Robin Freeman

What are leaders made of? The role of individual experience in determining leader–follower relations in homing pigeons

Negotiating joint routes during group travel is one of the challenges faced by collectively moving animals, on spatial scales ranging from daily foraging trips to long-distance migrations. Homing pigeons, Columba livia, provide a useful model system for studying the mechanisms of group decision making in the context of navigation, owing to the combination of their gregarious nature and the depth of our understanding of their individual orientational strategies. Previous work has shown that during paired flight, if two birds’ individually preferred routes are sufficiently different, one bird will emerge as leader whom the other follows. What determines the identity of a leader has important implications for the efficiency of a moving collective, since leaders with higher navigational certainty can increase the accuracy of the group. We examined factors contributing to the establishment of leadership/followership, focusing on the role of previous navigational experience. We tested, on a homing task, pairs of pigeons in which the two partners had relatively greater and lesser prior experience, generated through individual training. Analysis of the GPS-tracked routes taken by such pairs revealed a negative correlation between homing experience and the probability that a pigeon would follow a co-navigating partner. Thus, the larger the difference in experience between two partners, the higher the likelihood the more experienced bird would emerge as leader. Our results contribute to a better understanding of the mechanisms and potential payoffs of collective navigational decision making in species that travel in mixed-experience groups.

Emerging Network-Based Tools in Movement Ecology

New technologies have vastly increased the available data on animal movement and behaviour. Consequently, new methods deciphering the spatial and temporal interactions between individuals and their environments are vital. Network analyses offer a powerful suite of tools to disentangle the complexity within these dynamic systems, and we review these tools, their application, and how they have generated new ecological and behavioural insights. We suggest that network theory can be used to model and predict the influence of ecological and environmental parameters on animal movement, focusing on spatial and social connectivity, with fundamental implications for conservation. Refining how we construct and randomise spatial networks at different temporal scales will help to establish network theory as a prominent, hypothesis-generating tool in movement ecology.

Wireless Sensor Network for habitat monitoring on Skomer Island

In the natural sciences, research often relies on extensive manual investigation. Such methods can be error-prone and obviously dont scale well. The development of autonomous data acquisition systems such as Wireless Sensor Networks (WSN) has provided a method to significantly reduce manual work and, as such, has the potential to enable researchers to address previously infeasible scientific questions. However, making the transition from WSN deployments in a laboratory to real-world deployments is still very challenging. Creating robust, error-free systems that are able to run autonomously in real-world environments without manual supervision has proven to be complex and, therefore, the number of successful collaborations between computer scientists and natural scientists is still limited. Here, we describe our successful attempt to design and deploy a WSN to monitor seabirds on Skomer Island, a UK National Nature Reserve. We summarize the evolution of the system over a period of three years, share insights on selected design decisions, and discuss both, our experience and the problems we have encountered.

Information visualisation for science and policy: engaging users and avoiding bias

Visualisations and graphics are fundamental to studying complex subject matter. However, beyond acknowledging this value, scientists and science-policy programmes rarely consider how visualisations can enable discovery, create engaging and robust reporting, or support online resources. Producing accessible and unbiased visualisations from complicated, uncertain data requires expertise and knowledge from science, policy, computing, and design. However, visualisation is rarely found in our scientific training, organisations, or collaborations. As new policy programmes develop [e.g., the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES)], we need information visualisation to permeate increasingly both the work of scientists and science policy. The alternative is increased potential for missed discoveries, miscommunications, and, at worst, creating a bias towards the research that is easiest to display.

Ten Simple Rules for Effective Computational Research

In order to attempt to understand the complexity inherent in nature, mathematical, statistical and computational techniques are increasingly being employed in the life sciences. In particular, the use and development of software tools is becoming vital for investigating scientific hypotheses, and a wide range of scientists are finding software development playing a more central role in their day-to-day research. In fields such as biology and ecology, there has been a noticeable trend towards the use of quantitative methods for both making sense of ever-increasing amounts of data [1] and building or selecting models [2]. n nAs Research Fellows of the “2020 Science” project (http://www.2020science.net), funded jointly by the EPSRC (Engineering and Physical Sciences Research Council) and Microsoft Research, we have firsthand experience of the challenges associated with carrying out multidisciplinary computation-based science [3]–[5]. In this paper we offer a jargon-free guide to best practice when developing and using software for scientific research. While many guides to software development exist, they are often aimed at computer scientists [6] or concentrate on large open-source projects [7]; the present guide is aimed specifically at the vast majority of scientific researchers: those without formal training in computer science. We present our ten simple rules with the aim of enabling scientists to be more effective in undertaking research and therefore maximise the impact of this research within the scientific community. While these rules are described individually, collectively they form a single vision for how to approach the practical side of computational science. n nOur rules are presented in roughly the chronological order in which they should be undertaken, beginning with things that, as a computational scientist, you should do before you even think about writing any code. For each rule, guides on getting started, links to relevant tutorials, and further reading are provided in the supplementary material (Text S1).

Lower foraging efficiency in immatures drives spatial segregation with breeding adults in a long-lived pelagic seabird

Competition and, ultimately, adaptive specialization are the major ecological forces behind spatial segregation in foraging distributions, and are commonly driven by size-related differences in competitiveness between individuals of different sex, age or social status. However, such segregation can also be observed in long-lived monomorphic species, often between immature and breeding individuals. In many of these species, individuals often forage in patchy and potentially unpredictable environments in which resources can be spread over large scales and be difficult to find, and efficient foraging may require advanced cognitive skills (for example in navigation and memory). Particularly in species with deferred breeding, experience rather than size may be an important driver of segregation and may lead to differences in competitiveness between young and old, but whether there is a relationship between age, foraging efficiency and spatial segregation has never been properly investigated. Here we tested this hypothesis by simultaneously tracking individuals at different life stages in a long-lived seabird, the Manx shearwater, Puffinus puffinus, during a period of central-place foraging around the colony, to investigate spatial segregation, and by measuring foraging efficiency by combining an ethoinformatics approach and mass gain. We found substantial spatial segregation between immature and breeding adults. Compared with adults, immatures gained less mass per unit of time spent foraging and foraged in less productive waters, suggesting lower foraging efficiency, probably because of inexperience.

