Remotely sensed hydroacoustics and observation data for predicting fish habitat suitability

This paper investigates the use of using remotely sensed observation and full coverage hydroacoustic datasets to quantify habitat suitability for a marine demersal fish, the blue-throated wrasse. Because of issues surrounding the detection of species using remotely sensed video techniques, the application of presence-only techniques are well suited for modeling demersal fish habitat suitability. Ecological-Niche Factor Analysis is used to compare analyses conducted using seafloor variables derived from hydroacoustics at three spatial scales; fine (56.25 m²), medium (506.25 m²) and coarse (2756.25 m²), to determine which spatial scale was most influential in predicting blue-throated wrasse habitat suitability. The coarse scale model was found to have the best predictive capabilities with a Boyce Index of 0.80±0.26. The global marginality and specialization values indicated that, irrespective of spatial scale, blue-throated wrasse prefer seafloor characteristics that are different to the mean available within the study site, but exhibit a relatively wide niche. Although variable importance varied over the three spatial scale models, blue-throated wrasse showed a strong preference for regions of shallow water, close to reef, with high rugosity and maximum curvature and low HSI-B values. Generally the spatial patterns in habitat suitability were well represented in the Marine National Park compared to adjacent waters. However, some significant differences in spatial patterns were observed. Interspersion and Juxtaposition Indexes for unsuitable and highly suitable habitat were significantly smaller inside the Marine National Park, while the Mean Shape Index of unsuitable habitat in the Marine National Park was significantly larger.

High individual repeatability and population differentiation in stable isotope ratios in winter-grown collared flycatcher Ficedula albicollis feathers

For migrants, we often lack complete information of their spatial distribution year round. Here, we used stable carbon, nitrogen and hydrogen isotope ratios extracted from feathers grown at the wintering sites of the long-distance migratory collared flycatcher Ficedula albicollis, to study how individuals from different breeding populations are distributed at the wintering sites. A sub-sample of birds was also sampled in two consecutive years to test for the repeatability of isotope ratios. Birds from the same breeding populations had more similar isotope ratios compared to birds from other nearby populations (10–100 km apart). Furthermore, isotope repeatability within individuals was high, implying that the observed pattern of isotope variation is consistent between years. We put forward two hypotheses for these patterns; 1) strong wintering site philopatry and migratory connectivity, suggesting that migratory connectivity may potentially be found on a much smaller spatial scale than previously considered, and 2) consistent interpopulation differentiation of feeding ecology at their wintering site.

Layered dynamic probabilistic networks for spatio-temporal modelling

In applications such as tracking and surveillance in large spatial environments, there is a need for representing dynamic and noisy data and at the same time dealing with them at different levels of detail. In the spatial domain, there has been work dealing with these two issues separately, however, there is no existing common framework for dealing with both of them. In this paper, we propose a new representation framework called the Layered Dynamic Probabilistic Network (LDPN), a special type of Dynamic Probabilistic Network (DPN), capable of handling uncertainty and representing spatial data at various levels of detail. The framework is thus particularly suited to applications in wide-area environments which are characterised by large region size, complex spatial layout and multiple sensors/cameras. For example, a building has three levels: entry/exit to the building, entry/exit between rooms and moving within rooms. To avoid the problem of a relatively large state space associated with a large spatial environment, the LDPN explicitly encodes the hierarchy of connected spatial locations, making it scalable to the size of the environment being modelled. There are three main advantages of the LDPN. First, the reduction in state space makes it suitable for dealing with wide area surveillance involving multiple sensors. Second, it offers a hierarchy of intervals for indexing temporal data. Lastly, the explicit representation of intermediate sub-goals allows for the extension of the framework to easily represent group interactions by allowing coupling between sub-goal layers of different individuals or objects. We describe an adaptation of the likelihood sampling inference scheme for the LDPN, and illustrate its use in a hypothetical surveillance scenario.

