Newly emerging plants provide the best forage for herbivores. To exploit this fleeting resource, migrating herbivores align their movements to surf the wave of spring green-up. With new technology to track migrating animals, the Green Wave Hypothesis has steadily gained empirical support across a diversity of migratory taxa. This hypothesis assumes the green wave is controlled by variation in climate, weather, and topography, and its progression dictates the timing, pace, and extent of migrations. However, aggregate grazers that are also capable of engineering grassland ecosystems make some of the world's most impressive migrations, and it is unclear how the green wave determines their movements. Here we show that Yellowstone's bison (Bison bison) do not choreograph their migratory movements to the wave of spring green-up. Instead, bison modify the green wave as they migrate and graze. While most bison surfed during early spring, they eventually slowed and let the green wave pass them by. However, small-scale experiments indicated that feedback from grazing sustained forage quality. Most importantly, a 6-fold decadal shift in bison density revealed that intense grazing caused grasslands to green up faster, more intensely, and for a longer duration. Our finding broadens our understanding of the ways in which animal movements underpin the foraging benefit of migration. The widely accepted Green Wave Hypothesis needs to be revised to include large aggregate grazers that not only move to find forage, but also engineer plant phenology through grazing, thereby shaping their own migratory movements.
Limited mapping of migrations hampers conservation
Summary Prey switching occurs when a generalist predator kills disproportionately more of an abundant prey species and correspondingly spares a rarer species. Although this behaviour is a classic stabilizing mechanism in food web models, little is known about its operation in free‐living systems which often include dangerous prey species that resist predation. We used long‐term (1995–2015) data from a large mammal system in northern Yellowstone National Park, USA, to understand how prey preference of a wild, generalist predator (Canis lupus) responds to a shift in prey species evenness involving rising numbers of dangerous prey (Bison bison) and dropping numbers of relatively safer prey (Cervus elaphus). Contrary to the prey switching hypothesis, wolves attacked and killed disproportionately more of the rarer, but safer, species. Wolves maintained a strong preference against bison even when this species was nearly twice as abundant as elk. [Correction added after online publication on 26 April 2017: ‘more than’ changed to ‘nearly’]. There was also evidence that wolves were increasingly averse to hunting bison as relative bison abundance increased. Wolves seldom hunted bison because capture success was limited to a narrow set of conditions: larger packs (>11 wolves) chasing smaller herds (10–20 bison) with calves. Wolves scavenged bison carrion instead and did so more frequently as bison abundance increased. Our study demonstrates the overarching importance of prey vulnerability to understanding the prey preferences of generalist predators in ecological communities with dangerous prey. The formidable defences of such prey diminish the potential for switching and its stabilizing influence on population dynamics. In these communities, shifts from hunting to scavenging are perhaps more likely than shifts in prey preference. The assumption of switching may therefore overestimate the stability of multi‐prey systems that include dangerous prey species. A http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.12866/suppinfo is available for this article.
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Abstract. The bison (Bison bison) of the Yellowstone ecosystem, USA, exemplify the difficulty of conserving large mammals that migrate across the boundaries of conservation areas. Bison are infected with brucellosis (Brucella abortus) and their seasonal movements can expose livestock to infection. Yellowstone National Park has embarked on a program of adaptive management of bison, which requires a model that assimilates data to support management decisions. We constructed a Bayesian state-space model to reveal the influence of brucellosis on the Yellowstone bison population. A frequency-dependent model of brucellosis transmission was superior to a density-dependent model in predicting out-of-sample observations of horizontal transmission probability. A mixture model including both transmission mechanisms converged on frequency dependence. Conditional on the frequency-dependent model, brucellosis median transmission rate was 1.87 yr À1 . The median of the posterior distribution of the basic reproductive ratio (R 0 ) was 1.75. Seroprevalence of adult females varied around 60% over two decades, but only 9.6 of 100 adult females were infectious. Brucellosis depressed recruitment; estimated population growth rate k averaged 1.07 for an infected population and 1.11 for a healthy population. We used five-year forecasting to evaluate the ability of different actions to meet management goals relative to no action. Annually removing 200 seropositive female bison increased by 30-fold the probability of reducing seroprevalence below 40% and increased by a factor of 120 the probability of achieving a 50% reduction in transmission probability relative to no action. Annually vaccinating 200 seronegative animals increased the likelihood of a 50% reduction in transmission probability by fivefold over no action. However, including uncertainty in the ability to implement management by representing stochastic variation in the number of accessible bison dramatically reduced the probability of achieving goals using interventions relative to no action. Because the width of the posterior predictive distributions of future population states expands rapidly with increases in the forecast horizon, managers must accept high levels of uncertainty. These findings emphasize the necessity of iterative, adaptive management with relatively short-term commitment to action and frequent reevaluation in response to new data and model forecasts. We believe our approach has broad applications.
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