Search

Irruptions by boreal seed‐eating and frugivorous birds are assumed to be driven by the production of seeds and fruits, crops of which are highly variable between years. Using data from Sweden, we tested whether irruptions of Common Crossbills Loxia curvirostra were correlated with low Norway Spruce Picea abies seed production in the same year as the irruption and/or high seed production in the year prior to an irruption. Similar tests were made for Parrot Crossbill Loxia pytyopsittacus irruptions in relation to Scots Pine Pinus sylvestris seed production. In northern Europe, these conifers represent the key food species of the two crossbill species, respectively. Despite differing times that seeds take to mature and asynchronous seed production between the two conifer species, including a 3‐year cycle for Norway Spruce, the two crossbill species often irrupted in the same year as one another. Analyses showed that irruptions into Britain and other parts of western Europe by both crossbill species were correlated with low seed production by Norway Spruce in Sweden. Low seed production by Scots Pine had a marginally non‐significant additive effect on both crossbill species. In a second set of analyses, the best‐fitting model was one in which low seed production by both conifers in a given year and high seed production in the previous year were each correlated with large numbers of irrupting Common and Parrot Crossbills. The models indicate that the incidental co‐occurrence of low seed production of Norway Spruce and Scots Pine in a given year, after a year of high seed production, may result in an irruption. The seed production of Norway Spruce and Scots Pine in Sweden was correlated with production by the same species in Finland, indicating widespread synchrony of cropping across northern Europe. [ABSTRACT FROM AUTHOR]

Among other approaches, camera trap distance sampling (CTDS) is used to estimate animal abundance from unmarked populations. It was formulated for videos and observation distances are measured at predetermined 'snapshot moments'. Surveys recording still images with passive infrared motion sensors suffer from frequent periods where animals are not photographed, either because of technical delays before the camera can be triggered again (i.e. 'camera recovery time') or because they remain stationary and do not immediately retrigger the camera following camera recovery time (i.e. 'retrigger delays'). These effects need to be considered when calculating temporal survey effort to avoid downwardly biased abundance estimates. Here, we extend the CTDS model for passive infrared motion sensor recording of single images or short photo series. We propose estimating 'mean time intervals between triggers' as combined mean camera recovery time and mean retrigger delays from the time interval distribution of pairs of consecutive pictures, using a Gamma and Exponential function, respectively. We apply the approach to survey data on red deer, roe deer and wild boar. Mean time intervals between triggers were very similar when estimated empirically and when derived from the model‐based approach. Depending on truncation times (i.e. the time interval between consecutive pictures beyond which data are discarded) and species, we estimated mean time intervals between retriggers between 8.28 and 15.05 s. Using a predefined snapshot interval, not accounting for these intervals, would lead to underestimated density by up to 96% due to overestimated temporal survey effort. The proposed approach is applicable to any taxa surveyed with camera traps. As programming of cameras to record still images is often preferred over video recording due to reduced consumption of energy and memory, we expect this approach to find broad application, also for other camera trap methods than CTDS. [ABSTRACT FROM AUTHOR]

Birds are often obligate to specific habitats which can result in study areas with complex boundaries due to sudden changes in vegetation or other features. This can result in study areas with concave arcs or that include holes of unsuitable habitat such as lakes or agricultural fields. Spatial models used to produce species' distribution and density estimates need to respect such boundaries to make informed decisions for species conservation and management. The soap film smoother is one model for complex study regions which controls the boundary behaviour, ensuring realistic values at the edges of the region. We apply the soap film smoother to account for boundary effects and compare it with thin plate regression spline (TPRS) smooth and design-based conventional distance sampling methods to produce abundance estimates from point-transect distance sampling collected data on Hawai'i 'Ākepa Loxops coccineus in the Hakalau Forest Unit of the Big Island National Wildlife Refuge Complex, Hawai'i Island, USA. The soap film smoother predicted zero or near zero densities in the northern part of the domain and two hotspots (in the southern and central parts of the domain). Along the boundary the soap film model predicted relatively high densities where 'Ākepa occur in the adjacent forest and near zero elsewhere. The design-based and soap film abundance estimates were nearly identical. The width of the soap film confidence interval was 16.5% and 0.8% wider than the width of the TPRS smooth and design-based confidence intervals, respectively. The peaks in predicted densities along the boundary indicates leakage by the TPRS smooth. We provide a discussion of the statistical methods, biological findings and management implications of applying soap film smoothers to estimate forest bird population status. [ABSTRACT FROM AUTHOR]