Statistical power of dynamic occupancy models to identify temporal change: Informing the North American Bat Monitoring Program

Banner, Katharine M.; Irvine, Kathryn M.; Rodhouse, Thomas J.; Donner, Deahn M.; Litt, Andrea R.

doi:10.1016/j.ecolind.2019.05.047

Cited by 22 publications

(32 citation statements)

References 36 publications

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“…In 2003-2010 (Period 1), a combination of capture and acoustic surveys was conducted across the region in 241 grid F I G U R E 1 The study area, Oregon and Washington, USA, overlaid with the gridbased sampling frame, average % forest cover of each frame sample unit (grid cell), and the 190 sample units surveyed during 2016-2018 (black squares) that follow a spatially balanced master sample design. In 2016-2018 (Period 2), acoustic surveys were conducted in 190 grid cells, informed by a statistical power analysis (Banner, Irvine, Rodhouse, Donner, & Litt, 2019; Figure 1). In 2016-2018 (Period 2), acoustic surveys were conducted in 190 grid cells, informed by a statistical power analysis (Banner, Irvine, Rodhouse, Donner, & Litt, 2019; Figure 1).…”

Section: Study Survey Designmentioning

confidence: 99%

“…The area where white-nose syndrome has been confirmed circa 2019 is circled in red cells (see Rodhouse et al, 2015Rodhouse et al, , p. 1404. In 2016-2018 (Period 2), acoustic surveys were conducted in 190 grid cells, informed by a statistical power analysis (Banner, Irvine, Rodhouse, Donner, & Litt, 2019; Figure 1). During Period 1, grid cells were selected using a combination of constrained simple random sampling and nonrandom contributions from land management agencies and researchers using compatible methodology (see Rodhouse et al, 2015 for additional details).…”

Section: Study Survey Designmentioning

confidence: 99%

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Evidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors

Rodhouse

Rodriguez

Banner

et al. 2019

Ecology and Evolution

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Strategic conservation efforts for cryptic species, especially bats, are hindered by limited understanding of distribution and population trends. Integrating long‐term encounter surveys with multi‐season occupancy models provides a solution whereby inferences about changing occupancy probabilities and latent changes in abundance can be supported. When harnessed to a Bayesian inferential paradigm, this modeling framework offers flexibility for conservation programs that need to update prior model‐based understanding about at‐risk species with new data. This scenario is exemplified by a bat monitoring program in the Pacific Northwestern United States in which results from 8 years of surveys from 2003 to 2010 require updating with new data from 2016 to 2018. The new data were collected after the arrival of bat white‐nose syndrome and expansion of wind power generation, stressors expected to cause population declines in at least two vulnerable species, little brown bat (Myotis lucifugus) and the hoary bat (Lasiurus cinereus). We used multi‐season occupancy models with empirically informed prior distributions drawn from previous occupancy results (2003–2010) to assess evidence of contemporary decline in these two species. Empirically informed priors provided the bridge across the two monitoring periods and increased precision of parameter posterior distributions, but did not alter inferences relative to use of vague priors. We found evidence of region‐wide summertime decline for the hoary bat (trueλ^ = 0.86 ± 0.10) since 2010, but no evidence of decline for the little brown bat (trueλ^ = 1.1 ± 0.10). White‐nose syndrome was documented in the region in 2016 and may not yet have caused regional impact to the little brown bat. However, our discovery of hoary bat decline is consistent with the hypothesis that the longer duration and greater geographic extent of the wind energy stressor (collision and barotrauma) have impacted the species. These hypotheses can be evaluated and updated over time within our framework of pre–post impact monitoring and modeling. Our approach provides the foundation for a strategic evidence‐based conservation system and contributes to a growing preponderance of evidence from multiple lines of inquiry that bat species are declining.

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Section: Study Survey Designmentioning

confidence: 99%

Section: Study Survey Designmentioning

confidence: 99%

Evidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors

Rodhouse

Rodriguez

Banner

et al. 2019

Ecology and Evolution

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“…The sample units with the lowest GRTS order within a given spatial domain are considered the ''highest priority'' to survey. The number of grid cells to survey should be informed by power analyses (e.g., Banner et al 2019).…”

Section: The Master Sample Concept: a Catalyst For Collaborative Monimentioning

confidence: 99%

“…CPW and CNHP selected grid cells by first sub-setting the NABat master sample to all grid cells that overlapped Colorado (n = 2811). Based upon power analyses of NABat survey data (Banner et al 2019), 3% of the grid cells were designated as high priority (84 of 2811). Of the top 84 priority grid cells, 55 were selected for survey (52 by CNHP, 2 by neighboring state partners, 1 by US Geological Survey), while 32 were high priority but not selected because of a lack of access or resources.…”

