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319 results for “Population estimation”
Data from: When can model-based estimates replace surveys of wildlife populations that span many discrete management units?
<p>Monitoring widely distributed species on a budget presents challenges for the spatio-temporal allocation of survey effort. When there are multiple discrete units to monitor, survey alternatives such as model-based estimates can be useful to fill information-gaps but may not reliably reflect biological complexity and change. The spatio-temporal allocation of survey effort that minimizes uncertainty for the greatest number of units within a budget can help to ensure monitoring efforts are optimized.</p> <p>We used aerial survey-based population estimates of moose (Alces alces) across 30 Wildlife Management Units (WMUs) in Ontario, Canada to parameterize simulated populations and test the performance of different monitoring scenarios in capturing WMU-specific annual variation and trends. Firstly, we tested scenarios that prioritized conducting a survey for a unit based on one of three management criteria: population state, population uncertainty, or number of years between surveys. Also incorporated in the decision framework were WMU-specific costs and annual budget constraints. Secondly, we tested how using model-based estimates to fill information-gaps improved population and trend estimates. Lastly, we assessed how the utility (based on minimizing population uncertainty) of using a model-based estimate rather than conducting a survey was impacted by population density, severity of environmental stressors, and years since the last survey.</p> <p>Interval-based monitoring that minimized the number of years between surveys captured accurate trends for the highest number of WMUs, but annual variation was poorly captured regardless of management criteria prioritized. Using model-based estimates to fill information gaps improved trend estimation. Further, the utility of conducting a survey increased with time since the last survey and was greater for populations with low densities when the severity of environmental stressors was high, while being greater for populations with high densities when environmental severity was low.</p> <p>Overall, the utility of aerial survey monitoring was strongly associated with WMU-specific monitoring precision and the predictive power of model-based estimates. If long-term trends are evident then there is greater value in using alternatives such as model-based predictions to replace surveys, but model-based estimates may be a poor substitute when there is strong annual variation and when using a simple model.</p>
Data from: Passive acoustic monitoring provides reliable under-estimates of population size and longevity in wild Savannah Sparrows
<p>Many breeding birds produce conspicuous sounds, providing tremendous opportunities to study free-living birds through acoustic recordings. Traditional methods for studying population size and demographic features depend on labour-intensive field research. Passive acoustic monitoring provides an alternative method for quantifying population size and demographic parameters, but this approach requires careful validation. To determine the accuracy of passive acoustic monitoring for estimating population size and demographic parameters, we used autonomous recorders to sample an island-living population of Savannah Sparrows (<em>Passerculus sandwichensis</em>) over a six-year period. Using the individually distinctive songs of males, we estimated male population size as the number of unique songs detected in the recordings. We analyzed songs across six years to estimate birth year, death year, and longevity. We then compared the estimates to field data in a blind analysis. Estimates of male population size through passive acoustic monitoring were, on average, 72% of the true male population size, with higher accuracy in lower-density years. Estimates of demographic rates were lower than true values by 29% for birth year, 23% for death year, and 29% for longevity. This is the first investigation to estimate longevity with passive acoustic monitoring, and adds to a growing number of studies that have used passive acoustic monitoring to estimate population size. Although passive acoustic monitoring under-estimated true population parametersfeatures, likely due to the high similarity among many male songs, our findings suggest that autonomous recorders can provide reliable estimates of population size and demographic characteristicslongevity in a wild songbird.</p>
