Reviews

P. Race, 500 Tips on Group Learning, London: Kogan Page, 2000. ISBN: 0–7494–2884–8. Softback, vii + 135 pages, £15.99

Ecological Historicity, Functional Goals, and Novelty in the Anthropocene

While many recognize that rigid historical and compositional goals are inadequate in a world where climate and other global systems are undergoing unprecedented changes, others contend that promoting ecosystem services and functions encourages practices that can ultimately lower the bar of ecological management. These worries are foregrounded in discussions about Novel Ecosystems (NEs); where some researchers and conservationists claim that NEs provide a license to trash nature as long as some ecosystem services are provided. This criticism arises from what we call the “anything goes” problem created by the release of historical conditions. After explaining the notion of NE, we identify numerous substantive motivations for worrying about the anything-goes-problem and then go on to show the problem can be solved by correcting two mistaken assumptions. In short, we argue that the problem is a product of adopting an overly sparse functional perspective and one that assumes an unrealistically high degree of convergence in the trajectories of natural processes. Our analysis illuminates why such assumptions are unwarranted. Finally, we further argue that adopting an appropriate ethical framework is essential to overcoming the anything-goes-problem and suggest that a certain virtue ethics conception of ecological management provides useful resources for framing and resolving the problem

Ecological Historicity, Functional Goals, and Novelty in the Anthropocene

While many recognize that rigid historical and compositional goals are inadequate in a world where climate and other global systems are undergoing unprecedented changes, others contend that promoting ecosystem services and functions encourages practices that can ultimately lower the bar of ecological management. These worries are foregrounded in discussions about Novel Ecosystems (NEs); where some researchers and conservationists claim that NEs provide a license to trash nature as long as some ecosystem services are provided. This criticism arises from what we call the “anything goes” problem created by the release of historical conditions. After explaining the notion of NE, we identify numerous substantive motivations for worrying about the anything-goes-problem and then go on to show the problem can be solved by correcting two mistaken assumptions. In short, we argue that the problem is a product of adopting an overly sparse functional perspective and one that assumes an unrealistically high degree of convergence in the trajectories of natural processes. Our analysis illuminates why such assumptions are unwarranted. Finally, we further argue that adopting an appropriate ethical framework is essential to overcoming the anything-goes-problem and suggest that a certain virtue ethics conception of ecological management provides useful resources for framing and resolving the problem

Reviews

Danny Saunders and Nina Smalley (eds.), The International Simulation and Gaming Research Yearbook — Volume 8: Simulations and Games for Transition and Change, London: Kogan Page, 2000. ISBN: 0–7494–3397–3. Hardback, viii+271 pages, £40.00

Cementoblastoma in the Maxilla: A Case Report

The cementoblastoma is a rare, benign odontogenic tumour. It is generally asymptomatic and usually occurs in young adults. It is more common in males. It is most often associated with mandibular molar teeth and has a characteristic radiographic appearance. This case report is of a young male patient who presented with a cementoblastoma associated with a maxillary second molar. The initial presentation was to his dentist with pain, which was assumed to be of dental origin. This case highlights the importance of adequate radiographic investigation and appropriate interpretation in forming a diagnosis. It also serves as a reminder that rare odontogenic tumours may present initially in primary care and it is essential that all practitioners are aware of them. </jats:p

Environmental Grain, Organism Fitness, and Type Fitness

Abstract Natural selection is the result of organisms&apos; interactions with their environment, but environments vary in space and time, sometimes in extreme ways. Such variation is generally thought to play an important role in evolution by natural selection, maintaining genetic variation within and between populations, increasing the chance of speciation, selecting for plasticity of responses to the environment, and selecting for behaviors such as habitat selection and niche construction. Are there different roles that environmental variation plays in natural selection? When biologists make choices about how to divide up an environment for the sake of modeling or empirical research, are there any constraints on these choices? Since diverse evolutionary models relativize fitnesses to component environments within a larger environment, it would be useful to understand when such practices capture real aspects of evolutionary processes, and when they count as mere modeling conveniences. In this paper, I try to provide a general framework for thinking about how fitness and natural selection depend on environmental variation. I&apos;ll give an account of how the roles of environmental conditions in natural selection differ depending the probability of being experienced repeatedly by organisms, and how environmental conditions combine probabilistically to help determine fitness. My view has implications for what fitness is, and suggests that some authors have misconceived its nature

