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110 results for “Spatially explicit”
Spatially explicit habitat selection: testing contagion and the ideal free distribution with culex mosquitoes
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Spatially explicit genetic capture-recapture data from black bears in Ontario, Canada
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Prioritizing road-kill mitigation areas: a spatially explicit national-scale model for an elusive carnivore
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Estimating migration of the cold-tolerant leaf beetle Gonioctena quinquepunctata inside a mountain range in a spatially explicit context
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Data from: Modeling multilocus selection in an individual-based, spatially-explicit landscape genetics framework
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Data and code from: Spatially-explicit foraging by an apex predator linked to nearshore prey and their accessibility in lakes
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Data for fitting spatial capture-recapture (SCR) models to estimate spatially explicit demographics of Mojave desert tortoises on a demography plot in California, USA
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MetaSqueeze: A spatially-explicit metapopulation model for Banksia hookeriana in south-west Australia
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A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning
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Evaluating spatially explicit sharing-sparing scenarios for multiple environmental outcomes
<p>1. Understanding how to allocate land for the sustainable delivery of multiple, competing objectives is a major societal challenge. The land sharing-sparing framework presents a heuristic for understanding the trade-off between food production and biodiversity conservation by comparing region-wide land use scenarios which are equivalent in terms of overall food production.</p> <p>2. Here, for two contrasting regions of lowland England (The Fens and Salisbury Plain), we use empirical data and predictive models to compare a suite of spatially explicit scenarios reflecting the full range of the sharing-sparing continuum, including mixed scenarios which combine elements of both sharing and sparing. We evaluate a range of outcomes (bird populations, global warming potential (GWP), nitrogen and phosphorus pollution and outdoor recreation), in order to identify approaches to regional land use planning with the potential to deliver multiple societal benefits.</p> <p>3. Land-sharing scenarios (which reduce the dominance of productive agricultural land in farmed areas and the area of larger unfarmed areas) result in negative outcomes, particularly for birds and GWP. In contrast, many land-sparing scenarios (including mixed scenarios which increase the area of lower-yield farmland alongside larger unfarmed areas) resulted in improvements in all or most outcomes, although for recreation and nutrient export differences between scenarios were modest.</p> <p>4. Importantly, environmental outcomes also depended on the spatial arrangement of spared land, the types of natural or semi-natural habitat promoted on spared land, whether some lower-yield farmland is delivered alongside larger unfarmed areas, and the overall region-wide food production target.</p> <p>5. <i>Policy implications</i>. Our study suggests that land-sparing strategies which increase the area of natural and seminatural areas can improve environmental outcomes, despite the costs associated with high-yield agriculture. However, high-yield agriculture should not compromise future production or ecosystem services on spared land, and explicit policies such as certification or payments for ecosystem services are required to link sustainable high-yield production to habitat conservation. Our study also highlights the importance of mitigating projected increases in food <a>demand.</a></p>
Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
Inferring the dynamics of populations in time and space is a central challenge in ecology. Intra-specific structure (for example genetically distinct sub-populations or meta-populations) may require methods that can jointly infer the dynamics of multiple populations. This is of particular importance for harvested species, for which management must balance utilization of productive populations with protection of weak ones. Here we present a novel method for simultaneous learning about the spatio-temporal dynamics of multiple populations that combines genetic data with prior information about abundance and movement in an integrated population modelling approach. We apply the Bayesian genetic mixed stock analysis to 17 wild and 10 hatchery-reared Baltic salmon (S. salar) stocks, quantifying uncertainty in stock composition in time and space, and in population dynamics parameters such as migration timing and speed. Our results indicate that the commonly used "equal prior probabilities" assumption may not be appropriate for all mixed stock analyses. Incorporation of prior information about stock abundance and movement resulted in more precise and plausible estimates of mixture compositions in time and space. Inclusion of a population dynamics model also allowed robust interpolation of expected catch composition at areas and times with no genetic observations. The genetic data were informative about stock-specific movement patterns, updating priors for migration path, timing and speed. The model we present here forms the basis for optimizing the spatial and temporal allocation of harvest to support the management of mixed populations of migratory species.