Predators select against high growth rates and risk-taking behaviour in domestic trout populations

Domesticated (farm) salmonid fishes display an increased willingness to accept risk while foraging, and achieve high growth rates not observed in nature. Theory predicts that elevated growth rates in domestic salmonids will result in greater risk–taking to access abundant food, but low survival in the presence of predators. In replicated whole–lake experiments, we observed that domestic trout (selected for high growth rates) took greater risks while foraging and grew faster than a wild strain. However, survival consequences for greater growth rates depended upon the predation environment. Domestic trout experienced greater survival when risk was low, but lower survival when risk was high. This suggests that animals with high intrinsic growth rates are selected against in populations with abundant predators, explaining the absence of such phenotypes in nature. This is, to our knowledge, the first large–scale field experiment to directly test this theory and simultaneously quantify the initial invasibility of domestic salmonid strains that escape into the wild from aquaculture operations, and the ecological conditions affecting their survival.

Contextually Learnt Detection of Unusual Motion-Based Behaviour in Crowded Public Spaces

In this paper we are interested in analyzing behaviour in crowded publicplaces at the level of holistic motion. Our aim is to learn, without user input, strong scene priors or labelled data, the scope of ‘‘normal behaviour’’ for a particular scene and thus alert to novelty in unseen footage. The first contribution is a low-level motion model based on what we term tracklet primitives, which are scenespecific elementary motions. We propose a clustering-based algorithm for tracklet estimation from local approximations to tracks of appearance features. This is followed by two methods for motion novelty inference from tracklet primitives: (a) an approach based on a non-hierarchial ensemble of Markov chains as a means of capturing behavioural characteristics at different scales, and (b) a more flexible alternative which exhibits a higher generalizing power by accounting for constraints introduced by intentionality and goal-oriented planning of human motion in a particular scene. Evaluated on a 2 h long video of a busy city marketplace, both algorithms are shown to be successful at inferring unusual behaviour, the latter model achieving better performance for novelties at a larger spatial scale.

Migratory animals couple biodiversity and ecosystem functioning worldwide

Animal migrations span the globe, involving immense numbers of individuals from a wide range of taxa. Migrants transport nutrients, energy, and other organisms as they forage and are preyed upon throughout their journeys. These highly predictable, pulsed movements across large spatial scales render migration a potentially powerful yet underappreciated dimension of biodiversity that is intimately embedded within resident communities. We review examples from across the animal kingdom to distill fundamental processes by which migratory animals influence communities and ecosystems, demonstrating that they can uniquely alter energy flow, food-web topology and stability, trophic cascades, and the structure of metacommunities. Given the potential for migration to alter ecological networks worldwide, we suggest an integrative framework through which community dynamics and ecosystem functioning may explicitly consider animal migrations.

Strong influence of local habitat structure on mammals reveals mismatch with edge effects models

Context: What determines mammal occurrence across wildland-urban edges? A better understanding of the variables involved will help update edge effects theory and improve our ability to conserve biota in urbanizing landscapes. Objectives: For the first time, we tested whether the occurrence of mammals across urban-forest edges and forest interiors was best predicted by: (1) edge variables (i.e. edge type and distance to an urban boundary), (2) local habitat structure (e.g. proportion of understory cover), or (3) edge variables after accounting for local habitat structure. Methods: Using 77 camera stations in South-Eastern Australia, we quantified the factors influencing the occurrence of five native mammals (brown antechinus, bush rat, common brushtail possum, black wallaby and long-nosed bandicoot) and three non-native mammals (red fox, cat, and dog). Results: The occurrence of most native and non-native mammals was best predicted by local habitat structure rather than by edge variables. Although edge variables had effects on most species occurrences, local habitat structure outweighed the impacts of edge effects. Conclusions: Our findings are important for management and urban planning as they suggest that local-scale management of habitat and habitat retention at urban edges will mitigate urban impacts on fauna. Our work reveals a critical mismatch in the spatial scale of predictive variables commonly used in edge effects models (edge types and distance to a boundary) compared with the smaller scale of local habitat variables, which underlie most species occurrence. We emphasize the need to consider heterogeneity within patches in predictive frameworks of edge effects.

