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Sirén APK, Berube J, Clarfeld LA, Sullivan CF, Simpson B, Wilson TL. Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies. Ticks Tick Borne Dis 2024; 15:102342. [PMID: 38613901 DOI: 10.1016/j.ttbdis.2024.102342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 03/20/2024] [Accepted: 04/01/2024] [Indexed: 04/15/2024]
Abstract
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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Affiliation(s)
- Alexej P K Sirén
- Department of Natural Resources and the Environment, University of New Hampshire, Durham, NH, USA; Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, USA; Department of Environmental Conservation, University of Massachusetts, Amherst, MA, USA.
| | - Juliana Berube
- Department of Environmental Conservation, University of Massachusetts, Amherst, MA, USA
| | - Laurence A Clarfeld
- Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, USA
| | - Cheryl F Sullivan
- Entomology Research Laboratory, University of Vermont, Burlington, VT, USA
| | - Benjamin Simpson
- Penobscot Nation Department of Natural Resources, Indian Island, ME, USA
| | - Tammy L Wilson
- U.S. Geological Survey, Massachusetts Cooperative Fish and Wildlife Research Unit, Department of Environmental Conservation, University of Massachusetts, Amherst, MA, USA
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Schmidt JH, Thompson WL, Wilson TL, Reynolds JH. Distance sampling surveys: using components of detection and total error to select among approaches. WILDLIFE MONOGRAPHS 2022. [DOI: 10.1002/wmon.1070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Joshua H. Schmidt
- U.S. National Park Service Central Alaska Network 4175 Geist Road Fairbanks AK 99709 USA
| | | | - Tammy L. Wilson
- U.S. National Park Service, Southwest Alaska Network 240 W. 5th Avenue Anchorage AK 99501 USA
| | - Joel H. Reynolds
- U.S. National Park Service 240 W. 5th Avenue Anchorage AK 99501 USA
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Schmidt JH, Wilson TL, Thompson WL, Mangipane BA. Integrating distance sampling survey data with population indices to separate trends in abundance and temporary immigration. J Wildl Manage 2022. [DOI: 10.1002/jwmg.22185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Joshua H. Schmidt
- U.S. National Park Service Central Alaska Network 4175 Geist Road Fairbanks 99709 AK USA
| | - Tammy L. Wilson
- U.S. Geological Survey, Massachusetts Cooperative Fish and Wildlife Research Unit, Department of Environmental Conservation University of Massachusetts 160 Holdsworth Way Amherst 01003 MA USA
| | | | - Buck A. Mangipane
- U.S. National Park Service Lake Clark National Park and Preserve, General Delivery, Port Alsworth 99653 AK USA
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Christensen SA, Farr MT, Williams DM. Assessment and novel application of
N
‐mixture models for aerial surveys of wildlife. Ecosphere 2021. [DOI: 10.1002/ecs2.3725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Affiliation(s)
- Sonja A. Christensen
- Department of Fisheries and Wildlife Boone and Crockett Quantitative Wildlife Center Michigan State University East Lansing Michigan 48824 USA
| | - Matthew T. Farr
- Department of Integrated Biology Ecology, Evolutionary Biology, and Behavior Program Michigan State University East Lansing Michigan 48824 USA
| | - David M. Williams
- Department of Fisheries and Wildlife Boone and Crockett Quantitative Wildlife Center Michigan State University East Lansing Michigan 48824 USA
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Schmidt JH, Robison HL, Parrett LS, Gorn TS, Shults BS. Brown Bear Density and Estimated Harvest Rates in Northwestern Alaska. J Wildl Manage 2021. [DOI: 10.1002/jwmg.21990] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Joshua H. Schmidt
- Central Alaska Network U.S. National Park Service 4175 Geist Road Fairbanks AK 99709 USA
| | - Hillary L. Robison
- Western Arctic National Parklands U.S. National Park Service P.O. Box 1029 Kotzebue AK 99752 USA
| | - Lincoln S. Parrett
- Division of Wildlife Conservation Alaska Department of Fish and Game 1300 College Road Fairbanks AK 99701 USA
| | - Tony S. Gorn
- Division of Wildlife Conservation Alaska Department of Fish and Game P.O. Box 1148 Nome AK 99762 USA
| | - Brad S. Shults
- Western Arctic National Parklands U.S. National Park Service 4175 Geist Road Fairbanks AK 99709 USA
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Elenga G, Bonenfant C, Péron G. Distance sampling of duikers in the rainforest: Dealing with transect avoidance. PLoS One 2020; 15:e0240049. [PMID: 33031377 PMCID: PMC7544111 DOI: 10.1371/journal.pone.0240049] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 09/17/2020] [Indexed: 11/27/2022] Open
Abstract
