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Features 12 super-smart animals found around the world, including their life cycles, habitats, and traits.

Many animals, including humans, acquire valuable skills and knowledge by copying others. Scientists refer to this as social learning. It is one of the most exciting and rapidly developing areas of behavioral research and sits at the interface of many academic disciplines, including biology, experimental psychology, economics, and cognitive neuroscience.Social Learningprovides a comprehensive, practical guide to the research methods of this important emerging field. William Hoppitt and Kevin Laland define the mechanisms thought to underlie social learning and demonstrate how to distinguish them experimentally in the laboratory. They present techniques for detecting and quantifying social learning in nature, including statistical modeling of the spatial distribution of behavior traits. They also describe the latest theory and empirical findings on social learning strategies, and introduce readers to mathematical methods and models used in the study of cultural evolution. This book is an indispensable tool for researchers and an essential primer for students. Provides a comprehensive, practical guide to social learning researchCombines theoretical and empirical approachesDescribes techniques for the laboratory and the fieldCovers social learning mechanisms and strategies, statistical modeling techniques for field data, mathematical modeling of cultural evolution, and more

Learn all the dolphin basics, as well as new information scientists are finding out about these fascinating creatures!

Vocal learning, the substrate of human language acquisition, has rarely been described in other mammals. Often, group-specific vocal dialects in wild populations provide the main evidence for vocal learning. While social learning is often the most plausible explanation for these intergroup differences, it is usually impossible to exclude other driving factors, such as genetic or ecological backgrounds. Here, we show the formation of dialects through social vocal learning in fruit bats under controlled conditions. We raised 3 groups of pups in conditions mimicking their natural roosts. Namely, pups could hear their mothers' vocalizations but were also exposed to a manipulation playback. The vocalizations in the 3 playbacks mainly differed in their fundamental frequency. From the age of approximately 6 months and onwards, the pups demonstrated distinct dialects, where each group was biased towards its playback. We demonstrate the emergence of dialects through social learning in a mammalian model in a tightly controlled environment. Unlike in the extensively studied case of songbirds where specific tutors are imitated, we demonstrate that bats do not only learn their vocalizations directly from their mothers, but that they are actually influenced by the sounds of the entire crowd. This process, which we term \"crowd vocal learning,\" might be relevant to many other social animals such as cetaceans and pinnipeds.

Why vocal learning has evolved in songbirds, parrots, and hummingbirds but not in other avian groups remains an unanswered question. The difficulty in providing an answer stems not only from the challenge of reconstructing the conditions that favored vocal learning among ancestors of these groups but also from our incomplete knowledge of extant birds. Here we provide multiple lines of evidence for a previously undocumented, evolutionarily independent origin of vocal learning among the suboscine passerines. Working with bellbirds (Procnias spp.), we show that (1) a captive-reared Bare-throated Bellbird (P. nudicollis) deprived of conspecific song not only developed abnormal conspecific songs but also learned the calls of a Chopi Blackbird (Gnorimopsar chopi) near which it was housed; (2) songs of Three-wattled Bellbirds (P. tricarunculata) occur in three geographically distinct dialects (from north to south: Nicaragua, Monteverde, and Talamanca); (3) Three-wattled Bellbirds at Monteverde, Costa Rica, are often bilingual, having learned the complete song repertoire of both the Monteverde and Talamanca dialects; (4) immature bellbirds have an extended period of song development, lasting the 6 years in which they are in subadult plumage; and (5) adult male Three-wattled Bellbirds continually relearn their songs, visiting each others' song perches and adjusting their songs to track population-wide changes. Perhaps female preferences and strong sexual selection have favored vocal learning among bellbirds, and additional surveys for vocal learning among other lekking cotingas and other suboscines may reveal patterns that help determine the conditions that promote the evolution of vocal learning.

