Determining trends and environmental drivers from long-term marine mammal and seabird data : examples from Southern Australia

Climate change is acknowledged as an emerging threat for top-order marine predators, yet obtaining evidence of impacts is often difficult. In south-eastern Australia, a marine global warming hotspot, evidence suggests that climate change will profoundly affect pinnipeds and seabirds. Long-term data series are available to assess some species' responses to climate. Researchers have measured a variety of chronological and population variables, such as laying dates, chick or pup production, colony-specific abundance and breeding success. Here, we consider the challenges in accurately assessing trends in marine predator data, using long-term data series that were originally collected for other purposes, and how these may be driven by environmental change and variability. In the past, many studies of temporal changes and environmental drivers used linear analyses and we demonstrate the (theoretical) relationship between the magnitude of a trend, its variability, and the duration of a data series required to detect a linear trend. However, species may respond to environmental change in a nonlinear manner and, based on analysis of time-series from south-eastern Australia, it appears that the assumptions of a linear model are often violated, particularly for measures of population size. The commonly measured demographic variables exhibit different degrees of variation, which influences the ability to detect climate signals. Due to their generally lower year-to-year variability, we illustrate that monitoring of variables such as mass and breeding chronology should allow detection of temporal trends earlier in a monitoring programme than observations of breeding success and population size. Thus, establishing temporal changes with respect to climate change from a monitoring programme over a relatively short time period requires careful a priori choice of biological variables. © 2014 Springer-Verlag Berlin Heidelberg.

The top 10 oomycete pathogens in molecular plant pathology

Oomycetes form a deep lineage of eukaryotic organisms that includes a large number of plant pathogens which threaten natural and managed ecosystems. We undertook a survey to query the community for their ranking of plant-pathogenic oomycete species based on scientific and economic importance. In total, we received 263 votes from 62 scientists in 15 countries for a total of 33 species. The Top 10 species and their ranking are: (1) Phytophthora infestans; (2, tied) Hyaloperonospora arabidopsidis; (2, tied) Phytophthora ramorum; (4) Phytophthora sojae; (5) Phytophthora capsici; (6) Plasmopara viticola; (7) Phytophthora cinnamomi; (8, tied) Phytophthora parasitica; (8, tied) Pythium ultimum; and (10) Albugo candida. This article provides an introduction to these 10 taxa and a snapshot of current research. We hope that the list will serve as a benchmark for future trends in oomycete research.

Towards a cohesive, holistic view of top predation: a definition, synthesis and perspective

Research on the ecology of top predators - upper trophic level consumers that are relatively free from predation once they reach adult size - has provided regular contributions to general ecology and is a rapidly expanding and increasingly experimental, multidisciplinary and technological endeavour. Yet, an exponentially expanding literature coupled with rapid disintegration into specialized, disconnected subfields for study (e.g. vertebrate predators versus invertebrate predators, community ecology versus biological control etc.) increasingly means that we are losing a coherent, integrated understating of the role and importance of these species in ecosystems. This process of canalization is likely to hinder sharing of scientific discovery and continued progress, especially as there is a growing need to understand the generality of the top-down forcing, as demonstrated for some members of this group. Here, we propose ways to facilitate synthesis by promoting changes in mentality and awareness among specialists through increased debate and collaboration, conceptual reviews and a series of exemplary case studies. The strategy will rely on the collective contribution by all scientists in the field and will strive to consolidate and formalise top-order predation as a holistic, cohesive, cross-taxonomical field of research studying the ecology, evolution and behaviour of apex predators and their capability to exert top-down forcing on lower trophic levels. © 2014 The Authors.

Mining mechanism of top-k influential nodes based on voting algorithm in mobile social networks

In recent years, evaluating the influence of nodes and finding top-k influential nodes in social networks, has drawn a wide attention and has become a hot-pot research issue. Considering the characteristics of social networks, we present a novel mechanism to mine the top-k influential nodes in mobile social networks. The proposed mechanism is based on the behaviors analysis of SMS/MMS (simple messaging service / multimedia messaging service) communication between mobile users. We introduce the complex network theory to build a social relation graph, which is used to reveal the relationship among people's social contacts and messages sending. Moreover, intimacy degree is also introduced to characterize social frequency among nodes. Election mechanism is hired to find the most influential node, and then a heap sorting algorithm is used to sort the voting results to find the k most influential nodes. The experimental results show that the mechanism can finds out the most influential top-k nodes efficiently and effectively. © 2013 IEEE.

Leaning too far? PISA, policy and Australia's ‘top five’ ambitions

 Australia has declared its ambition to be within the ‘top five’ in the Programme for International Student Assessment (PISA) by 2025. So serious is it about this ambition, that the Australian Government has incorporated it into the Australian Education Act, 2013. Given this focus on PISA results and rankings, we go beyond average scores to take a close look at Australia’s performance in PISA, examining rankings by different geographical units, by item content and by test completion. Based on this analysis and using data from interviews with measurement and policy experts, we show how uninformative and even misleading the ‘average performance scores’, on which the rankings are based, can be. We explore how a more nuanced understanding would point to quite different policy actions. After considering the PISA data and Australia’s ‘top five’ ambition closely, we argue that neither the rankings nor such ambitions should be given much credence.

