A hierarchical model combining distance sampling and time removal to estimate detection probability during avian point counts

Amundson, Courtney L.; Royle, J. Andrew; Handel, Colleen M.

doi:10.1642/auk-14-11.1

Cited by 109 publications

(229 citation statements)

References 52 publications

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“…Thus, it is apparent that trends for these strata will require reliance either on off-road point counts or BBS style surveys (e.g., via all-terrain vehicle) such as those conducted for bird atlases (Blancher et al 2009), dedicated regional monitoring programs (e.g., Machtans et al 2014), or reliance on trends from the Christmas Bird Count (Niven et al 2004) or migration monitoring in combination with methods to infer catchment areas (Hobson et al 2015). As such, it would be beneficial to build on other work (e.g., Sólymos et al 2013, Amundson et al 2014 to develop and validate a framework for trend estimation from combining on-road and off-road point counts.…”

Section: Resultsmentioning

confidence: 99%

Biased representation of disturbance rates in the roadside sampling frame in boreal forests: implications for monitoring design

Wilgenburg¹,

Beck²,

Obermayer³

et al. 2015

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ABSTRACT. The North American Breeding Bird Survey (BBS) is the principal source of data to inform researchers about the status of and trend for boreal forest birds. Unfortunately, little BBS coverage is available in the boreal forest, where increasing concern over the status of species breeding there has increased interest in northward expansion of the BBS. However, high disturbance rates in the boreal forest may complicate roadside monitoring. If the roadside sampling frame does not capture variation in disturbance rates because of either road placement or the use of roads for resource extraction, biased trend estimates might result. In this study, we examined roadside bias in the proportional representation of habitat disturbance via spatial data on forest "loss," forest fires, and anthropogenic disturbance. In each of 455 BBS routes, the area disturbed within multiple buffers away from the road was calculated and compared against the area disturbed in degree blocks and BBS strata. We found a nonlinear relationship between bias and distance from the road, suggesting forest loss and forest fires were underrepresented below 75 and 100 m, respectively. In contrast, anthropogenic disturbance was overrepresented at distances below 500 m and underrepresented thereafter. After accounting for distance from road, BBS routes were reasonably representative of the degree blocks they were within, with only a few strata showing biased representation. In general, anthropogenic disturbance is overrepresented in southern strata, and forest fires are underrepresented in almost all strata. Similar biases exist when comparing the entire road network and the subset sampled by BBS routes against the amount of disturbance within BBS strata; however, the magnitude of biases differed. Based on our results, we recommend that spatial stratification and rotating panel designs be used to spread limited BBS and off-road sampling effort in an unbiased fashion and that new BBS routes be established where sufficient road coverage exists.Représentativité biaisée des taux de perturbation dans le plan d'échantillonnage en bord de routes en forêt boréale : répercussions dans l'élaboration de suivis RÉSUMÉ. Le Relevé des oiseaux nicheurs (BBS) nord-américain est la principale source de données sur le statut et la tendance des oiseaux de la forêt boréale pour les chercheurs. Malheureusement, la couverture du BBS en forêt boréale est faible et les préoccupations grandissantes sur le statut des espèces nichant dans ce milieu ont amené les chercheurs à considérer étendre la couverture du BBS vers le nord. Toutefois, les taux élevés de perturbation en forêt boréale pourraient compliquer les suivis effectués le long des routes. Si l'échantillonnage réalisé en bord de routes ne tient pas compte des taux de perturbation variables tant dans l'emplacement des routes que dans l'utilisation des routes pour l'extraction des ressources, les estimations de la tendance qui en résultent pourraient être faussées. Nous avons examiné si les suivis en bord de routes p...

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Section: Resultsmentioning

confidence: 99%

Biased representation of disturbance rates in the roadside sampling frame in boreal forests: implications for monitoring design

Wilgenburg¹,

Beck²,

Obermayer³

et al. 2015

ACE

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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

