Integrating Distance Sampling and Minimum Count Data: A Reply to Becker and Herreman

Schmidt, Joshua H.; Reynolds, Joel H.; White, Kevin S.; Schertz, Dylan; Morton, John M.; Kim, H. Sharon

doi:10.1002/jwmg.22021

Cited by 1 publication

(4 citation statements)

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“…The magnitude of any failure to meet the g (0) = 1 assumption is directly proportional to estimator bias (Buckland et al 2001:30, Laake and Borchers 2004:109, Schmidt et al 2021). For example, if g (0) = 0.8, estimates of density and abundance would be negatively biased by 20% if CDS was used (Laake et al 2008).…”

Section: Detection (Pp Pa Pd)mentioning

confidence: 99%

“…Even though pooling robustness breaks down when g (0) < 1 in the sense that covariates may be required to account for heterogeneity in the MR data, MCDS is not required when assuming PI. This is an important and potentially misunderstood concept in terms of the importance of heterogeneity and the role of covariates for analysis in an MRDS framework (Schmidt et al 2021).…”

Section: Detection (Pp Pa Pd)mentioning

confidence: 99%

“…The observed minimum counts are assumed to be the sum of all groups in the unit, thinned by the overall detection probability, and through scaling we ensure that the sampled population (i.e., transect vs. unit) is equivalent among surveys. An additional advantage is that overall detection probability for the minimum counts becomes estimable, providing a measure of the proportion of individuals that are not detected during minimum counts (see Schmidt et al 2019, 2021 for further details).…”

Section: Path Forward: Leveraging Informationmentioning

confidence: 99%

“…The power and flexibility of integrated approaches, such as the mountain goat example, suggest that design decisions may be less rigid than often considered. For example, formally planning to conduct cheaper minimum counts annually at the local scale and landscape‐scale CDS surveys at some longer return interval (e.g., every 5 years), practitioners could leverage the strengths of both approaches and generate more information in support of management (Schmidt et al 2019, 2021, 2022). We encourage practitioners to think beyond the design requirements for a single abundance survey and instead consider how other existing information can be leveraged to improve estimation and provide additional information on the system of interest.…”

Section: Path Forward: Leveraging Informationmentioning

confidence: 99%

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Distance sampling surveys: using components of detection and total error to select among approaches

Schmidt

Thompson

Wilson

et al. 2022

Wildlife Monographs

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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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Section: Detection (Pp Pa Pd)mentioning

confidence: 99%

Section: Detection (Pp Pa Pd)mentioning

confidence: 99%