Using by-catch camera trapping data for estimating the population size of spotted hyena (Crocuta crocuta)
<p>Spotted hyenas (<em>Crocuta crocuta</em>) are an important carnivore species whose dual role of scavenger and predator is vital to trophic energy flows of systems in which they are found. Where populations of spotted hyenas are small, the environment has few cleaners and carcasses can remain unprocessed. Despite being largely characterized as scavengers, spotted hyenas actively hunt and take down live prey and at high densities can have depressing effects on fragile or choice ungulate populations. In addition, they can alter the structure and composition dynamics of the carnivore guild through direct conflict or indirectly through competition for food and space. Despite their importance to ecosystem function and balance, reliable estimates of spotted hyena densities are rare. This is because unlike lions and leopards, spotted hyenas are generally not regarded as a charismatic species and, as such, survey resources, which are costly, are seldom solely allocated towards surveying them. Nonetheless, being able to confidently estimate spotted hyena numbers is important for the effective management of carnivore and herbivore populations whose dynamics they influence.</p>
Data availability: Random encounter model is a reliable method for estimating population density of multiple species using camera traps
<p>Data of the paper entitled "Random encounter model is a reliable method for estimating population density of multiple species using camera traps" published on Remote Sensing in Ecology and Conservation</p>
Estimation of breeding population size using DNA-based pedigree reconstruction in brown bears
Robust estimates of demographic parameters are critical for effective wildlife conservation and management, but are difficult to obtain for elusive species. We estimated the breeding and adult population sizes, as well as the minimum population size, in a high-density brown bear population on the Shiretoko Peninsula, in Hokkaido, Japan, using DNA-based pedigree reconstruction. A total of 1,288 individuals, collected in and around the Shiretoko Peninsula between 1998 and 2020, were genotyped at 21 microsatellite loci. Among them, 499 individuals were identified by intensive genetic sampling conducted in two consecutive years (2019 and 2020) mainly by noninvasive methods (e.g., hair and fecal DNA). Among them, both parents were assigned for 330 bears, and either maternity or paternity was assigned to 47 and 76 individuals, respectively. The subsequent pedigree reconstruction indicated a range of breeding and adult (≥4 years old) population sizes: 128–173 for female breeders and 66–91 male breeders, and 155–200 for female adults and 84–109 male adults. The minimum population size was estimated to be 449 (252 females and 197 males) in 2019. Long-term continuous genetic sampling prior to a short-term intensive survey would enable parentage to be identified in a population with a high probability, thus enabling reliable estimates of breeding population size for elusive species. --
Figure 4 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 4. Sum of individuals counted at each observation date at site A (above) and site B (below).
Death and population dynamics affect mutation rate estimates and evolvability under stress in bacteria
<p>Code and data for Frenoy & Bonhoeffer 2018 (Death and population dynamics affect mutation rate estimates and evolvability under stress in bacteria, in PLoS Biology)</p> <p>See README for details and steps to reproduce the full analysis from the raw data</p>
LD Estimated from 1k Genomes CEU Population
<p>These data contain estimated pairwise r^2 for variants with allele frequency greater than 0.05 in the 1000 Genomes CEU population. They were estimated using LDshrink (https://github.com/stephenslab/LDshrink). R^2 is only reported when the estimate is greater than 0.1.</p> <p> </p> <p>For each chromosome there are two files:</p> <p>chr<chr>_AF0.5_0.1.RDS is an R object containing a data frame with three columns: rowsnp, colsnp, and r2</p> <p>chr<chr>_AF0.5_snpdata.RDS is an R object containing a data frame with information for every SNP meeting the allele frequency cutoff.</p>
Dakar population estimates at 100x100m spatial resolution - grid layer - Dasymetric mapping
