Transportation networks play a crucial role in human mobility, the exchange of goods and the spread of invasive species. With 90 per cent of world trade carried by sea, the global network of merchant ships provides one of the most important modes of transportation. Here, we use information about the itineraries of 16 363 cargo ships during the year 2007 to construct a network of links between ports. We show that the network has several features that set it apart from other transportation networks. In particular, most ships can be classified into three categories: bulk dry carriers, container ships and oil tankers. These three categories do not only differ in the ships' physical characteristics, but also in their mobility patterns and networks. Container ships follow regularly repeating paths whereas bulk dry carriers and oil tankers move less predictably between ports. The network of all ship movements possesses a heavy-tailed distribution for the connectivity of ports and for the loads transported on the links with systematic differences between ship types. The data analysed in this paper improve current assumptions based on gravity models of ship movements, an important step towards understanding patterns of global trade and bioinvasion.

According to migration theory and several empirical studies, long-distance migrants are more time-limited during spring migration and should therefore migrate faster in spring than in autumn. Competition for the best breeding sites is supposed to be the main driver, but timing of migration is often also influenced by environmental factors such as food availability and wind conditions.Using GPS tags, we tracked 65 greater white-fronted geese Anser albifrons migrating between western Europe and the Russian Arctic during spring and autumn migration over six different years. Contrary to theory, our birds took considerably longer for spring migration (83 days) than autumn migration (42 days). This difference in duration was mainly determined by time spent at stopovers.Timing and space use during migration suggest that the birds were using different strategies in the two seasons: In spring they spread out in a wide front to acquire extra energy stores in many successive stopover sites (to fuel capital breeding), which is in accordance with previous results that white-fronted geese follow the green wave of spring growth. In autumn they filled up their stores close to the breeding grounds and waited for supportive wind conditions to quickly move to their wintering grounds. Selection for supportive winds was stronger in autumn, when general wind conditions were less favourable than in spring, leading to similar flight speeds in the two seasons. In combination with less stopover time in autumn this led to faster autumn than spring migration.White-fronted geese thus differ from theory that spring migration is faster than autumn migration. We expect our findings of different decision rules between the two migratory seasons to apply more generally, in particular in large birds in which capital breeding is common, and in birds that meet other environmental conditions along their migration route in autumn than in spring.

and 8 Vogeltrekstation -Dutch Centre for Avian Migration and Demography (NIOO-KNAW), 6708 PB Wageningen, The Netherlands Summary 1. Herbivorous birds are hypothesized to migrate in spring along a seasonal gradient of plant profitability towards their breeding grounds (green wave hypothesis). For Arctic breeding species in particular, following highly profitable food is important, so that they can replenish resources along the way and arrive in optimal body condition to start breeding early. 2. We compared the timing of migratory movements of Arctic breeding geese on different flyways to examine whether flyways differed in the predictability of spring conditions at stopovers and whether this was reflected in the degree to which birds were following the green wave. 3. Barnacle geese (Branta leucopsis) were tracked with solar GPS/ARGOS PTTs from their wintering grounds to breeding sites in Greenland (N = 7), Svalbard (N = 21) and the Barents Sea (N = 12). The numerous stopover sites of all birds were combined into a set of 16 general stopover regions. 4. The predictability of climatic conditions along the flyways was calculated as the correlation and slope between onsets of spring at consecutive stopovers. These values differed between sites, mainly because of the presence or absence of ecological barriers. Goose arrival at stopovers was more closely tied to the local onset of spring when predictability was higher and when geese attempted breeding that year. 5. All birds arrived at early stopovers after the onset of spring and arrived at the breeding grounds before the onset of spring, thus overtaking the green wave. This is in accordance with patterns expected for capital breeders: first, they must come into condition; at intermediate stopovers, arrival with the food quality peak is important to stay in condition, and at the breeding grounds, early arrival is favoured so that hatching of young can coincide with the peak of food quality. 6. Our results suggest that a chain of correlations between climatic conditions at subsequent stopovers enables geese to closely track the green wave. However, the birds' precision of migratory timing seems uninfluenced by ecological barriers, indicating partly fixed migration schedules. These might become non-optimal due to climate warming and preclude accurate timing of long-distance migrants in the future.