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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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“…Counts based on point-count surveys are considered an index of the actual population present at a given site because some portion of the population is not present on the breeding grounds during the time of the survey (i.e., probability of presence), is present but does not signal its presence during a survey (i.e., availability bias), or signals but is not detected by the surveyor (i.e., perceptibility bias) (Rosenstock et al 2002;Elphick 2008;Nichols et al 2009). This ''imperfect detection'' may not affect the interpretation of results as long as the ratio of detection and counts are unbiased or relatively constant (Johnson 2008), but several studies have shown that detection probability often varies by observers (Diefenbach et al 2003;Alldredge et al 2007;Simons et al 2007;Reidy et al 2014), by habitat (Pacifici et al 2008;Amundson et al 2014), with weather conditions Amundson et al 2014), and through time (Selmi and Boulinier 2003;Diefenbach et al 2007). Further, if detection probability differs between habitats but is not accounted for, differences in detection may be misattributed to species habitat associations (Amundson et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…This ''imperfect detection'' may not affect the interpretation of results as long as the ratio of detection and counts are unbiased or relatively constant (Johnson 2008), but several studies have shown that detection probability often varies by observers (Diefenbach et al 2003;Alldredge et al 2007;Simons et al 2007;Reidy et al 2014), by habitat (Pacifici et al 2008;Amundson et al 2014), with weather conditions Amundson et al 2014), and through time (Selmi and Boulinier 2003;Diefenbach et al 2007). Further, if detection probability differs between habitats but is not accounted for, differences in detection may be misattributed to species habitat associations (Amundson et al 2014). Monitoring programs are increasingly incorporating various methods to account for detection bias (e.g., double observer, distance sampling, repeated surveys; Nichols et al 2000;Buckland et al 2001;Royle 2004;Nichols et al 2009), and recent analytical advancements, namely hierarchical N-mixture modeling (Royle 2004), have facilitated simultaneous estimation of detection probability and population density from spatially replicated wildlife surveys.…”

Section: Introductionmentioning

confidence: 99%

“…Monitoring programs are increasingly incorporating various methods to account for detection bias (e.g., double observer, distance sampling, repeated surveys; Nichols et al 2000;Buckland et al 2001;Royle 2004;Nichols et al 2009), and recent analytical advancements, namely hierarchical N-mixture modeling (Royle 2004), have facilitated simultaneous estimation of detection probability and population density from spatially replicated wildlife surveys. However, evaluation of model performance is typically non-existent or based on simulated data because the underlying quantity being estimated (i.e., true abundance or density) is usually not known (Cassey and McArdle 1999;Royle 2004;Amundson et al 2014). Peak (2011) and Hunt et al (2012) demonstrated point-count surveys may be unreliable predictors of actual observed density of golden-cheeked warblers.…”

Section: Introductionmentioning

confidence: 99%

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Landscape and local effects on occupancy and densities of an endangered wood-warbler in an urbanizing landscape

et al. 2015

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Context Golden-cheeked warblers (Setophaga chrysoparia), an endangered wood-warbler, breed exclusively in woodlands co-dominated by Ashe juniper (Juniperus ashei) in central Texas. Their breeding range is becoming increasingly urbanized and habitat loss and fragmentation are a main threat to the species' viability.Objectives We investigated the effects of remotely sensed local habitat and landscape attributes on point occupancy and density of warblers in an urban preserve and produced a spatially explicit density map for the preserve using model-supported relationships. Methods We conducted 1507 point-count surveys during spring 2011-2014 across Balcones Canyonlands Preserve (BCP) to evaluate warbler habitat associations and predict density of males. We used hierarchical Bayesian models to estimate multiple components of detection probability and evaluate covariate effects on detection probability, point occupancy, and density. Results Point occupancy was positively related to landscape forest cover and local canopy cover; mean occupancy was 0.83. Density was influenced more by local than landscape factors. Density increased with greater amounts of juniper and mixed forest and decreased with more open edge. There was a weak negative relationship between density and landscape urban land cover. Conclusions Landscape composition and habitat structure were important determinants of warbler occupancy and density, and the large intact patches of juniper and mixed forest on BCP ([2100 ha) supported a high density of warblers. Increasing urbanization and fragmentation in the surrounding landscape will likely result in lower breeding density due to loss of juniper and mixed forest and increasing urban land cover and edge.

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A hierarchical model combining distance sampling and time removal to estimate detection probability during avian point counts

Cited by 109 publications

References 52 publications

Biased representation of disturbance rates in the roadside sampling frame in boreal forests: implications for monitoring design

Biased representation of disturbance rates in the roadside sampling frame in boreal forests: implications for monitoring design

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

Landscape and local effects on occupancy and densities of an endangered wood-warbler in an urbanizing landscape

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