<p>This dataset contains the a raster layer with the population estimates obtained using a dasymetric mapping procedure (top-down approach). For a detailed description of the methodology, please refer to the following paper:</p> <p>Grippa, Taïs, Catherine Linard, Moritz Lennert, Stefanos Georganos, Nicholus Mboga, Sabine Vanhuysse, Assane Gadiaga, and Eléonore Wolff. 2019. “Improving Urban Population Distribution Models with Very-High Resolution Satellite Information.” <em>Data</em> 4 (1): 13. <a href="https://doi.org/10.3390/data4010013">https://doi.org/10.3390/data4010013</a>.</p> <p>Funding and aknowledgement: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be/">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be/">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p> <p>The authors gratefully thanks the \href{http://assess-sn.org/}{ASSESS project}, funded by the \href{https://www.ares-ac.be}{ARES-CDD}, that provided the access to the census data.</p>
Population estimation from mobile network traffic metadata
<p><em><strong>Please cite our paper if you publish material based on those datasets</strong></em></p> <blockquote> <p>G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata", in IEEE Trans. on Mobile Computing, 2018 (in Press). <a href="http://dx.doi.org/10.1109/TMC.2018.2871156">10.1109/TMC.2018.2871156</a></p> </blockquote> <p> </p> <p><strong>Abstract</strong></p> <p>Communication-enabled devices that are physically carried by individuals are today pervasive,<br> which opens unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by a mobile operator. We show that our approach suits the estimation of static population densities, i.e., of the distribution of dwelling units per urban area contained in traditional censuses. Specifically, it achieves higher accuracy than that granted by previous equivalent solutions. In addition, our approach enables the estimation of dynamic population densities, i.e., the time-varying distributions of people in a conurbation. Our results build on significant real-world mobile network metadata and relevant ground-truth information in multiple urban scenarios.</p> <p><strong>Dataset Columns</strong></p> <p>This dataset cover one month of data taken during the month of April 2015 for three Italian cities: Rome, Milan, Turin. The raw data has been provided during the Telecom Italia Big Data Challenge (http://www.telecomitalia.com/tit/en/innovazione/archivio/big-data-challenge-2015.html)</p> <p>1. <strong>grid_id</strong>: the coordinate of the grid can be retrieved with the shapefile of a given city<br> 2. <strong>date</strong>: format Y-M-D H:M:S<br> 4. <strong>landuse_label</strong>: the land use label has been computed by through method described in [2]<br> 5. <strong>population</strong>: Census population of a given grid block as defined by the Istituto nazionale di statistica (ISTAT https://www.istat.it/en/censuses) in 2011<br> 6. <strong>estimation</strong>: Dynamics density population estimation (in person) as the result of the method described in [1]<br> 7. <strong>area</strong>: surface of the "grid id" considered in km^2<br> 8. <strong>geometry</strong>: the shape of the area considered with the EPSG:3003 coordinate system (only with quilt)</p> <p><strong>Note</strong></p> <p>Due to legal constraints, we cannot share directly the original data from the Telecom Italia Big Data Challenge we used to build this dataset.</p> <p><strong>Easy access to this dataset with quilt</strong></p> <p>Install the dataset repository:</p> <p>$ quilt install vgauthier/DynamicPopEstimate</p> <p>Use the dataset with a Panda Dataframe</p> <p>>>> from quilt.data.vgauthier import DynamicPopEstimate<br> >>> import pandas as pd<br> >>> df = pd.DataFrame(DynamicPopEstimate.rome())<br> <br> Use the dataset with a GeoPanda Dataframe<br> <br> >>> from quilt.data.vgauthier import DynamicPopEstimate<br> >>> import geopandas as gpd<br> >>> df = gpd.DataFrame(DynamicPopEstimate.rome())</p> <p><strong>References</strong></p> <p>[1] G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Population estimation from mobile network traffic metadata", in proc of the 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1 - 9, 2016. </p> <p>[2] A. Furno, M. Fiore, R. Stanica, C. Ziemlicki, and Z. Smoreda, "A tale of ten cities: Characterizing signatures of mobile traffic in urban areas," IEEE Transactions on Mobile Computing, Volume: 16, Issue: 10, 2017.<br> </p>
Geospatial micro-estimates of slum populations in 129 Global South countries using machine learning and public data
