Spatio-temporal Co-occurrence Research Articles

Spatiotemporal interactions of native and introduced salmonid top predators in a large lake: implications for species restoration

Animal interactions are an integral part of a community’s function, with influences ranging from the spatiotemporal habitat use of species to population effects to ecosystem management. Numerous non-native species are established or maintained through stocking in freshwater ecosystems with the potential to affect restoration of native species. Using acoustic telemetry, this study quantified the spatiotemporal co-occurrence of the native top-predator lake trout (Salvelinus namaycush) with non-native Chinook salmon (Oncorhynchus tshawytscha) in Lake Ontario over 2.5 years. Core home range overlap was observed during the summer, with depth acting as a mechanism of segregation, but with potential for interactions during vertical exploration. Fine-scale individual pairwise interactions confirmed the home range results. No horizontal overlap was observed during the winter and spring, but confidence was lower due to poor instrument coverage in deeper water, which the two species may frequent in these seasons. These results demonstrate the importance of depth in understanding fish interactions and highlight the usefulness of considering pairwise species interactions for understanding ecosystem community function to resource managers with multiple projects involving both native and non-native species.

The spatio-temporal co-occurrence of free-ranging common fallow deer and domestic caprines on the island of Rhodes, Greece

Abstract The Dama dama dama population of the island of Rhodes has an important conservation significance because of its unique genetic characters. Currently, many are the factors that threaten its survival. Aim of this paper is to provide the first data on the spatio-temporal co-occurrence between the only free-ranging Greek population of common fallow deer and local domestic caprines. Kilometric abundance index (KAI) was derived from spotlight counts. The unattended domestic caprines appear to impact on fallow deer, in particular during fawning season. Livestock monitoring over time can act as a proxy indicator of the status of this deer population.

A Spatio-Temporal Co-Clustering Framework for Discovering Mobility Patterns: A Study of Manhattan Taxi Data

Research on clustering spatio-temporal data to extract mobility patterns requires further development, as most existing studies do not simultaneously integrate data along both spatial dimensions and temporal dimensions but instead focus on only one dimension or separate the dimensions in analyses and applications, which could lead to discoveries that are not representative of the overall data or are dificult to interpret. To simultaneously reveal the spatial and temporal patterns of urban mobility datasets, we propose an analytical framework that is based on co-clustering and enables mobility behaviors to be distinguished in spatial and temporal dimensions. We use one month of taxi GPS data from the Manhattan area to explore spatio-temporal co-occurrence patterns. The spatial and temporal dimensions of taxi trip data were co-clustered by using the Bregman Block Average co-clustering algorithm with I-divergence (BBAC_I). We performed this process on weekdays and holidays and compared the mobility differences between these two periods. The experimental results demonstrated the effectiveness of this analytical framework, with which we can reveal the spatial patterns and their temporal dynamics as well as temporal patterns and their spatial dynamics in mobility data.

Density regulation of co-occurring herbivores via two indirect effects mediated by biomass and non-specific induced plant defenses

Two herbivorous species that share a single plant can interact indirectly with one another, even without direct interaction. One type of indirect interaction is exploitative resource competition, which results from a reduction in plant biomass; another type is that caused by changes in plant traits. These are referred to as indirect effects, mediated, respectively, by biomass and plant traits. The two indirect effect types often occur simultaneously, and they are difficult to partition. To investigate the roles of the two indirect effects on both herbivores, a dynamic one-plant, two-herbivore system model was analyzed assuming the spatiotemporal co-occurrence of the herbivores and the plants’ non-specific induced defenses. Our analysis revealed that the densities of coexisting competitively superior and subordinate herbivores were regulated by negative indirect effects mediated by plant biomass and defense, respectively. This indicates that indirect effects mediated by plant biomass and plant traits can be important regulators of herbivore population size in equilibrium with herbivore coexistence. Our results could be generally applicable to plant–herbivore interactions with non-specific plant defense that is induced through both intra- and transgenerational responses.