Temporal variation in the diet of gentoo penguins at the Falkland Islands

Knowledge of diet is critical in interpreting the ecological roles of marine top predators and provides information towards their conservation and management. The Falkland Islands hold the largest number of breeding gentoo penguins. Yet knowledge of gentoo penguin diet at the Falklands is limited to either broad taxonomic divisions of prey items or dietary samples collected only on a single day. This study is the first to investigate gentoo penguin diet at Cow Bay, Falklands, to the species level, over repeated sampling intervals during the breeding period. Through stomach content analysis, we determined diet over a large temporal scale (2002/2003/2004–2011/2012/2013) and between the guard and crèche periods of chick rearing. The principle prey item by reconstituted mass was rock cod fish Patagonotothen spp., for all periods (47–78 %) except that of the 2012/2013 crèche period (19 %) when Falkland herring Sprattus fugensis made up the bulk of the diet (52 %). Of the cephalopods recovered, Patagonian squid Doryteuthis gahi was prominent (1–24 %), while crustaceans contributed negligibly to gentoo penguin diet. Our findings revealed that gentoo penguins breeding at the Falkland Islands were primarily demersal foragers with an ability for pelagic feeding. Diet choice appears to reflect prey availability.

Selecting umbrella species for conservation: a test of habitat models and niche overlap for beach-nesting birds

Umbrella species are rarely selected systematically from a range of candidate species. On sandy beaches, birds that nest on the upper beach or in dunes are threatened globally and hence are prime candidates for conservation intervention and putative umbrella species status. Here we use a maximum-likelihood, multi-species distribution modeling approach to select an appropriate conservation umbrella from a group of candidate species occupying similar habitats. We identify overlap in spatial extent and niche characteristics among four beach-nesting bird species of conservation concern, American oystercatchers (Haematopus palliatus), black skimmers (Rynchops niger), least terns (Sterna antillarum) and piping plovers (Charadrius melodus), across their entire breeding range in New Jersey, USA. We quantify the benefit and efficiency of using each species as a candidate umbrella on the remaining group. Piping plover nesting habitat encompassed 86% of the least tern habitat but only 15% and 13% of the black skimmer and American oystercatcher habitat, respectively. However, plovers co-occur with all three species across 66% of their total nesting habitat extent (~ 649 ha), suggesting their value as an umbrella at the local scale. American oystercatcher nesting habitat covers 100%, 99% and 47% of piping plover, least tern and black skimmer habitat, making this species more appropriate conservation umbrellas at a regional scale. Our results demonstrate that the choice of umbrella species requires explicit consideration of spatial scale and an understanding of the habitat attributes that an umbrella species represents and to which extent it encompasses other species of conservation interest. Notwithstanding the attractiveness of the umbrella species concept, local conservation interventions especially for breeding individuals in small populations may still be needed.

The SLUGGS survey: a new mask design to reconstruct the stellar populations and kinematics of both inner and outer galaxy regions

Integral field unit spectrographs allow the 2D exploration of the kinematics and stellar populations of galaxies, although they are generally restricted to small fields-of-view. Using the large field-of-view of the DEIMOS multislit spectrograph on Keck and our Stellar Kinematics using Multiple Slits technique, we are able to extract sky-subtracted stellar light spectra to large galactocentric radii. Here, we present a new DEIMOS mask design named SuperSKiMS that explores large spatial scales without sacrificing high spatial sampling. We simulate a set of observations with such a mask design on the nearby galaxy NGC 1023, measuring stellar kinematics and metallicities out to where the galaxy surface brightness is orders of magnitude fainter than the sky. With this technique we also reproduce the results from literature integral field spectroscopy in the innermost galaxy regions. In particular, we use the simulated NGC 1023 kinematics to model its total mass distribution to large radii, obtaining comparable results with those from published integral field unit observation. Finally, from new spectra of NGC 1023, we obtain stellar 2D kinematics and metallicity distributions that show good agreement with integral field spectroscopy results in the overlapping regions. In particular, we do not find a significant offset between our Stellar Kinematics using Multiple Slits and the ATLAS3D stellar velocity dispersion at the same spatial locations.

