Papers by Jennifer A Miller
Movement in the context of species distribution models (SDMs) generally refers to a species' abil... more Movement in the context of species distribution models (SDMs) generally refers to a species' ability to access suitable habitat. Movement ability can be determined by some combination of dispersal constraints or migration rates, landscape factors such as patch configuration, disturbance, and barriers, and demographic factors related to age at maturity, mortality, and fecundity. Including movement ability can result in more precise projections that help to distinguish suitable habitat that is or can be potentially occupied, from suitable habitat that is inaccessible. While most SDM studies have ignored movement or conceptualized it in overly simplistic ways (e.g. no dispersal versus unlimited dispersal), it is increasingly important to incorporate realistic information on movement ability, particularly for studies that aim to project future distributions such as climate change forecasting and invasive species applications. This progress report addresses the increasingly complex ways in which movement has been incorporated in SDM and outlines directions for further study.
New developments in global positioning systems (GPS) and related satellite tracking technologies ... more New developments in global positioning systems (GPS) and related satellite tracking technologies have facilitated the collection of highly accurate data on moving objects, far surpassing the ability to analyze them. Within geographic information science, 'movement pattern analysis' (MPA) has developed as a subfield that addresses concepts and theories used to explore the spatio-temporal structure in data, although the methodological and analytical framework associated with MPA is new and still evolving. Interactions between individuals can be considered a second order property of movement and have been far less studied. The nature of interactions between individuals in a population is a fundamental aspect of a species' behavioral ecology and information on the frequency and duration of these interactions is vital to understanding mating and territorial behavior, resource use, and infectious disease epidemiology. The focus of this work was to explore how spatially explicit simulated data can be used to analyse dynamic interactions between individuals. Five different techniques that have been used to quantify dynamic interactions based on GPS data of pairs of individuals were utilised, and all were compared in the context of spatially explicit simulated data intended to represent biologically realistic null models for individual movement, and subsequently paired interactions.
The ability to measure dynamic interactions, such as attraction or avoidance, is crucial to under... more The ability to measure dynamic interactions, such as attraction or avoidance, is crucial to understanding socio-spatial behaviors related to territoriality and mating as well as for exploring resource use and the potential spread of infectious epizootic diseases. In spite of the importance of measuring dynamic interactions , it has not been a main research focus in movement pattern analysis. With very few exceptions (see Benhamou et al. 2014), no new metrics have been developed in the past 20 years to accommodate the fundamental shift in the type of animal movement data now being collected and there have been few comparison or otherwise critical studies of existing dynamic interaction metrics (but see Long et al. 2014; Miller 2012). This research borrows from the null model approach commonly used in community ecology to compare six currently used dynamic interaction metrics using data on five brown hyena dyads in Northern Botswana. There was disconcerting variation among the dynamic interaction results depending on which metric and which null model was used, and these results highlight the need for more extensive research on measuring and interpreting dynamic interactions in order to avoid making potentially misleading inferences about socio-spatial behaviors.
Stratified Sampling for Field Survey of Environmental Gradients in the Mojave Desert Ecoregion
GIS and Remote Sensing Applications in Biogeography and Ecology, 2001
... used, and data depicting them, for a second level of stratification at the local scale; 5 ...... more ... used, and data depicting them, for a second level of stratification at the local scale; 5 ... effects of terrain on vegetation, nested within the climate-geology stratification, in this desert landscape. Those effects are the influence of slope angle and drainage basin position on soil texture ...
Photogrammetric Engineering & Remote Sensing, 2003
We monitored land-cover change in San Diego County (1990County ( -1996 using multitemporal Landsa... more We monitored land-cover change in San Diego County (1990County ( -1996 using multitemporal Landsat TM data. Change vectors of Kauth Thomas features were combined with stable multitemporal Kauth Thomas features and a suite of ancillary variables within a classification tree classifier. A combination of aerial photointerpretation and field measurements yielded training and validation data. Maps of land-cover change were generated for three hierarchical levels of change classification of increasing detail: change vs. no-change; four classes representing broad increase and decrease classes; and nine classes distinguishing increases or decreases in tree canopy cover, shrub cover, and urban change. The multitemporal Kauth Thomas (both stable and change features representing brightness, greenness, and wetness) provided information for magnitude and direction of land-cover change. Overall accuracies of the land-cover change maps were high (72 to 92 percent). Ancillary variables representing elevation, fire history, and slope were most significant in mapping the most complicated level of land-cover change, contributing 15 percent to overall accuracy. Classification trees have not previously been used operationally with remotely sensed and ancillary data to map land-cover change at this level of thematic detail.
Remote Sensing of Environment, 2008
Large area land-cover monitoring scenarios, involving large volumes of data, are becoming more pr... more Large area land-cover monitoring scenarios, involving large volumes of data, are becoming more prevalent in remote sensing applications. Thus, there is a pressing need for increased automation in the change mapping process. The objective of this research is to compare the performance of three machine learning algorithms (MLAs); two classification tree software routines (S-plus and C4.5) and an artificial neural network (ARTMAP), in the context of mapping land-cover modifications in northern and southern California study sites between 1990/91 and 1996. Comparisons were based on several criteria: overall accuracy, sensitivity to data set size and variation, and noise. ARTMAP produced the most accurate maps overall (∼ 84%), for two study areasin southern and northern California, and was most resistant to training data deficiencies. The change map generated using ARTMAP has similar accuracies to a human-interpreted map produced by the U.S. Forest Service in the southern study area. ARTMAP appears to be robust and accurate for automated, large area change monitoring as it performed equally well across the diverse study areas with minimal human intervention in the classification process.
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Papers by Jennifer A Miller