Modeling Avian Abundance From Replicated Counts Using Binomial Mixture Models

Abstract

Abundance estimation in ecology is usually accomplished by capture-recapture, removal, or distance sampling methods. These may be hard to implement at large spatial scales. In contrast, binomial mixture models enable abundance estimation without individual identification, based simply on temporally and spatially replicated counts. Here, we evaluate mixture models using data from the national breeding bird monitoring program in Switzerland, where some 250 1-km 2 quadrats are surveyed using the territory mapping method three times during each breeding season. We chose eight species with contrasting distribution (wide-narrow), abundance (high-low), and detectability (easy-difficult). Abundance was modeled as a random effect with a Poisson or negative binomial distribution, with mean affected by forest cover, elevation, and route length. Detectability was a logitlinear function of survey date, survey date-by-elevation, and sampling effort (time per transect unit). Resulting covariate effects and parameter estimates were consistent with expectations. Detectability per territory (for three surveys) ranged from 0.66 to 0.94 (mean 0.84) for easy species, and from 0.16 to 0.83 (mean 0.53) for difficult species, depended on survey effort for two easy and all four difficult species, and changed seasonally for three easy and three difficult species. Abundance was positively related to route length in three high-abundance and one low-abundance (one easy and three difficult) species, and increased with forest cover in five forest species, decreased for two nonforest species, and was unaffected for a generalist species. Abundance estimates under the most parsimonious mixture models were between 1.1 and 8.9 (median 1.8) times greater than estimates based on territory mapping; hence, three surveys were insufficient to detect all territories for each species. We conclude that binomial mixture models are an important new approach for estimating abundance corrected for detectability when only repeated-count data are available. Future developments envisioned include estimation of trend, occupancy, and total regional abundance.

Biodiversity promotes primary productivity and growing season lengthening at the landscape scale

“…In each 1-km 2 plot, vascular plant and butterfly species were monitored along 2,500 × 5 m transects following standardized field protocols (44). Breeding bird species were monitored along a plot-specific route with an average length of 5 km following the standardized method of the Common Breeding Bird Survey (45). Monitoring events took place in 5-y intervals.…”

Detecting diversity: emerging methods to estimate species diversity

“…multispecies version of the N-mixture model that has been used to estimate the abundance of individual species [46][47][48][49][50]. It is structurally similar to the MSOM (Box 1), but replaces the ecological and observation process in the MSOM with distributions suitable for counts.…”

Inference About Species Richness and Community Structure Using Species-Specific Occupancy Models in the National Swiss Breeding Bird Survey MHB

“…Mean duration of a single survey in 2001 was 228 min (range 75-410), mean survey effort (time per unit transect length) 48 min km -1 (range 14-157) and mean survey dates were 10 May, 29 May, and 9 June, respectively. Although MHB yields abundance information for about 150 detected species (see, e.g., Kéry et al 2005;Royle et al 2005;Royle et al 2007b), we only used survey-specific detection/nondetection records for each of the 134 diurnal species observed anywhere in the 254 surveyed quadrats in 2001. Hence, we are modelling a three-dimensional data matrix X where element x k (i, j) denotes detection (1) or nondetection (0) of species i (i = 1, .…”

“…In particular, in the MHB there are four important components that affect detection of a species in a quadrat; route length, duration of survey, season of survey and observer identity. All were previously shown to have considerable effects on counts of species or individuals in avian surveys such as the North American BBS or the Swiss MHB (Sauer et al 1994;Link and Sauer 1998;Kéry et al 2005;Royle et al 2005;Kéry and Schmid 2006;Royle et al 2007b). In our analysis, we account for all of them except the last one; we note that it would be entirely possible to include a random observer effect if the same observer surveys different quadrats.…”