DOI: 10.5061/dryad.tmpg4f561
title: Data from: Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies

description: 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.
description: <strong>Methods</strong> To
 illustrate the problems that arise from not accounting for the detection
 process while estimating tick abundance, we performed two simulations that
 mirrored tick dragging studies and used common statistical frameworks for
 modeling tick data. For both simulations, we chose 5 temporal replicate
 surveys of 100 plots and specified a positive relationship of temperature
 on abundance and detection probability; average abundance (λ) was
 arbitrarily set to 20 ticks. Our choice of replicate surveys is a common
 field design for studying ticks (Dobson, 2013), and environmental factors
 such as temperature influence tick abundance and activity (Gilbert, 2021;
 Klarenberg and Wisely, 2019) and are often used to model tick abundance.
 For our first simulation, we specified low detection probability (ρ = 0.2)
 as tick dragging surveys often only collect ~10–20% of questing ticks
 (Drew and Samuel, 1985; Nyrhilä et al., 2020). We assumed perfect
 detection (ρ = 1) for our second simulation, meaning that all ticks would
 be captured by dragging or flagging surveys if they were
 present. We simulated count data arising from a
 negative binomial distribution using the 'simNmix' function from
 AHMbook R package (Kéry et al., 2022) as tick abundance data often have a
 high variance-to-mean ratio due to aggregated and high counts (Elston et
 al., 2001). Following simulations, we estimated abundance and evaluated
 relationships between average seasonal temperature and tick abundance
 using 3 common approaches for modeling tick count data (linear models
 [LM], generalized linear models [GLM], and generalized linear
 mixed-effects models [GLMM]). For the LM analysis, we added 1 to the tick
 counts and log-transformed counts to meet assumptions of normality. This
 approach, although problematic, is a standard method to force count data
 into a linear modeling framework (O’Hara and Kotze, 2010) and common in
 tick studies (e.g., Allen et al., 2019). For the GLM and GLMM analyses, we
 used the raw counts and specified models with negative binomial errors. We
 used the ‘lm’ and ‘glm’ functions in the <em>base</em> R
 package for the LM and GLM analyses, respectively, and the ‘glmmTMB’
 function in the <em>glmmTMB</em> R package (Brooks et al.,
 2017) for the GLMM analysis. To highlight the
 shortcomings of the preceding analytical approaches, we compared inference
 with an N-mixture or binomial mixture model – a type of HM that is often
 used for estimating abundance when count data are imperfectly detected
 (Kéry and Royle, 2015). We fit the N-mixture model using the ‘pcount’
 function in the <em>unmarked</em> R package (Fiske and
 Chandler, 2011) and specified average temperature across all sampling
 occasions as a covariate on abundance (λ) and temperature during each
 sampling occasion as a covariate on detection probability (ρ).
 We then evaluated how well each statistical approach recovered
 average abundance (λ = 20) and relationships between average seasonal
 temperature and abundance when detection is assumed to be imperfect (ρ =
 0.2) and perfect (ρ = 1). All simulations and statistical analyses were
 performed using R software (R Core Team, 2022), and predictive plots were
 created using the <em>ggplot2</em> R package (Wickham,
 2016).
 <strong>References</strong> Allen,
 D., Borgmann-Winter, B., Bashor, L., Ward, J., 2019. The density of the
 Lyme disease vector <em>Ixodes scapularis</em> (blacklegged
 tick) differs between the Champlain Valley and Green Mountains, Vermont.
 Northeast. Nat. 26, 545–560.
 https://doi.org/10.1656/045.026.0307 Brooks, M.E.,
 Kristensen, K., van Benthem, K.J., Magnusson, A., Berg, C.W., Nielsen, A.,
 Skaug, H.J., Mächler, M., Bolker, B.M., 2017. glmmTMB balances speed and
 flexibility among packages for zero-inflated generalized linear mixed
 modeling. R J. 9, 378–400. https://doi.org/10.32614/rj-2017-066
 Dobson, A.D.M., 2013. Ticks in the wrong boxes: Assessing error
 in blanket-drag studies due to occasional sampling. Parasites and Vectors
 6, 1–6. https://doi.org/10.1186/1756-3305-6-344 Drew,
 M.L., Samuel, W.M., 1985. Factors affecting transmission of larval winter
 ticks, <em>Dermacentor albipictus</em> (Packard), to moose,
 <em>Alces alces</em> L., in Alberta, Canada. J. Wildl. Dis.
