Hierarchy, Individuality and Paleoecosystems

1990 ◽  
Vol 5 ◽  
pp. 31-47 ◽  
Author(s):  
William Miller
Keyword(s):  
Spatial Variation ◽  
Temporal Dynamics ◽  
Data Sets ◽  
Field Observations ◽  
Important Research ◽  
Temporal Scales ◽  
Shell Beds ◽  
Spatio Temporal ◽  
Patterns And Processes ◽  
Fossil Data

Techniques and field observations that detect “spatial variation” and “temporal dynamics” in fossil deposits have become important research programs in paleosynecology. These studies attempt to delineate aggregates and sequences of fossils at varied scales that appear to result from processes encompassing larger areas and greater time spans than the processes familiar to neoecologists. Description and modeling of patterns and processes at these scales would be significant contributions to historical biology, but little attention has been given to the ontology of “natural” multispecies units discernable in fossil data sets at varied spatio-temporal scales of resolution. Do patterns at any of these nested levels of variation – patches within shell beds, shell beds within biofacies, and so on – represent the elusive original community of organisms?

scholarly journals Spatio-Temporal Dynamics in Collective Frog Choruses Examined by Mathematical Modeling and Field Observations

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Ikkyu Aihara ◽  
Takeshi Mizumoto ◽  
Takuma Otsuka ◽  
Hiromitsu Awano ◽  
Kohei Nagira ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Temporal Dynamics ◽  
Field Observations ◽  
Spatio Temporal

scholarly journals Millennial-scale faunal record reveals differential resilience of European large mammals to human impacts across the Holocene

2016 ◽  
Vol 283 (1827) ◽  
pp. 20152152 ◽  
Author(s):  
Jennifer J. Crees ◽  
Chris Carbone ◽  
Robert S. Sommer ◽  
Norbert Benecke ◽  
Samuel T. Turvey
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Human Impacts ◽  
Biodiversity Loss ◽  
Mammal Species ◽  
The Holocene ◽  
Range Change ◽  
Spatio Temporal ◽  
Patterns And Processes ◽  
Species Specific

The use of short-term indicators for understanding patterns and processes of biodiversity loss can mask longer-term faunal responses to human pressures. We use an extensive database of approximately 18 700 mammalian zooarchaeological records for the last 11 700 years across Europe to reconstruct spatio-temporal dynamics of Holocene range change for 15 large-bodied mammal species. European mammals experienced protracted, non-congruent range losses, with significant declines starting in some species approximately 3000 years ago and continuing to the present, and with the timing, duration and magnitude of declines varying individually between species. Some European mammals became globally extinct during the Holocene, whereas others experienced limited or no significant range change. These findings demonstrate the relatively early onset of prehistoric human impacts on postglacial biodiversity, and mirror species-specific patterns of mammalian extinction during the Late Pleistocene. Herbivores experienced significantly greater declines than carnivores, revealing an important historical extinction filter that informs our understanding of relative resilience and vulnerability to human pressures for different taxa. We highlight the importance of large-scale, long-term datasets for understanding complex protracted extinction processes, although the dynamic pattern of progressive faunal depletion of European mammal assemblages across the Holocene challenges easy identification of ‘static’ past baselines to inform current-day environmental management and restoration.

scholarly journals Similarity-based fusion of MEG and fMRI reveals spatio-temporal dynamics in human cortex during visual object recognition

2015 ◽  
Author(s):  
Radoslaw Cichy ◽  
Dimitrios Pantazis ◽  
Aude Oliva
Keyword(s):  
Object Recognition ◽  
Temporal Dynamics ◽  
Imaging Techniques ◽  
Visual Object ◽  
Data Sets ◽  
Visual Object Recognition ◽  
Occipital Pole ◽  
Spatio Temporal ◽  
The Brain

Every human cognitive function, such as visual object recognition, is realized in a complex spatio-temporal activity pattern in the brain. Current brain imaging techniques in isolation cannot resolve the brain's spatio-temporal dynamics because they provide either high spatial or temporal resolution but not both. To overcome this limitation, we developed a new integration approach that uses representational similarities to combine measurements from different imaging modalities - magnetoencephalography (MEG) and functional MRI (fMRI) - to yield a spatially and temporally integrated characterization of neuronal activation. Applying this approach to two independent MEG-fMRI data sets, we observed that neural activity first emerged in the occipital pole at 50-80ms, before spreading rapidly and progressively in the anterior direction along the ventral and dorsal visual streams. These results provide a novel and comprehensive, spatio-temporally resolved view of the rapid neural dynamics during the first few hundred milliseconds of object vision. They further demonstrate the feasibility of spatially unbiased representational similarity based fusion of MEG and fMRI, promising new insights into how the brain computes complex cognitive functions.

scholarly journals How to Draw a Neighborhood? The Potential of Big Data, Regionalization, and Community Detection for Understanding the Heterogeneous Nature of Urban Neighborhoods.

