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22 results for “landscape planning”
Data from: Constrained optimization of landscape indices in conservation planning to support ecological restoration in New Caledonia
<p>1. Curbing habitat loss, reducing fragmentation, and restoring connectivity are frequent concerns of conservation planning. In this respect, the incorporation of spatial constraints, fragmentation, and connectivity indices into optimization procedures is an important challenge for improving decision support.</p> <p>2. Here we present a novel optimization approach developed to accurately represent a broad range of conservation planning questions with spatial constraints and landscape indices. Relying on constraint programming, a technique from artificial intelligence based on automatic reasoning, this approach provides both constraint satisfaction and optimality guarantees.</p> <p>3. We applied this approach in a real case study to support managers of the ``Côte Oubliée -- `Woen Vùù -- Pwa Pereeù'' provincial park project, in the biodiversity hotspot of New Caledonia. Under budget, accessibility, and equitable allocation constraints, we identified restorable areas optimal for reducing forest fragmentation and improving inter-patch structural connectivity, respectively measured with the effective mesh size and the integral index of connectivity.</p> <p>4. Synthesis and applications. Our work contributes to more effective and policy-relevant conservation planning by providing a spatially-explicit and problem-focused optimization approach. By allowing an exact representation of spatial constraints and landscape indices, it can address new questions and ensure whether the solutions will be socio-economically feasible, through optimality and satisfiability guarantees. Our approach is generic and flexible, thus applicable to a wide range of conservation planning problems such as ecological restoration planning, reserve or corridor design.</p>
Exemplary landscape planning results using different qualities of LU/LC data to derive implications for practical planning.
<p>This Dataset consist of two GIS Shape files showing examples of common landscape planning activities, namely the an assessment of the climate protection function and planning towards locating environmental protection measures using different data basis. Using overlay operation it is possible to use descriptive statistics to assess uncertainties and inconsistencies between different land use / land cover data sets and highlight their implications for practical planning.</p> <p>Once published, the results of such analysis can be seen in the related publication "Uncertainties in land use data may have substantial effects on environmental planning recommendations: a plea for careful consideration. Submitted to PLOS ONE."</p>
Data and code from: Thresholding species distribution models: Simple approaches for land-use planning in multifunctional landscapes
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Data from: Constrained optimization of landscape indices in conservation planning to support ecological restoration in New Caledonia
Open the record for dataset details and reuse information.
A Conceptual Model Framework for Analyzing, Simulating, Planning and Controlling of Supply-Demand Matching of Ecosystem Services in Agricultural Landscapes
<p>The video gives an explanation of our conceptual approach for the investigation of the adaptation of societal demands for ecosystem services in agricultural landscapes.</p>
Planning Landscape Analysis for Self-Adaptive Systems
<p>This is the code and data for paper "Planning Landscape Analysis for Self-Adaptive Systems".</p>
Plan-form evolution of drainage basins in response to tectonic changes: Insights from experimental and numerical landscapes
<p>The following data contains raw DEMs from the experiment in the simulations referred to in the mentioned manuscript, as well as MATLAB scripts (and legend) used for the analysis of the data.</p> <p>Experiment DEMs are named after the time they were acquired within the experiment, i.e., 4.tif is the DEM of the landscape after a duration of 4 hours of experiment.</p> <p>In MATLAB scripts, the following legend is used:</p> <p>E = Experiment</p> <p>SL = Low concavity simulation</p> <p>SH = High concavity simulation</p> <p>HL = Hack's law analysis</p> <p>A = Asymmetry analysis</p> <p>C = Chi analysis</p> <p>CP = Chi prime analysis</p> <p>ADCP = Across-divide chi prime analysis</p> <p>DAI = Divide asymmetry index analysis</p> <p>CreateStructuredGrid = Function that generates GRIDobj from ASCII files</p> <p>Num_DEM_Load = Script that generates GRIDobj from ASCII files</p>
Supplementary material 1 from: Palomo-Campesino S, Palomo I, Moreno J, González J (2018) Characterising the rural-urban gradient through the participatory mapping of ecosystem services: insights for landscape planning. One Ecosystem 3: e24487. https://doi.org/10.3897/oneeco.3.e24487
List of the 19 socio-demographic and ecological parameters used for the study area clustering and their values for each municipality of the study area.
Supplementary material 2 from: Palomo-Campesino S, Palomo I, Moreno J, González J (2018) Characterising the rural-urban gradient through the participatory mapping of ecosystem services: insights for landscape planning. One Ecosystem 3: e24487. https://doi.org/10.3897/oneeco.3.e24487
Eigenvalue, percentage of explained variablity and accumulated percentage of explained variability of the five factors (with an eingenvalue higher than 1) result of the PCA analysis and used for the HCA analysis.
