Expert system for modelling stopover site selection by barnacle geese

The study of stopover sites has received a lot of attention in avian ecology, being especially important for many long-distance migrants, some of which have to pause several times during migration. The survival of many migratory birds depends primarily on food availability at these stopovers. However, previous studies show that there is a lack of knowledge about site selection where migratory birds stop to refuel energy stores. In the present study, a Bayesian expert system has been used to incorporate environmental parameters, to determine their relationship with the presence of barnacle geese at stopover sites. Data on stopover sites was obtained from satellite-tracked barnacle geese (Branta leucopsis) for three different breeding populations in the Western Palearctic (i.e. Russian, Svalbard and Greenland). The results from the present study showed that the posterior probability of presence at the stopover sites obtained from the Bayesian model was close to one. Therefore, the Bayesian expert system detected the stopover sites of the geese correctly and can be used as a proper method for modelling the presence of barnacle geese at the stopover sites in the future. This study introduces a new method into movement ecology to identify and predict the importance of different environmental parameters for stopover site selection by migratory geese. This is particularly important from both a conservation and an agro-economic point of view with the goal of reducing possible conflicts between geese and agricultural interests

Environmental parameters linked to the last migratory stage of barnacle geese en route to their breeding sites

The migration timing of birds can be controlled by endogenous parameters. However, little is known about how environmental parameters influence the timing of migration and which have the greatest influence at different stages of migration. In this study we identified the main environmental parameters that correlate with the timing of the last stage of spring migration for the barnacle goose, Branta leucopsis. GPS tracking data were registered for 12 barnacle geese (in 2008–2010) on the Russian flyway and 17 (2006–2010) on the Svalbard flyway. A linear mixed-effect model and principal component analysis were used to retrieve statistically significant parameters. Departure date from the last staging site on the Russian flyway was related to daylength, temperature, cloud cover and barometric pressure, and on the Svalbard flyway to a food availability index and daylength. Arrival date at the Russian breeding site was related to cloud cover and barometric pressure en route and the food availability index and temperature at the breeding site. For the Svalbard flyway, temperature and cloud cover en route and the food availability index, wind, temperature and cloud cover at the breeding site were significantly related to arrival date at the breeding site. Our study highlights the importance of environmental parameters including food, weather and daylength for the last stage of goose spring migration. We found different priorities in selecting the environmental parameters in migration timing decisions between Svalbard and Russian barnacle geese which fly over sea and over land, respectively. Identifying the key factors that act as cues during the final stages of spring migration is important when assessing the possible effects of climate change on the timing of migration for a highly selective herbivore such as the barnacle goose

Satellite- versus temperature-derived green wave indices for predicting the timing of spring migration of avian herbivores

According to the green wave hypothesis, herbivores follow the flush of spring growth of forage plants during their spring migration to northern breeding grounds. In this study we compared two green wave indices for predicting the timing of the spring migration of avian herbivores: the satellite-derived green wave index (GWI), and an index of the rate of acceleration in temperature (GDDjerk). The GWI was calculated from MODIS normalized difference vegetation index (NDVI) satellite imagery and GDDjerk from gridded temperature data using products from the global land data assimilation system (GLDAS). To predict the timing of arrival at stopover and breeding sites, we used four years (2008–2011) of tracking data from 12 GPS-tagged barnacle geese, a long-distance herbivorous migrant, wintering in the Netherlands,breeding in the Russian Arctic. The stopover and breeding sites for these birds were identified and there lations between date of arrival with the date of 50% GWI and date of peak GDDjerk at each site were analyzed using mixed effect linear regression. A cross-validation method was used to compare the predictive accuracy of the GWI and GDDjerk indices. Significant relationships were found between the arrival date sat the stopover and breeding sites for the dates of 50% GWI as well as the peak GDDjerk (p < 0.01). The goose arrival dates at both stopover and breeding sites were predicted more accurately using GWI (R2cv= 0.68,RMSDcv= 5.9 and R2cv= 0.71, RMSDcv= 3.9 for stopover and breeding sites, respectively) than GDDjerk.The GDDjerk returned a lower accuracy for prediction of goose arrival dates at stopover ( R2cv= 0.45,RMSDcv= 7.79) and breeding sites (R2cv= 0.55, RMSDcv= 4.93). The positive correlation between the absolute residual values of the GDDjerk model and distance to the breeding sites showed that this index is highly sensitive to latitude. This study demonstrates that the satellite-derived green wave index (GWI)can accurately predict the timing of goose migration, irrespective of latitude and therefore is suggested as a reliable green wave index for predicting the timing of avian herbivores spring migration

To pace or not to pace? A review of what abnormal repetitive behavior tells us about zoo animal management

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Performance of abnormal repetitive behavior (ARB) is noted in many captive wild species. ARB can be categorized into 2 basic forms: those whose aim appears to be to compulsively reach an inappropriate goal and those whose performance is linked to an inappropriate motor function. Although the negative welfare connotations of ARBs are well known, the precise reason for their performance remains the subject of debate. As zoos move forward in collection planning and to gather more evidence on the biological needs of the species being kept, the idea that ARBs may be part of a coping function adds more weight to arguments that some species may not be suitable for the zoo at all. Modern-day definitions of animal welfare tell us to measure the well-being of the individual based on its attempts at coping with its immediate environment. A failure to cope, and hence performance of ARB, is an objective and measurable welfare metric that may highlight which species are appropriate for captivity. As conservation pressures on zoos mount, and the need to take in more captive-naive species increases, research on why captive wild animals develop ARB can be used to inform practice. In this article, we aim to review the welfare issues across 3 basic categories of zoo animal (mammals, birds, and ectothermic vertebrates) and critique how research into ARBs can be used by zoos to promote wild-type behavior patterns by providing biologically relevant management and husbandry regimes, which allow animals the key components of control and choice over what they do and how they do it