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Recently, physics-informed neural networks (PINNs) have offered a powerful new paradigm for solving problems relating to differential equations. Compared to classical numerical methods, PINNs have several advantages, for example their ability to provide mesh-free solutions of differential equations and their ability to carry out forward and inverse modelling within the same optimisation problem. Whilst promising, a key limitation to date is that PINNs have struggled to accurately and efficiently solve problems with large domains and/or multi-scale solutions, which is crucial for their real-world application. Multiple significant and related factors contribute to this issue, including the increasing complexity of the underlying PINN optimisation problem as the problem size grows and the spectral bias of neural networks. In this work, we propose a new, scalable approach for solving large problems relating to differential equations called finite basis physics-informed neural networks (FBPINNs). FBPINNs are inspired by classical finite element methods, where the solution of the differential equation is expressed as the sum of a finite set of basis functions with compact support. In FBPINNs, neural networks are used to learn these basis functions, which are defined over small, overlapping subdomains. FBINNs are designed to address the spectral bias of neural networks by using separate input normalisation over each subdomain and reduce the complexity of the underlying optimisation problem by using many smaller neural networks in a parallel divide-and-conquer approach. Our numerical experiments show that FBPINNs are effective in solving both small and larger, multi-scale problems, outperforming standard PINNs in both accuracy and computational resources required, potentially paving the way to the application of PINNs on large, real-world problems. [ABSTRACT FROM AUTHOR]

First-person captioning is significant because it provides veracious descriptions of egocentric scenes in a unique perspective. Also, there is a need to caption the scene, a.k.a. life-logging, for patients, travellers, and emergency responders in an egocentric narrative. Ego-captioning is indeed non-trivial since (1) Ego-images can be noisy due to motion and angles; (2) Describing a scene in a first-person narrative involves drastically different semantics; (3) Empirical implications have to be made on top of visual appearance because the cameraperson is often outside the field of view. We note we humans make good sense out of casual footage thanks to our contextual awareness in judging when and where the event unfolds, and whom the cameraperson is interacting with. This inspires the infusion of such "contexts" for situation-aware captioning. We create EgoCap which contains 2.1K ego-images, over 10K ego-captions, and 6.3K contextual labels, to close the gap of lacking ego-captioning datasets. We propose EgoFormer , a dual-encoder transformer-based network which fuses both contextual and visual features. The context encoder is pre-trained on ImageNet before fine tuning with context classification tasks. Similar to visual attention, we exploit stacked multi-head attention layers in the captioning decoder to reinforce attention to the context features. The EgoFormer has realized state-of-the-art performance on EgoCap achieving a CIDEr score of 125.52. The EgoCap dataset and EgoFormer are publicly available at https://github.com/zdai257/EgoCap-EgoFormer. • First quantitative study of image captioning in an egocentric perspective. • Introduction of transformer-based context fusion architecture. • EgoCap, a sizable first-person image caption dataset, is released. [ABSTRACT FROM AUTHOR]

In the last few decades, Structure from Motion (SfM) and visual Simultaneous Localization and Mapping (visual SLAM) techniques have gained significant interest from both the computer vision and robotic com)munities. Many variants of these techniques have started to make an impact in a wide range of applications, including robot navigation and augmented reality. However, despite some remarkable results in these areas, most SfM and visual SLAM techniques operate based on the assumption that the observed environment is static. However, when faced with moving objects, overall system accuracy can be jeopardized. In this article, we present for the first time a survey of visual SLAM and SfM techniques that are targeted toward operation in dynamic environments. We identify three main problems: how to perform reconstruction (robust visual SLAM), how to segment and track dynamic objects, and how to achieve joint motion segmentation and re)construction. Based on this categorization, we provide a comprehensive taxonomy of existing approaches. Finally, the advantages and disadvantages of each solution class are critically discussed from the perspective of practicality and robustness. [ABSTRACT FROM AUTHOR]