Supplementary material 3 from: Palomo-Campesino S, Palomo I, Moreno J, González J (2018) Characterising the rural-urban gradient through the participatory mapping of ecosystem services: insights for landscape planning. One Ecosystem 3: e24487. https://doi.org/10.3897/oneeco.3.e24487
Factors' loadings for each of the 19 socio-demographic and ecological variables analysed. Values in bold are statistically significant at P>0.05.
Where and what kind – a better understanding of local and landscape features in planning the urban flower meadows for supporting bee communities
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Figure 2 from: Balzan MV, Tomaskinova J, Collier MJ, Dicks L, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A (2020) Building capacity for mainstreaming nature-based solutions into environmental policy and landscape planning. Research Ideas and Outcomes 6: e58970. https://doi.org/10.3897/rio.6.e58970
Figure 2 (a) Assessing the relationship between green infrastructure cover (GI) in each local council and average ES capacity and (b) population density (Adapted from: Balzan 2017).
Supplementary material 2 from: Lessi BF, Geneletti D, Cortinovis C, Dias MM, Reis MG (2024) Bird richness and Ecosystems Services across an urban to natural gradient in south-eastern Brazil: implications for landscape planning and future scenarios. One Ecosystem 9: e114955. https://doi.org/10.3897/oneeco.9.e114955
Supplementary material 2
Supplementary material 3 from: Lessi BF, Geneletti D, Cortinovis C, Dias MM, Reis MG (2024) Bird richness and Ecosystems Services across an urban to natural gradient in south-eastern Brazil: implications for landscape planning and future scenarios. One Ecosystem 9: e114955. https://doi.org/10.3897/oneeco.9.e114955
Supplementary material 3
Supplementary material 1 from: Lessi BF, Geneletti D, Cortinovis C, Dias MM, Reis MG (2024) Bird richness and Ecosystems Services across an urban to natural gradient in south-eastern Brazil: implications for landscape planning and future scenarios. One Ecosystem 9: e114955. https://doi.org/10.3897/oneeco.9.e114955
Supplementary material 1
Reorganization of HoxD regulatory landscapes during the evolution of a snake-like body plan
GEO Series GSE79048. Mus musculus; Pantherophis guttatus. 25 samples. Type: Other; Genome binding/occupancy profiling by high throughput sequencing.
Reorganization of HoxD regulatory landscapes during the evolution of a snake-like body plan (ChIP-seq)
GEO Series GSE79047. Mus musculus; Pantherophis guttatus. 9 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Figure 1 from: Balzan MV, Tomaskinova J, Collier MJ, Dicks L, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A (2020) Building capacity for mainstreaming nature-based solutions into environmental policy and landscape planning. Research Ideas and Outcomes 6: e58970. https://doi.org/10.3897/rio.6.e58970
Figure 1 The Maltese Islands (source: Balzan et al. (2018) and OpenStreetMaps).
Figure 3 from: Balzan MV, Tomaskinova J, Collier MJ, Dicks L, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A (2020) Building capacity for mainstreaming nature-based solutions into environmental policy and landscape planning. Research Ideas and Outcomes 6: e58970. https://doi.org/10.3897/rio.6.e58970
Figure 3 The Valletta urban agglomeration (Source: Balzan et al. submitted)
Data from: Big biology meets microclimatology: Defining thermal niches of ectotherms at landscape scales for conservation planning
Temperature profoundly affects ecology, a fact ever more evident as the ability to measure thermal environments increases and global changes alter these environments. The spatial structure of thermalscapes is especially relevant to the distribution and abundance of ectothermic organisms but the ability to describe biothermal relationships at extents and grains relevant to conservation planning has been limited by small or sparse datasets. Here, we combine a large occurrence database of >23,000 aquatic species surveys with stream microclimate scenarios supported by an equally large temperature database for a 149,000-km mountain stream network to describe thermal relationships for 14 fish and amphibian species. Species occurrence probabilities peaked across a wide range of temperatures (7.0–18.8 °C) but distinct warm- or cold-edge distribution boundaries were apparent for all species and represented environments where populations may be most sensitive to thermal changes. Warm-edge boundary temperatures for a native species of conservation concern were used with geospatial datasets and a habitat occupancy model to highlight subsets of the network where conservation measures could benefit local populations by maintaining cool temperatures. Linking that strategic approach to local estimates of habitat impairment remains a key challenge but is also an opportunity to build relationships and develop synergies between the research, management, and regulatory communities. As with any data mining or species distribution modeling exercise, care is required in analysis and interpretation of results, but the use of large biological datasets with accurate microclimate scenarios can provide valuable information about the thermal ecology of many ectotherms and a spatially-explicit way of guiding conservation investments.
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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.