The LizLand model : geomorphic landform and surface composition analysis of lizard habitat in the California Mojave Desert

This dissertation had three main objectives: 1) investigate the role of macro and micro geomorphology in determining lizard distributions in the Southern California Mojave Desert; 2) develop a spatially explicit lizard habitat model based upon geomorphology; and, 3) determine the interactive effects of geomorphology, roads, and land use in defining lizard habitat. To achieve these objectives a new concept of reptile habitat was developed. Primary field data on presence/absence of lizards in relation to macro and micro geomorphology were collected for three conspicuous and widely distributed Mojave Desert lizard species: Cnemidophorus tigris, Callisaurus draconoides, and Uta stansburiana. Study sites were distributed between the Marine Air Ground Task Force Training Command Marine Corps Air Ground Combat Center (MCAGCC) and Joshua Tree National Park (JOTR) in the Southern California Mojave Desert. Chapter 2 develops a new concept of reptile habitat in and environments. This concept is based upon geomorphic landform and surface composition, which are used to distinguish lizard macro and micro-habitat components. The micro-habitat requirements of individual species are linked to macro geomorphic landforms via their mutual microhabitat characterizations. Chapter 3 describes the development of the spatially explicit LizLand habitat model. LizLand is based on the concept of reptile habitat developed and described in Chapter 2. Digital data used to construct the model include geomorphic landform, surface composition, hydrologic, and elevation data. The model is validated for MCAGCC using secondary species locational data and developed for two additional species for which no primary data were collected. The implications of LizLand for military land management are discussed. The final paper explores in greater depth the relationship between Callisaurus draconoides and the geomorphic landform habitat class alluvial plains. It compares C. draconoides observations on unpaved alluvial plain roads to surrounding habitats and observations between MCAGCC and JOTR. Finally, it considers the use of C. draconoides as an indicator species given its affinity for unpaved alluvial plain roads

Identifying the domains of context important to implementation science: a study protocol

Background There is growing recognition that “context” can and does modify the effects of implementation interventions aimed at increasing healthcare professionals’ use of research evidence in clinical practice. However, conceptual clarity about what exactly comprises “context” is lacking. The purpose of this research program is to develop, refine, and validate a framework that identifies the key domains of context (and their features) that can facilitate or hinder (1) healthcare professionals’ use of evidence in clinical practice and (2) the effectiveness of implementation interventions. Methods/design A multi-phased investigation of context using mixed methods will be conducted. The first phase is a concept analysis of context using the Walker and Avant method to distinguish between the defining and irrelevant attributes of context. This phase will result in a preliminary framework for context that identifies its important domains and their features according to the published literature. The second phase is a secondary analysis of qualitative data from 13 studies of interviews with 312 healthcare professionals on the perceived barriers and enablers to their application of research evidence in clinical practice. These data will be analyzed inductively using constant comparative analysis. For the third phase, we will conduct semi-structured interviews with key health system stakeholders and change agents to elicit their knowledge and beliefs about the contextual features that influence the effectiveness of implementation interventions and healthcare professionals’ use of evidence in clinical practice. Results from all three phases will be synthesized using a triangulation protocol to refine the context framework drawn from the concept analysis. The framework will then be assessed for content validity using an iterative Delphi approach with international experts (researchers and health system stakeholders/change agents). Discussion This research program will result in a framework that identifies the domains of context and their features that can facilitate or hinder: (1) healthcare professionals’ use of evidence in clinical practice and (2) the effectiveness of implementation interventions. The framework will increase the conceptual clarity of the term “context” for advancing implementation science, improving healthcare professionals’ use of evidence in clinical practice, and providing greater understanding of what interventions are likely to be effective in which contexts

A comparison of random forest and Adaboost tree in ecosystem classification in east Mojave Desert

We compared two basic ensemble methods, namely random forest and Adaboost tree for the classification of ecosystems in Clark County, Nevada, USA through multitemporal multisource LANDSAT TM/ETM+ images and terrain-related GIS data layers. Random forest generates decision trees by randomly selecting a limited number features from all available features for node splitting, and each tree cast a vote for the final decision. On the other hand, Adaboost tree is an iterative approach to improve the performance of a weak classifier by assigning weights to training samples, and incorrectly classified training samples will gain a larger weight in the process. We discuss the properties of these two tree-based ensemble methods and compare their classification performances in ecosystem classification. The results show that Adaboost tree can provide higher classification accuracy than random forest in multitemporal multisource dataset, while the latter could be more efficient in computation

Olfaction-based Detection Distance: A Quantitative Analysis of How Far Away Dogs Recognize Tortoise Odor and Follow It to Source

