Data from: Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies

Ixodid (hard) ticks play important ecosystem roles and have significant
impacts on animal and human health via tick-borne diseases and
physiological stress from parasitism. Tick occurrence, abundance,
behavior, and key life-history traits are highly influenced by host
availability, weather, microclimate, and landscape features. As such,
changes in the environment can have profound impacts on ticks, their
hosts, and the spread of diseases. Researchers interested in enumerating
questing ticks attempt to integrate this heterogeneity by conducting
replicate sampling bouts spread over the tick questing period as common
field methods notoriously underestimate ticks. However, it is unclear how
(or if) tick studies account for this heterogeneity in the modeling
process. This step is critical as unaccounted variance in detection can
lead to biased estimates of occurrence and abundance. We performed a
descriptive review to evaluate the extent to which studies account for the
detection process while modeling tick data. We also categorized the types
of analyses that are commonly used to model tick data. We used
hierarchical models (HMs) that account for imperfect detection to analyze
simulated and empirical tick data, demonstrating that inference is muddled
when detection probability is not accounted for in the modeling process.
Our review indicates that only 5 of 412 (1%) papers explicitly accounted
for imperfect detection while modeling ticks. By comparing HMs with the
most common approaches used for modeling tick data (e.g., ANOVA), we show
that population estimates are biased low for simulated and empirical data
when using non-HMs, and that confounding occurs due to not explicitly
modeling factors that influenced both detection and abundance. Our review
and analysis of simulated and empirical data shows that it is important to
account for our ability to detect ticks using field methods with imperfect
detection. Not doing so leads to biased estimates of occurrence and
abundance which could complicate our understanding of parasite-host
relationships and the spread of tick-borne diseases. We highlight the
resources available for learning HM approaches and applying them to
analyzing tick data.