Data from: The shift from plant–plant facilitation to competition under severe water deficit is spatially explicit
The stress-gradient hypothesis predicts a higher frequency of facilitative interactions as resource limitation increases. Under severe resource limitation, it has been suggested that facilitation may revert to competition, and identifying the presence as well as determining the magnitude of this shift is important for predicting the effect of climate change on biodiversity and plant community dynamics. In this study, we perform a meta-analysis to compare temporal differences of species diversity and productivity under a nurse plant (Retama sphaerocarpa) with varying annual rainfall quantity to test the effect of water limitation on facilitation. Furthermore, we assess spatial differences in the herbaceous community under nurse plants in situ during a year with below-average rainfall. We found evidence that severe rainfall deficit reduced species diversity and plant productivity under nurse plants relative to open areas. Our results indicate that the switch from facilitation to competition in response to rainfall quantity is nonlinear. The magnitude of this switch depended on the aspect around the nurse plant. Hotter south aspects under nurse plants resulted in negative effects on beneficiary species, while the north aspect still showed facilitation. Combined, these results emphasize the importance of spatial heterogeneity under nurse plants for mediating species loss under reduced precipitation, as predicted by future climate change scenarios. However, the decreased water availability expected under climate change will likely reduce overall facilitation and limit the role of nurse plants as refugia, amplifying biodiversity loss.
Data from: Counting cats: spatially explicit population estimates of cheetah (Acinonyx jubatus) using unstructured sampling data
Many ecological theories and species conservation programmes rely on accurate estimates of population density. Accurate density estimation, especially for species facing rapid declines, requires the application of rigorous field and analytical methods. However, obtaining accurate density estimates of carnivores can be challenging as carnivores naturally exist at relatively low densities and are often elusive and wide-ranging. In this study, we employ an unstructured spatial sampling field design along with a Bayesian sex-specific spatially explicit capture-recapture (SECR) analysis, to provide the first rigorous population density estimates of cheetahs (Acinonyx jubatus) in the Maasai Mara, Kenya. We estimate adult cheetah density to be between 1.22 ± 0.301 and 1.28 ± 0.322 individuals/100km2 across four candidate models specified in our analysis. Our spatially explicit approach revealed 'hotspots' of cheetah density, highlighting that cheetah are distributed heterogeneously across the landscape. The SECR models incorporated a movement range parameter which indicated that male cheetah moved four times as much as females, possibly because female movement was restricted by their reproductive status and/or the spatial distribution of prey. We show that SECR can be used for spatially unstructured data to successfully characterise the spatial distribution of a low density species and also estimate population density when sample size is small. Our sampling and modelling framework will help determine spatial and temporal variation in cheetah densities, providing a foundation for their conservation and management. Based on our results we encourage other researchers to adopt a similar approach in estimating densities of individually recognisable species.
Data from: A spatially explicit hierarchical model to characterize population viability
Many of the processes that govern the viability of animal populations vary spatially, yet population viability analyses (PVAs) that account explicitly for spatial variation are rare. We develop a PVA model that incorporates autocorrelation into the analysis of local demographic information to produce spatially explicit estimates of demography and viability at relatively fine spatial scales across a large spatial extent. We use a hierarchical, spatial autoregressive model for capture-recapture data from multiple locations to obtain spatially explicit estimates of adult survival (Φad), juvenile survival (Φjuv), and juvenile-to-adult transition rates (ψ), and a spatial autoregressive model for recruitment data from multiple locations to obtain spatially explicit estimates of recruitment (R). We combine local estimates of demographic rates in stage-structured population models to estimate the rate of population change (λ), then use estimates of λ (and its uncertainty) to forecast changes in local abundance and produce spatially explicit estimates of viability (probability of extirpation, Pex). We apply the model to demographic data for the Sonoran desert tortoise (Gopherus morafkai) collected across its geographic range in Arizona. There was modest spatial variation in λ (0.94–1.03), which reflected spatial variation in Φad (0.85–0.95), Φjuv (0.70–0.89), and ψ (0.07–0.13). Recruitment data were too sparse for spatially explicit estimates, therefore we used a range-wide estimate (R = 0.32 one-year old females per female per year). Spatial patterns in demographic rates were complex, but Φad, Φjuv, and λ tended to be lower and ψ higher in the northwestern portion of the range. Spatial patterns in Pex varied with local abundance. For local abundances > 500, Pex was near zero (Pex approached one in the northwestern portion of the range and remained low elsewhere. When local abundances were Pex > 0.25). This approach to PVA offers the potential to reveal spatial patterns in demography and viability that can inform conservation and management at multiple spatial scales, provide insight into scale-related investigations in population ecology, and improve basic ecological knowledge of landscape-level phenomena.
