AREAdata: A worldwide climate dataset averaged across spatial units at different scales through time
Ontology highlight
ABSTRACT:
SUBMITTER: Smith T
PROVIDER: S-EPMC9278028 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Alien species invasion represents a global threat to biodiversity and ecosystems. Explaining invasion patterns in terms of environmental constraints will help us to assess invasion risks and plan control strategies. We aim to identify plant invasion patterns in the Basque Country (Spain), and to determine the effects of climate and human pressure on that pattern. We modeled the regional distribution of 89 invasive plant species using two approaches. First, distance-based Moran's eigenvector maps were used to partition variation in the invasive species richness, S, into spatial components at broad and fine scales; redundancy analysis was then used to explain those components on the basis of climate and human pressure descriptors. Second, we used generalized additive mixed modeling to fit species-specific responses to the same descriptors. Climate and human pressure descriptors have different effects on S at different spatial scales. Broad-scale spatially structured temperature and precipitation, and fine-scale spatially structured human population density and percentage of natural and semi-natural areas, explained altogether 38.7% of the total variance. The distribution of 84% of the individually tested species was related to either temperature, precipitation or both, and 68% was related to either population density or natural and semi-natural areas, displaying similar responses. The spatial pattern of the invasive species richness is strongly environmentally forced, mainly by climate factors. Since individual species responses were proved to be both similarly constrained in shape and explained variance by the same environmental factors, we conclude that the pattern of invasive species richness results from individual species' environmental preferences.
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Project description:Managing flood risk, i.e. both the hazard and the potential consequences, is an important aspect of adapting to global change and has gained much traction in recent decades. As a result, a priori flood risk assessments have become an important part of flood management practices. Many methodologies have been set up, ranging from global risk assessments for the world as a whole, to local assessments for a particular stretch of a river/coast or small town. Most assessment frameworks generally follow a similar approach, but there are also notable differences between assessments at different spatial scales. This review article examines these differences, for instance those related to the methodology, use of assessments and uncertainties. From this review, future research needs are identified in order to improve flood risk assessments at different scales. At global/continental scale, there is a clear need for harmonised information on flood defences to improve assessments. Furthermore, inclusions of indirect economic effects at the macro-/meso-scale would give a better indication of the total effects of catastrophic flooding. At the meso-/micro-scale, there is an urgent need to improve our understanding of the effects of flooding on critical infrastructures, given their importance to society, the economy, emergency management and reconstruction. An overarching theme at all scales is the validation of flood risk assessments, which is often limited. More detailed post-disaster information would allow for improved calibration, validation and thus performance of flood risk models. Lastly, the link between spatial scales also deserves attention, for instance up- or downscaling methodologies.
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Project description:BackgroundWe introduce a new dataset of woody plants on 60 small tropical islands located in the Raja Ampat archipelago in Indonesia. The dataset includes incidence, abundance and functional trait data for 57 species. All islands were sampled using a standardised transect and plot design providing detailed information on plant occurrences at different spatial scales ranging from the local (plot and transect scale) to the island scale. In addition, the dataset includes information on key plant functional traits linked to species dispersal, resource acquisition and competitive strategies. The dataset can be used to address ecological questions connected to the species-area relationship and community assembly processes on small islands and in isolated habitats.New informationThe dataset yields detailed information on plant community structure and links incidence, abundance and functional trait data at different spatial scales. Furthermore, this is the first plant-island dataset for the Raja Ampat archipelago, a remote and poorly studied region, and provides important new information on species occurrences.
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Project description:MotivationIn order to understand how species evolutionarily responded to Plio-Pleistocene climate oscillations (e.g. in terms of speciation, extinction, migration and adaptation), it is first important to have a good understanding of those past climate changes per se. This, however, is currently limited due to the lack of global-scale climatic datasets with high temporal resolution spanning the Plio-Pleistocene. To fill this gap, I here present Oscillayers, a global-scale and region-specific bioclim dataset, facilitating the study of climatic oscillations during the last 5.4 million years at high spatial (2.5 arc-minutes) and temporal (10 kyr time periods) resolution. This dataset builds upon interpolated anomalies (Δ layers) between bioclim layers of the present and the Last Glacial Maximum (LGM) that are scaled relative to the Plio-Pleistocene global mean temperature curve, derived from benthic stable oxygen isotope ratios, to generate bioclim variables for 539 time periods. Evaluation of the scaled, interpolated estimates of palaeo-climates generated for the Holocene, Last Interglacial and Pliocene showed good agreement with independent general circulation models (GCMs) for respective time periods in terms of pattern correlation and absolute differences. Oscillayers thus provides a new tool for studying spatial-temporal patterns of evolutionary and ecological processes at high temporal and spatial resolution.Main types of variable containedNineteen bioclim variables for time periods throughout the Plio-Pleistocene. Input data and R script to recreate all 19 bioclim variables.Spatial location and grainGlobal at 2.5 arc-minutes (4.65 x 4.65 = 21.62 km2 at the equator).Time period and grainThe last 5.4 million years. The grain is 10 kyr (= 539 time periods).Level of measurementData are for terrestrial climates (excluding Antarctica) taking sea level changes into account.Software formatAll data are available as ASCII grid files.