Section: Example Increasing Statistical Power By Coordinated Sharingmentioning

confidence: 99%

“…NABat partners are expected to survey the same grid cells annually following national guidance (Loeb et al 2015) and regionally specific standard operating procedures (e.g., Rodriguez et al 2019). Power analyses tailored to regional specifications guide the required survey effort needed to achieve measurable objectives for estimating status and trends (Banner et al 2019). If partners are no longer able to survey a cell, (lack of resources, retirement, etc.)…”

Section: Modern Tools Support Top-down Direction While Motivating Parmentioning

confidence: 99%

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NABat: A top-down, bottom-up solution to collaborative continental-scale monitoring

Reichert

Bayless

Cheng

et al. 2021

Ambio

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Collaborative monitoring over broad scales and levels of ecological organization can inform conservation efforts necessary to address the contemporary biodiversity crisis. An important challenge to collaborative monitoring is motivating local engagement with enough buy-in from stakeholders while providing adequate top-down direction for scientific rigor, quality control, and coordination. Collaborative monitoring must reconcile this inherent tension between top-down control and bottom-up engagement. Highly mobile and cryptic taxa, such as bats, present a particularly acute challenge. Given their scale of movement, complex life histories, and rapidly expanding threats, understanding population trends of bats requires coordinated broad-scale collaborative monitoring. The North American Bat Monitoring Program (NABat) reconciles top-down, bottom-up tension with a hierarchical master sample survey design, integrated data analysis, dynamic data curation, regional monitoring hubs, and knowledge delivery through web-based infrastructure. NABat supports collaborative monitoring across spatial and organizational scales and the full annual lifecycle of bats.

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Detection criteria and post‐field sample processing influence results and cost efficiency of occupancy‐based monitoring

Lonsinger

Knight

Waits

2021

Ecological Applications

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Optimization of occupancy‐based monitoring has focused on balancing the number of sites and surveys to minimize field efforts and costs. When survey techniques require post‐field processing of samples to confirm species detections, there may be opportunities to further improve efficiency. We used scat‐based noninvasive genetic sampling for kit foxes (Vulpes macrotis) in Utah, USA, as a model system to assess post‐field data processing strategies, evaluate the impacts of these strategies on estimates of occupancy and associations between parameters and predictors, and identify the most cost‐effective approach. We identified scats with three criteria that varied in costs and reliability: (1) field‐based identification (expert opinion), (2) statistical‐based morphological identification, and (3) genetic‐based identification (mitochondrial DNA). We also considered four novel post‐field sample processing strategies that integrated statistical and genetic identifications to reduce costly genetic procedures, including (4) a combined statistical‐genetic identification, (5) a genetic removal design, (6) a within‐survey conditional‐replicate design, and (7) a single‐genetic‐replicate with false‐positive modeling design. We considered results based on genetic identification as the best approximation of truth and used this to evaluate the performance of alternatives. Field‐based and statistical‐based criteria prone to misidentification produced estimates of occupancy that were biased high (˜1.8 and 2.1 times higher than estimates without misidentifications, respectively). These criteria failed to recover associations between parameters and predictors consistent with genetic identification. The genetic removal design performed poorly, with limited detections leading to estimates that were biased high with poor precision and patterns inconsistent with genetic identification. Both statistical‐genetic identification and the conditional‐replicate design produced occupancy estimates comparable to genetic identification, while recovering the same model structure and associations at cost reductions of 67% and 74%, respectively. The false‐positive design had the lowest cost (88% reduction) and recovered patterns consistent with genetic identification but had occupancy estimates that were ˜32% lower than estimated occupancy based on genetic identification. Our results demonstrate that careful consideration of detection criteria and post‐field data processing can reduce costs without significantly altering resulting inferences. Combined with earlier guidance on sampling designs for occupancy modeling, these findings can aid managers in optimizing occupancy‐based monitoring.

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Statistical power of dynamic occupancy models to identify temporal change: Informing the North American Bat Monitoring Program

Cited by 22 publications

References 36 publications

Evidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors

Evidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors

NABat: A top-down, bottom-up solution to collaborative continental-scale monitoring

Detection criteria and post‐field sample processing influence results and cost efficiency of occupancy‐based monitoring

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