Spatio-temporal patterns of herbage availability and livestock movements: A cross-border analysis in the Chinese-Mongolian Altay

Due to increasing population and the recent implementation of policies to intensify the use of land and water resources, the transhumant pastoral systems in the Chinese-Mongolian Altay-Dzungarian region are rapidly changing, leading to modifications of herd size, herd composition and spatial distribution of livestock grazing. This may have major consequences for the supply and quality of rangeland biomass. Despite similar topographic settings, the socio-political framework for Chinese and Mongolian pastoralists differs significantly, leading to differences in rangeland utilization. To substantiate these claims, the long-distance transhumance routes, frequency of pasture changes, daily grazing itineraries and size of pastures were recorded by means of GPS tracking of cattle and goats on 1,535 (China) and 1,396 (Mongolia) observation days. The status quo of the main seasonal pastures was captured by measuring the herbage offer and its nutritive value in 869 sampling spots. In the Altay-Dzungarian region, small ruminant herds covered up to 412 km (Mongolia) and grazed on up to nine pastures per year (China). In Mongolia, the herds’ average duration of stay at an individual pasture was longer than in China, particularly in spring and autumn. Herbage allowance at the onset of a grazing period (kg dry matter per sheep unit and day) ranged from 34/17 to 91/95 (China/Mongolia). Comparing crude protein and phosphorous concentrations of herbage, in China, the highest concentrations were measured for spring and summer pastures, whereas in Mongolia, the highest concentrations were determined for autumn and winter pastures. Based on our data, we conclude that regulation of animal numbers and access to pastures seemingly maintained pasture productivity in China, especially at high altitudes. However, this policy may prohibit flexible adaptation to sudden environmental constraints. In contrast, high stocking densities and grazing of pastures before flowering of herbaceous plants negatively affected rangeland productivity in Mongolia, especially for spring and summer pastures.

Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis

Background: Preventing risk factor exposure is vital to reduce the high burden from lung cancer. The leading risk factor for developing lung cancer is tobacco smoking. In Australia, despite apparent success in reducing smoking prevalence, there is limited information on small area patterns and small area temporal trends. We sought to estimate spatio-temporal patterns for lung cancer risk factors using routinely collected population-based cancer data. Methods: The analysis used a Bayesian shared component spatio-temporal model, with male and female lung cancer included separately. The shared component reflected exposure to lung cancer risk factors, and was modelled over 477 statistical local areas (SLAs) and 15 years in Queensland, Australia. Analyses were also run adjusting for area-level socioeconomic disadvantage, Indigenous population composition, or remoteness. Results: Strong spatial patterns were observed in the underlying risk factor exposure for both males (median Relative Risk (RR) across SLAs compared to the Queensland average ranged from 0.48-2.00) and females (median RR range across SLAs 0.53-1.80), with high exposure observed in many remote areas. Strong temporal trends were also observed. Males showed a decrease in the underlying risk across time, while females showed an increase followed by a decrease in the final two years. These patterns were largely consistent across each SLA. The high underlying risk estimates observed among disadvantaged, remote and indigenous areas decreased after adjustment, particularly among females. Conclusion: The modelled underlying exposure appeared to reflect previous smoking prevalence, with a lag period of around 30 years, consistent with the time taken to develop lung cancer. The consistent temporal trends in lung cancer risk factors across small areas support the hypothesis that past interventions have been equally effective across the state. However, this also means that spatial inequalities have remained unaddressed, highlighting the potential for future interventions, particularly among remote areas.

Temporal and spatial patterns in α- and γ-hexachlorocyclohexane concentrations in industrially contaminated rivers

Fluxes of HCH isomers α- and γ-HCH dynamics were determined in four industrial U.K. rivers feeding the North Sea. Sampling was conducted weekly basis over a 2-year period. This was complemented by discrete studies of events where two hourly sampling periods were used to investigate the fine time scale dynamics of fluxes. Two intensively industrialized rivers had average isomer concentrations of ~20 ng L^-1 for both isomers, while average concentrations in the two less industrialized rivers ranged between 1.5 and 5.0 ng L^-1. α-HCH concentrations showed no strong temporal patterns on any river, which contrasts with γ-HCH levels that increased considerably during late summer/early autumn following sustained periods of low river flow. Sampling during high river flow events on rivers with differing HCH pollution histories both showed the same dynamics in HCH isomer concentrations. γ-HCH concentrations decreased 4-fold during events while α-HCH-concentrations stayed constant. The increases in γ-HCH concentrations under low flow conditions and the rapid dilution of this isomer during events indicate that γ-HCH has current inputs to these river systems. It was calculated that these four rivers export 30.8 kg yr^-1 of γ-HCH and 14.8 kg yr^-1 of α-HCH to the North Sea.

