Satellite tracking reveals unusual diving characteristics for a marine reptile, the olive ridley turtle : Lepidochelys olivacea

The movements, diving behaviour and thermal environment occupied by 4 adult female olive ridley turtles Lepidochelys olivacea in northern Australia were determined through satellite telemetry. Patterns of behaviour recorded were rather unusual compared to other sea turtles in that dives were mainly deep, largely benthic and exceptionally long (>2 h) in some cases, characteristics typical of over-wintering turtles in colder environments. One individual occupied shallow coastal foraging zones, while the others foraged far from land (probably on the seabed) in relatively deep water (>100 m). Individuals performed long dives (frequently >100 min), but from the short post-dive intervals we suggest that these dives were mainly aerobic. Maximum dive depth recorded was 200 ± 20 m (mean maximum depths ranged from 20.1 to 46.7 m across individuals; n = 17328 dives in total; depths ≥3 m were considered ‘dives’) and the maximum duration was 200 ± 20 min (mean durations ranged from 24.5 to 48.0 min across individuals). Temperature profiles indicate that turtles experienced temperatures ranging from 23 to 29°C at the surface, with the lowest temperature recorded (18.7°C) at a depth of 98 m. Only 6.9% of the dives were in water <20°C. From time-allocation at depth (TAD) scores, we demonstrated that many dives reaching the known or inferred sea bottom were U-shaped, but there was no apparent diel signal in dive depth. This suggests that many benthic dives were not associated exclusively with resting behaviour and likely had a foraging component as well. The ability to perform long benthic dives allows this species to exploit deeper benthic environments in addition to the shallow coastal areas more generally occupied by adult hard-shelled sea turtles (e.g. green and hawksbill turtles). Deep benthic dives also occur in certain marine mammals (e.g. narwhals) and sea birds (e.g. rockhopper penguins) and therefore seem to be a general foraging strategy exploited by animals that can perform long dives.

Inferring activity budgets in wild animals to estimate the consequences of disturbances

Activity budgets can provide a direct link to an animal's bioenergetic budget and is thus a valuable unit of measure when assessing human-induced nonlethal effects on wildlife conservation status. However, activity budget inference can be challenging for species that are difficult to observe and require multiple observational variables. Here, we assessed whether whalewatching boat interactions could affect the activity budgets of minke whales (Balaenoptera acutorostrata). We used a stepwise modeling approach to quantitatively record, identify, and assign activity states to continuous behavioral time series data, to estimate activity budgets. First, we used multiple behavioral variables, recorded from continuous visual observations of individual animals, to quantitatively identify and define behavioral types. Activity states were then assigned to each sampling unit, using a combination of hidden and observed states. Three activity states were identified: nonfeeding, foraging, and surface feeding (SF). From the resulting time series of activity states, transition probability matrices were estimated using first-order Markov chains. We then simulated time series of activity states, using Monte Carlo methods based on the transition probability matrices, to obtain activity budgets, accounting for heterogeneity in state duration. Whalewatching interactions reduced the time whales spend foraging and SF, potentially resulting in an overall decrease in energy intake of 42%. This modeling approach thus provides a means to link short-term behavioral changes resulting from human disturbance to potential long-term bioenergetic consequences in animals. It also provides an analytical framework applicable to other species when direct observations of activity states are not possible.

Negotiating place and space in a school-wide curriculum innovation: a place for the animals

School-wide curriculum innovations are complex fields of practice, held together by a cast of heterogeneous actors who put various and diverse discourses to work in their everyday efforts to shape their work. This paper draws upon ethnographic data collected in a large regional primary school that since the beginning of 2012 has implemented a school-wide science specialism. In this paper, we focus on one feature of the initiative - classroom animals. We explore the discursive construction of ‘classroom animals' in relation to teachers work and student learning, framing our discussion around three dichotomies found in the data that raise questions about: the nature of science education; what it means to be a good teacher in this context; and, what it means to be a good classroom animal. Tensions between canonical, disciplinary approaches to science education and broader, cross-curricular and critical approaches contribute to the broad context of this paper, and implications for embedding science teaching and learning in the everyday work of primary teachers and students are discussed.

Integrating multibeam backscatter angular response, mosaic and bathymetry data for benthic habitat mapping

Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management.

Quantification of spatial and thematic uncertainty in the application of underwater video for benthic habitat mapping

This study presents an analysis of the application of underwater video data collected for training and validating benthic habitat distribution models. Specifically, we quantify the two major sources of error pertaining to collection of this type of reference data. A theoretical spatial error budget is developed for a positioning system used to co-register video frames to their corresponding locations at the seafloor. Second, we compare interpretation variability among trained operators assessing the same video frames between times over three hierarchical levels of a benthic classification scheme. Propagated error in the positioning system described was found to be highly correlated with depth of operation and varies from 1.5m near the surface to 5.7m in 100m of water. In order of decreasing classification hierarchy, mean overall observer agreement was found to be 98% (range 6%), 82% (range 12%) and 75% (range 17%) for the 2, 4, and 6 class levels of the scheme, respectively. Patterns in between-observer variation are related to the level of detail imposed by each hierarchical level of the classification scheme, the feature of interest, and to the amount of observer experience. © 2014 Copyright © Taylor & Francis Group, LLC.

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.