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133 results for “conservation models”
Data supplement for PhD thesis "Pattern Formation with Mass Conservation - From Passive to Active Models"
<p>Here, we provide <em>Mathematica</em> files that provide the details of the weakly nonlinear analysis employed in the PhD thesis "Pattern Formation with Mass Conservation - From Passive to Active Models" submitted by Tobias Frohoff-Hülsmann.</p> <p> </p>
Modeling liquid transport in the Earth's mantle as two-phase flow: Effect of an enforced positive porosity on liquid flow and mass conservation
<p>These data files include the raw data for the figures shown in Lee et al. titled as 'Modeling liquid transport in the Earth’s mantle as two-phase flow: Effect of an enforced positive porosity on liquid flow and mass conservation'. <br> </p>
Data from: The contributions of flower strips to wild bee conservation in agricultural landscapes can be predicted using pollinator habitat suitability models
<p>Sowing flower strips along field edges is a widely adopted method for conserving pollinating insects in agricultural landscapes. To maximize the effect of flower strips given limited resources, we need spatially explicit tools that can prioritize their placement, and for identifying plant species to include in seed mixtures.</p> <p>We sampled bees and plant species as well as their interactions in a semi-controlled field experiment with roadside/field edge pairs with/without a sown flower strip at 31 sites in Norway and used a regional spatial model of solitary bee species richness to test if the effect of flower strips on bee species richness was predictable from the modelled solitary bee species richness.</p> <p>We found that sites with flower strips were more bee species rich compared to sites without flower strips and that this effect was greatest in areas that the regional solitary bee species richness model had identified to be particularly important for bees. Spatial models revealed that even within small landscapes there were pronounced differences between field edges in the predicted effect of sowing flower strips.</p> <p>Of the plant species that attracted the most bee species, the majority mainly attracted bumblebees and only few species also attracted solitary bees. Considering both the taxonomic diversity of bees and the species richness of bees attracted by plants we suggest that seed mixes containing <em>Hieracium </em>spp. such as <em>Hieracium umbellatum </em>and <em>Pilosella officinarum</em>; <em>Taraxacum</em> spp; <em>Trifolium repens</em>;<em> Lotus corniculatus</em>; S<em>tellaria graminea</em>; and <em>Achillea millefolium</em> would provide resources for diverse bee communities in our region.</p> <p>Spatial prediction models of bee diversity can be used to identify locations where flower strips are likely to have the largest effect and can thereby provide managers with an important tool for prioritizing how funding for agri-environmental schemes such as flower strips should be allocated. Such flower strips should contain plant species that are attractive to both solitary and bumblebees, and do not need to be particularly plant species rich as long as the selected plants complement each other.</p>
Data from: The contributions of flower strips to wild bee conservation in agricultural landscapes can be predicted using pollinator habitat suitability models
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A gap analysis modeling framework to prioritize collecting for ex situ conservation of crop landraces
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Modelling the genetic aetiology of complex disease: human-mouse conservation of noncoding features and disease-associated loci
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Benefits of modelling abundance for rare species conservation: a case study with multiple birds across one million hectares
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Data from: Statistical stream temperature modelling with SSN and INLA: An introduction for conservation practitioners
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Targeting fin whale conservation in the North-Western Mediterranean Sea: Insights on movements and behaviour from biologging and habitat modelling
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Random forest modelling of multi-scale, multi-species habitat associations within KAZA transfrontier conservation area using spoor data
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Data from: Evaluating population viability and efficacy of conservation management using integrated population models
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Data from: Sequential use of niche and occupancy models identifies conservation and research priority areas for two data-poor endemic birds from the Colombian Andes
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In-situ and ex-situ conservation priorities and distribution of lentil wild relatives under climate change: A modeling approach
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The modeled distribution of corals and sponges surrounding the Salas y Gómez and Nazca ridges with implications for high seas conservation
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Multiple-model stock assessment frameworks for precautionary management and conservation on fishery-targeted coastal dolphin populations off Japan
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Provisioning forest and conservation science with European tree species distribution models under climate change
<p>Estimating shifts in the current range of forest tree species is crucial for formulating adaptive management strategies such as assisted migration. Ecological niche models have been the most widely used tools to estimate the potential climatic suitability of species worldwide. The reliability of such estimations depends on the model algorithm and the input data such as climate and species occurrence. We developed a dataset of the potential distribution of seven ecologically and economically important tree species of Europe in terms of their climatic suitability with an ensemble approach while accounting for uncertainty due to model algorithms. The distribution models shall be the basis for follow-up studies in forest and conservation science.</p>
Combining Satellite Remote Sensing and Climate Data in Species Distribution Models to Improve the Conservation of Iberian White Oaks (Quercus L.)