Behavioral innovations are likely to contribute to the persistence of native species in developed areas. Innovativeness has been well-studied in birds, and the frequency with which they innovate is related to their relative brain size. However, the mechanisms by which behavioral innovations emerge and spread remain poorly known. Two major mechanisms are thought to play a fundamental role: the independent appearance of the same innovation in different individuals and innovation diffusion by social learning. Here, we describe observations of multiple Blue-faced Honeyeaters (Entomyzon cyanotis) collecting sugar packets, a technical innovation that had not been published in that species. We also demonstrate that this behavior emerged in 2 developed areas separated by 1,200 km, with multiple individuals engaging in the behavior within one of the sites, such that both independent innovation and social diffusion are likely to have occurred. Using brain size data on 62 species of the Meliphagidae family, we then discuss the likely importance of relative brain size in determining innovativeness in this family, and suggest that anatomical specialization such as the curvature of beaks used in nectar foraging could constrain the emergence of new behaviors in some large-brained species. Received 15 March 2018. Accepted 2 September 2018. Key words: Acanlhorhynchus temtirostris, behavioral innovation, brain size, Entomyzon cyanotis, innovation spread, Meliphagidae Les innovations comportementales sont susceptibles de contribuer a la persistance d'especes natives dans les zones developpees. La capacite d'innovation a ete bien etudiee chez les oiseaux et la frequence a laquelle les oiseaux innovent est liee a la taille relative de leur cerveau. Cependant, les mecanismes par lesquels les innovations comportementales emergent et se repandent restent mal connus. Deux mecanismes principaux jouent probablement un role fondamental : l'apparition independante d'une meme innovation chez differents individus et la diffusion de l'innovation par l'apprentissage social. Nous decrivons ici les observations de plusieurs meliphages a oreillons bleus (Entomyzon evanotis) collectant des sachets de sucre, une innovation technique qui n'avait pas ete publiee chez cette espece. Nous demontrons egalement que ce comportement est apparu dans 2 zones developpees separees de 1 200 km, plusieurs individus se livrant a ce comportement sur l'un des sites, si bien que l'emergence independante de la meme innovation, mais aussi la diffusion sociale de cette innovation ont probablement eu lieu. En utilisant les donnees de taille du cerveau de 62 especes de la famille des meliphagides, nous discutons ensuite de l'importance probable de la taille relative du cerveau dans la determination des differences de capacite d'innovation au sein de cette famille, et suggerons que certaines specialisations anatomiques telles que la courbure des becs utilises pour la collecte de nectar pourraient contraindre l'emergence de nouveaux comportements chez certaines especes a gros cerveau. Mots clefs : Acanthorhynchus tenuirostris, innovation comportementale, diffusion de l'innovation, taille du cerveau, Entomyzon cyanotis, meliphagide

The excessive reliance on fossil fuels has resulted in an energy crisis, environmental pollution, and health problems, calling for alternative fuels such as biodiesel. Here, we review computational chemistry and machine learning for optimizing biodiesel production from waste. This article presents computational and machine learning techniques, biodiesel characteristics, transesterification, waste materials, and policies encouraging biodiesel production from waste. Computational techniques are applied to catalyst design and deactivation, reaction and reactor optimization, stability assessment, waste feedstock analysis, process scale-up, reaction mechanims, and molecular dynamics simulation. Waste feedstock comprise cooking oil, animal fat, vegetable oil, algae, fish waste, municipal solid waste and sewage sludge. Waste cooking oil represents about 10% of global biodiesel production, and restaurants alone produce over 1,000,000 m3 of waste vegetable oil annual. Microalgae produces 250 times more oil per acre than soybeans and 7–31 times more oil than palm oil. Transesterification of food waste lipids can produce biodiesel with a 100% yield. Sewage sludge represents a significant biomass waste that can contribute to renewable energy production.