Towards Better Detection and Analysis of Massive Spatiotemporal Co-Occurrence Patterns

With the rapid development of sensing technologies, massive spatiotemporal data have been acquired from the urban space with respect to different domains, such as transportation and environment. Numerous co-occurrence patterns (e.g., traffic speed <; 10km/h, weather = foggy, and air quality = unhealthy) between the transportation data and other types of data can be obtained with given spatiotemporal constraints (e.g., within 3 kilometers and lasting for 2 hours) from these heterogeneous data sources. Such patterns present valuable implications for many urban applications, such as traffic management, pollution diagnosis, and transportation planning. However, extracting and understanding these patterns is beyond manual capability because of the scale, diversity, and heterogeneity of the data. To address this issue, a novel visual analytics system called CorVizor is proposed to identify and interpret these co-occurrence patterns. CorVizor comprises two major components. The first component is a co-occurrence mining framework involving three steps, namely, spatiotemporal indexing, co-occurring instance generation, and pattern mining. The second component is a visualization technique called CorView that implements a level-of-detail mechanism by integrating tailored visualizations to depict the extracted spatiotemporal co-occurrence patterns. The case studies and expert interviews are conducted to demonstrate the effectiveness of CorVizor.

Spatio-temporal co-occurrence of hotspots of tuberculosis, poverty and air pollution in Lima, Peru

Growing evidence suggests pollution and other environmental factors have a role in the development of tuberculosis (TB), however, such studies have never been conducted in Peru. Considering the association between air pollution and specific geographic areas, our objective was to determine the spatial distribution and clustering of TB incident cases in Lima and their co-occurrence with clusters of fine particulate matter (PM2.5) and poverty. We found co-occurrences of clusters of elevated concentrations of air pollutants such as PM2.5, high poverty indexes, and high TB incidence in Lima. These findings suggest an interplay of socio-economic and environmental in driving TB incidence.

Spatiotemporal co-occurrence and overlap of two sympatric mongoose species in an urban environment

Abstract Small carnivores are becoming increasingly common in urban areas. What has received less attention is whether and how resource partitioning among sympatric species in urban areas facilitates their coexistence. We examined the spatial, temporal and combined spatiotemporal occurrence and overlap of co-existing yellow mongoose Cynictis penicillata and slender mongoose Galerella sanguinea in an urban estate in South Africa. The reserve comprised two parts, an Eco-Estate where human residential and natural areas are interspersed and wildlife has greater contact with people, and a Nature Estate, where contact is reduced by palisade fencing between people and natural areas. Using photographic data from camera traps collected over 11 consecutive months, we found a moderate level of spatial overlap between the mongoose species. Differences between the species occurred at a finer habitat scale: yellow mongooses were more common in open habitats located near human residents whereas the slender mongooses were more common in covered areas further away from human residents. The detection probability of the yellow mongoose, however, was greater than that of the slender mongoose, and the occupancy probability of the slender mongoose was reduced in the presence of the yellow mongoose. Although both species demonstrated bimodal diurnal peaks in activity, they varied in their active periods, with temporal overlap being greater during colder than warmer months. No complete spatiotemporal overlap (occurrence in the same place at the same time/within a 10-min period) occurred. This may have been as a result of the difference in detection and occupancy probabilities of the two species. Resource availability (food), however, appears to influence the different habitat selection, space use, and activity patterns of yellow and slender mongoose in the study area. Therefore, we conclude that partitioning along the spatial and somewhat on the temporal dimensions aids in the coexistence of these mongoose species in an urban environment.

The opportunity cost of spiny dogfish (Squalus acanthias) bycatch in a ling (Genypterus blacodes) longline fishery

The economic impact of spiny dogfish in a New Zealand ling longline fishery was assessed for the purpose of guiding early-stage bycatch reduction device (BRD) development. Ling fishermen describe being “dogged out”, events where high dogfish catch per unit effort (CPUE) is assumed to cause low ling CPUE. Overall, fisheries observer data showed a significant, weak positive relationship between these species' CPUE, which was expected given their spatio-temporal co-occurrence. On fishing lines in the upper quartile for spiny dogfish CPUE (193 dogfish per thousand hooks, a proxy for large “dogged out” events), there was a significant, moderate, negative relationship between dogfish and ling CPUE, providing support for the view of ling fishermen. An opportunity cost for these events was estimated where the value of ling ‘lost’ was approximated by using the ratio of ling to dogfish per line. These lines caught almost 16,000 dogfish, which equated to just over 6000 lost ling worth 75,000 NZD. This cost was adjusted to all hooks in the dataset, as large dogfish events cannot be discerned in advance, equating to 23 cents per hook, per fishing event. A per-hook BRD that eliminates this opportunity cost must reduce dogfish CPUE to below the “dogged out” threshold and cost <23 cents per unit, per fishing event. A 23 cent, single-use BRD would meet this standard, as would a $23 BRD that can be used 100 times. This approach could be used to establish BRD price and performance standards for any bycatch problem that reduces fishing efficiency.

