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8 results for “spatial density modeling”
French Guianan mammal and bird population densities with spatial-capture recapture, line transect distance sampling, and 'unmarked' density models
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Data from: An open spatial capture–recapture model for estimating density, movement, and population dynamics from line-transect surveys
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Potential map generated by the RF spatial model to define ideal zones for the occurrence of high density of giant trees in the Amazon
<p>The provided image is a theoretical map of giant tree density in the Amazon, generated from a spatial model based on the **Random Forest** algorithm. The map displays the spatial distribution of tree density, representing the number of trees taller than 60 meters per square kilometer (trees/km²). The model was developed using climatic, topographic, and soil variables to predict areas with higher concentrations of these giant trees.</p> <p>The areas are color-coded according to different density ranges, where:<br>- Lighter shades indicate lower tree density (≤ 5 trees/km²),<br>- Darker shades indicate higher density (up to 141 trees/km²).</p> <p>Biogeographic provinces within the Amazon biome, such as the **Guiana Shield**, **Xingu-Tapajós**, and **Roraima**, are highlighted, showing distinct density patterns across the Amazon region. This map is a valuable tool for understanding the spatial distribution of giant trees in the Amazon and plays a crucial role in conservation efforts and ecological monitoring in the region.</p>
Data from: Joint spatial modeling of cluster size and density for a heavily hunted primate persisting in a heterogeneous landscape
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Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
Density estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of "marked" wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of "unmarked" wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates.
Estimating red fox density using non-invasive genetic sampling and spatial capture–recapture modelling
<p>Data and scripts for our paper:</p> <p>Lindsø, L.K., Dupont, P., Rød-Eriksen, L. <em>et al.</em> Estimating red fox density using non-invasive genetic sampling and spatial capture–recapture modelling. <em>Oecologia</em> <strong>198</strong>, 139–151 (2022). https://doi.org/10.1007/s00442-021-05087-3</p>
Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
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Spatially explicit re-harmonized terrestrial carbon densities for calibrating Integrated human-Earth System Models
<p>Soil and vegetation carbon densities play a critical role in global and regional human-Earth system models. These densities affect variables such as land use change emissions and also influence land use change pathways under climate mitigation scenarios where terrestrial carbon is assigned a carbon price. Recently, more spatially explicit, fine resolution data have become available for both soil and vegetation carbon. However, for models to effectively use these data the fine resolution data need to be reharmonized to initial land use and land cover conditions represented by these models. Without such reharmonization the carbon values may be very inaccurate for particular land types and places where the source data and the model disagree on the land use/cover type. Here we present reharmonized soil and vegetation carbon densities both at the grid cell level at 5 arcmin resolution and also aggregated to 235 water sheds for 4 different land use and 15 land cover types. These data are particularly useful as initial land carbon conditions for global Multisectoral Dynamic Models (MSD). Moreover, these data include six different statistical states calculated using distinct resampling methods for each of the land use, land cover types. These statistical states are used to define a range of possible carbon values for each land classification, and any state can be used for defining initial conditions of soil and vegetation carbon in MSD models. We make use of these statistical states to calculate spatially distinct uncertainties in the carbon densities by land type. We have implemented these data in a state-of-the-art multi sector dynamics model, namely the Global Change Analysis Model (GCAM), and show that these new data improve several land use responses in the model, especially when terrestrial carbon is assigned a carbon price. The statistical states in our data are validated against similar estimates in the literature both at a grid cell level and at a regional level. </p> <p>This is a data record which corresponds to the paper "Spatially explicit re-harmonized terrestrial carbon densities for calibrating Integrated Multisectoral Models" (Narayan et al. 2023, under review)</p> <p>We have now also added a tabular version of the dataset aggregated to GTAP's AEZ definitions as opposed to GCAM's GLUs</p> <p> </p>
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