5  Theme: Tick Populations

What: Amblyomma americanum nymphal tick abundance per sampled area

Where: 9 NEON sites

When: Forecasts for 52 weeks into the future using a weekly time-step. Forecasts that start in the current week are due by Sunday at 12:59 pm (UTC).

Why: There is a correlation between tick population abundance and disease incidence, meaning forecasts for tick abundance have the potential to aid in our understanding of disease risk through time and space.

Who: Open to any individual or team that registers

How: REGISTER your team and submit forecast. If you registered for the Round 1 (2021) and are using the same team and method then you do not need to re-register.

The video below is an overview of the Tick Populations Challenge that was recorded for the 2021 Early Career Annual Meeting

We held a Q&A session on March 24, 2021. You can find a recording from that session HERE.

5.1 Overview

Target species for the population forecasts are Amblyomma americanum nymphal ticks. A. americanum is a vector of ehrlichiosis, tularemia, and southern tick-associated rash illness. The species is present in the eastern United States, and their populations are expanding. There is a correlation between tick population abundance and disease incidence, meaning forecasts for tick abundance have the potential to aid in our understanding of disease risk through time and space.

5.2 Challenge

The challenge is open to any individual, group, or institution that may want to participate. The goals of this challenge are to forecast the density of Amblyomma americanum nymphs (ticks/1600m^2) each epidemiological week (Sun-Sat) at nine NEON sites.

Teams must post information about any additional data they wish to use on the theme Slack channel so that other teams can potentially use the data as well.

5.3 Data: Targets

The challenge uses the following NEON data products:

DP1.10093.001: Ticks sampled using drag cloths

A file with previously released NEON data that has been processed into “targets” is provided below. The same processing will be applied to new data that are used for forecast evaluation. We provide the processing script here.

5.3.1 Amblyomma americanum nymphs


The density of Amblyomma americanum nymphs per week. Density is defined as the total number of individuals caught in a week across the forested plots divided by the total area sampled in the forested plots during the week. Densities are presented as ticks per 1600m^2, as 1600m^2 is the size of an individual NEON tick plot.


We chose to use the density of Amblyomma americanum nymphs for several reasons. The first is that Amblyomma americanum is a vector of multiple pathogens, many of which cause human disease, and a forecast for their abundance could aid decisions in public health and personal protective measures. For simplicity, we chose to focus on one species for the abundance challenge, and the Amblyomma americanum nymphs are the most abundant tick observed in the NEON data. Most ticks are observed in to forested plots, and by standardizing the data to density of ticks observed per unit effort in the forested plots, we hope to avoid forecasters predicting sampling effort. We scaled the density to be representative of ticks per plot, which is more interpretable than ticks per square meter. Also, tick drags occur every three weeks. By having the challenge be for forecasting every week, participants won’t have to predict which weeks drags occur.

5.3.2 Focal sites

Information on the sites can be found here:

site_data <- readr::read_csv("https://raw.githubusercontent.com/eco4cast/neon4cast-targets/main/NEON_Field_Site_Metadata_20220412.csv") |> 
  dplyr::filter(ticks == 1)

See Table at end for more information on the sites

5.3.3 Target data calculation

Tick drags occur every three weeks at the NEON sites used in this challenge. The sampling season at each site is determined by phenological milestones, beginning and ending within two weeks of green-up and senescence, respectively. The 1m^2 cloth is dragged for 160m (and at least 80m), and ticks are collected intermittently. They are then sent to a lab for taxonomic identification. Ticks are then identified by life stage and taxonomic rank. The target data is for Amblyomma americanum nymphs that were identified to the species level; i.e. ticks identified as being in the Amblyomma genus are not included.

5.3.4 Target file

Here is the format of the target file

readr::read_csv("https://data.ecoforecast.org/neon4cast-targets/ticks/ticks-targets.csv.gz", guess_max = 1e6)
# A tibble: 578 × 5
   datetime   site_id variable             observation iso_week
   <date>     <chr>   <chr>                      <dbl> <chr>   
 1 2015-04-20 BLAN    amblyomma_americanum        0    2015-W17
 2 2015-05-11 BLAN    amblyomma_americanum        9.82 2015-W20
 3 2015-06-01 BLAN    amblyomma_americanum       10    2015-W23
 4 2015-06-08 BLAN    amblyomma_americanum       19.4  2015-W24
 5 2015-06-22 BLAN    amblyomma_americanum        3.14 2015-W26
 6 2015-07-13 BLAN    amblyomma_americanum        3.66 2015-W29
 7 2015-08-03 BLAN    amblyomma_americanum        0    2015-W32
 8 2015-08-24 BLAN    amblyomma_americanum        0    2015-W35
 9 2015-09-14 BLAN    amblyomma_americanum        0    2015-W38
10 2015-10-12 BLAN    amblyomma_americanum        0    2015-W42
# … with 568 more rows
  • datetime: YYYY-MM-DD (the Monday marking the week of sample collection (for training data) or forecast (submission). Per ISO standards, Monday marks the first day of each week.)
  • site_id: Site where ticks are observed.
  • variable: amblyomma_americanum (density of Amblyomma americanum ticks; ticks / 1600m^2)
  • observation: value for variable
  • iso_week: The ISO-week