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Project description:We present historical monthly spatial models of temperature and precipitation generated from the North American dataset version "j" from the National Oceanic and Atmospheric Administration's (NOAA's) National Centres for Environmental Information (NCEI). Monthly values of minimum/maximum temperature and precipitation for 1901-2016 were modelled for continental United States and Canada. Compared to similar spatial models published in 2006 by Natural Resources Canada (NRCAN), the current models show less error. The Root Generalized Cross Validation (RTGCV), a measure of the predictive error of the surfaces akin to a spatially averaged standard predictive error estimate, averaged 0.94?°C for maximum temperature models, 1.3?°C for minimum temperature and 25.2% for total precipitation. Mean prediction errors for the temperature variables were less than 0.01?°C, using all stations. In comparison, precipitation models showed a dry bias (compared to recorded values) of 0.5?mm or 0.7% of the surface mean. Mean absolute predictive errors for all stations were 0.7?°C for maximum temperature, 1.02?°C for minimum temperature, and 13.3?mm (19.3% of the surface mean) for monthly precipitation.
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Project description:Humans navigate across a range of spatial scales, from rooms to continents, but the brain systems underlying spatial cognition are usually investigated only in small-scale environments. Do the same brain systems represent and process larger spaces? Here we asked subjects to compare distances between real-world items at six different spatial scales (room, building, neighborhood, city, country, continent) under functional MRI. Cortical activity showed a gradual progression from small to large scale processing, along three gradients extending anteriorly from the parahippocampal place area (PPA), retrosplenial complex (RSC) and occipital place area (OPA), and along the hippocampus posterior-anterior axis. Each of the cortical gradients overlapped with the visual system posteriorly and the default-mode network (DMN) anteriorly. These results suggest a progression from concrete to abstract processing with increasing spatial scale, and offer a new organizational framework for the brain's spatial system, that may also apply to conceptual spaces beyond the spatial domain.
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Project description:Proxy temperature data records featuring local time series, regional averages from areas all around the globe, as well as global averages, are analyzed using the Slow Feature Analysis (SFA) method. As explained in the paper, SFA is much more effective than the traditional Fourier analysis in identifying slow-varying (low-frequency) signals in data sets of a limited length. We find the existence of a striking gap from ~1000 to about ~20,000 years, which separates intrinsic climatic oscillations with periods ranging from ~60 years to ~1000 years, from the longer time-scale periodicities (20,000 year+) involving external forcing associated with Milankovitch cycles. The absence of natural oscillations with periods within the gap is consistent with cumulative evidence based on past data analyses, as well as with earlier theoretical and modeling studies.
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Project description:Functional organization units of the cerebral cortex exist over a wide range of spatial scales, from local circuits to entire cortical areas. In the last decades, scale-space representations of neuroimaging data suited to probe the multi-scale nature of cortical functional organization have been introduced and methodologically elaborated. For this purpose, responses are statistically detected over a range of spatial scales using a family of Gaussian filters, with small filters being related to fine and large filters-to coarse spatial scales. The goal of the present study was to investigate the degree of variability of fMRI-response patterns over a broad range of observation scales. To this aim, the same fMRI data set obtained from 18 subjects during a visuomotor task was analyzed with a range of filters from 4- to 16-mm full width at half-maximum (FWHM). We found substantial observation-scale-related variability. For example, using filter widths of 6- to 8-mm FWHM, in the group-level results, significant responses in the right secondary visual but not in the primary visual cortex were detected. However, when larger filters were used, the responses in the right primary visual cortex reached significance. Often, responses in probabilistically defined areas were significant when both small and large filters, but not intermediate filter widths were applied. This suggests that brain responses can be organized in local clusters of multiple distinct activation foci. Our findings illustrate the potential of multi-scale fMRI analysis to reveal novel features in the spatial organization of human brain responses.
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Project description:One of the main goals of community ecology is to measure the relative importance of environmental filters to understand patterns of species distribution at different temporal and spatial scales. Likewise, the identification of factors that shape symbiont metacommunity structures is important in disease ecology because resulting structures drive disease transmission. We tested the hypothesis that distributions of virus species and viral families from rodents and bats are defined by shared responses to host phylogeny and host functional characteristics, shaping the viral metacommunity structures at four spatial scales (Continental, Biogeographical, Zoogeographical, and Regional). The contribution of host phylogeny and host traits to the metacommunity of viruses at each spatial scale was calculated using a redundant analysis of canonical ordering (RDA). For rodents, at American Continental scale the coherence of viral species metacommunity increased while the spatial scale decreased and Quasi-Clementsian structures were observed. This pattern suggests a restricted distribution of viruses through their hosts, while in the Big Mass (Europe, Africa, and Asia), the coherence decreased as spatial scale decreased. Viral species metacommunities associated with bats was dominated by random structures along all spatial scales. We suggest that this random pattern is a result of the presence of viruses with high occupancy range such as rabies (73%) and coronavirus (27%), that disrupt such structures. At viral family scale, viral metacommunities associated with bats showed coherent structures, with the emergence of Quasi- Clementsian and Checkerboard structures. RDA analysis indicates that the assemblage of viral diversity associated with rodents and bats responds to phylogenetic and functional characteristics, which alternate between spatial scales. Several of these variations could be subject to the spatial scale, in spite of this, we could identify patterns at macro ecological scale. The application of metacommunity theory at symbiont scales is particularly useful for large-scale ecological analysis. Understanding the rules of host-virus association can be useful to take better decisions in epidemiological surveillance, control and even predictions of viral distribution and dissemination.Electronic supplementary materialSupplementary material is available for this article at 10.1556/168.2018.19.2.9 and is accessible for authorized users.
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