Spatial and temporal analysis of Barmah Forest virus disease in Queensland, Australia

Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios. Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads. Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒÓ2 = 7587, p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. A cluster analysis was used to detect the hot spots/clusters of BFV disease at a SLA level. Most likely spatial and space-time clusters are detected at the same locations across coastal Queensland (p<0.05). The study demonstrates heterogeneity of disease risk at a SLA level and reveals the spatial and temporal clustering of BFV disease in Queensland. Discriminant analysis was employed to establish a link between wetland classes, climate zones and BFV disease. This is because the importance of wetlands in the transmission of BFV disease remains unclear. The multivariable discriminant modelling analyses demonstrate that wetland types of saline 1, riverine and saline tidal influence were the most significant risk factors for BFV disease in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. The model accuracies were 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV disease risk varied with wetland class and climate zone. The study suggests that wetlands may act as potential breeding habitats for BFV vectors. Multivariable spatial regression models were applied to assess the impact of spatial climatic, socio-economic and tidal factors on the BFV disease in Queensland. Spatial regression models were developed to account for spatial effects. Spatial regression models generated superior estimates over a traditional regression model. In the spatial regression models, BFV disease incidence shows an inverse relationship with minimum temperature, low tide and distance to coast, and positive relationship with rainfall in coastal areas whereas in whole Queensland the disease shows an inverse relationship with minimum temperature and high tide and positive relationship with rainfall. This study determines the most significant spatial risk factors for BFV disease across Queensland. Empirical models were developed to forecast the future risk of BFV disease outbreaks in coastal Queensland using existing climatic, socio-economic and tidal conditions under climate change scenarios. Logistic regression models were developed using BFV disease outbreak data for the existing period (2000-2008). The most parsimonious model had high sensitivity, specificity and accuracy and this model was used to estimate and forecast BFV disease outbreaks for years 2025, 2050 and 2100 under climate change scenarios for Australia. Important contributions arising from this research are that: (i) it is innovative to identify high-risk coastal areas by creating buffers based on grid-centroid and the use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression method was used to account for spatial dependence and heterogeneity of data in the study area; (iii) it determined a range of potential spatial risk factors for BFV disease; and (iv) it predicted the future risk of BFV disease outbreaks under climate change scenarios in Queensland, Australia. In conclusion, the thesis demonstrates that the distribution of BFV disease exhibits a distinct spatial and temporal variation. Such variation is influenced by a range of spatial risk factors including climatic, demographic, socio-economic, ecological and tidal variables. The thesis demonstrates that spatial regression method can be applied to better understand the transmission dynamics of BFV disease and its risk factors. The research findings show that disease notification data can be integrated with multi-factorial risk factor data to develop build-up models and forecast future potential disease risks under climate change scenarios. This thesis may have implications in BFV disease control and prevention programs in Queensland.

Spatial-temporal epidemiological analyses of two sympatric, co-endemic alphaviral diseases in Queensland, Australia

Background: The two most reported mosquito-borne diseases in Queensland, a northern state of Australia, are Ross River virus (RRV) disease and Barmah Forest virus (BFV) disease. Both diseases are endemic in Queensland and have similar clinical symptoms and comparable transmission cycles involving a complex inter-relationship between human hosts, various mosquito vectors, and a range of nonhuman vertebrate hosts, including marsupial mammals that are unique to the Australasian region. Although these viruses are thought to share similar vectors and vertebrate hosts, RRV is four times more prevalent than BFV in Queensland. Methods: We performed a retrospective analysis of BFV and RRV human disease notification data collected from 1995 to 2007 in Queensland to ascertain whether there were differences in the incidence patterns of RRV and BFV disease. In particular, we compared the temporal incidence and spatial distribution of both diseases and considered the relationship between their disease dynamics. We also investigated whether a peak in BFV incidence during spring was indicative of the following RRV and BFV transmission season incidence levels. Results: Although there were large differences in the notification rates of the two diseases, they had similar annual temporal patterns, but there were regional variations between the length and magnitude of the transmission seasons. During periods of increased disease activity, however, there was no association between the dynamics of the two diseases. Conclusions: The results from this study suggest that while RRV and BFV share similar mosquito vectors, there are significant differences in the ecology of these viruses that result in different epidemic patterns of disease incidence. Further investigation is required into the ecology of each virus to determine which factors are important in promoting RRV and BFV disease outbreaks.

