A goodness‐of‐fit test for occupancy models with correlated within‐season revisits

Wright, Wilson J.; Irvine, Kathryn M.; Rodhouse, Thomas J.

doi:10.1002/ece3.2292

Cited by 27 publications

(36 citation statements)

References 28 publications

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“…For the camera trap data, we assessed model fit using the parametric bootstrap procedure (MacKenzie & Bailey, ) implemented within program presence . We were unable to assess fit of the correlated detections models to the sign survey data (Wright, Irvine, & Rodhouse, ) as sites had variable numbers of spatial replicates, requiring assessment of fit separately by cohort (a set of sites with the same pattern of missing observations). Of the 18 cohorts among 58 sites, most contained very few sites (e.g.…”

Section: Discussionmentioning

confidence: 99%

Substituting space for time: Empirical evaluation of spatial replication as a surrogate for temporal replication in occupancy modelling

Srivathsa

Puri

Kumar

et al. 2017

Journal of Applied Ecology

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Occupancy models that account for detection probability are important analytical tools in conservation monitoring. Traditionally, occupancy models relied on detection/non‐detection data generated from temporal replicates for estimating detectability. Due to logistical challenges and financial costs involved, many large‐scale field studies instead use spatial replication as a surrogate. The efficacy of the two approaches and their statistical validity has generally sought support from simulation‐based inferences rather than empirical data. Using the sloth bear Melursus ursinus as an example, we compared estimates of occupancy and detection probabilities obtained from temporal and spatial sampling designs. We carried out temporally replicated camera trap surveys and spatially replicated sign surveys across a 754‐km2 area around Bhadra Tiger Reserve in the Western Ghats of India. We sampled along forest/coffee plantation roads in 58 grid cells of 13 km2 each, treating these cells as independent sites. We used the standard single‐season model for the camera trap survey data, and the single‐season correlated detections model (with Markovian dependence) for the sign survey data, and incorporated ecological covariates that likely influenced occupancy and detection probabilities. Occupancy estimates from the two surveys and corresponding modelling approaches were similar [trueψ^cfalse(trueSE^false) = 0.58 (0.03) for camera trap surveys; trueψ^sfalse(trueSE^false) = 0.56 (0.03) for sign surveys]. In both cases, the influence of covariates corroborated our a priori predictions. Site‐level estimates of occupancy from the two methods were highly correlated (r = .78). We generated a combined estimate of sloth bear occupancy in the region as an inverse‐variance weighted average of the two estimates [normalψtrue^false(trueSE^false) = 0.57 (0.02)]. Synthesis and applications. Studies that aim to evaluate occupancy models should account for spatial variation in occupancy/detection probabilities, particularly when making inferences on species–habitat relationships. We show that spatial replication can serve as a good surrogate for temporal replication in occupancy studies, which may be useful for distribution assessments of species when field resources are limited or logistical challenges preclude traditional survey approaches that yield temporally replicated data. Our results therefore provide a basis for efficient targeting of funds and field resources, particularly for practitioners involved in monitoring species at large landscape scales.

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Section: Discussionmentioning

confidence: 99%

Substituting space for time: Empirical evaluation of spatial replication as a surrogate for temporal replication in occupancy modelling

Srivathsa

Puri

Kumar

et al. 2017

Journal of Applied Ecology

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“…An optimization analysis is required to determine the optimal number of recordings to transcribe, and it is also important to consider that adding sampling times and dates to BBS surveys is in violation of the closure assumption for most abundance estimators (Farnsworth et al 2002, Rota et al 2009). More work will be needed to determine the best modeling framework to address this issue, e.g., occupancy and N-mixture models to quantify superpopulation and account for serially correlated counts (Amundson et al 2014, Wright et al 2016.…”

Section: Discussionmentioning

confidence: 99%

Potential benefits of augmenting road-based breeding bird surveys with autonomous recordings

Mahon¹,

Haché²,

Sólymos³

et al. 2017

ACE

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ABSTRACT. The North American Breeding Bird Survey (BBS) is one of the longest annual avian surveys and has the greatest spatiotemporally extensive coverage in the Western Hemisphere. Although this important survey provides trend estimates for more than 400 species, it has limited coverage in the boreal forest and biases in representation and detectability that complicate inference. Thus, there is a need to evaluate the potential of new technologies and analytical approaches to increase coverage and improve monitoring efficiency. We documented variation in counts between BBS surveys (hereafter "human BBS") and different on-road and forest-edge surveys using autonomous recording units (ARUs) from 3 routes in the Northwest Territories, Canada. Specifically, we quantified percent differences (i.e., bias in counts) in species richness, abundance indices of birds, and species-specific variation in counts between human BBS and ARU-based surveys conducted on-road and at the forest edge at different dates and times of day. We also generated on-road effective detection radius (EDR) estimates for 15 species and tested for species-specific differences in EDR to explain bias in counts between on-road and forest-edge ARU surveys. Overall, species richness and abundance indices in human BBS surveys were higher than forest-edge ARU surveys conducted simultaneously and when similar forest-edge ARU surveys were conducted at sunset and a week earlier in June. However, there was no difference when comparing values from human BBS with on-road ARU BBS and forest-edge ARU surveys conducted at sunrise. Extracting the maximum count per species from 4 types of 3-minute forest-edge surveys increased counts by 62% and 64% for species richness and abundance indices, respectively, relative to human BBS, but the importance of this bias differed considerably among the 10 most common species in the study area. Our results suggest that false-negative bias in species detection could be corrected with appropriate methods, and ARUs deployed at the forest edge near BBS stops could be used to increase data quality of on-road surveys. When combined with appropriate correction factors to adjust for surveys done at the forest edge, ARUs could also be used to increase the geographic coverage of boreal surveys by allowing inexperienced volunteers to collect BBS data along winter or secondary roads in remote locations. Avantages

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“…Multiple-night replicates were avoided in order to maintain backward compatibility with the Period 1 revisit design and because Wright, Irvine, and Rodhouse (2016; and others, see Hayes, 1997) found evidence of serial correlation suggesting a lack of independence in bat activity among consecutive nights. Multiple-night replicates were avoided in order to maintain backward compatibility with the Period 1 revisit design and because Wright, Irvine, and Rodhouse (2016; and others, see Hayes, 1997) found evidence of serial correlation suggesting a lack of independence in bat activity among consecutive nights.…”

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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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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A goodness‐of‐fit test for occupancy models with correlated within‐season revisits

Cited by 27 publications

References 28 publications

Substituting space for time: Empirical evaluation of spatial replication as a surrogate for temporal replication in occupancy modelling

Substituting space for time: Empirical evaluation of spatial replication as a surrogate for temporal replication in occupancy modelling

Potential benefits of augmenting road-based breeding bird surveys with autonomous recordings

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

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