We establish intra-individual and inter-annual variability in European badger (Meles meles) autumnal nightly activity in relation to fine-scale climatic variables, using tri-axial accelerometry. This contributes further to understanding of causality in the established interaction between weather conditions and population dynamics in this species. Modelling found that measures of daylight, rain/humidity, and soil temperature were the most supported predictors of ACTIVITY, in both years studied. In 2010, the drier year, the most supported model included the SOLAR*RH interaction, RAIN, and30cmTEMP (w = 0.557), while in 2012, a wetter year, the most supported model included the SOLAR*RH interaction, and the RAIN*10cmTEMP (w = 0.999). ACTIVITY also differed significantly between individuals. In the 2012 autumn study period, badgers with the longest per noctem activity subsequently exhibited higher Body Condition Indices (BCI) when recaptured. In contrast, under drier 2010 conditions, badgers in good BCI engaged in less per noctem activity, while badgers with poor BCI were the most active. When compared on the same calendar dates, to control for night length, duration of mean badger nightly activity was longer (9.5 hrs ±3.3 SE) in 2010 than in 2012 (8.3 hrs ±1.9 SE). In the wetter year, increasing nightly activity was associated with net-positive energetic gains (from BCI), likely due to better foraging conditions. In a drier year, with greater potential for net-negative energy returns, individual nutritional state proved crucial in modifying activity regimes; thus we emphasise how a ‘one size fits all’ approach should not be applied to ecological responses. [ABSTRACT FROM AUTHOR]

Tuning the parameters that control the operation of a wireless sensor network, such as sampling rate, is not a simple task. This is partly due to the distributed nature of the problem, but is also a result of the time-varying dynamics that a network experiences. Inspired by the way in which cells alter their behaviour in response to diffused protein concentrations, an abstract representation, termed a discrete gene regulatory network (dGRN), is introduced. Each node runs an identical dGRN controller which controls node activity and interaction. The controllers are authored automatically using an evolutionary algorithm. The communication that occurs between nodes is neither specified nor designed, but emerges naturally. As a particular example, we illustrate that our approach can generate effective strategies for nodes to cooperatively track a moving target. The obtained strategies vary according to the user's accuracy requirements and the speed of the target, and are similar to those which would be expected from a network engineer. We also present results from our proof-of-concept dGRN implementation on T-Mote Sky nodes. Our approach takes high-level user application requirements and from these, automatically generates distributed parameter tuning algorithms. The dGRN framework thus greatly reduces the amount of effort involved in adjusting a sensor network's operation. [ABSTRACT FROM AUTHOR]

The simulation of seismic waves is a core task in many geophysical applications. Numerical methods such as finite difference (FD) modelling and spectral element methods (SEMs) are the most popular techniques for simulating seismic waves, but disadvantages such as their computational cost prohibit their use for many tasks. In this work, we investigate the potential of deep learning for aiding seismic simulation in the solid Earth sciences. We present two deep neural networks which are able to simulate the seismic response at multiple locations in horizontally layered and faulted 2-D acoustic media an order of magnitude faster than traditional finite difference modelling. The first network is able to simulate the seismic response in horizontally layered media and uses a WaveNet network architecture design. The second network is significantly more general than the first and is able to simulate the seismic response in faulted media with arbitrary layers, fault properties and an arbitrary location of the seismic source on the surface of the media, using a conditional autoencoder design. We test the sensitivity of the accuracy of both networks to different network hyperparameters and show that the WaveNet network can be retrained to carry out fast seismic inversion in the same media. We find that are there are challenges when extending our methods to more complex, elastic and 3-D Earth models; for example, the accuracy of both networks is reduced when they are tested on models outside of their training distribution. We discuss further research directions which could address these challenges and potentially yield useful tools for practical simulation tasks. [ABSTRACT FROM AUTHOR]

We study the problem of recovering an underlying 3D shape from a set of images. Existing learning based approaches usually resort to recurrent neural nets, e.g., GRU, or intuitive pooling operations, e.g., max/mean poolings, to fuse multiple deep features encoded from input images. However, GRU based approaches are unable to consistently estimate 3D shapes given different permutations of the same set of input images as the recurrent unit is permutation variant. It is also unlikely to refine the 3D shape given more images due to the long-term memory loss of GRU. Commonly used pooling approaches are limited to capturing partial information, e.g., max/mean values, ignoring other valuable features. In this paper, we present a new feed-forward neural module, named AttSets, together with a dedicated training algorithm, named FASet, to attentively aggregate an arbitrarily sized deep feature set for multi-view 3D reconstruction. The AttSets module is permutation invariant, computationally efficient and flexible to implement, while the FASet algorithm enables the AttSets based network to be remarkably robust and generalize to an arbitrary number of input images. We thoroughly evaluate FASet and the properties of AttSets on multiple large public datasets. Extensive experiments show that AttSets together with FASet algorithm significantly outperforms existing aggregation approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high resolution of $\boldsymbol{256^3}$ 256 3 by recovering the occluded/missing regions. The key idea is to combine the generative capabilities of 3D encoder-decoder and the conditional adversarial networks framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets and real-world Kinect datasets show that the proposed 3D-RecGAN++ significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects. [ABSTRACT FROM AUTHOR]