Fig. 1 in Estimating Migration of Gonioctena quinquepunctata (Coleoptera: Chrysomelidae) Inside a Mountain Range in a Spatially Explicit Context
Fig. 1. Strategy developed in this study to compare observed and simulated SNP data in a spatially explicit context, using the new DSVSF statistic. Allele frequencies per SNP are recorded for each sampled grid cell and organized in an array. Frequency arrays can then be categorized into patterns by associating them with one of three categories, according to their frequency (category 0 corresponds to a frequency ≤ 0.1, category 2 to a frequency ≥ 0.9, and category 1 otherwise). We then compared the resulting observed and simulated distributions of pattern frequencies using a standard dissimilarity index (see text). Each simulated distribution shown at the bottom of the figure corresponds to one specific combination of Ne and m values.
Fig. 5 in Estimating Migration of Gonioctena quinquepunctata (Coleoptera: Chrysomelidae) Inside a Mountain Range in a Spatially Explicit Context
Fig. 5. Results of the validation analyses. Each dot presents, for one ABC analysis run on a pseudo-observed data set, the estimated value of Nm as a function of its known value. Dots are black when the real value was included inside the 95% highest posterior density interval, white otherwise. Real values are along the red line.
Fig. 4 in Estimating Migration of Gonioctena quinquepunctata (Coleoptera: Chrysomelidae) Inside a Mountain Range in a Spatially Explicit Context
Fig. 4. Principal component analysis (PCA) graph resulting from analyzing all SNPs from all sampled individuals. Each population is identified by a different color.
Fig. 3 in Estimating Migration of Gonioctena quinquepunctata (Coleoptera: Chrysomelidae) Inside a Mountain Range in a Spatially Explicit Context
Fig. 3. Grids defining the spatially explicit context used in the coalescence models for simulating SNP data with PhyloGeoSim. Insects are absent from white cells but are potentially present in orange cells. Blue dots on the right grid (15,000 a—0) depict sampling locations.The left grid describes the spatial context assumed during the last glaciation.
Supplementary material 2 from: Polce C, Maes J, Brander L, Cescatti A, Baranzelli C, Lavalle C, Zulian G (2016) Global change impacts on ecosystem services: a spatially explicit assessment for Europe. One Ecosystem 1: e9990. https://doi.org/10.3897/oneeco.1.e9990
Key between LUISA (Baranzelli et al., 2014) and CORINE LULC types, and scores for the potential contribution of each LULC type to provide relevant ES. Scores are based on the ES potential matrix by Burkhard et al. (2014), after appropriate rescaling. See main text for additional details.
Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction
<p>This dataset contains the spatially-explicit gridded database based on social media data for over 150 different tourism-related classes that depicts tourism density (supply and demand) and perceived satisfaction in Europe, and the related exposure to selected climate extreme events. Information on tourism density (supply and demand) and perceived satisfaction are categorised for Attractions, Culinary, and Hospitality, while the exposure analysis of those clases are provided in separate, specific files. The provided dataset is made accessible to support large-scale and regional tourism research and extends its relevance to other fields that are part of tourism as a complex system, such as risk assessment and vulnerability studies. For citing this work, please refer to the research article "Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction", DOI 10.1088/1748-9326/ad3e91. Suggested citation: "Camatti, N., Hrast Essenfelder, A., & Giove, S. (2024). Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction. Environmental Research Letters."</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.