 21, 274–282. https://doi.org/10.7589/0090-3558-21.3.274
 Elston, D.A., Moss, R., Boulinier, T., Arrowsmith, C., Lambin,
 X., 2001. Analysis of aggregation, a worked example: Numbers of ticks on
 red grouse chicks. Parasitology 122, 563–569.
 https://doi.org/10.1017/S0031182001007740 Gilbert, L.,
 2021. The impacts of climate change on ticks and tick-borne disease risk.
 Annu. Rev. Entomol. 66, 273–288.
 https://doi.org/10.1146/annurev-ento-052720-094533
 Kéry, Marc, Royle, J.A., Meredith, M., 2022. AHMbook: Functions
 and Data for the Book “Applied Hierarchical Modeling in Ecology” Vols 1
 and 2. Kéry, M., Royle, J.A., 2015. Applied
 hierarchical modeling in ecology: Analysis of distribution, abundance, and
 species richness in R an BUGS. Academic Press.
 https://doi.org/https://doi.org/10.1016/C2015-0-04070-9
 Klarenberg, G., Wisely, S.M., 2019. Evaluation of NEON data to
 model spatio-temporal tick dynamics in Florida. Insects 10.
 https://doi.org/10.3390/insects10100321 Nyrhilä, S.,
 Sormunen, J.J., Mäkelä, S., Sippola, E., Vesterinen, E.J., Klemola, T.,
 2020. One out of ten: low sampling efficiency of cloth dragging challenges
 abundance estimates of questing ticks. Exp. Appl. Acarol. 82, 571–585.
 https://doi.org/10.1007/s10493-020-00564-5 O’Hara,
 R.B., Kotze, D.J., 2010. Do not log-transform count data. Methods Ecol.
 Evol. 1, 118–122.
 https://doi.org/10.1111/j.2041-210x.2010.00021.x R Core
 Team, 2022. R: A Language and Environment for Statistical Computing. R
 Foundation for Statistical Computing, Vienna, Austria.
 https://www.R-project.org/. R Found. Stat. Comput. Vienna, Austria. URL
 http//www.R-project.org/. Wickham, H., 2016. ggplot2:
 Elegant graphics for data analysis, First Ed. ed. Springer, New York, NY.
 https://doi.org/https://doi.org/10.1007/978-0-387-98141-3
description: # Data from: Accounting for missing ticks: Use (or lack thereof) of
 hierarchical models in tick ecology studies
 [https://doi.org/10.5061/dryad.tmpg4f561](https://doi.org/10.5061/dryad.tmpg4f561) 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 used hierarchical models (HMs) that account for imperfect detection to analyze simulated tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. 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 data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. We provide an R function `simTicks` to simulate tick data and the code we used to analyze the simulated tick data to determine how well HMs and non-HMs recovered the known parameters. ## Description of the data and file structure The R function `simTicks` and the R code `Tick Simulations.Rmd` are desribed as follows: * `simTicks`: We modified code the `simNmix` function from the `AHMbook` R package (Kéry, Marc, Royle, J.A., Meredith, M., 2022. AHMbook: Functions and Data for the Book “Applied Hierarchical Modeling in Ecology” Vols 1 and 2.) to simulate tick data and renamed it `simTicks`. First, we changed the probability distribution from a Poisson to a negative binomial. Next, made the detection and abundance covariates correlated with each other. All other attributes of the simulation function developed by Kéry et al. (2022) were retained. * `Tick Simulations.Rmd`: This is the code we developed for analyzing data simulated using the `simTicks` function with non-HMs and HMs. A more thorough description of the variables and code are included in the `Tick Simulations.Rmd` file. ## Sharing/Access information Data and R code used in this study are available at Dryad (Sirén et al. 2024): [https://doi.org/10.5061/dryad.tmpg4f561](https://doi.org/10.5061/dryad.tmpg4f561). ## Code/Software The R function to simulate count data `simTicks` and R code to analyze the simulated tick data using non-HMs and HMs `Tick Simulations.Rmd` is desribed above.