2014
DOI: 10.1002/ece3.1101
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Observer aging and long‐term avian survey data quality

Abstract: Long-term wildlife monitoring involves collecting time series data, often using the same observers over multiple years. Aging-related changes to these observers may be an important, under-recognized source of error that can bias management decisions. In this study, we used data from two large, independent bird surveys, the Atlas of the Breeding Birds of Ontario (“OBBA”) and the North American Breeding Bird Survey (“BBS”), to test for age-related observer effects in long-term time series of avian presence and a… Show more

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Cited by 34 publications
(34 citation statements)
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“…Similarly, observer effects associated with age‐related hearing loss also lower count estimates for certain species and may additionally contribute to nondetection (Farmer et al. ). First‐year observer and observer effects are not expected to exert a directional bias in range margin location through time, although they could potentially have a minor effect on abundance‐weighted estimates for the few species in our study that are affected.…”
Section: Methodsmentioning
confidence: 99%
“…Similarly, observer effects associated with age‐related hearing loss also lower count estimates for certain species and may additionally contribute to nondetection (Farmer et al. ). First‐year observer and observer effects are not expected to exert a directional bias in range margin location through time, although they could potentially have a minor effect on abundance‐weighted estimates for the few species in our study that are affected.…”
Section: Methodsmentioning
confidence: 99%
“…Observation error arises from multiple sources and, similar to population processes, may have both systematic and stochastic components. Systematic biases can arise from factors like variation in detection among and within species amid different habitats (Schieck 1997, Pacifici et al 2008, directional changes in observer abilities over time such as declining hearing (Farmer et al 2014), seasonal variation in detectability or spatial variation in observer skill (Johnston et al 2018), improved confidence and training (Kendall et al 1996, Sauer et al 1994, Tingley and Beissinger 2013, changes in microphone sensitivity for ARUs (Turgeon et al 2017), or sampling protocols failing to track the advancement of the breeding season because of climate change (McClure et al 2011). The systematic and stochastic components can be modeled to reduce their impacts on population inferences when surveys are appropriately designed (Matsuoka et al 2014) and relevant covariates are recorded (Sauer et al 1994, Pacifici et al 2008, Farmer et al 2014, Johnston et al 2018).…”
Section: Taking Observation Error Into Accountmentioning
confidence: 99%
“…double counting) or sub-estimations of abundance (e.g. variable experience and age of observers, Hancock et al, 1999;Farmer et al, 2014; variable male lek count attendance in black grouse, Baines, 1996;acoustic limitations, Simons et al 2007; low or variable probability of detection in tetraonids, Zimmerman et al, 2007, Fremgen et al, 2016. Several observers are often required for fieldwork and current protocols already tend to minimize or avoid double counts (see Method section).…”
Section: Black Grouse Monitoring and Cofactorsmentioning
confidence: 99%