Search results for "Spatial Ecology"
showing 10 items of 191 documents
Spatio-temporal dynamics of a planktonic system and chlorophyll distribution in a 2D spatial domain: matching model and data
2017
AbstractField data on chlorophyll distribution are investigated in a two-dimensional spatial domain of the Mediterranean Sea by using for phytoplankton abundances an advection-diffusion-reaction model, which includes real values for physical and biological variables. The study exploits indeed hydrological and nutrients data acquired in situ, and includes intraspecific competition for limiting factors, i.e. light intensity and phosphate concentration. As a result, the model allows to analyze how both the velocity field of marine currents and the two components of turbulent diffusivity affect the spatial distributions of phytoplankton abundances in the Modified Atlantic Water, the upper layer…
Dynamics of Two Picophytoplankton Groups in Mediterranean Sea: Analysis of the Deep Chlorophyll Maximum by a Stochastic Advection-Reaction-Diffusion …
2013
A stochastic advection-reaction-diffusion model with terms of multiplicative white Gaussian noise, valid for weakly mixed waters, is studied to obtain the vertical stationary spatial distributions of two groups of picophytoplankton, i.e., picoeukaryotes and Prochlorococcus, which account about for 60% of total chlorophyll on average in Mediterranean Sea. By numerically solving the equations of the model, we analyze the one-dimensional spatio-temporal dynamics of the total picophytoplankton biomass and nutrient concentration along the water column at different depths. In particular, we integrate the equations over a time interval long enough, obtaining the steady spatial distributions for th…
The spatial pattern of a forest ecosystem
1998
Abstract Statistical analysis of stands of trees as a whole need suitable methods of spatial statistics. Obviously, trees within a stand affect development and survival of their neighbours. They interact and therefore have to be considered as a system of dependent random variates from an unknown stochastic process. One such statistical model which considers the spatial dependence among trees in a forest and their characteristics is a marked point process. The `points', called events in spatial statistics, are the tree positions and the `marks' are tree characteristics such as crown lengths or tree species. A minimal prerequisite for any serious attempt to model an observed pattern is to tes…
A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion
2015
article i nfo The focus of the current study is to compare data fusion methods applied to sensors with medium- and high- spatial resolutions. Two documented methods are applied, the spatial and temporal adaptive reflectance fusion model (STARFM) and an unmixing-based method which proposes a Bayesian formulation to incorporate prior spectral information.Furthermore, thestrengths of both algorithms arecombined ina novel data fusionmethod: the Spatial and Temporal Reflectance Unmixing Model (STRUM). The potential of each method is demonstrated using simulation imagery and Landsat and MODIS imagery. The theoretical basis of the algorithms causes STARFM and STRUM to produce Landsat-like reflecta…
Insight into Disrupted Spatial Patterns of Human Connectome in Alzheimer’s Disease via Subgraph Mining
2012
Alzheimer’s disease (AD) is the most common cause of age-related dementia, which prominently affects the human connectome. In this paper, the authors focus on the question how they can identify disrupted spatial patterns of the human connectome in AD based on a data mining framework. Using diffusion tractography, the human connectomes for each individual subject were constructed based on two diffusion derived attributes: fiber density and fractional anisotropy, to represent the structural brain connectivity patterns. After frequent subgraph mining, the abnormal score was finally defined to identify disrupted subgraph patterns in patients. Experiments demonstrated that our data-driven approa…
Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data
2015
Abstract The accurate quantification of carbon fluxes at continental spatial scale is important for future policy decisions in the context of global climate change. However, many elements contribute to the uncertainty of such estimate. In this study, the uncertainties of eight days gross primary production (GPP) predicted by Random Forest (RF) machine learning models were analysed at the site, ecosystem and European spatial scales. At the site level, the uncertainties caused by the missing of key drivers were evaluated. The most accurate predictions of eight days GPP were obtained when all available drivers were used (Pearson's correlation coefficient, ρ ~ 0.84; Root Mean Square Error (RMSE…
Predictive habitat suitability models to aid the conservation of elasmobranchs in Isla del Coco National Park (Costa Rica)
2021
Abstract Worldwide there is increasing concern for elasmobranch species given that their biological and ecological characteristics make them highly vulnerable to fishing pressure. The disappearance of these species could affect the structure and function of marine ecosystems, which would induce changes in trophic interactions at the community level. For effective conservation and management of elasmobranchs detailed knowledge of their habitat preferences is essential. Yet, there is a poor understanding of their spatial ecology. Isla del Coco National Park is an oceanic island in Pacific Costa Rica and renowned for being a sanctuary for migratory pelagic species, such as elasmobranchs and ot…
Micro-geographies of creative industries clusters in Europe: From hot spots to assemblages
2014
The aim of this paper is to provide basic stylized facts about the spatial patterns of location and co-location of clusters of creative industries in Europe. The research proposes a novel methodology for detailing the spatial delimitation of clusters, based on a geo-statistical algorithm and firm-based micro-data. The procedure is applied to a continuous space of 16 European countries and 15 creative industries in 2009. The investigation reveals that creative firms are highly clustered, and that clusters are concentrated in a ‘creative belt’ stretching from the South of England to the South-east of Germany. These clusters are predominantly metropolitan, heterogeneous, cross borders, and may…
Pattern formation in clouds via Turing instabilities
2020
Pattern formation in clouds is a well-known feature, which can be observed almost every day. However, the guiding processes for structure formation are mostly unknown, and also theoretical investigations of cloud patterns are quite rare. From many scientific disciplines the occurrence of patterns in non-equilibrium systems due to Turing instabilities is known, i.e. unstable modes grow and form spatial structures. In this study we investigate a generic cloud model for the possibility of Turing instabilities. For this purpose, the model is extended by diffusion terms. We can show that for some cloud models, i.e special cases of the generic model, no Turing instabilities are possible. However,…
Not everything is everywhere: the distance decay of similarity in a marine host-parasite system
2009
Aim We test the similarity–distance decay hypothesis on a marine host–parasite system, inferring the relationships from abundance data gathered at the lowest scale of parasite community organization (i.e. that of the individual host). Location Twenty-two seasonal samples of the bogue Boops boops (Teleostei: Sparidae) were collected at seven localities along a coastal positional gradient from the northern North-East Atlantic to the northern Mediterranean coast of Spain. Methods We used our own, taxonomically consistent, data on parasite communities. The variations in parasite composition and structure with geographical and regional distance were examined at two spatial scales, namely loca…