The simulation of seismic waves is a core task in many geophysical applications. Numerical methods such as Finite Difference (FD) modelling and Spectral Element Methods (SEM) are the most popular techniques for simulating seismic waves in complex media, but for many tasks their computational cost is prohibitively expensive. In this work we present two types of deep neural networks as fast alternatives for simulating seismic waves in horizontally layered and faulted 2D acoustic media. In contrast to the classical methods both networks are able to simulate the seismic response at multiple locations within the media in a single inference step, without needing to iteratively model the seismic wavefield through time, resulting in an order of magnitude reduction in simulation time. This speed improvement could pave the way to real-time seismic simulation and benefit seismic inversion algorithms based on forward modelling, such as full waveform inversion. Our first network is able to simulate seismic waves in horizontally layered media. We use a WaveNet network architecture and show this is more accurate than a standard convolutional network design. Furthermore we show that seismic inversion can be carried out by retraining the network with its inputs and outputs reversed, offering a fast alternative to existing inversion techniques. Our second network is significantly more general than the first; it is able to simulate seismic waves in faulted media with arbitrary layers, fault properties and an arbitrary location of the seismic source on the surface of the media. It uses a convolutional autoencoder network design and is conditioned on the input source location. We investigate the sensitivity of different network designs and training hyperparameters on its simulation accuracy. We compare and contrast this network to the first network. To train both networks we introduce a time-dependent gain in the loss function which improves convergence. We discuss the relative merits of our approach with FD modelling and how our approach could be generalised to simulate more complex Earth models. [ABSTRACT FROM AUTHOR]

While climatic effects on species biogeographic distributions are well documented, less mobile species must compensate for climate change in situ via behavioral plasticity. Despite this being a critical mechanism, behavioral plasticity is rarely modeled explicitly. Here, we use novel accelerometer and active-RFID transponder technology to quantify weather-driven modification of activity, mechanical energy expenditure, and ranging behavior, using the European badger as a model species. We then examine how these behaviors could respond to future climate change. From multi-model inference, activity was promoted significantly by a quadratic relationship with temperature, but inhibited by a quadratic relationship with humidity, and the amount of solar radiation. Drier conditions also encouraged more movement. Modeled against IPCC SRES low and high emissions climate change scenarios, milder and drier conditions projected for the next century would likely produce a change to badgers’ current phenology, with elevated levels of activity being maintained into the winter. This increased activity could necessitate up to a 15% increase in energy expenditure. Furthermore, conditions projected under the high emissions scenario may also lead to substantially increased movement, with implications for road traffic mortality rates. We contend that behavioral adaptation must be better incorporated into conservation strategies, versus the assumption of non-adaptive failure. [ABSTRACT FROM AUTHOR]

We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing steps, most existing approaches are only able to be trained and operate over small-scale point clouds. In this paper, we introduce RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Although remarkably computation and memory efficient, random sampling can discard key features by chance. To overcome this, we introduce a novel local feature aggregation module to progressively increase the receptive field for each 3D point, thereby effectively preserving geometric details. Comparative experiments show that our RandLA-Net can process 1 million points in a single pass up to 200× faster than existing approaches. Moreover, extensive experiments on five large-scale point cloud datasets, including Semantic3D, SemanticKITTI, Toronto3D, NPM3D and S3DIS, demonstrate the state-of-the-art semantic segmentation performance of our RandLA-Net. [ABSTRACT FROM AUTHOR]

