Counting young birds: A simple tool for the determination of avian population parameters

Abstract

Population parameters are usually determined from mark-recapture experiments requiring laborious field work. Here, we present a model-based approach that can be applied for the determination of avian population parameters such as average individual life expectancy, average age in the population, and generation length from age-differentiated bird counts. Moreover, the method presented can also create age-specific results from lifetime averages using a deterministic exponential function for the calculation of parameters of interest such as age-dependent mortality and age distribution in the population. The major prerequisites for application of this method are that young and adult birds are easily distinguishable in the field as well as the existence of sufficiently large data sets for error minimization. Large data sets are nowadays often available through the existence of so-called “citizen science” databases. Examples for the determination of population parameters are given for long-living migratory birds which travel as families in large groups such as the Common Crane and the Whooper Swan. Other examples include long-living partially migratory birds staying together in large flocks which do not travel as families such as the Black-headed Gull, and also short-living songbirds where at least from one sex young and adult birds are easily differentiable such as the male Black Redstart.

Automatic Detection of Feral Pigeons in Urban Environments Using Deep Learning

“…In Equation (1), Precision (p): the proportion of samples that are positive among all samples that are positive in the detection result, where TP is True Positives and FP is False Positives, and the calculation formula is shown in Equation (2). Recall (r): the proportion of samples that are correctly detected as positive among all samples that are positive, where FN is False Negatives, and the formula is shown in Equation (3).…”

“…Consequently, monitoring and quantifying feral pigeon populations is essential for evaluating their distribution and identifying instances of overpopulation, ultimately informing the design of effective intervention strategies. Traditional studies of feral pigeons typically utilize the mark–recapture method [ 3 ], point–count surveys [ 4 ], and nest-site surveys [ 5 ] to record the number of pigeons. These methods are labor-intensive and inaccurate due to observation difficulties.…”

“…(2) We developed Swin-Mask R-CNN with SAHI model for pigeon detection, incorporating the SAHI tool to preserve fine details of smaller targets during the inference phase, thereby enhancing detection accuracy. (3) We further improved the model with SAHI tool and enabled it to encompass a greater number of feral pigeons by utilizing large-scale images (4032 × 3024), achieving broader detection coverage.…”