Rebecca Loraamm

Voxel-based probabilistic space-time prisms for analysing animal movements and habitat use

Time-geographic analysis has been limited in the past by its capacity to model only potential locations for moving objects, without sufficiently evaluating which locations are more probable. This paper expands upon existing research in probabilistic time geography by accomplishing two main tasks. First, a new geocomputational approach is presented for generating probabilistic space-time prisms. Here, probabilistic space-time prisms are represented as three-dimensional rasters of volume elements, or voxels, that record the probability that an object was located at any location at any time. After describing the geocomputational approach, its utility is illustrated through a detailed analysis of tracking data collected for a Muscovy duck (Cairina mochata). Specifically, probabilistic space-time prisms are used to map the duck’s fine-scale movement patterns over five complete days of global positioning system (GPS)-tracking. Then, the space-time prisms are used in conjunction with a detailed habitat map of the study area in order to quantify the duck’s habitat usage over the course of each day. This application highlights the utility of probabilistic space-time prisms for understanding the movements and activities of animals at fine temporal and spatial scales.

Strategically Locating Wildlife Crossing Structures for Florida Panthers Using Maximal Covering Approaches

Crossing structures are an effective method for mitigating habitat fragmentation and reducing wildlife-vehicle collisions, although high construction costs limit the number that can be implemented in practice. Therefore, optimizing the placement of crossing structures in road networks is suggested as a strategic conservation planning method. This research explores two approaches for using the maximal covering location problem (MCLP) to determine optimal sites to install new wildlife crossing structures. The first approach is based on records of traffic mortality, while the second uses animal tracking data for the species of interest. The objective of the first is to cover the maximum number of collision sites, given a specified number of proposed structures to build, while the second covers as many animal tracking locations as possible under a similar scenario. These two approaches were used to locate potential wildlife crossing structures for endangered Florida panthers (Puma concolor coryi) in Collier, Lee, and Hendry Counties, Florida, a population whose survival is threatened by excessive traffic mortality. Historical traffic mortality records and an extensive radio-tracking dataset were used in the analyses. Although the two approaches largely select different sites for crossing structures, both models highlight key locations in the landscape where these structures can remedy traffic mortality and habitat fragmentation. These applications demonstrate how the MCLP can serve as a useful conservation planning tool when traffic mortality or animal tracking data are available to researchers.

Spatial epidemiology of eastern equine encephalitis in Florida

BackgroundEastern Equine Encephalitis virus (EEEV) is an alphavirus with high pathogenicity in both humans and horses. Florida continues to have the highest occurrence of human cases in the USA, with four fatalities recorded in 2010. Unlike other states, Florida supports year-round EEEV transmission. This research uses GIS to examine spatial patterns of documented horse cases during 2005–2010 in order to understand the relationships between habitat and transmission intensity of EEEV in Florida.MethodsCumulative incidence rates of EEE in horses were calculated for each county. Two cluster analyses were performed using density-based spatial clustering of applications with noise (DBSCAN). The first analysis was based on regional clustering while the second focused on local clustering. Ecological associations of EEEV were examined using compositional analysis and Euclidean distance analysis to determine if the proportion or proximity of certain habitats played a role in transmission.ResultsThe DBSCAN algorithm identified five distinct regional spatial clusters that contained 360 of the 438 horse cases. The local clustering resulted in 18 separate clusters containing 105 of the 438 cases. Both the compositional analysis and Euclidean distance analysis indicated that the top five habitats positively associated with horse cases were rural residential areas, crop and pastureland, upland hardwood forests, vegetated non-forested wetlands, and tree plantations.ConclusionsThis study demonstrates that in Florida tree plantations are a focus for epizootic transmission of EEEV. It appears both the abundance and proximity of tree plantations are factors associated with increased risk of EEE in horses and therefore humans. This association helps to explain why there is are spatially distinct differences in the amount of EEE horse cases across Florida.