<p>The Iberian Peninsula hosts a high diversity of oak species, being a hot-spot for the conservation of European White Oaks (Quercus) due to their environmental heterogeneity and its critical role as a phylogeographic refugium. Identifying and ranking the drivers that shape the distribution of White Oaks in Iberia requires that environmental variables operating at distinct scales are considered. These include climate, but also ecosystem functioning attributes (EFAs) related to energy–matter exchanges that characterize land cover types under various environmental settings, at finer scales. Here, we used satellite-based EFAs and climate variables in species distribution models (SDMs) to assess how variables related to ecosystem functioning improve our understanding of current distributions and the identification of suitable areas for White Oak species in Iberia. We developed consensus ensemble SDMs targeting a set of thirteen oaks, including both narrow endemic and widespread taxa. Models combining EFAs and climate variables obtained a higher performance and predictive ability (true-skill statistic (TSS): 0.88, sensitivity: 99.6, specificity: 96.3), in comparison to the climate-only models (TSS: 0.86, sens.: 96.1, spec.: 90.3) and EFA-only models (TSS: 0.73, sens.: 91.2, spec.: 82.1). Overall, narrow endemic species obtained higher predictive performance using combined models (TSS: 0.96, sens.: 99.6, spec.: 96.3) in comparison to widespread oaks (TSS: 0.80, sens.: 92.6, spec.: 87.7). The Iberian White Oaks show a high dependence on precipitation and the inter-quartile range of Normalized Difference Water Index (NDWI) (i.e., seasonal water availability) which appears to be the most important EFA variable. Spatial projections of climate–EFA combined models contribute to identify the major diversity hotspots for White Oaks in Iberia, holding higher values of cumulative habitat suitability and species richness. We discuss the implications of these findings for guiding the long-term conservation of IberianWhite Oaks and provide spatially explicit geospatial information about each oak species (or set of species) relevant for developing biogeographic conservation frameworks.</p>
Combining conservation status and species distribution models for planning assisted colonisation under climate change
<p>Effects of climate change are particularly important in the Mediterranean Biodiversity hotspot where rising temperatures and drought are negatively affecting several plant taxa, including endemic species. Assisted Colonisation (AC) represents a useful tool for reducing the effect of climate change on endemic plant species threatened by climate change.</p> <p>We combined SDMs for 188 taxa endemic to Italy with the IUCN red listing range loss threshold under criterion A (30%) to define: a) the number of AC (measured as 2×2 km grid cells that should be occupied by new populations, that is grid cells = new populations) required to fully compensate for predicted range loss and to halt the decline below the 30% of range loss; b) The number of cells necessary to compensate for range loss was calculated as the number of currently occupied cells lost under future climate due to unsuitable conditions. We used two Representative Concentration Pathways, +2.6 and +8.5 W/m2, optimistic and pessimistic scenarios, respectively. Availability of suitable areas for AC was also assessed within the current species distribution and within protected areas.</p> <p>Under the optimistic scenario, no taxa would lose more than 30% of their range and AC would not be required. Under the pessimistic scenario, roughly 90% of taxa showed a cell loss higher than 30%. Eight taxa were predicted to lose >95% of their range. For these species, AC was required from 13 to 16 new populations (= 13 to 16 grid cells) per taxon to cap the range loss at 30%. For currently VU or EN species, an average number of 32 to 35 AC attempts would be necessary to fully compensate for their range loss under a pessimistic scenario. Suitable recipient sites within protected areas falling in their projected range were identified, allowing for short-distance AC.</p> <p>Synthesis. Combining SDMs and red listing thresholds under Criterion A has enabled the strategic planning of multiple-species AC minimising the effort in terms of new populations to be created and maximising the conservation benefit in terms of range loss compensation.</p>
Data from: Seascape genetics and biophysical connectivity modelling support conservation of the seagrass Zostera marina in the Skagerrak-Kattegat region of the eastern North Sea
