AGRICULTURAL BIOSECURITY: HARNESSING DATA FUSION TO MEET EMERGING CHALLENGES TO CATTLE FEVER TICK ERADICATION IN A CHANGING WORLD

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: ACTIVE
• Funding Source: AFRI COMPETITIVE GRANT
• Reporting Frequency: Annual
• Accession No.: 1032196
• Grant No.: 2024-67015-42369
• Proposal No.: 2023-08046
• Project Start Date: Jul 1, 2024
• Project End Date: Jun 30, 2027
• Grant Year: 2024
• Program Code: [A1181]- Tactical Sciences for Agricultural Biosecurity

Recipient Organization
TEXAS A&M UNIVERSITY
750 AGRONOMY RD STE 2701
COLLEGE STATION,TX 77843-0001

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%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
807	3120	1070	50%
312	3120	1130	50%

Knowledge Area
807 - Disaster Preparedness, Mitigation, Response, and Recovery; 312 - External Parasites and Pests of Animals;

Subject Of Investigation
3120 - Spiders, mites, ticks, and other arthropods;

Field Of Science
1130 - Entomology and acarology; 1070 - Ecology;

Keywords

cattle ticks

data fusion

invasion risk

near-term projection

partnerships for data integration

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.

Progress 07/01/24 to 06/30/25

Outputs
Target Audience:During this reporting period our target audience has been the members of our Advisory Board, as well as the Director and technical staff of the Knipling-Bushland US Livestock Insect Research Laboratory. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Training webinars for artificial intelligence, spatial and computational modeling tools. Interactions with state and federal regulatory and research professionals working on numerous cattle fever tick issues. How have the results been disseminated to communities of interest?To date, our results have been shared within our community of project participants including our Advisory Board members and ARS research personnel. What do you plan to do during the next reporting period to accomplish the goals?Obj. 1: Integrate validated data layers via data fusion approaches. Obj. 2: Develop Species Distribution Models based on current and future climates. Obj. 3: Integrate trace-out data, the United States Animal Movement Model (USAMM) and SDMs, and develop ABIPMs. Obj. 4: Begin integration of fused CFT-related datasets into the CFTEPTs platform, and development of training tutorial.