A wildlife movement approach to optimally locate wildlife crossing structures

Transportation networks negatively impact wildlife populations by limiting the physical movement of the individual animal. In extreme cases road presence can lead to collisions between vehicles and animals, resulting in direct mortality if an animal attempts to cross the road. Crossing structures are one commonly used method for reducing wildlife–vehicle collisions. However, limited funding often reduces the amount of structures that may be constructed in practice. Therefore, areas that have the highest probability for animal interactions with roads should be targeted for locating new structures to provide the best possible outcome. This research uses a probabilistic time-geographic strategy coupled with a site selection phase handled by a classical optimization model to site wildlife crossing structures. To achieve optimal site selection, crossing locations are first identified where wildlife frequently cross roads, and then a maximum covering location problem is applied to these areas as demand nodes. The objective is to cover the largest area having the highest probability of interaction given a finite number of crossing structures available to be located. Coverage is defined in terms of fencing distance associated with a particular structure. The approach was demonstrated using Florida panther telemetry data identifying potential crossing structures across two counties in south Florida. The maximal covering location problem (MCLP) was solved for four coverage distances using radio telemetry tracking data, which captured frequent contact with roads. The results identify that the most effective coverage distance is 2000 m, which incrementally covers more total animal–road interaction probability than that of lower fencing distances in the case of the Florida panther. The results illustrate how this new time-geographic approach, combined with location modeling, measures animal–road interactions probabilistically for finding the optimum placement of wildlife crossing structures.

Testing time-geographic density estimation for home range analysis using an agent-based model of animal movement

ABSTRACT Time-geographic density estimation (TGDE) is a method of movement pattern analysis that generates a continuous intensity surface from a set of tracking data. TGDE has recently been proposed as a method of animal home range estimation, where the goal is to delineate the spatial extents that an animal occupies. This paper tests TGDE’s effectiveness as a home range estimator using simulated movement data. First, an agent-based model is used to simulate tracking data under 16 movement scenarios representing a variety of animal life history traits (habitat preferences, homing behaviour, mobility) and habitat configurations (levels of habitat fragmentation). Second, the accuracy of TGDE is evaluated for four temporal sampling frequencies using three adaptive velocity parameters for 30 sample data sets from each scenario. Third, TGDE accuracy is compared to two other common home range estimation methods, kernel density estimation (KDE) and characteristic hull polygons (CHP). The results demonstrate that TGDE is the most effective at estimating core areas, home ranges and total areas at high sampling frequencies, while CHP performs better at low sampling frequencies. KDE was ineffective across all scenarios explored.

Towards Food Service Sustainability in Suburban Environments by Optimally Locating Shared Anaerobic Digester Units

Anaerobic digestion is an effective method for reducing food waste at the consumer level. Drawbacks associated with this strategy include high construction costs for multiple digester units and limited public awareness of the method’s commercial potential. Given the large scale problem of food waste, an approach establishing community partnerships between local businesses and primary schools is offered to combat the problem of food waste. Optimizing the placement of shared digester units enabling utilization by multiple stakeholders is the suggested mitigation method. This research explores application of the p-median problem to determine the set of optimal site(s) for shared anaerobic digester units among restaurants in a suburban setting, using schools as potential digester sites. The p-median problem was applied to determine optimal sites for locating these facilities in the study area. The p-median problem seeks to locate p facilities at i locations while minimizing the total transportation cost necessary to satisfy customer demand, where each customer is supplied from the closest facility. Five scenarios were evaluated, including runs with a p equal to 1, 5, 10, 25 and 50 facilities respectively, using an estimated acceptable transportation distance of 250 meters between waste producers and digester facilities. Results were evaluated in terms of the number of producer locations serviced relative to the p number of digester facilities desired. This application demonstrates how the pmedian problem can serve as a useful planning tool, providing options to planners interested in effective facilities placement for studies aimed at food waste reduction.