2019 ◽  
Author(s):  
Ate Poorthuis
Keyword(s):  
Big Data ◽  
Social Life ◽  
Technical Problem ◽  
Data Availability ◽  
Data Sets ◽  
Temporal Scales ◽  
Data Set ◽  
Heterogeneous Nature ◽  
Spatio Temporal ◽  
Neighborhood Boundaries

How to draw neighborhood boundaries, or spatial regions in general, has been a long‐standing focus in Geography. This article examines this question from a methodological perspective, often referred to as regionalization, with an empirical study of neighborhoods in New York City. I argue that methodological advances, combined with the affordances of big data, enable a different, more nuanced approach to regionalization than has been possible in the past. Conventional data sets often dictate constraints in terms of data availability and spatio‐temporal granularity. However, big data is now available at much finer spatio‐temporal scales and covers a wider array of aspects of social life. The emergence of these data sets supports the notion that neighborhoods can be fuzzy and highly dependent on spatio‐temporal scales and socio‐economic variables. As such, these new data sets can help to bring quantitative analysis in line with social theory that has long emphasized the heterogeneous nature of neighborhoods. This article uses a data set of geotagged tweets to demonstrate how different “sets” of neighborhoods may exist at different spatio‐temporal scales and for different algorithms. Such varying neighborhood boundaries are not a technical problem in need of a solution but rather a reflection of the complexity of the underlying urban fabric.

scholarly journals Geostatistical tools for the study of insect spatial distribution: practical implications in the integrated management of orchard and vineyard pests

2014 ◽  
Vol 50 (No. 2) ◽  
pp. 97-110 ◽  
Author(s):  
A. Sciarretta ◽  
P. Trematerra
Keyword(s):  
Temporal Dynamics ◽  
Integrated Management ◽  
Field Studies ◽  
Agricultural Systems ◽  
Practical Applications ◽  
Insect Populations ◽  
Spatio Temporal ◽  
Patterns And Processes ◽  
Fruit Orchards ◽  
Practical Implications

Spatial heterogeneity in agricultural systems is recognised as an important source of variability to be investigated. In the evolution of Integrated Pest Management (IPM), patterns and processes that influence spatio-temporal dynamics in insect populations tend to assume more importance compared to the classical theory. Geostatistics represent a valuable tool to investigate the spatial pattern of insect populations and to support pest control. After an explanation of the geostatistical analysis, in the present paper we provided an overview of practical applications in managing pests, focusing on fruit orchards and vineyards. The utility of geostatistical tools is illustrated with examples taken from field studies, with attention to the analysis of spatial patterns, monitoring schemes, use of traps, scale issues, precision targeting, and risk assessment maps. Potential approaches in the context of IPM are discussed in relation to future perspectives.    

scholarly journals A MANY-TO-MANY FULLY CONVOLUTIONAL RECURRENT NETWORK FOR MULTITEMPORAL CROP RECOGNITION

2019 ◽  
Vol IV-2/W7 ◽  
pp. 25-32 ◽  
Author(s):  
J. A. Chamorro ◽  
J. D. Bermudez ◽  
P. N. Happ ◽  
R. Q. Feitosa
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Temporal Dynamics ◽  
Agricultural Practices ◽  
Remote Sensing Data ◽  
Recurrent Network ◽  
Data Sets ◽  
Tropical Regions ◽  
Spatio Temporal ◽  
Multitemporal Remote Sensing