Conceptual domain of the matrix in fragmented landscapes

In extensively modified landscapes, how the matrix is managed determines many conservation outcomes. Recent publications revise popular conceptions of a homogeneous and static matrix, yet we still lack an adequate conceptual model of the matrix. Here, we identify three core effects that influence patch-dependent species, through impacts associated with movement and dispersal, resource availability, and the abiotic environment. These core effects are modified by five 'dimensions': spatial and temporal variation in matrix quality; spatial scale; temporal scale of matrix variation; and adaptation. The conceptual domain of the matrix, defined as three core effects and their interaction with these five dimensions, provides a much-needed framework to underpin management of fragmented landscapes and highlights new research priorities.

Disrupted fine-scale population processes in fragmented landscapes despite large-scale genetic connectivity for a widespread and common cooperative breeder : the superb fairy-wren (Malurus cyaneus)

Understanding how habitat fragmentation affects population processes (e.g. dispersal) at different spatial scales is of critical importance to conservation. We assessed the effects of habitat fragmentation on dispersal and regional and fine-scale population structure in a currently widespread and common cooperatively breeding bird species found across south-eastern Australia, the superb fairy-wren Malurus cyaneus. Despite its relative abundance and classification as an urban tolerant species, the superb fairy-wren has declined disproportionately from low tree-cover agricultural landscapes across the Box-Ironbark region of north-central Victoria, Australia. Loss of genetic connectivity and disruption to its complex social system may be associated with the decline of this species from apparently suitable habitat in landscapes with low levels of tree cover. To assess whether reduced structural connectivity has had negative consequences for genetic connectivity in the superb fairy-wren, we used a landscape-scale approach to compare patterns of genetic diversity and gene flow at large (landscape/regional) and fine (site-level) spatial scales. In addition, using genetic distances, for each sex, we tested landscape models of decreased dispersal through treeless areas (isolation-by-resistance) while controlling for the effect of isolation-by-distance. Landscape models indicated that larger-scale gene flow across the Box-Ironbark region was constrained by distance rather than by lack of structural connectivity. Nonetheless, a pattern of isolation-by-resistance for males (the less-dispersive sex) and lower genetic diversity and higher genetic similarity within sites in low-cover fragmented landscapes indicated disruption to fine-scale gene flow mechanisms and/or mating systems. Although loss of structural connectivity did not appear to impede gene flow at larger spatial scales, fragmentation appeared to affect fine-scale population processes (e.g. local gene flow mechanisms and/or mating systems) adversely and may contribute to the decline of superb fairy-wrens in fragmented landscapes in the Box-Ironbark region. © 2012 British Ecological Society.

Large-scale statistical modeling of motion patterns : a Bayesian nonparametric approach

We propose a novel framework for large-scale scene understanding in static camera surveillance. Our techniques combine fast rank-1 constrained robust PCA to compute the foreground, with non-parametric Bayesian models for inference. Clusters are extracted in foreground patterns using a joint multinomial+Gaussian Dirichlet process model (DPM). Since the multinomial distribution is normalized, the Gaussian mixture distinguishes between similar spatial patterns but different activity levels (eg. car vs bike). We propose a modification of the decayed MCMC technique for incremental inference, providing the ability to discover theoretically unlimited patterns in unbounded video streams. A promising by-product of our framework is online, abnormal activity detection. A benchmark video and two surveillance videos, with the longest being 140 hours long are used in our experiments. The patterns discovered are as informative as existing scene understanding algorithms. However, unlike existing work, we achieve near real-time execution and encouraging performance in abnormal activity detection.