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Sirén, A.P.K., J. Berube, L.A. Clarfeld, C.F.Sullivan, B. Simpson, T.L. Wilson. 2024. Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies. Ticks and Tickborne Diseases 15:102342. DOI: 10.1016/j.ttbdis.2024.102342.
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Abstract
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Publisher Website
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April 2024
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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, activity, 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 recognize that spatial and temporal factors influence activity and abundance and attempt to account for both by conducting replicate sampling bouts spread over the tick questing period. However, common field methods notoriously underestimate abundance, and it is unclear how (or if) tick studies model the confounding effects of factors influencing activity and abundance. 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.
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Schmidt, J.H., W.L. Thompson, T.L. Wilson, J.H. Reynolds. 2022. Understanding the impacts of the detection process on distance sampling surveys: selecting among approaches and minimizing total error. Wildlife Monographs 210: e1070. https://doi.org/10.1002/wmon.1070
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Abstract
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July 2022
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Wildlife population estimators often require formal adjustment for imperfect detection of individuals during surveys. Conventional distance sampling (CDS) and its extensions (mark‐recapture distance sampling [MRDS], temporary emigration distance sampling [TEDS]) are popular approaches for producing unbiased estimators of wildlife abundance. However, despite extensive discussion and development of distance sampling theory in the literature, deciding which of these alternatives is most appropriate for a particular scenario can be confusing. Some of this confusion may stem from an incomplete understanding of how each approach addresses the components of the detection process. Here we describe the proper application of CDS, MRDS, and TEDS approaches and use applied examples to help clarify their differing assumptions with respect to the components of the detection process. To further aid the practitioner, we summarize the differences in a decision tree that can be used to identify cases where a more complex alternative (e.g., MRDS or TEDS) may be appropriate for a given survey application. Although the more complex approaches can account for additional sources of bias, in practical applications one also must consider estimator precision. Therefore, we also review the concept of total estimator error in the context of comparing competing methods for a given application to aid in the selection of the most appropriate distance sampling approach. Finally, we detail how the use of more advanced techniques (i.e., informed priors, open‐population distance sampling models, and integrated modeling approaches) can further reduce total estimator error by leveraging information from existing and ongoing data collection .By synthesizing the existing literature on CDS, MRDS, TEDS and their extensions, in conjunction with the concepts of total estimator error and the components of the detection process, we provide a comprehensive guide that can be used by the practitioner to more efficiently, effectively, and appropriately apply distance sampling in a variety of settings.
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Berube, J., A.P.K. Sirén, B. Simpson, K.B. Klingler, T.L. Wilson. 2024. Monitoring off-host winter tick abundance on traditional moose hunting lands. Journal of Wildlife Management 88:e22630. DOI: 10.1002/jwmg.22630.
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Abstract
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Publisher Website
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July 2024
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An important symbolic and subsistence animal for many Native American Tribes, the moose (<i>Alces alces</i>; mos in Algonquin, Penobscot language) has been under consistent threat in the northeastern United States because of winter tick (<i>Dermacentor albipictus</i>) parasitism over the past several decades, causing declines in moose populations throughout the region. This decline has raised concern for Tribes and agencies that are invested in moose. Given this concern, it is increasingly important to effectively monitor and develop strategies to manage winter ticks to address consistent population declines of moose due to winter ticks. The Penobscot Nation developed a novel strategy to sample questing winter ticks (i.e., ticks that are actively seeking hosts) using a plot-based sampling protocol that may be suitable for heterogeneous habitats. We deployed this protocol in the northeastern United States in 2022 during the tick questing period (Sep–Dec) on Penobscot Nation sovereign trust lands, the White Mountain National Forest and Umbagog National Wildlife Refuge, and western-central Massachusetts, USA. We analyzed the data using occupancy and N-mixture models. Detection probability peaked during mid-October and tick occupancy and abundance were greatest at sites with intermediate understory vegetation height. The sampling protocol was successful at sampling ticks in Massachusetts, where abundances were expected to be low, indicating that it may be useful for studies planning to monitor winter tick distribution and abundance in areas with sub-optimal moose habitat and where winter tick abundance is expected to be low. This approach may also benefit managers or researchers intending to monitor many species of hard ticks, and where imperfect detection is expected.
