Inferring the rules of social interaction in migrating caribou

Social interactions are a significant factor that influence the decision-making of species ranging from humans to bacteria. In the context of animal migration, social interactions may lead to improved decision-making, greater ability to respond to environmental cues, and the cultural transmission of optimal routes. Despite their significance, the precise nature of social interactions in migrating species remains largely unknown. Here we deploy unmanned aerial systems to collect aerial footage of caribou as they undertake their migration from Victoria Island to mainland Canada. Through a Bayesian analysis of trajectories we reveal the fine-scale interaction rules of migrating caribou and show they are attracted to one another and copy directional choices of neighbours, but do not interact through clearly defined metric or topological interaction ranges. By explicitly considering the role of social information on movement decisions we construct a map of near neighbour influence that quantifies the nature of information flow in these herds. These results will inform more realistic, mechanism-based models of migration in caribou and other social ungulates, leading to better predictions of spatial use patterns and responses to changing environmental conditions. Moreover, we anticipate that the protocol we developed here will be broadly applicable to study social behaviour in a wide range of migratory and non-migratory taxa. This article is part of the theme issue ‘Collective movement ecology’

Status of the Major Oyster Diseases in Virginia 2003 A Summary of the Annual Monitoring Program

Low temperatures and salinities brought abatement in the oyster diseases caused by Perkinsus marinus (Dermo) and Haplosporidium nelsoni (MSX) for the first time since 1998. In the James River, P. marinus prevalences were the lowest they had been since 1998. In summer and fall, when P. marinus is normally most prevalent, it was found in a maximum of 72% of oysters at Wreck Shoal and in less than half the oysters at Horsehead Rock and Point of Shoals. Advanced infections were very rare. Haplosporidium nelsoni had disappeared completely from quarterly James River samples by Jul

Retrieval cues fail to influence contextualized evaluations.

Initial evaluations generalise to new contexts, whereas counter-attitudinal evaluations are context-specific. Counter-attitudinal information may not change evaluations in new contexts because perceivers fail to retrieve counter-attitudinal cue-evaluation associations from memory outside the counter-attitudinal learning context. The current work examines whether an additional, counter-attitudinal retrieval cue can enhance the generalizability of counter-attitudinal evaluations. In four experiments, participants learned positive information about a target person, Bob, in one context, and then learned negative information about Bob in a different context. While learning the negative information, participants wore a wristband as a retrieval cue for counter-attitudinal Bob-negative associations. Participants then made speeded as well as deliberate evaluations of Bob while wearing or not wearing the wristband. Internal meta-analysis failed to find a reliable effect of the counter-attitudinal retrieval cue on speeded or deliberate evaluations, whereas the context cues influenced speeded and deliberate evaluations. Counter to predictions, counter-attitudinal retrieval cues did not disrupt the generalisation of first-learned evaluations or the context-specificity of second-learned evaluations (Experiments 2-4), but the counter-attitudinal retrieval cue did influence evaluations in the absence of context cues (Experiment 1). The current work provides initial evidence that additional counter-attitudinal retrieval cues fail to disrupt the renewal and generalizability of first-learned evaluations

Linking Resilience Theory and Diffusion of Innovations Theory to Understand the Potential for Perennials in the U.S. Corn Belt

In the last 200 yr, more than 80% of the land in the U.S. Corn Belt agro-ecosystem has been converted from natural perennial vegetation to intensive agricultural production of row crops. Despite research showing how re-integration of perennial vegetation, e.g., cover crops, pasture, riparian buffers, and restored wetlands, at strategic landscape positions can bolster declining regional ecosystem functions, the amount of land area devoted to row crop production in the Corn Belt continues to increase. As this region enters a time of fast-paced and uncertain reorganization driven by the emerging bioeconomy, changes in land use will continue to take place that will impact the resilience of the Corn Belt’s linked social and ecological systems for years to come. Both resilience theory and the diffusion of innovations theory investigate how change is brought about in systems through the adaptation and innovation of social actors. In this paper, we integrate these two frameworks in the analysis of 33 in-depth interviews to improve our understanding of how rural Corn Belt stakeholders make conservation decisions in the midst of an uncertain future. Interview data indicate that the adoption of conservation practices is based not only on immediate profitability but also on the interplay between contextual factors at three distinct levels of the system: compatibility with farm priorities, profitability, practices, and technologies; community-level reinforcement through local social networks, norms, and support structures; and consistent, straightforward, flexible, and well-targeted incentives and regulations issuing from regional institutions. Interviewees suggest that the multiscale drivers that currently support the continued expansion of row crop production could be realigned with conservation objectives in landscapes of the future. Adaptation of social actors through collaborative learning at the community level may be instrumental in brokering the sort of multiscale system change that would lead to more widespread adoption of perennial cover types in the Corn Belt