<p><span>Reliable estimation of populations living in slums or slum-like conditions is crucial for urban planning, humanitarian resource allocation, and human well-being improvement. We generate the micro-estimate of slum population at a neighborhood level (~</span><span>3.63 arc-minutes</span><span>, preserving the privacy of vulnerable people) for 129 Global South countries in 2018. The estimates are built based on the Sustainable Development Goals 11.1 indicator framework and machine learning algorithms to heterogeneous data from household-based surveys and satellite images, as well as grided population data. Our integrated regional models show strong predictive capabilities for cluster-level slums proxy, explaining 82% to 96% of the variation in ground-truth surveys conducted in Global South countries, with root mean squared error ranging from 4.85% to 10.47%. The models perform match or surpass benchmarks established by previous studies.</span><span> </span><span>Cross-comparison with independent data sources at multi-scales suggest that our approach can yield reliable and consistent slum population estimates.</span></p>
Estimates for US foreign-born population size and immigration flows, 2000-2019
<p>Detailed results files for "Bayesian evidence synthesis to infer unobserved population dynamics: an application to international migration into the United States, 2000-2019"</p>
Data from: RAD sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference
Restriction site-associated DNA sequencing (RADseq) provides researchers with the ability to record genetic polymorphism across thousands of loci for non-model organisms, potentially revolutionising the field of molecular ecology. However, as with other genotyping methods, RADseq is prone to a number of sources of error that may have consequential effects for population genetic inferences, and these have received only limited attention in terms of the estimation and reporting of genotyping error rates. Here we use individual sample replicates, under the expectation of identical genotypes, to quantify genotyping error in the absence of a reference genome. We then use sample replicates to (1) optimize de novo assembly parameters within the program Stacks, by minimizing error and maximizing the retrieval of informative loci, and; (2) quantify error rates for loci, alleles and SNPs. As an empirical example we use a double digest RAD dataset of a non-model plant species, Berberis alpina, collected from high altitude mountains in Mexico.
Fig. 2 in Estimating Population Size And Distribution Of Hume'S Pheasant In Northern Thailand
Fig. 2. Predicted habitat of Hume's Pheasant in northern Thailand.
Evaluating noninvasive methods for estimating cestode prevalence in a wild carnivore population
<p>This repository holds the datasets and R code files needed to run the models in: Brandell et al., 2022. Evaluating noninvasive methods for estimating cestode prevalence in a wild carnivore population. <em>PLOS ONE</em>.</p> <p>Excel files have associated KEYs for each data column; CSVs are analyzed with their associated R code.</p>
Open-population SCR model to estimate spatiotemporal variation in individual birth locations, detection rates, and survival
<p>This is an open population SCR model developed by R. Chandler and K. Engebretsen. The full model incorporates 4 spatial covariates in birth location density submodel, 3 location-specific, temporal covariates in the detection submodel, and 4 spatial covariates in the survival submodel. </p> <p>Formatted data is provided for the 2015 and 2016 fawning season in south Florida and the model can be fit using the script fitFawnModel.R.</p>
Data from: Genetic mark-recapture analysis of winter faecal pellets allows estimation of population size in sage grouse Centrocercus urophasianus
