Accounting for incomplete detection: What are we estimating and how might it affect long-term passerine monitoring programs?

Schmidt, Joshua H.; McIntyre, Carol L.; MacCluskie, Margaret C.

doi:10.1016/j.biocon.2013.01.007

Cited by 64 publications

(82 citation statements)

References 56 publications

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“…One assumption for lek counts is that males do not move among leks, and if they do, movement rates are equal to and from each lek (Sedinger ), but we observed trends of unequal movement to leks at high elevations, and to larger leks. Interlek movements contribute to an availability bias, in which males are not present at leks when counted, and the availability bias should be quantified to relate lek counts to population abundance (Diefenbach et al , Kéry and Schmidt , Blomberg et al , Schmidt et al , WAFWA, ).…”

Section: Discussionmentioning

confidence: 99%

Male greater sage‐grouse movements among leks

et al. 2017

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Movements among leks by breeding birds (i.e., interlek movements) could affect the population's genetic flow, complicate use of lek counts as a population index, and indicate a change in breeding behavior following a disturbance. We used a Bayesian multi‐state mark‐recapture model to assess the daily probability of male greater sage‐grouse (Centrocercus urophasianus) interlek movements and estimate factors influencing movements among leks. We fitted 145 males with solar Argos global positioning systems platform transmitter terminals over 4 years (2011–2014) in Carbon County, Wyoming, USA. The daily probability of a male sage‐grouse moving among leks ranged 0.003 (95% CI = 0.000–0.010) in 2011 to 0.010 (95% CI = 0.001–0.021) in 2013, indicating high daily lek fidelity throughout the season, although there was a 5–42% chance annually a male would move at least once to another lek throughout the season (x¯ days analyzed/M = 55 ± 3.3 days [SE]). Interlek movement probabilities were strongly affected by day of year, peaking early in the lek season. Interlek movements were positively associated with elevation. Seasonal interlek movements occurred more frequently than previously reported, and can bias lek counts in early spring as males move from low to high elevation leks, which reinforces interlek movements as a critical component of lek ecology. © 2016 The Wildlife Society.

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Section: Discussionmentioning

confidence: 99%

Male greater sage‐grouse movements among leks

et al. 2017

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“…In single‐visit distance sampling surveys, abundance and detection probability are conditional on the probabilities of presence and availability, p p , and p a , respectively. However, variation in the temporary emigration composite process, p p p a , may be confounded to an unknown degree with that present in abundance, negatively biasing abundance estimates (Chandler et al., ; Schmidt et al., ). Temporary emigration processes are particularly problematic for surveys of species that show large variation in cue production over a survey season and highly mobile species, including: birds (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…We used 02:00 a.m. to calculate the survey timing covariate because there is no sunrise/sunset in June for our study area and this hour corresponds to the initiation of singing for many species. We included the linear and quadratic effects of survey date to account for variation in the probability of availability over a survey period that began prior to the arrival of a subset of individuals and then extended past peak singing (Kéry, Royle, Plattner, & Dorazio, ; Schmidt et al., ). Lastly, we included a survey‐level random effect, δ j,k , to accommodate unexplained variation in the model (Buckland et al., ; Oedekoven, Laake, & Skaug, ), in part due to the effects of weather.…”

Section: Methodsmentioning

confidence: 99%

“…By failing to address p p and p a directly, single‐visit approaches make the implicit assumption that these components of the detection process are relatively constant through space and time. Should this assumption prove untenable, then inference is limited to some unknown proportion of the superpopulation and abundance estimates are negatively biased (Chandler, Royle, & King, ; Nichols et al., ; Schmidt, McIntyre, & MacCluskie, ). Thus, estimates of superpopulation abundance require fewer implicit assumptions and, therefore, should have greater sensitivity for assessing population and spatial dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Accommodating temporary emigration in spatial distance sampling models

Mizel

Schmidt

Lindberg

2017

Journal of Applied Ecology

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Model‐based distance sampling is commonly used to understand spatial variation in the density of wildlife species. The standard approach assumes that individuals are distributed uniformly and models spatial variation in density using plot‐level effects. Thinned point process (TPP) models for surveys of unmarked populations (spatial distance sampling) better leverage the spatial information underlying individual encounters, and in the presence of within‐plot variation in density, may explain a larger proportion of the spatial variation in density. However, existing spatial distance sampling approaches are conditioned on the assumption that all individuals are present and available for sampling. Temporary emigration of individuals can therefore result in biased estimates of abundance. We extended spatial distance sampling models to accommodate temporary emigration (TPP model). Using simulations of a thinned inhomogeneous point process, we assessed the performance of the TPP model relative to the temporary emigration distance sampling (TEDS) model, which implies a uniform distribution of individuals. In addition, we compared inferences between TPP and TEDS models using data for two passerine species in Alaska. Parameter estimates from the TPP model exhibited improved coverage probability and precision relative to the TEDS model including a 26% reduction in the coefficient of variation (CV) of the population size estimate. In the applied example, the TEDS model indicated weak relationships between abundance and habitat covariates, whereas the TPP model indicated strong relationships for those same effects, suggesting that spatial distance sampling models can provide considerably stronger inference in the presence of within‐plot variation in density. In addition, the CV of the population size estimates for the two passerine species were 32% and 4% smaller under the TPP model. Synthesis and applications. We expect our extension accommodating temporary emigration will be a critical specification for spatial distance sampling models, particularly for studies assessing changes in the distribution and abundance of highly mobile species including passerines.

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“…N-mixture models allow the estimation of abundance from repeated counts of unmarked individuals in a closed population by assuming that observed counts are a function of detection probability and abundance (Royle 2004, Chandler et al 2011, Schmidt et al 2013). More recently, open versions of the N-mixture model have also been developed (Dail andMadsen 2011, Zipkin et al 2014b), relaxing the closure assumption and allowing the estimation of population dynamics parameters.…”

Section: Introductionmentioning

confidence: 99%

Estimating demographic parameters using a combination of known‐fate and open N‐mixture models

Schmidt

Johnson

Lindberg

et al. 2015

Ecology

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Abstract. Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Known-fate data from marked individuals are commonly used to estimate survival rates, whereas N-mixture models use count data from unmarked individuals to estimate multiple demographic parameters. However, a joint approach combining the strengths of both analytical tools has not been developed. Here we develop an integrated model combining known-fate and open N-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. We demonstrate our approach through both simulations and an applied example using four years of known-fate and pack count data for wolves (Canis lupus). Simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. Results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. Our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from markresight data sets. We provide implementations in both the BUGS language and an R package.

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Accounting for incomplete detection: What are we estimating and how might it affect long-term passerine monitoring programs?

Cited by 64 publications

References 56 publications

Male greater sage‐grouse movements among leks

Male greater sage‐grouse movements among leks

Accommodating temporary emigration in spatial distance sampling models

Estimating demographic parameters using a combination of known‐fate and open N‐mixture models

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