Olfaction is a key sense, enabling animals to locate forage, select mates, navigate their environment, and avoid predation. Wind is an important abiotic factor that modulates the strength of olfactory information detected by animals. In theory, when airflow is unidirectional, an animal can increase odor detection probability and maximize the amount of olfactory information gained by moving crosswind. Given energetic costs inherent to activity and locomotion, behavioral search strategies that optimize the benefit-cost ratio should be advantageous. We tested whether African lions (Panthera leo) modify their movement directionality and distance according to wind speed and direction during hours of darkness when they are most active. We tracked 29 lions in southern Zimbabwe using GPS collars and deployed a weather station to collect detailed abiotic data. We found that when wind speeds increased lions were more likely to move crosswind. We also found that female lions, which tend to hunt more often than males, traveled farther when wind speeds were stronger. The results of our analysis suggest that lions adjust their movement behavior according to wind speed and direction. We inferred that this was a behavioral decision to maximize the amount of olfactory information gained per unit of energy spent. Our findings not only offer one of the first detailed insights on large carnivore anemotaxis (movement direction relative to wind) but also make an important contribution towards understanding the influence of wind on predator ecology in general which remains understudied to date. [ABSTRACT FROM AUTHOR]

Ferrous and highly conductive materials distort low-frequency magnetic fields and can significantly increase magnetoinductive positioning errors. In this paper, we use the image theory in order to formulate an analytical channel model for the magnetic field of a quasi-static magnetic dipole positioned above a perfectly conducting half-space. The proposed model can be used to compensate for the distorting effects that metallic reinforcement bars (rebars) within the floor impose on the magnetic field of a magnetoinductive transmitter node in an indoor single-story environment. Good agreement is observed between the analytical solution and numerical solutions obtained from 3-D finite-element simulations. Experimental results indicate that the image theory model shows improvement over the free-space dipole model in estimating positions in the distorted environment, typically reducing positioning errors by 22% in 90% of the cases and 26% in 40% of the cases. No prior information on the geometry of the metallic distorters was available, making this essentially a “blind” technique. [ABSTRACT FROM PUBLISHER]

Underground mines are characterized by a network of intersecting tunnels and sharp turns, an environment which is particularly challenging for radiofrequency based positioning systems due to extreme multipath, non-line-of-sight propagation, and poor anchor geometry. Such systems typically require a dense grid of devices to enable 3-D positioning. Moreover, the precise position of each anchor node needs to be precisely surveyed, a particularly challenging task in underground environments. Magneto-inductive (MI) positioning, which provides 3-D position and orientation from a single transmitter and penetrates thick layers of soil and rock without loss, is a more promising approach, but so far has only been investigated in simple point-to-point contexts. In this paper, we develop a novel MI positioning approach to cover an extended underground 3-D space with unknown geometry using a rapidly deployable anchor network. The key to our approach is that the position of only a single anchor needs to be accurately surveyed—the positions of all secondary anchors are determined using an iterative refinement process using measurements obtained from receivers within the network. This avoids the particularly challenging and time-intensive task in an underground environment of accurately surveying the positions of all of the transmitters. We also demonstrate how measurements obtained from multiple transmitters can be fused to improve localization accuracy. We validate the proposed approach in a man-made cave and show that, with a portable system that took 5 min to deploy, we were able to provide accurate through-the-earth location capability to nodes placed along a suite of tunnels. [ABSTRACT FROM PUBLISHER]