Bushmeat is a major source of protein and income in tropical regions but is often over-harvested. A better monitoring of bushmeat stocks could help achieve sustainability. We used a combination of simulations and transect survey data collected from blue duikers (Philantomba monticola) in the Lomako wildlife reserve, Democratic Republic of the Congo, to investigate the use of transect-based distance sampling to monitor bushmeat stocks. The comparison of dung piles and direct observations of duikers evidenced that animals avoided both the transects in the absence of observers, and the observers themselves. This type of behavioural response appeared common in a literature survey. It causes a negative bias in the estimates of population densities from the standard distance sampling methodology. This negative bias would lead to over-pessimistic predictions of population viability, especially if the behavioural response is more intense in the locations where the animals are hunted. In turn, this would lead to excessively conservative management recommendations. To correct for the effect of the behavioural response of the animals to either the transects or the observers, we recommend recording both the forward and perpendicular distances to the observers (2D distance sampling), not just the perpendicular distance. We also recommend multiple-observer protocols. As a cautionary note, we also demonstrate a scenario where the intensity of the behavioural response is too high to reliably estimate the abundance of the population. As a perspective, we outline the general principles of a local stakeholder-based program combining distance sampling with less intensive types of ecological indicators to monitor wildlife populations.
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Affiliation(s)
- Gaïus Elenga
- Laboratoire de Biométrie et Biologie Évolutive UMR5558, Univ Lyon, Université Lyon 1, CNRS, Villeurbanne, France
- Department of the Environment, Faculty of Sciences, University of Kinshasa, Kinshasa, Democratic Republic of Congo
| | - Christophe Bonenfant
- Laboratoire de Biométrie et Biologie Évolutive UMR5558, Univ Lyon, Université Lyon 1, CNRS, Villeurbanne, France
| | - Guillaume Péron
- Laboratoire de Biométrie et Biologie Évolutive UMR5558, Univ Lyon, Université Lyon 1, CNRS, Villeurbanne, France
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Crupi AP, Gregovich DP, White KS. Steep and deep: Terrain and climate factors explain brown bear (Ursus arctos) alpine den site selection to guide heli-skiing management. PLoS One 2020; 15:e0238711. [PMID: 32966287 PMCID: PMC7511016 DOI: 10.1371/journal.pone.0238711] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 08/21/2020] [Indexed: 11/28/2022] Open
Abstract
Winter recreation and tourism continue to expand worldwide, and where these activities overlap with valuable wildlife habitat, there is greater potential for conservation concerns. Wildlife populations can be particularly vulnerable to disturbance in alpine habitats as helicopters and snowmachines are increasingly used to access remote backcountry terrain. Brown bears (Ursus arctos) have adapted hibernation strategies to survive this period when resources and energy reserves are limited, and disturbance could negatively impact fitness and survival. To help identify areas of potential conflict between helicopter skiing and denning brown bears in Alaska, we developed a model to predict alpine denning habitat and an associated data-based framework for mitigating disturbance activities. Following den emergence in spring, we conducted three annual aerial surveys (2015-2017) and used locations from three GPS-collared bears (2008-2014) to identify 89 brown bear dens above the forest line. We evaluated brown bear den site selection of land cover, terrain, and climate factors using resource selection function (RSF) models. Our top model supported the hypothesis that bears selected dens based on terrain and climate factors that maximized thermal efficiency. Brown bears selected den sites characterized by steep slopes at moderate elevations in smooth, well-drained topographies that promoted vegetation and deep snow. We used the RSF model to map relative probability of den selection and found 85% of dens occurred within terrain predicted as prime denning habitat. Brown bear exposure to helicopter disturbance was evident as moderate to high intensities of helicopter flight tracking data overlapped prime denning habitat, and we quantified where the risk of these impact was greatest. We also documented evidence of late season den abandonment due to disturbance from helicopter skiing. The results from this study provide valuable insights into bear denning habitat requirements in subalpine and alpine landscapes. Our quantitative framework can be used to support conservation planning for winter recreation industries operating in habitats occupied by denning brown bears.