Impacts
What was accomplished under these goals? Activities in FY2024 Obj. 1. Collected essential data sets and improved dataset validation and integration techniques through data fusion Data collection and initial processing To develop SDMs: We received historical cattle fever tick data maps derived from a publication of John L. Wilbur for TAHC (Texas Animal Health Commission) from USDA-ARS, Knipling-Bushland US Livestock Insects Research Lab. We downloaded both BIOCLIM data for 1970 - 2000 from WorldClim version 2.1 (https://www.worldclim.org/data/worldclim21.html) at the spatial resolution of 2.5 arc-minutes (~25 km2). We downloaded Elevation data from the Shuttle Radar Topography Mission (SRTM; https://www2.jpl.nasa.gov/srtm/). We downloaded Land Use / Land Cover (LULC) data from the EarthExplorer (https://earthexplorer.usgs.gov/). We also set the above data layers as Geographic Coordinate System as WGS 1984 (EPSG 4326) using ArcGIS Pro. We collected 2,241 nilgai (Boselaphus tragocamelus) locality data from Global Biodiversity Information Facility (GBIF; https://www.gbif.org/). We processed the data by removing those occurrences that were duplicates, with errors, or with no GPS coordinates. This resulted in 817 (36.45%) occurrences being removed. Leaving a total of 1,424 (63.55%) occurrences including 360 (25.26%) in Texas, 2 (0.14%) in Mexico, 1,010 (70.88%) in India, and 52 (3.65%) in Nepal. We had an in-person visit at USDA-ARS, Knipling-Bushland US Livestock Insects Research Lab, Kerrville, TX on November 8, 2024, to understand the CFTEP data collection history, data properties, data type, data availability, and limitation of data usage. We received various types of data related to CFTEP from USDA-ARS, Knipling-Bushland US Livestock Insects Research Laboratory. To develop ABIPMs,we developed a first-stage model: (1) whose purpose was to simulate the population dynamics of cattle fever ticks in response to trends and fluctuations in major environmental variables, (2) that had produced acceptable estimates of fluctuations in both off- and on-host tick densities under a wide range of environmental conditions, and (3) whose context included a wide geographical range, a wide range of non-catastrophic environmental changes, and situations in which cattle were the primary host before integrating all proposed components into the ABIPMs. Dataset validation An Access file containing inspection records from Form 722s and G-cards from 1976 to 2014 was designed to be a complete capture of quarantines to be used in the analysis of infestation trends. However, it was focused on Zapata County for data collection prior to 2014 due to the accessibility of impeccable record keeping at that time. However, there is some missing information. We are working on filling the gaps (missing information) through the "Excel workbook." The Zapata County CFT quarantine history is quite relevant to our project as it involves both cattle and white-tailed deer in an area that has experienced changes in land use, ownership, and property size consistent with broad changes in land trends in Texas. An Excel workbook documenting telephone report data for infested premises from 2006 to present contains 1,607 data entries. Based on the difference of data arrangement methods among these files, we are finding the commonalities and will propose a generic arrangement for USDA-ARS Knipling-Bushland US Livestock Insects Research Lab and each county office. Polygon quarantine files (.shp files) have been checked individually. Obj. 2. Refine SDM and design methods to analyze collected CFT-related data. 2.1. Refine SDM (Species Distribution Model) SDMs can project potential geographic ranges of cattle fever ticks. To achieve this goal, we identified the R-package "biomod 2" which contains algorithms for SDM development, calibration, and evaluation, ensemble of SDMs, and ESDM forecasting and visualization. The twelve SDM algorithms in "biomod 2" include artificial neural network (ANN), classification tree analysis (CTA), flexible discriminant analysis (FDA), generalized additive model (GAM), gradient boosting model (GBM), generalized linear model (GLM), multiple adaptive regression splines (MARS), maximum entropy (MAXENT), random forest (RF), random forest down-sampled (RFd), surface range envelop (SRE), and eXtreme gradient boosting training (XGBOOST). As soon as we collect enough cattle fever tick locality data, we will conduct SDM development using all algorithms. 2.2. Method design Based on the data from the "Access file containing inspection records from Form 722s and G-cards from 1976 to 2014," we plan to use a statistical method, logistic regression, and a combined method of statistics and machine learning, boosted regression tree, to analyze the factors affecting whether the premises could be infested or not using (1) all data and (2) data in Zapata county only. Based on the data from the "Excel workbook documentation of telephone report data for infested premises from 2006 to present," we plan to use descriptive statistics to analyze (1) the infestation trend from 2006 to now and (2) understand the commonalities among all infested premises. If we find a clear trend and common properties of infestation, we will use a statistical method, multinomial logit regression, and a combined method of statistics and machine learning, boosted regression tree, to quantify the factors affecting the recursion of cattle fever ticks. We developed a stage-structured model that simulates tick population dynamics in response to the effects of climate, landscape, and cattle density, and produces estimates of fluctuations in off- and on-host tick densities using STELLA Professional® (ISSE Systems, inc.). We calibrated the model to represent conditions in Brownsville and Corpus Christi, Texas, USA. The model was developed following the life cycle of cattle fever ticks. Obj. 4: Get familiar with the CFTEPTs platform We had an in-person visit at USDA-ARS, Knipling-Bushland US Livestock Insects Research Lab, Kerrville, TX on November 8, 2024 to look at the practice of CFTEPTs and understand its use, its functions and relationships to the CFT program, as well as follow up email correspondence. The current CFTEPTs platform includes four major functions: CFTEP Editor Interface Web Maps, CFTEP Field Maps, CFTEP Web Map Viewer: Current Quarantines, and CFTEP Dashboards.

Publications