk individually (single-scene analyses) and collectively (time-series). My results showed that time-series analyses were best suited to distinguish tamarisk from other vegetation and landscape features (AUC = 0.96, overall accuracy = 90%). June, August and September were the best months to detect unique phenological attributes that are likely related to the species' extended growing season and green-up during peak growing months. These studies demonstrate that new techniques can further our understanding of tamarisk's impacts on ecosystem processes, predict potential distribution and new invasions, and improve our ability to detect occurrence using remote sensing techniques. Collectively, the results of my studies may increase our ability to map tamarisk distributions and better quantify its impacts over multiple spatial and temporal scales.