<p><span>Sex ratio, and the extent to which it varies over time, is an important factor in the demography, management, and conservation of wildlife populations. We estimated pre-breeding sex ratio of greater sage-grouse (Centrocercus urophasianus) in a peripheral, geographically isolated population in northwestern Colorado during two consecutive winters using closed-population, robust-design, multi-state, genetic mark-recapture models in program MARK (White and Burnham 1999). This data release includes the data files (.inp format) used in those models, as described in Shyvers et al. 2023. The data include capture histories and auxiliary data for individual greater sage-grouse collected during two study seasons: Season 1 (winter 2012-2013) and Season 2 (winter 2013-2014) and are readable using program MARK or notepad. Each data row includes the unique bird identification number (GMR-ID); the bird's encounter history for n= sampling occasions coded as a static state (M = male, F = female); the group ID; and a region covariate (0 = North, 1 = South). The data were adapted from those originally developed for Shyvers et al. 2020 and applied using Closed Robust Design Multi-state (CRDMS) Huggins' p and c w/state probabilities in program MARK to obtain estimates of Omega, enabling estimation of sex ratio with associated confidence intervals (see Shyvers et al. 2023).</span></p> <p>References:</p> <p>Shyvers, J.E., Walker, B.L., Oyler-McCance, S.J., Fike, J.A. and Noon, B.R. 2023. Genetic mark-recapture analysis reveals large annual variation in pre-breeding sex ratio of greater sage-grouse. Wildlife Biology (https://doi.org/10.1002/wlb3.01085)</p> <p>Shyvers, J.E., Walker, B.L., Oyler‐McCance, S.J., Fike, J.A. and Noon, B.R., 2020. Genetic mark-recapture analysis of winter faecal pellets allows estimation of population size in Sage Grouse Centrocercus urophasianus. Ibis, 162(3), pp.749-765.</p> <p>White, G. C., and K. P. Burnham. 1999. Program Mark: survival estimation from populations of marked animals. – Bird Study 46:120–139.</p>
Dataset for density estimation for an island population of raccoon dogs in Japan
<p><span>Estimation of the population</span><span> size</span><span> is essential for understanding population dynamics</span><span>. Estimating animal density using multiple methods and/or multiple attempts is required for accurate estimations. Raccoon dog (<em>Nyctereutes</em> <em>procyonoides</em>) is native to East Asia, including Japan, and has become an invasive species in Europe. Information on raccoon dog density in their native range is important to understand their invasion; however, relatively few studies have been conducted on raccoon dog density in their native range. In this study, we extracted DNA from fecal samples of raccoon dogs inhabiting a small island in Japan and conducted density estimation over two periods using DNA capture-recapture methods: CAPWIRE and SECR. We also investigated sex ratio</span> <span>using genetic sex identification. Density estimates using SECR were approximately threefold different between the two study periods: </span><span>17.2</span><span> individuals per km<sup>2</sup> in 2018 and </span><span>49.0 </span><span>individuals per km<sup>2</sup> in 2020. In contrast, estimates using CAPWIRE were relatively stable: </span><span>21.7</span><span> individuals per km<sup>2</sup> in 2018 and </span><span>24.3</span><span> individuals per km<sup>2</sup> in 2020. A drastic increase or decrease is not expected during the study period, and thus</span><span>, density estimates using CAPWIRE are more reasonable than </span><span>those using SECR. The small number of samples per individual might result in low accuracy of density estimates by SECR. The density estimated by CAPWIRE was similar to that in the main island in Japan</span> <span>and higher than that in Europe. Feeding competition with other omnivorous carnivores and/or predation risk by wolves might maintain the low density in Europe. The sex ratio of raccoon dogs was 1:1, which was similar to </span><span>the values in invasive raccoon dogs and </span><span>other canids. Further genetic census</span><span>, including sex identification in various landscapes in their native and invasive range, will enable us to understand not only the ecology of raccoon dogs but also their adaptations to </span><span>their invading areas.</span></p>
Abundance and population growth estimates for bare-nosed wombats
<p><span>Wildlife managers often rely on population estimates, but estimates can be challenging to obtain for geographically widespread species. Spotlight surveys provide abundance data for many species and, when conducted over wide spatial scales, have the potential to provide population estimates of geographically widespread species. The bare-nosed wombat (<em>Vombatus</em> <em>ursinus</em>) has a broad geographic range and is subject to spotlight surveys. We used 19 years (2002–2020) of annual spotlight surveys to provide the first estimates of population abundance for two of the three extant bare-nosed wombat subspecies: <em>V. u. ursinus</em> on Flinders Island; and <em>V. u. tasmaniensis</em> on the