Many migratory herbivores seem to follow the flush of plant growth during migration in order to acquire the most nutrient-rich plants. This has also been hypothesized for arctic-breeding geese, but so far no test of this so-called green wave hypothesis has been performed at the individual level. During four years, a total of 30 greater white-fronted geese Anser albifrons albifrons was tracked using GPS transmitters, of which 13 yielded complete spring migration tracks. From those birds we defined stopover sites and related the date of arrival at each of these stopovers to temperature sum (growing degree days, GDD), snow cover, accumulated photoperiod and latitude. We found that geese arrived at spring stopovers close to the peak in GDD jerk; the 'jerk' is the third derivative, or the rate of change in acceleration, and GDD jerk maxima therefore represent the highest acceleration of daily temperature per site. Day of snow melt also correlated well with the observed arrival of the geese. Factors not closely related to onset of spring, i.e. accumulated photoperiod and latitude, yielded poorer fits. A comparison with published data revealed that the GDD jerk occurs 1-2 weeks earlier than the onset of spring derived from NDVI, and probably represents the very start of spring growth. Our data therefore suggest that white-fronted geese track the front of the green wave in spring.

Many migrating herbivores rely on plant biomass to fuel their life cycles and have adapted to following changes in plant quality through time. The green wave hypothesis predicts that herbivorous waterfowl will follow the wave of food availability and quality during their spring migration. However, testing this hypothesis is hampered by the large geographical range these birds cover. The satellite-derived normalized difference vegetation index (NDVI) time series is an ideal proxy indicator for the development of plant biomass and quality across a broad spatial area. A derived index, the green wave index (GWI), has been successfully used to link altitudinal and latitudinal migration of mammals to spatio-temporal variations in food quality and quantity. To date, this index has not been used to test the green wave hypothesis for individual avian herbivores. Here, we use the satellite-derived GWI to examine the green wave hypothesis with respect to GPS-tracked individual barnacle geese from three flyway populations (Russian n = 12, Svalbard n = 8, and Greenland n = 7). Data were collected over three years (2008–2010). Our results showed that the Russian and Svalbard barnacle geese followed the middle stage of the green wave (GWI 40–60%), while the Greenland geese followed an earlier stage (GWI 20–40%). Despite these differences among geese populations, the phase of vegetation greenness encountered by the GPS-tracked geese was close to the 50% GWI (i.e. the assumed date of peak nitrogen concentration), thereby implying that barnacle geese track high quality food during their spring migration. To our knowledge, this is the first time that the migration of individual avian herbivores has been successfully studied with respect to vegetation phenology using the satellite-derived GWI. Our results offer further support for the green wave hypothesis applying to long-distance migrants on a larger scale.

Recently, Lévy walks have been put forward as a new paradigm for animal search and many cases have been made for its presence in nature. However, it remains debated whether Lévy walks are an inherent behavioural strategy or emerge from the animal reacting to its habitat. Here, we demonstrate signatures of Lévy behaviour in the search movement of mud snails (Hydrobia ulvae) based on a novel, direct assessment of movement properties in an experimental set-up using different food distributions. Our experimental data uncovered clusters of small movement steps alternating with long moves independent of food encounter and landscape complexity. Moreover, size distributions of these clusters followed truncated power laws. These two findings are characteristic signatures of mechanisms underlying inherent Lévy-like movement. Thus, our study provides clear experimental evidence that such multi-scale movement is an inherent behaviour rather than resulting from the animal interacting with its environment.

Ecological theory uses Brownian motion as a default template for describing ecological movement, despite limited mechanistic underpinning. The generality of Brownian motion has recently been challenged by empirical studies that highlight alternative movement patterns of animals, especially when foraging in resource-poor environments. Yet, empirical studies reveal animals moving in a Brownian fashion when resources are abundant. We demonstrate that Einstein's original theory of collision-induced Brownian motion in physics provides a parsimonious, mechanistic explanation for these observations. Here, Brownian motion results from frequent encounters between organisms in dense environments. In density-controlled experiments, movement patterns of mussels shifted from Lévy towards Brownian motion with increasing density. When the analysis was restricted to moves not truncated by encounters, this shift did not occur. Using a theoretical argument, we explain that any movement pattern approximates Brownian motion at high-resource densities, provided that movement is interrupted upon encounters. Hence, the observed shift to Brownian motion does not indicate a density-dependent change in movement strategy but rather results from frequent collisions. Our results emphasize the need for a more mechanistic use of Brownian motion in ecology, highlighting that especially in rich environments, Brownian motion emerges from ecological interactions, rather than being a default movement pattern.

The Arctic is entering a new ecological state, with alarming consequences for humanity. Animal-borne sensors offer a window into these changes. Although substantial animal tracking data from the Arctic and subarctic exist, most are difficult to discover and access. Here, we present the new Arctic Animal Movement Archive (AAMA), a growing collection of more than 200 standardized terrestrial and marine animal tracking studies from 1991 to the present. The AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. With AAMA-based case studies, we document climatic influences on the migration phenology of eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, and species-specific changes in terrestrial mammal movement rates in response to increasing temperature.