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Alexej P. K. Sirén, Hallworth, M.T., Kilborn, J.R., Bernier, C.A., Fortin, N.L., Geider, K.D., Patry, R.K., Cliché, R.M., Prout, L.S., Gifford, S.J., Wixsom, S., Morelli, T.L. and Wilson, T.L. (2024). Monitoring animal populations with cameras using open, multistate, N mixture models. Ecology and Evolution, 14(12):e70583. doi:https://doi.org/10.1002/ece3.70583.
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Abstract
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December 2024
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Remote cameras have become a mainstream tool for studying wildlife populations. For species whose developmental stages are identifiable in photographs, there are opportunities for tracking population changes and estimating demographic rates. Recent developments in hierarchical models allow for the estimation of ecological states and rates over time for unmarked animals whose stages are known. However, this powerful class of models has been underutilized as they are computationally intensive and model outputs can be difficult to interpret. Here, we use simulation to show how camera data can be analyzed with open, multistate, N-mixture (hereafter multistate DM) models to estimate abundance, survival, and recruitment. We evaluated 4 commonly encountered scenarios arising from camera trap data (low and high abundance and 25% and 50% missing data) each with 18 different sample size combinations (camera sites = 40, 250; surveys = 4, 8, 12; and years = 2, 5, 10) and evaluated the bias and precision of abundance, survival, and recruitment estimates. We also analyzed our empirical camera data on moose (<i>Alces alces</i>) with multistate DM models and compared inference with telemetry studies from the same time and region to assess the accuracy of camera studies to track moose populations. Most scenarios recovered the known parameters from our simulated data with higher accuracy and increased precision for scenarios with more sites, surveys, and/or years. Large amounts of missing data and fewer camera sites, especially at higher abundances, reduced accuracy and precision of survival and recruitment. Our empirical analysis provided biologically realistic estimates of moose survival and recruitment and recovered the known pattern of moose abundance across the region. Multistate DM models can be used for estimating demographic parameters from camera data when developmental stages are clearly identifiable. We discuss several avenues for future research and caveats for using these models for large-scale population monitoring.
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Alexej P. K. Sirén, Hallworth, M.T., Kilborn, J.R., Bernier, C.A., Fortin, N.L., Geider, K.D., Patry, R.K., Cliché, R.M., Prout, L.S., Gifford, S.J., Wixsom, S., Morelli, T.L. and Wilson, T.L. (2024). Monitoring animal populations with cameras using open, multistate, N mixture models. Ecology and Evolution, 14(12):e70583. doi:https://doi.org/10.1002/ece3.70583.
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Abstract
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December 2024
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Remote cameras have become a mainstream tool for studying wildlife populations. For species whose developmental stages are identifiable in photographs, there are opportunities for tracking population changes and estimating demographic rates. Recent developments in hierarchical models allow for the estimation of ecological states and rates over time for unmarked animals whose stages are known. However, this powerful class of models has been underutilized as they are computationally intensive and model outputs can be difficult to interpret. Here, we use simulation to show how camera data can be analyzed with open, multistate, N-mixture (hereafter multistate DM) models to estimate abundance, survival, and recruitment. We evaluated 4 commonly encountered scenarios arising from camera trap data (low and high abundance and 25% and 50% missing data) each with 18 different sample size combinations (camera sites = 40, 250; surveys = 4, 8, 12; and years = 2, 5, 10) and evaluated the bias and precision of abundance, survival, and recruitment estimates. We also analyzed our empirical camera data on moose (<i>Alces alces</i>) with multistate DM models and compared inference with telemetry studies from the same time and region to assess the accuracy of camera studies to track moose populations. Most scenarios recovered the known parameters from our simulated data with higher accuracy and increased precision for scenarios with more sites, surveys, and/or years. Large amounts of missing data and fewer camera sites, especially at higher abundances, reduced accuracy and precision of survival and recruitment. Our empirical analysis provided biologically realistic estimates of moose survival and recruitment and recovered the known pattern of moose abundance across the region. Multistate DM models can be used for estimating demographic parameters from camera data when developmental stages are clearly identifiable. We discuss several avenues for future research and caveats for using these models for large-scale population monitoring.
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