Tasmanian mainland. Using distance sampling methods, we estimated annual rates of change and 2020 population sizes for both sub-species. Tasmanian mainland surveys included habitat data, which allowed us to also look for evidence of habitat associations for <em>V. u. tasmaniensis</em>. The average wombat density estimate was higher on Flinders Island (0.42 ha<sup>-1</sup>, 95% CrI = 0.25 – 0.79) than on the Tasmanian mainland (0.11 ha<sup>-1</sup>, CrI = 0.07 – 0.19) and both wombat subspecies increased over the 19-year survey period with an estimated annual growth rate of 2.90% (CrI = -1.7 – 7.3) on Flinders Island and 1.20% (CrI = -1.1 – 2.9) on mainland Tasmania. Habitat associations for <em>V. u. tasmaniensis</em> were weak, possibly owing to survey design; however, we detected regional variation in density for this subspecies. We estimated the population size of <em>V. u. ursinus </em>to be 71,826 (CrI = 43,913 – 136,761) on Flinders Island, which when combined with a previously published estimate of 2,599 (CI = 2,254 – 2,858) from Maria Island, where the subspecies was introduced, provides a total population estimate. We also estimated 840,665 (CrI = 531,104 – 1,201,547) <em>V. u. tasmaniensis </em>on mainland Tasmania. These estimates may be conservative, owing to individual heterogeneity in when wombats emerge from burrows. Although these two sub-species are not currently threatened, our population estimates provide an important reference when assessing their population status in the future, and demonstrate how spotlight surveys can be valuable to inform management of geographically widespread species.</span></p>
Dataset for: Estimating pregnancy rate from blubber progesterone levels of a blindly biopsied beluga population poses methodological, analytical and statistical challenges
<p class="MsoBodyText"><span>Beluga (<em>Delphinapterus leucas</em>) from the St. Lawrence Estuary, Canada, have been declining since the early 2000s, suggesting recruitment issues as a result of low fecundity, abnormal abortion rates or poor calf or juvenile survival. Pregnancy is difficult to observe in cetaceans, making the ground-truthing of pregnancy estimates in wild individuals challenging. Blubber progesterone concentrations were contrasted among 62 SLE beluga with a known reproductive state (i.e., pregnant, resting, parturient, and lactating females), that were found dead in 1997–2019. The suitability of a threshold obtained from decaying carcasses to assess reproductive state and pregnancy rate of freshly-dead or free-ranging and blindly-sampled beluga was examined using three statistical approaches and two datasets (135 freshly-harvested carcasses in Nunavik, and 65 biopsy-sampled SLE beluga). Progesterone concentrations in decaying carcasses were considerably higher in known-pregnant (mean </span><span>±</span><span> sd: </span><span>365 </span><span>±</span><span> 244 ng g<sup>-1</sup> of tissue) </span><span>than resting (</span><span>3.1 </span><span>±</span><span> 4.5 ng g<sup>-1</sup> of tissue</span><span>) or lactating (</span><span>38.4 </span><span>±</span><span> 100 ng g<sup>-1</sup> of tissue</span><span>) females. An approach based on statistical mixtures of distributions and a logistic regression was compared to the commonly-used, fixed threshold approach (here, 100 ng g<sup>-1</sup>) for discriminating pregnant from non-pregnant females. The error rate for classifying individuals of known reproductive status was the lowest for the fixed threshold and logistic regression approaches, but the mixture approach required limited <em>a priori</em> knowledge for clustering individuals of unknown pregnancy status. Mismatches in assignations occurred at lipid content <10% of sample weight. Our results emphasize the importance of reporting lipid contents and progesterone concentrations in both units (ng g<sup>-1 </sup>of tissue and ng g<sup>-1</sup> of lipid) when sample mass is low. By highlighting ways to circumvent potential biases in field sampling associated with capturability of different segments of a population, this study also enhances the usefulness of the technique for estimating pregnancy rate of free-ranging population. </span></p>
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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)
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DANDI Archive for NWB datasets
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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.