The use of detector dogs has been demonstrated to be effective and safe for finding Mojave desert tortoises and provides certain advantages over humans in field surveys. Unlike humans who rely on visual cues for target identification, dogs use primarily olfactory cues and can therefore locate targets that are not visually obvious. One of the key benefits of surveying with dogs is their efficiency at covering ground and their ability to detect targets from long distances. Dogs may investigate potential targets using visual cues but confirm the presence of a target based on scent. Everything that emits odor does so via vapor-phase molecules and the components comprising a particular scent are carried primarily though bulk movement of the atmosphere. It is the ability to search for target odor and then go to its source that makes dogs ideal for rapid target recognition in the field setting. Using tortoises as targets, we quantified distances that dogs detected tortoise scent, followed it to source, and correctly identified tortoises as targets. Detection distance data were collected during experimental trials with advanced global positioning system (GPS) technology and then analyzed using geographic information system (GIS) modeling techniques. Detection distances ranged from 0.5 m to 62.8 m for tortoises on the surface. We did not observe bias with tortoise size, age class, sex or the degree to which tortoises were handled prior to being found by the dogs. The methodology we developed to quantify olfaction-based detection distance using dogs can be applied to other targets that dogs are trained to find

Detection and classification of invasive saltcedar through high spatial resolution airborne hyperspectral imagery

We evaluated the performance of airborne HyperSpecTIR (HST) images for detecting and classifying the invasive riparian vegetation saltcedar along the Muddy River in Clark County, Nevada. HyperSpecTIR image reflectance spectra (227 bands, 450–2450 nm) were acquired for the following four vegetation covers: invasive saltcedar, native honey mesquite, grassland patches and crops. We compared five feature reduction approaches: band selection based on Jeffreys–Matusita distance, principal component analysis (PCA), minimum noise fraction (MNF), segmented principal component transform (SPCT) and segmented minimum noise fraction (SMNF). In addition, maximum likelihood (ML) and two spectral angle mapper (SAM) classifiers were applied to all extracted bands or features. Classification accuracies were compared among all classification approaches. Although the overall accuracy of maximal likelihood classifiers generally surpassed that of SAM classifiers, the highest overall accuracy was achieved by a SMNF-SAM combination with adjusted angular thresholds for classes. We concluded that high spectral and spatial resolution imagery can be used to detect and classify invasive saltcedar in this arid area

Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data

The decision tree method has grown fast in the past two decades and its performance in classification is promising. The tree-based ensemble algorithms have been used to improve the performance of an individual tree. In this study, we compared four basic ensemble methods, that is, bagging tree, random forest, AdaBoost tree and AdaBoost random tree in terms of the tree size, ensemble size, band selection (BS), random feature selection, classification accuracy and efficiency in ecological zone classification in Clark County, Nevada, through multi-temporal multi-source remote-sensing data. Furthermore, two BS schemes based on feature importance of the bagging tree and AdaBoost tree were also considered and compared. We conclude that random forest or AdaBoost random tree can achieve accuracies at least as high as bagging tree or AdaBoost tree with higher efficiency; and although bagging tree and random forest can be more efficient, AdaBoost tree and AdaBoost random tree can provide a significantly higher accuracy. All ensemble methods provided significantly higher accuracies than the single decision tree. Finally, our results showed that the classification accuracy could increase dramatically by combining multi-temporal and multi-source data set

Ivanpah Valley simulation results over time.

Roadways and railways can reduce wildlife movements across landscapes, negatively impacting population connectivity. Connectivity may be improved by structures that allow safe passage across linear barriers, but connectivity could be adversely influenced by low population densities. The Mojave desert tortoise is threatened by habitat loss, fragmentation, and population declines. The tortoise continues to decline as disturbance increases across the Mojave Desert in the southwestern United States. While underground crossing structures, like hydrological culverts, have begun receiving attention, population density has not been considered in tortoise connectivity. Our work asks a novel question: How do culverts and population density affect connectivity and potentially drive genetic and demographic patterns? To explore the role of culverts and population density, we used agent-based spatially explicit forward-in-time simulations of gene flow. We constructed resistance surfaces with a range of barriers to movement and representative of tortoise habitat with anthropogenic disturbance. We predicted connectivity under variable population densities. Simulations were run for 200 non-overlapping generations (3400 years) with 30 replicates using 20 microsatellite loci. We evaluated population genetic structure and diversity and found that culverts would not entirely negate the effects of linear barriers, but gene flow improved. Our results also indicated that density is important for connectivity. Low densities resulted in declines regardless of the landscape barrier scenario (> 75% population census size, > 97% effective population size). Results from our simulation using current anthropogenic disturbance predicted decreased population connectivity over time. Genetic and demographic effects were detectable within five generations (85 years) following disturbance with estimated losses in effective population size of 69%. The pronounced declines in effective population size indicate this could be a useful monitoring metric. We suggest management strategies that improve connectivity, such as roadside fencing tied to culverts, conservation areas in a connected network, and development restricted to disturbed areas.</div