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Data from: Studying phenology by flexible modeling of seasonal detectability peaks

Citation

Strebel, Nicolas; Kéry, Marc; Schaub, Michael; Schmid, Hans (2014), Data from: Studying phenology by flexible modeling of seasonal detectability peaks, Dryad, Dataset, https://doi.org/10.5061/dryad.k20q2

Abstract

1.Many animals and plant species have advanced spring phenology in response to climate warming. The majority of avian phenological studies are based on arrival dates. Consequently knowledge on bird phenology is mainly based on migratory species. In addition, arrival dates of migratory birds may be substantially affected by en-route climate conditions; thus failing to provide good indicators for spring phenology on the breeding grounds. Correlating arrival dates with other phenological data or with environmental covariates may be meaningless in these cases. 2.We propose the date of highest singing activity, quantified by detection probability, as a powerful proxy for breeding phenology that is applicable to migratory and sedentary bird species alike. In contrast to arrival dates, breeding phenology is mainly (non-migrants) or at least partially (migrants) influenced by conditions experienced within the breeding area. 3.We developed a new method for flexible estimation of peak detectability date in spring by combining multi-season site-occupancy with semi-parametric regression modeling (thin-plate splines). We applied our approach to opportunistic observations of 27 bird species (mostly passerines) in Switzerland. 4.We found substantial differences among species in the date of spring peak detectability: late February to mid-April in sedentary and short-distance migratory species and mid-April to late May in long-distance migrants. Among 10 species with data for >9 years, five showed a trend in detectability peaks towards an earlier spring phenology by nine to 17 days within 10 years. The mean shift over all species was ~3.5 days per 10 years. 5.Our approach is widely applicable, especially for temporally and spatially large-scale data from monitoring or citizen-science programs. Besides using the detectability peak as measure of phenology, the estimated seasonal pattern in detectability can help designing monitoring programs for improved efficiency. Our approach may be applied to any species with pronounced acoustic displays or other behavioral traits strongly influencing detectability during the breeding period. We believe that it can contribute substantially to unraveling how species and communities respond to environmental change.

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