A statistical method for detecting spatiotemporal co-occurrence patterns

ABSTRACTSpatiotemporal co-occurrence patterns (STCOPs) are subsets of Boolean features whose instances frequently co-occur in both space and time. The detection of STCOPs is crucial to the investigation of the spatiotemporal interactions among different features. However, prevalent STCOPs reported by available methods do not necessarily indicate the statistically significant dependence among different features, which is likely to result in highly erroneous assessments in practice. To improve the reliability of results, this paper develops a statistical method to detect STCOPs and discern their statistical significance. The proposed method detects STCOPs against the null hypothesis that the spatiotemporal distributions of different features are independent of each other. To construct the null hypothesis, suitable spatiotemporal point-process models considering spatiotemporal autocorrelation are employed to model the distributions of different features. The performance of the proposed statistical method is assessed by synthetic experiments and a case study aimed at identifying crime patterns among multiple crime types in Portland City. The experimental results demonstrate that the proposed method is more effective for detecting meaningful STCOPs than the available alternative methods.

Research of MDCOP mining based on time aggregated graph for large spatio-temproal data sets

Discovering mixed-drove spatiotemporal co-occurrence patterns (MDCOPs) is important for network security such as distributed denial of service (DDoS) attack. There are usually many features when we are suffering from a DDoS attacks such as the server CPU is heavily occupied for a long time, bandwidth is hoovered and so on. In distributed cooperative intrusion, the feature information from multiple intrusion detection sources should be analyzed simultaneously to find the spatial correlation among the feature information. In addition to spatial correlation, intrusion also has temporal correlation. Some invasions are gradually penetrating, and attacks are the result of cumulative effects over a period of time. So it is necessary to discover mixed-drove spatiotemporal co-occurrence patterns (MDCOPs) in network security. However, it is difficult to mine MDCOPs from large attack event data sets because mining MDCOPs is computationally very expensive. In information security, the set of candidate co-occurrence attack event data sets is exponential in the number of object-types and the spatiotemporal data sets are too large to be managed in memory. To reduce the number of candidate co-occurrence instances, we present a computationally efficient MDCOP Graph Miner algorithm by using Time Aggregated Graph. which can deal with large attack event data sets by means of file index. The correctness, completeness and efficiency of the proposed methods are analyzed.

Action Recognition Using Spatio-temporal Co-occurrence Features and Improved VLAD

Action Recognition Using Spatio-temporal Co-occurrence Features and Improved VLAD

Spatio-Temporal Co-Occurrence Characterizations For Human Action Classification

The human action classification task is a widely researched topic and is still an open problem. Many state-ofthe- arts approaches involve the usage of bag-of-video-words with spatio-temporal local features to construct characterizations for human actions. In order to improve beyond this standard approach, we investigate the usage of co-occurrences between local features. We propose the usage of co-occurrences information to characterize human actions. A trade-off factor is used to define an optimal trade-off between vocabulary size and classification rate. Next, a spatio-temporal co-occurrence technique is applied to extract co-occurrence information between labeled local features. Novel characterizations for human actions are then constructed. These include a vector quantized correlogram-elements vector, a highly discriminative PCA (Principal Components Analysis) co-occurrence vector and a Haralick texture vector. Multi-channel kernel SVM (support vector machine) is utilized for classification. For evaluation, the well known KTH as well as the UCF-Sports action datasets are used. We obtained state-of-the-arts classification performance. We also demonstrated that we are able to fully utilize co-occurrence information, and improve the standard bag-of-video-words approach.

Vessel Spatio-temporal Knowledge Discovery with AIS Trajectories Using Co-clustering

Large volumes of data collected by the Automatic Identification System (AIS) provide opportunities for studying both single vessel motion behaviours and collective mobility patterns on the sea. Understanding these behaviours or patterns is of great importance to maritime situational awareness applications. In this paper, we leveraged AIS trajectories to discover vessel spatio-temporal co-occurrence patterns, which distinguish vessel behaviours simultaneously in terms of space, time and other dimensions (such as ship type, speed, width etc.). To this end, available AIS data were processed to generate spatio-temporal matrices and spatio-temporal tensors (i.e., multidimensional arrays). We then imposed a sparse bilinear decomposition on the matrices and a sparse multi-linear decomposition on the tensors. Experimental results on a real-world dataset demonstrated the effectiveness of this methodology, with which we show the existence of connection among regions, time, and vessel attributes.