Excessive algae growth can lead to negative consequences for ecosystem function, economic opportunity, and human and animal health. Due to the cost‐effectiveness and temporal availability of satellite imagery, remote sensing has become a powerful tool for water quality monitoring. The use of remotely sensed products to monitor water quality related to algae and cyanobacteria productivity during a bloom event may help inform management strategies for inland waters. To evaluate the ability of satellite imagery to monitor algae pigments and dissolved oxygen conditions in a small inland lake, chlorophyll‐a, phycocyanin, and dissolved oxygen concentrations are measured using a YSI EXO2 sonde during Sentinel‐2 and Sentinel‐3 overpasses from 2019 to 2022 on Lake Mendota, WI. Machine learning methods are implemented with existing algorithms to model chlorophyll‐a, phycocyanin, and Pc:Chla. A novel machine learning‐based dissolved oxygen modeling approach is developed using algae pigment concentrations as predictors. Best model results based on Sentinel‐2 (Sentinel‐3) imagery achieved R2 scores of 0.47 (0.42) for chlorophyll‐a, 0.69 (0.22) for phycocyanin, and 0.70 (0.41) for Pc:Chla. Dissolved oxygen models achieved an R2 of 0.68 (0.36) when applied to Sentinel‐2 (Sentinel‐3) imagery, and Pc:Chla is found to be the most important predictive feature. Random forest models are better suited to water quality estimations in this system given built in methods for feature selection and a relatively small data set. Use of these approaches for estimation of Pc:Chla and dissolved oxygen can increase the water quality information extracted from satellite imagery and improve characterization of algae conditions among inland waters. Plain Language Summary Agricultural runoff and wastewater discharge has fueled nutrient pollution in Lake Mendota over the last century. As a result, algae blooms have become a common summertime occurrence on Lake Mendota. Algae blooms are often made up of different algae species. Green algae are typically harmless, but cyanobacteria (blue‐green algae) can produce a range of toxins harmful to human and animal health. The ability to discriminate between cyanobacteria and green algae during a bloom may be useful for lake managers and public health officials in making decisions about closing waterfront areas and communicating with the public. In recent years, satellite imagery has become a powerful tool for monitoring water quality. In this study, we build models that use imagery from two satellites to estimate the abundance of cyanobacteria versus green algae in Lake Mendota. We also find that our algae estimates can be used to model dissolved oxygen, an important water quality indicator that cannot be directly measured from satellite imagery. The methods presented for satellite‐based monitoring of algae pigments, the Pc:Chla ratio, and dissolved oxygen has the potential to increase the water quality information extracted from satellite imagery, better characterize algae blooms, and inform management strategies for Lake Mendota. Key Points Chlorophyll‐a and phycocyanin are sampled from 2019 to 2022 on Lake Mendota, WI Sentinel‐2 and Sentinel‐3 are used to model chlorophyll‐a, phycocyanin, and Pc:Chla A model based in situ data allows for satellite‐based estimates of dissolved oxygen

The squeezing behavior of surrounding rock can be described as the time-dependent large deformation during tunnel excavation, which appears in special geological conditions, such as weak rock masses and high in situ stress. Several problems such as budget increase and construction period extension can be caused by squeezing in rock mass. It is significant to propose a model for accurate prediction of rock squeezing. In this research, the support vector machine (SVM) as a machine learning model was optimized by the whale optimization algorithm (WOA), WOA-SVM, to classify the tunnel squeezing based on 114 real cases. The role of WOA in this system is to optimize the hyper-parameters of SVM model for receiving a higher level of accuracy. In the established database, five input parameters, i.e., buried depth, support stiffness, rock tunneling quality index, diameter and the percentage strain, were used. In the process of model classification, different effective parameters of SVM and WOA were considered, and the optimum parameters were designed. To examine the accuracy of the WOA-SVM, the base SVM, ANN (refers to the multilayer perceptron) and GP (refers to the Gaussian process classification) were also constructed. Evaluation of these models showed that the optimized WOA-SVM is the best model among all proposed models in classifying the tunnel squeezing. It has the highest accuracy (approximately 0.9565) than other un-optimized individual classifiers (SVM, ANN, and GP). This was obtained based on results of different performance indexes. In addition, according to sensitivity analysis, the percentage strain is highly sensitive to the model, followed by buried depth and support stiffness. That means, ɛ, H and K are the best combination of parameters for the WOA–SVM model.