<p><strong>Abstract.</strong> Recently, recurrent neural networks have been proposed for crop mapping from multitemporal remote sensing data. Most of these proposals have been designed and tested in temperate regions, where a single harvest per season is the rule. In tropical regions, the favorable climate and local agricultural practices, such as crop rotation, result in more complex spatio-temporal dynamics, where the single harvest per season assumption does not hold. In this context, a demand arises for methods capable of recognizing agricultural crops at multiple dates along the multitemporal sequence. In the present work, we propose to adapt two recurrent neural networks, originally conceived for single harvest per season, for multidate crop recognition. In addition, we propose a novel multidate approach based on bidirectional fully convolutional recurrent neural networks. These three architectures were evaluated on public Sentinel-1 data sets from two tropical regions in Brazil. In our experiments, all methods achieved state-of-the-art accuracies with a clear superiority of the proposed architecture. It outperformed its counterparts in up to 3.8% and 7.4%, in terms of per-month overall accuracy, and it was the best performing method in terms of F1-score for most crops and dates on both regions.</p>

scholarly journals SPATIO-TEMPORAL DYNAMICS ALONG THE TERRAIN GRADIENT OF DIVERSE LANDSCAPE

2014 ◽  
Vol 22 (1) ◽  
pp. 50-63 ◽  
Author(s):  
T. V. Ramachandra ◽  
Settur Bharath ◽  
Aithal Bharath
Keyword(s):  
Land Cover ◽  
Environmental Changes ◽  
Temporal Dynamics ◽  
Demographic Data ◽  
Coastal Region ◽  
Data Sets ◽  
Lulc Change ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
The Impact

Land use (LU) land cover (LC) information at a temporal scale illustrates the physical coverage of the Earth’s terrestrial surface according to its use and provides the intricate information for effective planning and management activities. LULC changes are stated as local and location specifc, collectively they act as drivers of global environmental changes. Understanding and predicting the impact of LULC change processes requires long term historical restorations and projecting into the future of land cover changes at regional to global scales. The present study aims at quantifying spatio temporal landscape dynamics along the gradient of varying terrains presented in the landscape by multi-data approach (MDA). MDA incorporates multi temporal satellite imagery with demographic data and other additional relevant data sets. The gradient covers three different types of topographic features, planes; hilly terrain and coastal region to account the signifcant role of elevation in land cover change. The seasonality is another aspect to be considered in the vegetation dominated landscapes; variations are accounted using multi seasonal data. Spatial patterns of the various patches are identifed and analysed using landscape metrics to understand the forest fragmentation. The prediction of likely changes in 2020 through scenario analysis has been done to account for the changes, considering the present growth rates and due to the proposed developmental projects. This work summarizes recent estimates on changes in cropland, agricultural intensifcation, deforestation, pasture expansion, and urbanization as the causal factors for LULC change.

Spatial and temporal dynamics of Pacific capelin Mallotus catervarius in the Gulf of Alaska: implications for ecosystem-based fisheries management

2020 ◽  
Vol 637 ◽  
pp. 117-140 ◽  
Author(s):  
DW McGowan ◽  
ED Goldstein ◽  
ML Arimitsu ◽  
AL Deary ◽  
O Ormseth ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Marine Ecosystem ◽  
Gulf Of Alaska ◽  
Data Series ◽  
Limited Information ◽  
Important Species ◽  
Multiple Data ◽  
Spatial And Temporal Dynamics ◽  
Spawning Areas ◽  
Spatio Temporal

Pacific capelin Mallotus catervarius are planktivorous small pelagic fish that serve an intermediate trophic role in marine food webs. Due to the lack of a directed fishery or monitoring of capelin in the Northeast Pacific, limited information is available on their distribution and abundance, and how spatio-temporal fluctuations in capelin density affect their availability as prey. To provide information on life history, spatial patterns, and population dynamics of capelin in the Gulf of Alaska (GOA), we modeled distributions of spawning habitat and larval dispersal, and synthesized spatially indexed data from multiple independent sources from 1996 to 2016. Potential capelin spawning areas were broadly distributed across the GOA. Models of larval drift show the GOA’s advective circulation patterns disperse capelin larvae over the continental shelf and upper slope, indicating potential connections between spawning areas and observed offshore distributions that are influenced by the location and timing of spawning. Spatial overlap in composite distributions of larval and age-1+ fish was used to identify core areas where capelin consistently occur and concentrate. Capelin primarily occupy shelf waters near the Kodiak Archipelago, and are patchily distributed across the GOA shelf and inshore waters. Interannual variations in abundance along with spatio-temporal differences in density indicate that the availability of capelin to predators and monitoring surveys is highly variable in the GOA. We demonstrate that the limitations of individual data series can be compensated for by integrating multiple data sources to monitor fluctuations in distributions and abundance trends of an ecologically important species across a large marine ecosystem.

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