Machine learning to refine decision making within a syndromic surveillance service

Background: Worldwide, syndromic surveillance is increasingly used for improved and timely situational awareness and early identification of public health threats. Syndromic data streams are fed into detection algorithms, which produce statistical alarms highlighting potential activity of public health importance. All alarms must be assessed to confirm whether they are of public health importance. In England, approximately 100 alarms are generated daily and, although their analysis is formalised through a risk assessment process, the process requires notable time, training, and maintenance of an expertise base to determine which alarms are of public health importance. The process is made more complicated by the observation that only 0.1% of statistical alarms are deemed to be of public health importance. Therefore, the aims of this study were to evaluate machine learning as a tool for computer-assisted human decision-making when assessing statistical alarms. Methods: A record of the risk assessment process was obtained from Public Health England for all 67505 statistical alarms between August 2013 and October 2015. This record contained information on the characteristics of the alarm (e.g. size, location). We used three Bayesian classifiers- naïve Bayes, tree-augmented naïve Bayes and Multinets - to examine the risk assessment record in England with respect to the final ‘Decision’ outcome made by an epidemiologist of ‘Alert’, ‘Monitor’ or ‘No-action’. Two further classifications based upon tree-augmented naïve Bayes and Multinets were implemented to account for the predominance of ‘No-action’ outcomes. Results: The attributes of each individual risk assessment were linked to the final decision made by an epidemiologist, providing confidence in the current process. The naïve Bayesian classifier performed best, correctly classifying 51.5% of ‘Alert’ outcomes. If the ‘Alert’ and ‘Monitor’ actions are combined then performance increases to 82.6% correctly classified. We demonstrate how a decision support system based upon a naïve Bayes classifier could be operationalised within an operational syndromic surveillance system. Conclusions: Within syndromic surveillance systems, machine learning techniques have the potential to make risk assessment following statistical alarms more automated, robust, and rigorous. However, our results also highlight the importance of specialist human input to the process

A rapid multi-disciplinary biodiversity assessment of the Kamdebooberge (Sneeuberg, Eastern Cape, South Africa): implications for conservation

Botanical work since 2008 on the Sleeping Giant section of the Kamdebooberge (Sneeuberg mountain complex, Eastern Cape, South Africa) has indicated that these mountains may be of significant conservation value. Accordingly, a precursory, rapid multi-disciplinary biodiversity assessment was undertaken in January 2011, focusing on plants, tetrapod vertebrates and leafhoppers. The botanical results confirm the Kamdebooberge as being of high botanical conservation value, hosting three strict endemics, healthy populations of five other Sneeuberg endemics, and fynbos communities comprising species not found elsewhere in the Sneeuberg. The Kamdebooberge are important for herpetofauna (excluding serpentoids) and mammals, hosting several range-restricted and regional endemics. The expedition uncovered three new leafhopper species, together with several species previously only known from the Cape Floristic Region. Further detailed faunal work may provide further interesting results from these mountains, which show a high conservation value unique to the southern Escarpment

Growth of high quality yttrium iron garnet films using standard pulsed laser deposition technique

Thin films with properties comparable to bulk single crystals were grown by pulsed laser deposition using a substrate temperature of only 500 °C. This was achieved by a careful choice of both the oxygen pressure in the deposition chamber and the temperature of the air anneal. The best films were grown on gadolinium gallium garnet substrates but we also report data for films grown on the diamagnetic substrate yttrium aluminium garnet. The films were analysed using X-ray diffraction, near edge X-ray absorption and magnetometry. Our best films had a magnetisation of 143 emu/cm 3 and a coercive field of ~1 Oe