A generalised random encounter model for estimating animal density with remote sensor data

Summary Wildlife monitoring technology is advancing rapidly and the use of remote sensors such as camera traps and acoustic detectors is becoming common in both the terrestrial and marine environments. Current methods to estimate abundance or density require individual recognition of animals or knowing the distance of the animal from the sensor, which is often difficult. A method without these requirements, the random encounter model (REM), has been successfully applied to estimate animal densities from count data generated from camera traps. However, count data from acoustic detectors do not fit the assumptions of the REM due to the directionality of animal signals. We developed a generalised REM (gREM), to estimate absolute animal density from count data from both camera traps and acoustic detectors. We derived the gREM for different combinations of sensor detection widths and animal signal widths (a measure of directionality). We tested the accuracy and precision of this model using simulations of different combinations of sensor detection widths and animal signal widths, number of captures and models of animal movement. We find that the gREM produces accurate estimates of absolute animal density for all combinations of sensor detection widths and animal signal widths. However, larger sensor detection and animal signal widths were found to be more precise. While the model is accurate for all capture efforts tested, the precision of the estimate increases with the number of captures. We found no effect of different animal movement models on the accuracy and precision of the gREM. We conclude that the gREM provides an effective method to estimate absolute animal densities from remote sensor count data over a range of sensor and animal signal widths. The gREM is applicable for count data obtained in both marine and terrestrial environments, visually or acoustically (e.g. big cats, sharks, birds, echolocating bats and cetaceans). As sensors such as camera traps and acoustic detectors become more ubiquitous, the gREM will be increasingly useful for monitoring unmarked animal populations across broad spatial, temporal and taxonomic scales.

Modelling group navigation: transitive social structures improve navigational performance.

Collective navigation demands that group members reach consensus on which path to follow, a task that might become more challenging when the groups members have different social connections. Group decision-making mechanisms have been studied successfully in the past using individual-based modelling, although many of these studies have neglected the role of social connections between the groups interacting members. Nevertheless, empirical studies have demonstrated that individual recognition, previous shared experiences and inter-individual familiarity can influence the cohesion and the dynamics of the group as well as the relative spatial positions of specific individuals within it. Here, we use models of collective motion to study the impact of social relationships on group navigation by introducing social network structures into a model of collective motion. Our results show that groups consisting of equally informed individuals achieve the highest level of accuracy when they are hierarchically organized with the minimum number of preferred connections per individual. We also observe that the navigational accuracy of a group will depend strongly on detailed aspects of its social organization. More specifically, group navigation does not only depend on the underlying social relationships, but also on how much weight leading individuals put on following others. Also, we show that groups with certain social structures can compensate better for an increased level of navigational error. The results have broader implications for studies on collective navigation and motion because they show that only by considering a groups social system can we fully elucidate the dynamics and advantages of joint movements.

The Diversity-Weighted Living Planet Index: Controlling for Taxonomic Bias in a Global Biodiversity Indicator

As threats to species continue to increase, precise and unbiased measures of the impact these pressures are having on global biodiversity are urgently needed. Some existing indicators of the status and trends of biodiversity largely rely on publicly available data from the scientific and grey literature, and are therefore prone to biases introduced through over-representation of well-studied groups and regions in monitoring schemes. This can give misleading estimates of biodiversity trends. Here, we report on an approach to tackle taxonomic and geographic bias in one such indicator (Living Planet Index) by accounting for the estimated number of species within biogeographical realms, and the relative diversity of species within them. Based on a proportionally weighted index, we estimate a global population decline in vertebrate species between 1970 and 2012 of 58% rather than 20% from an index with no proportional weighting. From this data set, comprising 14,152 populations of 3,706 species from 3,095 data sources, we also find that freshwater populations have declined by 81%, marine populations by 36%, and terrestrial populations by 38% when using proportional weighting (compared to trends of -46%, +12% and +15% respectively). These results not only show starker declines than previously estimated, but suggests that those species for which there is poorer data coverage may be declining more rapidly.

Pairs of pigeons act as behavioural units during route learning and co-navigational leadership conflicts

SUMMARY In many species, group members obtain benefits from moving collectively, such as enhanced foraging efficiency or increased predator detection. In situations where the groups decision involves integrating individual preferences, group cohesion can lead to more accurate outcomes than solitary decisions. In homing pigeons, a classic model in avian orientation studies, individuals learn habitual routes home, but whether and how co-navigating birds acquire and share route-based information is unknown. Using miniature GPS loggers, we examined these questions by first training pairs (the smallest possible flocks) of pigeons together, and then releasing them with other pairs that had received separate pair-training. Our results show that, much like solitary individuals, pairs of birds are able to establish idiosyncratic routes that they recapitulate together faithfully. Also, when homing with other pairs they exhibit a transition from a compromise- to a leadership-like mechanism of conflict resolution as a function of the degree of disagreement (distance separating the two preferred routes) between the two pairs, although pairs tolerate a greater range of disagreements prior to the transition than do single birds. We conclude that through shared experiences during past decision-making, pairs of individuals can become units so closely coordinated that their behaviour resembles that of single birds. This has implications for the behaviour of larger groups, within which certain individuals have closer social affiliations or share a history of previous associations.