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Indigenous peoples living in Canada’s north have long-valued the livers of Burbot (Lota lota) as a traditional food source; however, there has been concern relating to liver quality and potential contaminants. In this study, livers of Burbot collected in lower Mackenzie River were ranked using a traditional appearance-based assessment. These rankings were compared to a variety of biological and contaminant metrics. Livers ranked “most palatable” had a significantly higher mass and lipid content and were from younger fish with greater hepatosomatic index and total mass and had lower parasite intensities. There were no differences in the concentrations of persistent organic pollutants or metals, except copper, which although still well below consumption guidelines, was significantly higher in fish with livers that appeared most palatable. The results of this study demonstrated that traditional methods effectively assessed the quality of livers by selecting for the most nutritious (high lipid levels) and safest (low parasite loading) food. This method could be incorporated into a community-based monitoring framework as a rough index of overall fish and ecosystem health; however, would not be effective in screening food for anthropogenic contaminants. This study highlights the importance and value of linking traditional knowledge into scientific studies.

A patchwork of disjunct lidar collections is rapidly developing across the USA, often acquired with different acquisition goals and parameters and without field data for forest inventory. Airborne lidar and coincident field data have been used to estimate forest attributes across individual lidar extents, where forest measurements are collected using project-specific inventory designs. This research explores predicting forest attributes at locations not represented in the training data by combining lidar and field measurements from ecologically similar forests. Using field measurements from six lidar units, random forests regression models were created by systematically withholding forest inventory data from one lidar unit and using the forest inventory data from the other five units to predict basal area and stem density at the withheld unit. Results indicate that BA models produce more accurate predictions than stem density models when transferred to a lidar unit that did not contain field data. Relative root mean square errors calculated from the withheld field plots ranged between 32.3%–50.1% for BA and 40.7%–67.3% for stem density models. It is concluded that forest managers may use predictive models constructed from ecologically similar forests to obtain a preliminary estimate of resources, until local field measurement can be obtained. RÉSUMÉ Un ensemble disparate de collections de données lidar se développe rapidement aux États-Unis, souvent acquises avec des objectifs et des paramètres différents et sans données de terrains relatives aux inventaires forestiers. Des données de terrain concomitantes sont utilisées pour estimer des attributs forestiers au sein d’étendues de lidar aéroporté individuelles, pour lesquelles les mesures d’intérêt correspondent à des modèles d’inventaires déterminés. En combinant des mesures de terrain et de lidar provenant de forêts écologiquement similaires, cette étude examine la prédiction d’attributs forestiers à des endroits où des données d’entrainement sont absentes. En utilisant des mesures de terrain, des modèles de régression produits par des forêts d’arbres décisionnels ont été créés pour six unités de lidar. Un procédé systématique a été suivi par lequel les données de chaque unité sont retenues et la surface terrière et densité des tiges de cette unité sont ensuite estimés avec les données des cinq unités restantes. Les résultats indiquent que les estimations de surface terrière sont plus robustes que pour la densité de tiges lorsque les modèles sont transférés à une unité de lidar pour laquelle les données de terrain sont absents. Les erreurs quadratiques moyennes relatives calculées pour les échantillons retenus sont entre 32.3 % et 50.1 % pour les modèles de surface terrière et entre 40.7 % et 67.3 % pour la densité des tiges. Nous concluons que, jusqu'à ce que des mesures de terrain locales puissent être acquises, il est possible d’obtenir des estimations préliminaires de ressources forestières en utilisant des modèles prédictifs construits à partir d'échantillons de forêts écologiquement similaires.

Mountain glaciation involves the erosion of cirques and troughs, which increase steep slopes but also produce gentle slopes in cirque floors and trough floors. This is expected to increase the variability of slope gradients at related altitudes. Taking a whole mountain range, its distributions of altitude and slope can be analysed to establish a signal of glacial modification. Frequency distributions of altitude (hypsometry) and gradient (clinometry) alone do not seem adequate. Taking these two variables together – hypsoclinometry, plotting slope gradient against altitude – is more promising. Frequency distributions of slope gradient at different altitudes are exemplified here for mountain ranges in British Columbia and Romania, together with altitudinal variations of steep or gentle slopes. Cirque headwalls give the clearest morphometric signature of glaciation. Steep (especially the steepest) slopes are concentrated at cirque altitudes, increasing mean, median, standard deviation (SD) and inter-quartile range (IQR) of gradients, especially above cirque floors. There is only a small increase in SD and IQR at cirque floor altitudes. Hypsometric maxima and increased proportions of gentle slopes at cirque floor altitudes are clear only in mountain ranges densely occupied by cirques. This relates to the small proportion of each cirque (about 28%) occupied by the floor. Concentrations of steep slope aspects in directions favoured by local glaciers provide further evidence of glacial modification. The most general morphometric effect of glaciation, however, is the increase in steep slopes at cirque headwall altitudes. Thus it is possible to rank mountain ranges by degree of glacial modification.

Recent developments in network theory have led to the creation of new models to describe spatial and social interaction and a reappraisal of existing models. Although designed with contemporary society in mind they are increasingly used by archaeologists to help explain the archaeological record. This leads to the problem that, with such a large range of models available it is likely that, if we hunt hard enough, we can find a model to justify whatever preconceptions we may have. In this paper we address this issue by re-examining the onset of centralisation in mainland Greek city states of the 9th and 8th centuries BCE. Pioneering work on this settlement structure was performed in 1987 by Rihll and Wilson, adapting a 'retail' model devised originally for 20th century urban planning. We highlight the problems of modelling archaeological data by contrasting this model to a recent cost-benefit model termed ariadne, developed by ourselves, and other simpler models. In particular, we examine what constitutes model 'robustness' and the way in which different models handle 'contingency' when describing periods of rapid change. In no way can we get what we want.