Statistical significance of sequential firing patterns in multi-neuronal spike trains

Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

Inferring neuronal network connectivity from spike data: A temporal data mining approach

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent d evelopments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.

Variabilidade espacial e temporal da distribuição do material particulado em suspensão nas proximidades do canal de navegação da baía de Sepetiba (RJ)

Este estudo teve como objetivo principal caracterizar os padrões de distribuição do material particulado em suspensão ao longo da baía de Sepetiba, associando as variabilidades espaciais e temporais com ciclos de maré. Além disso, o estudo também avalia a utilização de equipamentos acústicos como ferramentas à estimativa das concentrações de material particulado em suspensão. A aquisição de dados foi realizada num total de sete campanhas realizadas entre novembro de 2010 e dezembro de 2011. Sete estações foram posicionadas nas proximidades do canal principal de acesso à baía, ao longo de um transecto que se estende do seu interior até sua desembocadura. As sete campanhas amostrais se distribuem em duas séries longas, de 13 e 25 horas, de aquisição em um ponto fixo, e cinco amostragens ao longo das estações. A aquisição de dados envolve: coleta de amostras de água, utilizadas nas estimativas das concentrações de material particulado; coleta de sedimentos de fundo para caracterização granulométrica das estações amostradas; perfis de parâmetros físico-químicos; dados de correntômetria adquiridos junto ao fundo. O processamento das amostras de água e sedimentos foi realizado no laboratório de Geologia Marinha da Faculdade de Oceanografia da Universidade do Estado do Rio de Janeiro. As concentrações de material particulado em suspensão foram utilizadas na calibração de sensores acústicos e óticos, permitindo uma avaliação espaço-temporal mais detalhada dos padrões de distribuição junto ao fundo e ao longo da coluna dágua. Os dados observados permitiram identificar que as maiores concentrações de material particulado em suspensão ocorrem em condições de maré enchente, e estão associadas à assimetria de maré. A baía pode ser dividida em dois setores: um na porção mais interna, onde se observou maior influência do aporte fluvial, onde as concentrações de material particulado em suspensão respondem à propagação da pluma do canal de São Francisco; e outro que se estende da porção central até sua desembocadura, onde predomina o domínio marinho, com influência de eventos oriundos da plataforma continental. Também pode ser identificada a influência do fenômeno La Niña, que provavelmente foi responsável por: altas salinidades encontradas no interior da baía e ocorrência da Água Central do Atlântico Sul à baixas profundidades. Quanto à utilização de equipamentos acústicos nas estimativas das concentrações de material particulado, os dados se demonstraram ricos em detalhes, que permitiram avaliar o comportamento do material particulado junto ao fundo frente a diferentes condições de maré, turbulência e incidência de oscilações.

Etograma e orçamento temporal de um grupo de machos não pareados de Phyllostomus hastatus (Pallas 1767) (Phyllostomidae: Phyllostominae)

Embora exista uma enorme variação de histórias de vida e ecologia, a maioria das espécies de morcegos mostra-se, em algum nível, social. Os aspectos de ecologia comportamental de quirópteros tem sido estudados, embora a maior parte refira-se a colônias de maternidades em regiões de clima temperado. Repertórios comportamentais completos e orçamentos temporais são raros, especialmente para a região Neotropical. Para compreender melhor a sociabilidade neste grupo, mostra-se importante focar também em grupos neutros com relação a fatores associados à atividade reprodutiva. Neste sentido, o estudo de grupos de machos neotropicais pode apresentar respostas importantes. O comportamento de um grupo machos não pareados de Phyllostomus hastatus foi estudado dentro do seu abrigo por aproximadamente 100 horas entre os meses de janeiro e agosto de 2012. O grupo estava abrigado no forro de um telhado de uma casa em desuso da Vila Dois Rios, na Ilha Grande. Os comportamentos foram registrados com uma câmera sensível a infra-infravermelho Sony DCR-HC28 em modo night-vision. Quando necessário, utilizei uma luz de auxílio infravermelha. A partir dos vídeos eu elaborei primeiramente um etograma. Os comportamentos classificados como estados foram usados para fazer um orçamento temporal, usando metodologia de amostragem por varredura e amostragem instantânea. Adicionalmente, fiz algumas observações a respeito de horário de entrada e saída dos morcegos e do tamanho do grupo. Organizei os comportamentos em seis categorias, com um total de 24 comportamentos distintos. Os comportamentos descritos são consistentes com os publicados em outros etogramas de morcegos, inclusive de alguns megaquirópteros. Um comportamento mais notável foi ventilando, que parece raro entre os microquirópteros, mas provavelmente importante na termorregulação. Nos meses analisados os morcegos alocaram aproximadamente 50% do tempo ao estado dormindo; 14,6% ao estado parado; 15,3% ao estado ativo; 0,9% ao estado andando; 0,1% ao estado voando; 14,1% ao estado higiene; e 3,5% ao estado ventilando. O orçamento temporal foi semelhante aos descritos para outros microquirópteros no interior de abrigos, com uma maior prevalência do estado dormindo, e com picos de atividade (principalmente do estado higiene) antes e depois das saídas noturnas. A higiene parece ter um papel importante no controle de ectoparasitas, e talvez algum papel social, mas como a higiene de outros indivíduos só foi observada uma única vez, não pude concluir nada a respeito. O presente trabalho é o primeiro etograma para Phyllostomus hastatus e o primeiro etograma e orçamento temporal para um grupo de machos em Chiroptera. Observei algumas diferenças importantes do grupo estudado com trabalhos já publicados sobre essa espécie, e sugiro que essas diferenças sejam estudadas mais a fundo. Apesar desta dissertação trazer contribuições importantes, fica claro que ainda falta muito a ser examinado nesse campo.