Diving deeper into individual foraging specializations of a large marine predator, the southern sea lion

Despite global declines in the abundance of marine predators, knowledge of foraging ecology, necessary to predict the ecological consequences of large changes in marine predator abundance, remains enigmatic for many species. Given that populations suffering severe declines are of conservation concern, we examined the foraging ecology of southern sea lions (SSL) (Otaria flavescens)-one of the least studied otariids (fur seal and sea lions)-which have declined by over 90 % at the Falkland Islands since the 1930s. Using a combination of biologging devices and stable isotope analysis of vibrissae, we redress major gaps in the knowledge of SSL ecology and quantify patterns of individual specialization. Specifically, we revealed two discrete foraging strategies, these being inshore (coastal) and offshore (outer Patagonian Shelf). The majority of adult female SSL (72 % or n = 21 of 29 SSL) foraged offshore. Adult female SSL that foraged offshore travelled further (92 ± 20 vs. 10 ± 4 km) and dived deeper (75 ± 23 vs. 21 ± 8 m) when compared to those that foraged inshore. Stable isotope analysis revealed long-term fidelity (years) to these discrete foraging habitats. In addition, we found further specialization within the offshore group, with adult female SSL separated into two clusters on the basis of benthic or mixed (benthic and pelagic) dive behavior (benthic dive proportion was 76 ± 9 vs. 51 ± 8 %, respectively). We suggest that foraging specialization in depleted populations such as SSL breeding at the Falkland Islands, are influenced by foraging site fidelity, and could be independent of intraspecific competition. Finally, the behavioral differences we describe are crucial to understanding population-level dynamics, impediments to population recovery, and threats to population persistence.

Benefits of group foraging depend on prey type in a small marine predator, the Little Penguin

Group foraging provides predators with advantages in over-powering prey larger than themselves or in aggregating small prey for efficient exploitation. For group-living predatory species, cooperative hunting strategies provide inclusive fitness benefits. However, for colonial-breeding predators, the benefit pay-offs of group foraging are less clear due to the potential for intra-specific competition. We used animal-borne cameras to determine the prey types, hunting strategies, and success of little penguins (Eudyptula minor), a small, colonial breeding air-breathing marine predator that has recently been shown to display extensive at-sea foraging associations with conspecifics. Regardless of prey type, little penguins had a higher probability of associating with conspecifics when hunting prey that were aggregated than when prey were solitary. In addition, success was greater when individuals hunted schooling rather than solitary prey. Surprisingly, however, success on schooling prey was similar or greater when individuals hunted on their own than when with conspecifics. These findings suggest individuals may be trading-off the energetic gains of solitary hunting for an increased probability of detecting prey within a spatially and temporally variable prey field by associating with conspecifics.

Can camera traps monitor Komodo dragons a large ectothermic predator?

Camera trapping has greatly enhanced population monitoring of often cryptic and low abundance apex carnivores. Effectiveness of passive infrared camera trapping, and ultimately population monitoring, relies on temperature mediated differences between the animal and its ambient environment to ensure good camera detection. In ectothermic predators such as large varanid lizards, this criterion is presumed less certain. Here we evaluated the effectiveness of camera trapping to potentially monitor the population status of the Komodo dragon (Varanus komodoensis), an apex predator, using site occupancy approaches. We compared site-specific estimates of site occupancy and detection derived using camera traps and cage traps at 181 trapping locations established across six sites on four islands within Komodo National Park, Eastern Indonesia. Detection and site occupancy at each site were estimated using eight competing models that considered site-specific variation in occupancy (ψ)and varied detection probabilities (p) according to detection method, site and survey number using a single season site occupancy modelling approach. The most parsimonious model [ψ (site), p (site survey); ω = 0.74] suggested that site occupancy estimates differed among sites. Detection probability varied as an interaction between site and survey number. Our results indicate that overall camera traps produced similar estimates of detection and site occupancy to cage traps, irrespective of being paired, or unpaired, with cage traps. Whilst one site showed some evidence detection was affected by trapping method detection was too low to produce an accurate occupancy estimate. Overall, as camera trapping is logistically more feasible it may provide, with further validation, an alternative method for evaluating long-term site occupancy patterns in Komodo dragons, and potentially other large reptiles, aiding conservation of this species.

A learning method for Top-N recommendations with incomplete data

In this paper, we tackle the incompleteness of user rating history in the context of collaborative filtering for Top-N recommendations. Previous research ignore a fact that two rating patterns exist in the user × item rating matrix and influence each other. More importantly, their interactive influence characterizes the development of each other, which can consequently be exploited to improve the modelling of rating patterns, especially when the user × item rating matrix is highly incomplete due to the well-known data sparsity issue. This paper proposes a Rating Pattern Subspace to iteratively re-optimize the missing values in each user’s rating history by modelling both the global and the personal rating patterns simultaneously. The basic idea is to project the user × item rating matrix on a low-rank subspace to capture the global rating patterns. Then, the projection of each individual user on the subspace is further optimized according to his/her own rating history and the captured global rating patterns. Finally, the optimized user projections are used to improve the modelling of the global rating patterns. Based on this subspace, we propose a RapSVD-L algorithm for Top-N recommendations. In the experiments, the performance of the proposed method is compared with the state-of-the-art Top-N recommendation methods on two real datasets under various data sparsity levels. The experimental results show that RapSVD-L outperforms the compared algorithms not only on the all items recommendations but also on the long tail item recommendations in terms of accuracy.

Modelling personal preferences for Top-N movie recommendations

Modelling the temporal dynamics of personal preferences is still under-developed despite the rapid development of personalization. In this paper, we observe that the user preference styles tend to change regularly following certain patterns in the context of movie recommendation systems. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N movie recommendations. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation-Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N movie recommendations in terms of accuracy.