Environmental change reduces body condition, but not population growth, in a high-arctic herbivore

Environmental change influences fitness-related traits and demographic rates, which in herbivores are often linked to resource-driven variation in body condition. Coupled body condition-demographic responses may therefore be important for herbivore population dynamics in fluctuating environments, such as the Arctic. We applied a transient Life-Table Response Experiment (‘transient-LTRE’) to demographic data from Svalbard barnacle geese (Branta leucopsis), to quantify their population-dynamic responses to changes in body mass. We partitioned contributions from direct and delayed demographic and body condition-mediated processes to variation in population growth. Declines in body condition (1980-2017), which positively affected reproduction and fledgling survival, had negligible consequences for population growth. Instead, population growth rates were largely reproduction-driven, in part through positive responses to rapidly advancing spring phenology. The virtual lack of body condition-mediated effects indicates that herbivore population dynamics may be more resilient to changing body condition than previously expected, with implications for their persistence under environmental change.,Data of body mass was recorded during goose catches (during the moulting period at the breeding grounds at Ny-Ålesund Svalbard). Body mass (measured in g) was then entered into data bases along with the individual ID and age class (fledgling = FL or adult = Ad). Included in the dataset is the average annual body mass for a cohort , for fledglings (1991-2016) and adults (1980-2016), and the sample size for which the average was calculated.,Where average values of body mass are missing, there was no data available in this year.

(Table 1) Deuterium excess record during different time periods in Lomonosovfonna ice core

Abstract: In this paper we present a deuterium excess (d) record from an ice core drilled on a small ice cap in Svalbard in 1997. The core site is located at Lomonosovfonna at 1255 m asl, and the analyzed time series spans the period 1400-1990 A.D. The record shows pronounced multidecadal to centennial-scale variations coherent with sea surface temperature changes registered in the subtropical to southern middle-latitude North Atlantic during the instrumental period. We interpret the negative trend in the deuterium excess during the 1400s and 1500s as an indication of cooling in the North Atlantic associated with the onset of the Little Ice Age. Consistently positive anomalies of d after 1900, peaking at about 1950, correspond with well-documented contemporary warming. Yet the maximum values of deuterium excess during 1900-1990 are not as high as in the early part of the record (pre-1550). This suggests that the sea surface temperatures during this earlier period of time in the North Atlantic to the south of approximately 45°N were at least comparable with those registered in the 20th century before the end of the 1980s. We examine the potential for a cold bias to exist in the deuterium excess record due to increased evaporation from the local colder sources of moisture having isotopically cold signature. It is argued that despite a recent oceanic warming, the contribution from this local moisture to the Lomonosovfonna precipitation budget is still insufficient to interfere with the isotopic signal from the primary moisture region in the midlatitude North Atlantic. Supplemental Information: Data extracted in the frame of a joint ICSTI/PANGAEA IPY effort, see http://doi.pangaea.de/10.1594/PANGAEA.150150 Coverage: EVENT LABEL: Lomonosovfonna * LATITUDE: 78.864700 * LONGITUDE: 17.424400 * DATE/TIME: 1997-01-01T00:00:00 * ELEVATION: 1255.0 m * Recovery: 121 m * LOCATION: Svalbard * METHOD/DEVICE: Ice dril

Counting animals in aerial images with a density map estimation model (code)

Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual post-processing has been used extensively, however, volumes of such data are increasing, necessitating some level of automation, either for complete counting or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as Pygoscelis penguins. We present here a customized CNN-based density map estimation method for counting of penguins from low-resolution aerial photography. Our model, an indirect regression algorithm, performed significantly better in terms of counting accuracy than standard detection algorithm (Faster RCNN) when counting small objects from low-resolution images and gave an error rate of only 0.8 percent. Density map estimation methods as demonstrated here can vastly improve our ability to count animals in tight aggregations, and demonstrably improve monitoring efforts from aerial imagery