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Affiliation(s)
- Anthony P. Crupi
- Division of Wildlife Conservation, Alaska Department of Fish and Game, Douglas, Alaska, United States of America
| | - David P. Gregovich
- Division of Wildlife Conservation, Alaska Department of Fish and Game, Douglas, Alaska, United States of America
| | - Kevin S. White
- Division of Wildlife Conservation, Alaska Department of Fish and Game, Douglas, Alaska, United States of America
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Schmidt JH, Robison HL. Using Distance Sampling‐Based Integrated Population Models to Identify Key Demographic Parameters. J Wildl Manage 2020. [DOI: 10.1002/jwmg.21805] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Joshua H. Schmidt
- Central Alaska NetworkNational Park Service 4175 Geist Road Fairbanks AK 99709 USA
| | - Hillary L. Robison
- Western Arctic National ParklandsU.S. National Park Service P.O. Box 1029 Kotzebue AK 99752 USA
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Schmidt JH, Reynolds JH, Rattenbury KL, Phillips LM, White KS, Schertz D, Morton JM, Kim HS. Integrating distance sampling with minimum counts to improve monitoring. J Wildl Manage 2019. [DOI: 10.1002/jwmg.21691] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Joshua H. Schmidt
- U.S. National Park ServiceCentral Alaska Network 4175 Geist Road Fairbanks AK 99709 USA
| | - Joel H. Reynolds
- U.S. National Park ServiceAlaska Region 240 W. 5th Avenue Anchorage AK 99501 USA
| | - Kumi L. Rattenbury
- U.S. National Park ServiceArctic Network 4175 Geist Road Fairbanks AK 99709 USA
| | - Laura M. Phillips
- U.S. National Park ServiceDenali National Park and Preserve P.O. Box 9, Denali Park AK 99755 USA
| | - Kevin S. White
- Alaska Department of Fish and GameDivision of Wildlife Conservation P.O. Box 110024 Juneau AK 99811 USA
| | - Dylan Schertz
- Wildlife Conservation SocietyArctic Beringia Office 3550 Airport Way, Suite 5 Fairbanks AK 99709 USA
| | - John M. Morton
- U.S. Fish and Wildlife ServiceKenai National Wildlife Refuge P.O. Box 2139 Soldotna AK 99669 USA
| | - H. Sharon Kim
- U.S. National Park ServiceKenai Fjords National Park P.O. Box 1727 Seward AK 99664 USA
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Becker E, Christ A. Rejection of Schmidt et al.'s estimators for bear population size. Ecol Evol 2019; 9:6157-6164. [PMID: 31161027 PMCID: PMC6540835 DOI: 10.1002/ece3.5134] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 03/05/2019] [Accepted: 03/13/2019] [Indexed: 11/11/2022] Open
Abstract
Aerial distance sampling of bears to estimate population size has been used throughout many parts of Alaska. The distance sampling models are complex since they need to account for undetected bears and differences in detection probabilities. This will require covariates and mark-recapture data. The models proposed by Schmidt et al. do not use covariates or mark-recapture data and are inappropriate for these surveys.