Spatio-temporal co-occurrence of alien and native molluscs: a modelling approach using physical-chemical predictors

The invasion of alien species can have serious economic and ecological impacts. Ecologically, invasions often lead to an increased rate of native species replacement and decreased biodiversity. A critical step in the dominance of alien species is their successful co-occurrence with native species. In this study, we assessed the occurrence of alien molluscs and their co-occurrence with native molluscs and identified the determining physical-chemical variables. We expected that a combination of some key variables of water quality could provide suitable conditions promoting alien molluscs to occur and to co-occur with native molluscs. The analyses were based on 20-year data, collected from river systems across Flanders (Belgium). Classification Trees (CTs) were used to perform the analyses and to develop the predictive models. Based on CT models, the co-occurrence of alien and native molluscs could be reliably predicted based on physical-chemical variables. However, there was insufficient data to determine the environmental conditions in which alien taxa dominate. From the past to the present, spatial co-occurrence significantly increased. Sinuosity, ammonium and nitrate concentrations, chemical oxygen demand, pH and conductivity were the key determining variables. Our findings suggest that the co-occurrence of alien and native molluscs mainly occurs in straight rivers with good chemical water quality. These results provide insights into the ecology and behaviour of alien species which could support management practices and priority setting for conservation planning in surface waters of Flanders and Europe.

Mining At Most Top-K% Spatiotemporal Co-occurrence Patterns in Datasets with Extended Spatial Representations

Spatiotemporal co-occurrence patterns (STCOPs) in datasets with extended spatial representations are two or more different event types, represented as polygons evolving in time, whose instances often occur together in both space and time. Finding STCOPs is an important problem in domains such as weather monitoring, wildlife migration, and solar physics. Nevertheless, in real life, it is difficult to find a suitable prevalence threshold without prior domain-specific knowledge. In this article, we focus our work on the problem of mining at most top-K% of STCOPs from continuously evolving spatiotemporal events that have polygon-like representations, without using a user-specified prevalence threshold.

Geographical impacts on social networks from perspectives of space and place: an empirical study using mobile phone data

Space and place are two fundamental concepts in geography. Geographical factors have long been known as drivers of many aspects of people’s social networks. But whether and how space and place affect social networks differently are still unclear. The widespread use of location-aware devices provides a novel source for distinguishing the mechanisms of their impacts on social networks. Using mobile phone data, this paper explores the effects of space and place on social networks. From the perspective of space, we confirm the distance decay effect in social networks, based on a comparison between synthetic social ties generated by a null model and actual social ties derived from real-world data. From the perspective of place, we introduce several measures to evaluate interactions between individuals and inspect the trio relationship including distance, spatio-temporal co-occurrence, and social ties. We found that people’s interaction is a more important factor than spatial proximity, indicating that the spatial factor has a stronger impact on social networks in place compared to that in space. Furthermore, we verify the hypothesis that interactions play an important role in strengthening friendships.

Modelling food web structure using an end-to-end approach in the coastal ecosystem of the Gulf of Gabes (Tunisia)

Given the ecological importance and high socio-economic value of the fishery of the Gulf of Gabes, an end-to-end model was applied to its continental shelf ecosystem to characterize the structure of the food web in the 2000s. This approach consisted in forcing a high trophic level model (OSMOSE) with an existing biogeochemical model (Eco3M-MED) representing the seasonal dynamics of the low trophic levels. The two models were linked through trophic interactions to represent the ecosystem dynamics from primary producers to top predators. In this study, we developed the multispecies, individual-based model OSMOSE in the Gulf of Gabes (OSMOSE-GoG). This model aims to capture the main processes that influence species life cycle and simulate the functioning of the ecosystem according to opportunistic predation process based on size selection and spatio-temporal co-occurrence between a predator and its prey. The spatial distribution of the eleven modelled species was derived from a Multi-Scale Species Distribution Modelling approach. We calibrated OSMOSE-GoG model with available data of biomass and fishing yield, using an optimization method based on evolutionary algorithms which is suitable for complex and stochastic models. Finally, OSMOSE-GoG was validated against independent data sets at different hierarchical levels: the individual (diet composition), population (mean size of commercial catch) and community levels (mean trophic level) following the Pattern-Oriented Modelling approach. The model outputs were overall consistent with the diet compositions and mean trophic levels derived from the ECOPATH model of the Gulf of Gabes (ECOPATH-GoG) and the observations of mean size of catches. The OSMOSE-GoG can be considered as a baseline model to investigate ecosystem responses to environmental changes and fishing management measures in the Gulf of Gabes.