5.4 Timeline

Weekly-time step forecasts for a minimum of 4 weeks in the future submitted weekly by 12:59 pm ET on Mondays. A minimum of 4 week in the future must be forecasted for each submission, but they could be for longer. New forecasts can be submitted daily as new weather forecasts become available. The key is that submissions are predictions of the future.

Weekly submissions are allowed and encouraged as new observations and weather forecasts become available, therefore the automation of forecast generation may be ideal. There are many ways to automate scripts that are written to download observations and meteorology drivers, generate forecasts, and submit forecasts. Two tools that many have used are cron jobs (see the R package cronR) that execute tasks at user specifics times and github actions. See more in Chapter 12

The timeline evaluation is determined by the data latency provided by NEON. NEON currently lists 300 days as the latency between data collection and reporting data with taxonomic identification.

5.5 Submissions

The required names for forecasted variable: amblyomma_americanum

The required time unit: date for the Monday of the corresponding isoweek in YYYY-MM-DD format.

Instructions for submitting forecasts are found in Chapter 2

5.6 Meterological inputs for modeling

Information about forecasted meteorology that is available for you to use when generating your forecasts can be found in Chapter 9

5.7 Useful functions

Functions for validating, evaluating and submitting forecasts can be found in Chapter 10

Functions for downloading and working with the meteorology forecasts can be be found in Chapter 9

5.8 Null models

Two null models will be automatically generated each week: (1) the historical mean of the entire time-series at each site and (2) the historical mean for each week at each site. For weeks that don’t have observations, the forecast is a linear interpolation between the preceding and following weeks.

Code for the weekly mean model can be found here

Code for the mean model can be found here

5.9 FAQ

Answers to frequency asks questions can be found in Chapter 12

5.10 Design team

Matt Bitters, University of Colorado, Boulder
Melissa Chen, University of Colorado, Boulder
John Foster, Boston University
Leah Johnson, Virginia Tech
Shannon LaDeau, Cary Institute of Ecosystem Studies
Cat Lippi, University of Florida
Brett Melbourne, University of Colorado, Boulder
Wynne Moss, University of Colorado, Boulder
Sadie Ryan, University of Florida

5.11 Partners

Data used in the challenge are collected by the National Ecological Observatory Network (NEON; https://www.neonscience.org/).

5.12 Site list table

siteID site name vegetation type latitude longtitude NEON site URL
BLAN Blandy Experimental Farm NEON Deciduous Forest|Pasture/Hay 39.03370 -78.04179 https://www.neonscience.org/field-sites/blan
KONZ Konza Prairie Biological Station NEON Deciduous Forest|Grassland/Herbaceous 39.10077 -96.56307 https://www.neonscience.org/field-sites/konz
LENO Lenoir Landing NEON Deciduous Forest|Woody Wetlands 31.85386 -88.16118 https://www.neonscience.org/field-sites/leno
ORNL Oak Ridge NEON Deciduous Forest|Evergreen Forest|Pasture/Hay 35.96413 -84.28259 https://www.neonscience.org/field-sites/ornl
OSBS Ordway-Swisher Biological Station NEON Emergent Herbaceous Wetlands|Evergreen Forest|Woody Wetlands 29.68928 -81.99343 https://www.neonscience.org/field-sites/osbs
SCBI Smithsonian Conservation Biology Institute NEON Deciduous Forest|Evergreen Forest|Pasture/Hay 38.89292 -78.13949 https://www.neonscience.org/field-sites/scbi
SERC Smithsonian Environmental Research Center NEON Cultivated Crops|Deciduous Forest 38.89013 -76.56001 https://www.neonscience.org/field-sites/serc
TALL Talladega National Forest NEON Deciduous Forest|Evergreen Forest|Mixed Forest 32.95047 -87.39326 https://www.neonscience.org/field-sites/tall
UKFS University of Kansas Field Station NEON Deciduous Forest|Pasture/Hay 39.04043 -95.19215 https://www.neonscience.org/field-sites/ukfs