Performing Department
(N/A)
Non Technical Summary
Two species of cattle fever ticks are the only arthropods in North America that can transmit the pathogens that cause the highly fatal disease of cattle known as bovine babesiosis or Texas cattle fever. In the absence of drugs or vaccines to protect cattle from this disease, the best disease control is to prevent the tick vectors from becoming re-established in the US from Mexico where both the ticks and disease pathogens remain endemic. At risk are US cattle that are immunologically susceptible to infection through tick bite of cattle fever ticks.The risks ofincursions, establishment and outbreaks of cattle fever ticks in the US are associated with tick-host-habitat-climate interactions. Each type of interaction has been extensively studied by scientists and specialists of the state and federal regulatory programs that oversee the US Cattle Fever Tick Eradication Program. Different datasets and operational subprograms have been developedalong the lines of each type of interaction for use in detecting, containing and eliminating cattle fever ticks. The objectivesof this project are to assimilate the different datasets into an interactive computer-based platform that permits the examination, analysis, and interpretation of factors influencing cattle fever ticks, with the goal of developing a tool to identify everchangingrisks and how to best prevent and/or mitigate cattle fever tick infestations.
Animal Health Component
100%
Research Effort Categories
Basic
0%
Applied
100%
Developmental
0%
Goals / Objectives
The long-term project goal is to enable the Cattle Fever Tick Eradication Program Tools (CFTEPT)platform to provide cattle fever tick (CFT) invasion risk projections under scenarios representing 1) changing climatic conditions, 2) shifting land use patterns, 3) increasing acaricide resistance in CFT strains, and 4) involvement of wildlife hosts. Supporting project objectives (Figure 2) include:Objective 1: Use data fusion methods to integrate disparate CFT-related datasets.Objective 2: Refine methods of identification of areas of suitable CFT habitat.Objective 3: Assess the risk of range expansion by each CFT species and strain.Objective 4: Integrate CFT invasion risk projections into the CFTEPTs platform.
Project Methods
Objective 1: Use data fusion methods to integrate disparate Cattle Fever Tick (CFT)-related datasets. We will apply data fusion methods to adjust for biases among disparate datasets and to increase the accuracy and precision of parameter estimates.Data collection: We will use targeted types of datasets in the proposed project, including data collected in previous projects (Section 1.3.3) and new datasets related to specific system interactions. We will obtain (1) species- and strain-specific CFT locality data (2) climate surface data layers (3) monthly weather data layers(4) Land Use Land Cover (LULC) data (5) Normalized Difference Vegetation Index (NDVI) and Landsat Enhanced Vegetation Index (EVI) values (6) wildlife distribution data, (7) cattle shipment data. In addition, a dataset exists of livestock trace-outs from the 12-months prior to each CFT infestation (USDA-APHIS-VS and TAHC) that will provide in-state and out- of-state estimates of animal movement beyond the traditional marketing datasets.Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information by increasing inference and mitigating bias. We will discretize species- and strain-specific CFT locality data, climate data, weather data, land use data, wildlife distribution data, cattle GPS movement data, the trace-outs data, and output data from the USAMM into a lattice of distinct grid cells. This will allow us to 1) choose a predictive resolution appropriate to our CFT locality data resolutions and 2) project future CFT occurrence and risks of infestation spreadat an appropriate resolution.Objective 2: Refine methods of identification of areas of suitable CFT habitat. Our SDMs will draw upon species- and strain-specific CFT locality data, climate data, land use data, and wildlife distribution data. SDM fitting: This step includesmulticollinearity tests, variable pre-selection, choice of model settings and model complexity, andmodel selection/ensembles. We will use ensemble SDMs (ESDMs), which take individual SDMs and average or weight their output into one final prediction. SDM projections: We will use the optimal model to estimate the areas of suitable habitat for each CFT species and strain in the U.S.Objective 3: Assess the risk of range expansion by each CFT species and strain.Integration of the trace-outs data, USAMM, SDMs, and ABIPMs: We will assess the risk of range expansion by each CFT species and strain via development of spatially explicit agent-based invasion projection models (ABIPMs). These models, one for each CFT strain, will integrate trace- outs data and outputs from the USAMM and the SDMs and with data characterizing relevant spatiotemporal patterns exhibited by the environment (NDVI/EVI and weather data), by cattle (GPS cattle movement data), and by wildlife hosts (population dynamics and habitat use data).The risk of CFT range expansion depends on the availability of suitable habitats and access to those habitats.Objective 4: Integrate CFT invasion risk projections into the CFTEPTs platform.?We will integrate the invasion risk projections into the CFTEPTs platform via an easily accessible, data-driven, interactive module, which will be adaptable to other USDA PDI projects and will facilitate interdisciplinary collaboration and proactive management actions at local, state, andnational levels.To integrate results of our project into the CFTEPTs platform we will utilize a preexisting software package from ESRI known as ArcGIS Hub. The USDA PDI has utilized ESRI products for the development of all the current CFTEPTs. Our addition to the CFTEPTs will consist of a user interface website that provides access to datasets that can be downloaded and interactive mapsdeveloped from the data produced by Objectives 1 - 3.