Maintaining and enabling evolutionary processes within meta-populations is critical to resistance, resilience and adaptive potential. Knowledge about which populations act as sources or sinks, and the direction of gene flow, can help to focus conservation efforts more effectively and forecast how populations might respond to future anthropogenic and environmental pressures. As a foundation species and habitat provider, Zostera marina (eelgrass) is of critical importance to ecosystem functions including fisheries. Here we estimate connectivity of Z. marina in the Skagerrak-Kattegat region of the North Sea based on genetic and biophysical modelling. Genetic diversity, population structure and migration were analysed at 23 locations using 20 microsatellite loci and a suite of analytical approaches. Oceanographic connectivity was analysed using Lagrangian dispersal simulations based on contemporary and historical distribution data dating back to the late 19th century. Population clusters, barriers and networks of connectivity were found to be very similar based on either genetic or oceanographic analyses. A single-generation model of dispersal was not realistic, whereas multi-generation models that integrate stepping-stone dispersal and extant and historic distribution data were able to capture and model genetic connectivity patterns well. Passive rafting of flowering shoots along oceanographic currents is the main driver of gene flow at this spatial-temporal scale and extant genetic connectivity strongly reflects the "ghost of dispersal past" sensu Benzie 1999. The identification of distinct clusters, connectivity hotspots and areas where connectivity has become limited over the last century is critical information for spatial management, conservation and restoration of eelgrass.
Data from: A generalizable energetics-based model of avian migration to facilitate continental-scale waterbird conservation
Conserving migratory birds is made especially difficult because of movement among spatially disparate locations across the annual cycle. In light of challenges presented by the scale and ecology of migratory birds, successful conservation requires integrating objectives, management, and monitoring across scales, from local management units to ecoregional and flyway administrative boundaries. We present an integrated approach using a spatially explicit energetic-based mechanistic bird migration model useful to conservation decision-making across disparate scales and locations. This model moves a Mallard-like bird (Anas platyrhynchos), through spring and fall migration as a function of caloric gains and losses across a continental-scale energy landscape. We predicted with this model that fall migration, where birds moved from breeding to wintering habitat, took a mean of 27.5 d of flight with a mean seasonal survivorship of 90.5% (95% CI = 89.2%, 91.9%), whereas spring migration took a mean of 23.5 d of flight with mean seasonal survivorship of 93.6% (95% CI = 92.5%, 94.7%). Sensitivity analyses suggested that survival during migration was sensitive to flight speed, flight cost, the amount of energy the animal could carry, and the spatial pattern of energy availability, but generally insensitive to total energy availability per se. Nevertheless, continental patterns in the bird-use days occurred principally in relation to wetland cover and agricultural habitat in the fall. Bird-use days were highest in both spring and fall in the Mississippi Alluvial Valley and along the coast and near-shore environments of South Carolina. Spatial sensitivity analyses suggested that locations nearer to migratory endpoints were less important to survivorship; for instance, removing energy from a 1036 km2 stopover site at a time from the Atlantic Flyway suggested coastal areas between New Jersey and North Carolina, including the Chesapeake Bay and the North Carolina piedmont, are essential locations for efficient migration and increasing survivorship during spring migration but not locations in Ontario and Massachusetts. This sort of spatially explicit information may allow decision-makers to prioritize their conservation actions toward locations most influential to migratory success. Thus, this mechanistic model of avian migration provides a decision-analytic medium integrating the potential consequences of local actions to flyway-scale phenomena.
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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.