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Affiliation(s)
- Earl Becker
- Alaska Department of Fish and GameAnchorageAlaska
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Rattenbury KL, Schmidt JH, Swanson DK, Borg BL, Mangipane BA, Sousanes PJ. Delayed spring onset drives declines in abundance and recruitment in a mountain ungulate. Ecosphere 2018. [DOI: 10.1002/ecs2.2513] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Kumi L. Rattenbury
- Arctic Network U.S. National Park Service 4175 Geist Road Fairbanks Alaska 99709 USA
| | - Joshua H. Schmidt
- Central Alaska Network U.S. National Park Service 4175 Geist Road Fairbanks Alaska 99709 USA
| | - David K. Swanson
- Arctic Network U.S. National Park Service 4175 Geist Road Fairbanks Alaska 99709 USA
| | - Bridget L. Borg
- Denali National Park and Preserve P.O. Box 9 Denali Park Alaska 99755 USA
| | - Buck A. Mangipane
- Lake Clark National Park and Preserve U.S. National Park Service General Delivery Port Alsworth Alaska 99653 USA
| | - Pam J. Sousanes
- Arctic Network U.S. National Park Service 4175 Geist Road Fairbanks Alaska 99709 USA
- Central Alaska Network U.S. National Park Service 4175 Geist Road Fairbanks Alaska 99709 USA
- Denali National Park and Preserve P.O. Box 9 Denali Park Alaska 99755 USA
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12
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Schmidt JH, Rattenbury KL. An open‐population distance sampling framework for assessing population dynamics in group‐dwelling species. Methods Ecol Evol 2017. [DOI: 10.1111/2041-210x.12932] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Schmidt JH, Wilson TL, Thompson WL, Reynolds JH. Improving inference for aerial surveys of bears: The importance of assumptions and the cost of unnecessary complexity. Ecol Evol 2017; 7:4812-4821. [PMID: 28690810 PMCID: PMC5496527 DOI: 10.1002/ece3.2912] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Revised: 02/15/2017] [Accepted: 02/21/2017] [Indexed: 11/25/2022] Open
Abstract
Obtaining useful estimates of wildlife abundance or density requires thoughtful attention to potential sources of bias and precision, and it is widely understood that addressing incomplete detection is critical to appropriate inference. When the underlying assumptions of sampling approaches are violated, both increased bias and reduced precision of the population estimator may result. Bear (Ursus spp.) populations can be difficult to sample and are often monitored using mark‐recapture distance sampling (MRDS) methods, although obtaining adequate sample sizes can be cost prohibitive. With the goal of improving inference, we examined the underlying methodological assumptions and estimator efficiency of three datasets collected under an MRDS protocol designed specifically for bears. We analyzed these data using MRDS, conventional distance sampling (CDS), and open‐distance sampling approaches to evaluate the apparent bias‐precision tradeoff relative to the assumptions inherent under each approach. We also evaluated the incorporation of informative priors on detection parameters within a Bayesian context. We found that the CDS estimator had low apparent bias and was more efficient than the more complex MRDS estimator. When combined with informative priors on the detection process, precision was increased by >50% compared to the MRDS approach with little apparent bias. In addition, open‐distance sampling models revealed a serious violation of the assumption that all bears were available to be sampled. Inference is directly related to the underlying assumptions of the survey design and the analytical tools employed. We show that for aerial surveys of bears, avoidance of unnecessary model complexity, use of prior information, and the application of open population models can be used to greatly improve estimator performance and simplify field protocols. Although we focused on distance sampling‐based aerial surveys for bears, the general concepts we addressed apply to a variety of wildlife survey contexts.
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Affiliation(s)
- Joshua H Schmidt
- Central Alaska Network U.S. National Park Service Fairbanks AK USA
| | - Tammy L Wilson
- Southwest Alaska Network U.S. National Park Service Anchorage AK USA.,Department of Natural Resource Management South Dakota State University Brookings SD USA
| | - William L Thompson
- U.S. Fish and Wildlife Service Hadley MA USA.,Present address: U.S. National Park Service Kingston RI USA
| | - Joel H Reynolds
- Western Alaska Landscape Conservation Cooperative Anchorage AK USA.,Present address: U.S. National Park Service Anchorage AK USA
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