How solitary are white sharks: social interactions or just spatial proximity?

White sharks (Carcharodon carcharias) are circumglobally distributed large apex predators. While ecologically important, there is very limited study of their social behaviour. Although evident in other large, apex marine predators (e.g. toothed whales) and smaller elasmobranchs (e.g. blacktip reef sharks), the ability of any large pelagic elasmobranch to demonstrate social preferences, tolerance or grouping behaviour is largely unknown. Here, we test whether white sharks in a near-coastal environment form non-random associations with other conspecifics or simply share the same space at the same time. We photo-identified 323 individuals—74 % juvenile females (175–300 cm)—during chumming events at six different sites in Mossel Bay, South Africa, over a 6-year period (2008–2013), and tested for grouping behaviour. We found evidence for random associations among individuals, though spatio-temporal co-occurrence of white sharks in close proximity was weakly structured according to sex and, potentially, body size. Such biological traits may play a minor part in structuring co-occurrence of individuals at fine spatio-temporal scales, which could reflect ontogenetic preferences in diet and site fidelity, or differing tolerance levels for conspecifics of different sexes and sizes. Our study strengthens the evidence that large pelagic shark species are generally solitary and display limited social behaviour. Large pelagic shark species are important top predators, but we know little about their social behaviour. We tested the ability of white sharks (C. carcharias) to form groups and display social preferences for other individuals when they congregate at scavenging events in a coastal environment, where social interactions may be more likely. We found that white sharks co-occur at random, displaying no preferred or avoided associations for other individuals. Nevertheless, there was a minor influence of biological traits, with individuals aggregating according to gender and, possibly, body size. While we hypothesise these effects could represent preferences in diet and site fidelity, or more tolerance for similar-sized individuals of the same sex, our study strengthens the evidence that white sharks are mostly solitary foragers.

Mining spatiotemporal co-occurrence patterns in non-relational databases

Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of feature types whose instances are frequently co-occurring both in space and time. Spatiotemporal co-occurrences reflect the spatiotemporal overlap relationships among two or more spatiotemporal instances both in spatial and temporal dimensions. STCOPs can be potentially used to predict and understand the generation and evolution of different types of interacting phenomena in various scientific fields such as astronomy, meteorology, biology, geosciences. Meaningful and statistically significant data analysis for these scientific fields requires processing sufficiently large datasets. Due to the computationally expensive nature of spatiotemporal operations required for mining spatiotemporal co-occurrences, it is increasingly difficult to identify spatiotemporal co-occurrences and discover STCOPs in centralized system settings. As a solution, we developed a cloud-based distributed mining system for discovering STCOPs. Our system uses Accumulo, a column-oriented non-relational database management system as its backbone. In order to efficiently mine the STCOPs, we propose three data models for managing trajectory-based spatiotemporal data in Accumulo. We introduce an in-memory join-index structure and a join algorithm for effectively performing spatiotemporal join operations on spatiotemporal trajectories in non-relational databases. Lastly, with the experiments with artificial and real life datasets, we evaluate the performance of the proposed models for STCOP mining.

From faces to hands: Changing visual input in the first two years

Human development takes place in a social context. Two pervasive sources of social information are faces and hands. Here, we provide the first report of the visual frequency of faces and hands in the everyday scenes available to infants. These scenes were collected by having infants wear head cameras during unconstrained everyday activities. Our corpus of 143hours of infant-perspective scenes, collected from 34 infants aged 1month to 2years, was sampled for analysis at 1/5Hz. The major finding from this corpus is that the faces and hands of social partners are not equally available throughout the first two years of life. Instead, there is an earlier period of dense face input and a later period of dense hand input. At all ages, hands in these scenes were primarily in contact with objects and the spatio-temporal co-occurrence of hands and faces was greater than expected by chance. The orderliness of the shift from faces to hands suggests a principled transition in the contents of visual experiences and is discussed in terms of the role of developmental gates on the timing and statistics of visual experiences.