In the last decade, numerous supervised deep learning approaches have been proposed for visual–inertial odometry (VIO) and depth map estimation, which require large amounts of labelled data. To overcome the data limitation, self-supervised learning has emerged as a promising alternative that exploits constraints such as geometric and photometric consistency in the scene. In this study, we present a novel self-supervised deep learning-based VIO and depth map recovery approach (SelfVIO) using adversarial training and self-adaptive visual–inertial sensor fusion. SelfVIO learns the joint estimation of 6 degrees-of-freedom (6-DoF) ego-motion and a depth map of the scene from unlabelled monocular RGB image sequences and inertial measurement unit (IMU) readings. The proposed approach is able to perform VIO without requiring IMU intrinsic parameters and/or extrinsic calibration between IMU and the camera. We provide comprehensive quantitative and qualitative evaluations of the proposed framework and compare its performance with state-of-the-art VIO, VO, and visual simultaneous localization and mapping (VSLAM) approaches on the KITTI, EuRoC and Cityscapes datasets. Detailed comparisons prove that SelfVIO outperforms state-of-the-art VIO approaches in terms of pose estimation and depth recovery, making it a promising approach among existing methods in the literature. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) system typically employs vision-based sensors to observe the surrounding environment. However, the performance of such systems highly depends on the ambient illumination conditions. In scenarios with adverse visibility or in the presence of airborne particulates (e.g., smoke, dust, etc.), alternative modalities such as those based on thermal imaging and inertial sensors are more promising. In this article, we propose the first complete thermal–inertial SLAM system that combines neural abstraction in the SLAM front end with robust pose-graph optimization in the SLAM back end. We model the sensor abstraction in the front end by employing probabilistic deep learning parameterized by mixture density networks (MDNs). Our key strategies to successfully model this encoding from thermal imagery are the usage of normalized 14-b radiometric data, the incorporation of hallucinated visual (RGB) features, and the inclusion of feature selection to estimate the MDN parameters. To enable a full SLAM system, we also design an efficient global image descriptor that is able to detect loop closures from thermal embedding vectors. We performed extensive experiments and analysis using three datasets, namely self-collected ground robot and hand-held data taken in indoor environment, and one public dataset (SubT-tunnel) collected in underground tunnel. Finally, we demonstrate that an accurate thermal–inertial SLAM system can be realized in conditions of both benign and adverse visibility. [ABSTRACT FROM AUTHOR]

With the recent availability and affordability of commercial depth sensors and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets have been publicized to facilitate research in 3D computer vision. However, existing datasets either cover relatively small areas or have limited semantic annotations. Fine-grained understanding of urban-scale 3D scenes is still in its infancy. In this paper, we introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km 2 . Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset. In addition to the more commonly encountered categories such as road and vegetation, urban-level categories including rail, bridge, and river are also included in our dataset. Based on this dataset, we further build a benchmark to evaluate the performance of state-of-the-art segmentation algorithms. In particular, we provide a comprehensive analysis and identify several key challenges limiting urban-scale point cloud understanding. The dataset is available at http://point-cloud-analysis.cs.ox.ac.uk/. [ABSTRACT FROM AUTHOR]

Abstract: l-DOPA-2,3-dioxygenase from Streptomyces lincolnensis is a single-domain type I extradiol dioxygenase of the vicinal oxygen chelate superfamily and catalyzes the second step in the metabolism of tyrosine to the propylhygric acid moiety of the antibiotic, lincomycin. S. lincolnensis l-DOPA-2,3-dioxygenase was overexpressed, purified and reconstituted with Fe(II). The activity of l-DOPA-2,3-dioxygenase was kinetically characterized with l-DOPA (K M =38μM, k cat =4.2min−1) and additional catecholic substrates including dopamine, 3,4-dihydroxyhydrocinnamic acid, catechol and d-DOPA. 3,4-Dihydroxyphenylacetic acid was characterized as a competitive inhibitor of the enzyme (K i =2.2mM). Site-directed mutagenesis and its effects on enzymatic activity were used to identify His14 and His70 as iron ligands. [Copyright &y& Elsevier]

Previous research has shown that African lions (Panthera leo) have the ability to discriminate between conspecific vocalisations, but little is known about how individual identity is conveyed in the spectral structure of roars. Using acoustic – accelerometer biologgers that allow vocalisations to be reliably associated with individual identity, we test for vocal individuality in the fundamental frequency (f0) of roars from 5 male lions, firstly by comparing simple f0 summary features and secondly by modelling the temporal pattern of the f0 contour. We then assess the application of this method for discriminating between individuals using passive acoustic monitoring. Results indicate that f0 summary features only allow for vocal discrimination with 70.7% accuracy. By comparison, vocal discrimination can be achieved with an accuracy of 91.5% based on individual differences in the temporal pattern of the f0 sequence. We further demonstrate that passively recorded lion roars can be localised and differentiated with similar accuracy. The existence of individually unique f0 contours in lion roars and their relatively lower attenuation indicates a likely mechanism enabling individual lions to identify conspecifics over long distances. These differences can be exploited by researchers to track individuals across the landscape and thereby supplement conventional lion monitoring approaches. [ABSTRACT FROM AUTHOR]