Breeding behavior under temporal risk of predation in male root voles (Microtus oeconomus)

We examined breeding behavior responses of male root votes (Microtus oeconomus) to temporal risk of predation by using acute and chronic exposure to predator odor. The 2 series of exposure experiments provided 2 types of temporal patterns of risk: continuous safety with a brief period of risk and Sustained risk with a brief period of safety. Male root votes that were acutely exposed to predator odor for I h suppressed their breeding behavior, but bred immediately after exposure to control odor for I h. Those chronically exposed to predator odor for 20 days maintained behavioral suppression during the 1-h period of exposure to control odor. Acutely exposed males did not change their physiological patterns of breeding, but those chronically exposed to predator odor had reduced testosterone concentration and epididymis index. Our results indicate that breeding behavior in a given situation depends on the overall patterns of risk experienced by male root votes, and the acute and chronic stress responses that affect reproduction are responsible for different behavioral responses to the 2 types of temporal patterns of risk. We also discuss the reasons for conflicting results about breeding suppression of votes between previous studies in the laboratory and the field.

Sequential Memory with ART: A Self-Organizing Network Capable of Learning Sequences of Patterns

A model which extends the adaptive resonance theory model to sequential memory is presented. This new model learns sequences of events and recalls a sequence when presented with parts of the sequence. A sequence can have repeated events and different sequences can share events. The ART model is modified by creating interconnected sublayers within ART's F2 layer. Nodes within F2 learn temporal patterns by forming recency gradients within LTM. Versions of the ART model like ART I, ART 2, and fuzzy ART can be used.

Temporal pattern recognition in video clips detection

Temporal representation and reasoning plays an important role in Data Mining and Knowledge Discovery, particularly, in mining and recognizing patterns with rich temporal information. Based on a formal characterization of time-series and state-sequences, this paper presents the computational technique and algorithm for matching state-based temporal patterns. As a case study of real-life applications, zone-defense pattern recognition in basketball games is specially examined as an illustrating example. Experimental results demonstrate that it provides a formal and comprehensive temporal ontology for research and applications in video events detection.

Impacts of bioturbation on temporal variation in bacterial and archaeal nitrogen-cycling gene abundance in coastal sediments

In marine environments, macrofauna living in or on the sediment surface may alter the structure, diversity and function of benthic microbial communities. In particular, microbial nitrogen (N)-cycling processes may be enhanced by the activity of large bioturbating organisms. Here, we study the effect of the burrowing mud shrimp Upogebia deltaura upon temporal variation in the abundance of genes representing key N-cycling functional guilds. The abundance of bacterial genes representing different N-cycling guilds displayed different temporal patterns in burrow sediments in comparison with surface sediments, suggesting that the burrow provides a unique environment where bacterial gene abundances are influenced directly by macrofaunal activity. In contrast, the abundances of archaeal ammonia oxidizers varied temporally but were not affected by bioturbation, indicating differential responses between bacterial and archaeal ammonia oxidizers to environmental physicochemical controls. This study highlights the importance of bioturbation as a control over the temporal variation in nitrogen-cycling microbial community dynamics within coastal sediments.

Detecting localized homogeneous anomalies over spatio-temporal data

The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as – weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases – i) discovering homogeneous regions, and ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.

Geographical and temporal distribution of human giardiasis in Ontario, Canada

Affiliation: Pascal Michel : Département de pathologie et microbiologie, Faculté de médecine vétérinaire, Université de Montréal