Long-distance vocalization is a characteristic of African lion, Panthera leo , behaviour and is important for maintaining territorial boundaries as well as locating distant group members. Vocal signalling is, however, a flexible behaviour that involves varying costs and benefits depending on environmental, social and spatial factors. Motivated by previous data collection limitations, we developed a novel approach to investigate the influence of atmospheric conditions and animal home range geography on lion vocal behaviour using acoustic and accelerometer biologgers. To compensate for the short lifetime of the acoustic biologger, we trained a machine-learning model to detect lion roars from long-term acceleration signals which yielded over 500 nights of data from seven individual lions. Analysis of detected roar events revealed that vocalizations occurred mainly at night with a peak just before dawn. The relative likelihood of vocalization was negatively related to wind speed and temperature and positively related to absolute humidity suggesting that lions preferred to roar under conditions that reduce sound attenuation and thereby maximize calling area. Roar occurrence was found to be dependent on an animal's location relative to its home range with lions demonstrating an apparent avoidance for vocalizing beyond the home range boundary. Lions were also more likely to roar repeatedly while closer to rivers and water points within their home range. This study is the first of its kind and not only improves the understanding of lion vocal behaviour but can also inform new approaches for recording animal vocalizations remotely. • Acoustic accelerometer biologgers enable detection of lion roars from body movement. • Lions vocalize mainly at night with a peak just before dawn. • Roaring is preferred when temperature and wind speed are low and humidity is high. • Lions avoid roaring beyond their home range boundary. • Lions are more likely to roar repeatedly when closer to rivers and water points. [ABSTRACT FROM AUTHOR]

Acoustic localisation technology has been widely tested and applied for passive acoustic monitoring and ecological research, however, hardware costs of commercially available devices limit scalability. Furthermore, few studies have explored its use with low-density arrays. We present a low-cost, custom-designed hardware and software system termed 'CARACAL' that is able to extract and localise weak acoustic signals. The key to this is the use of four microphones on each logger, allowing for phase-based measurements and the ability to enhance signal-to-noise ratio through beamforming. As a proof of concept, we test the functionality of the CARACAL system by conducting a gunshot localisation experiment and demonstrate animal call detection and localisation from a lion predation event. Results show the system could locate gunshots with an average accuracy of 33.2 ± 15.3 m within an array of 7 stations 500 m apart. When applied to animal call positioning, we show long range (> 1 km) localisation of three different species' calls, Cape buffalo, chacma baboon and spotted hyaena. With a cost of approximately £150 per unit, the CARACAL system provides a cost-effective solution for acoustic localisation over large areas. The system is open source and can be customised to suit a variety of wildlife research applications. [ABSTRACT FROM AUTHOR]

It has been proposed that there is a thermal cost of the mane to male lions, potentially leading to increased body surface temperatures ( Ts), increased sperm abnormalities, and to lower food intake during hot summer months. To test whether a mane imposes thermal costs on males, we measured core body temperature ( Tb) continuously for approximately 1 year in 18 free-living lions. There was no difference in the 24-hr maximum Tb of males ( n = 12) and females ( n = 6), and males had a 24-hr mean Tb that was 0.2 ± 0.1°C lower than females after correcting for seasonal effects. Although feeding on a particular day increased 24-hr mean and 24-hr maximum Tb, this phenomenon was true of both male and female lions, and females had higher 24-hr mean and 24-hr maximum Tb than males, on both days when lions did not feed, and on days when lions did feed. Twenty-four-hour Tb was not influenced by mane length or color, and 24-hr mean Tb was negatively correlated with mane length. These data contradict the suggestion that there exists a thermal cost to male lions in possessing a long dark mane, but do not preclude the possibility that males compensate for a mane with increased heat loss. The increased insulation caused by a mane does not necessarily have to impair heat loss by males, which in hot environments is primarily through respiratory evaporative cooling, nor does in necessarily lead to increased heat gain, as lions are nocturnal and seek shade during the day. The mane may even act as a heat shield by increasing insulation. However, dominant male lions frequent water points more than twice as often as females, raising the possibility that male lions are increasing water uptake to facilitate increased evaporative cooling. The question of whether male lions with manes compensate for a thermal cost to the mane remains unresolved, but male lions with access to water do not have higher Tb than females or males with smaller manes. [ABSTRACT FROM AUTHOR]