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25 results for “N-mixture models”
Example code and data for ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework
<p>This repository contains an R script (grouse_example.R) and data (grouse_data.csv) used to reproduce the grouse abundance analysis described in Kellner, K. F., et al. (2021) ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods in Ecology and Evolution. The R script requires installation of the ubms R package, which can be obtained from CRAN (https://cran.r-project.org/package=ubms).</p> <p>The repository also contains an additional example occupancy analysis (occupancy_example.R) using the crossbill dataset included with the unmarked R package.</p>
Fig. 2 in Assessing The Abundance Of Caucasian Salamander, Mertensiella Caucasica (Caudata, Salamandridae), With N-Mixture Model In Northeastern Anatolia
Fig. 2. The average abundance of Caucasian salamanders from the East Black Sea Region, Turkey. X-axis shows the sampling plots number in each city; Y-axis shows the estimated population size.
Evidence of absence regression: a binomial N-mixture model for estimating fatalities at wind power facilities
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Monitoring animal populations with cameras using open, multistate, N-mixture models
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Estimating spawning Green Sturgeon (Acipenser medirostris Ayres, 1854) abundance using side scan sonar and N-mixture models
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Data from: Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements
Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space, and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi-state mark recapture methods using a hierarchical N-mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state-space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (Cervus elaphus nelsoni) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N-mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.
Data from: using camera traps and N-mixture models to estimate population abundance: model selection really matters
<p>Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the data collection and statistical methods, sampling and ecological variation, and sample size. N-mixture models are a common method which has been applied to a wide range of taxa for estimating population abundance from non-invasive data representing the distribution of the species. We used population estimates from an aerial survey of moose and videos from camera traps to assess the sensitivity of N-mixture models to ecological conditions, the spatial scale at which they were measured, the criteria used to define independent detections, and model choice based on the common statistical criterion of parsimony. The most parsimonious N-mixture models were considerably biased, producing implausibly large and considerably imprecise estimates of the abundance of moose. Most of the other models produced estimates of abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N-mixture models were not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size. Our results suggest that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N-mixture model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps can provide valuable information on behavior of the target species as well as insights into multiple species in the community.</p>
Fig. 1 in Assessing The Abundance Of Caucasian Salamander, Mertensiella Caucasica (Caudata, Salamandridae), With N-Mixture Model In Northeastern Anatolia
Fig. 1. General view of study area.
Data from: N-mixture models estimate abundance reliably: a field test on Marsh Tit using time-for-space substitution
<p>Imperfect detection in field studies on animal abundance, including birds, is common and can be corrected for in various ways. The binomial N-mixture (hereafter binmix) model developed for this task is widely used in ecological studies owing to its simplicity: it requires replicated count results as the input. However, it may overestimate abundance and be sensitive to even small violations of its assumptions. We used a 33-year dataset on the Marsh Tit, Poecile palustris, a sedentary forest passerine, from Białowieża Forest, Poland to validate inference from binmix models by comparing model-estimated abundances to the true number of breeding pairs within the plots, determined by exhaustive population study. The abundance estimates, derived from six springtime (April-May) counts of males on each plot in each year, were highly reliable: 116 out of 132 year-plot estimates (88%) included the true number of pairs within the 95% confidence intervals. Over- and underestimations were thus rare and similarly frequent (9 and 12 cases, respectively), with a tendency to overestimate at low densities and underestimate at high densities. Marsh Tits sing rarely but the frequency of countersinging increases with abundance, leading to non-independence in detections. When accounted for in a submodel for detection, the per-survey number of countersinging events positively affected detection probability but only weakly affected abundance estimates. Simulations further demonstrate that this property, overestimation at low densities and underestimation at high densities, may be a systematic bias of binmix model even if density-dependent detection is absent. While the behaviour of binmix models in specific situations requires more study, we conclude that these models are a valid tool to estimate abundance reliably when intensive population monitoring is not feasible.</p>
Northern shoveler data for "Sensitivity of binomial N-mixture models to overdispersion: the importance of assessing model fit"
<p>Repeated count data for Northern shoveler analyzed in Knape et al. Sensitivity of binomial N-mixture models to overdispersion: the importance of assessing model fit", Methods in Ecology and Evolution.</p> <p>count.csv contains Northern shoveler counts repeated 10 times at 50 sites in a 50 x 10 matrix. Each row corresponds to a specific site and columns correspond to visits.</p> <p>date.csv is a 50 x 10 matrix containing the julian date of each count, using the same ordering of visits (columns) and sites (rows) as in count.csv.</p> <p>site.csv contains covariates for each of the 50 sites. Sites (rows) are ordered in the same way as in count.csv and date.csv. The first column represents the area of water (ha) covered by the wetlands where the counts were conducted, the second columns is the percentage of the wetland area covered by reeds, and the third column is the latitude of the wetland in RT90 coordinates.</p> <p> </p> <p> </p>
A hierarchical N-mixture model to estimate behavioral variation and a case study of Neotropical birds
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Data from: N-mixture models estimate abundance reliably: a field test on Marsh Tit using time-for-space substitution
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Data from: Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements
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Data from: using camera traps and N-mixture models to estimate population abundance: model selection really matters
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Data from: Estimating transient populations of unmarked individuals at a migratory stopover site using generalized N-mixture models
1. Migration counts are popular indices used to monitor population trends over time. Advanced analytical methods for estimating abundance of unmarked, open populations now incorporate population growth models and simultaneously test for covariate effects on abundance and detection probability. However, estimating population abundance at a staging site is complicated by daily immigration and emigration of unmarked individuals. 2. We applied a set of generalized N-mixture models to simulated count data to test their applicability for transient populations. Using simulated datasets, parameters were unbiased when the apparent survival rate varied within a season or was mis-specified in a model, but not when the immigration or detection probability was mis-specified. 3. With knowledge from the simulated data, we applied these models to daily counts of staging migratory shorebirds and estimated daily abundances accounting for variation in the detection and immigration rates. Daily counts of ruddy turnstones (Arenaria interpres) staging at Westhampton Island, New York, were collected during northward migration (1997–1999). We tested the effects of weather and tides on detection probability, and we modeled within-season variation in immigration rates as a function of time. 4. Covariates affecting the detection probability differed among years, but tide height consistently was correlated with detection probability. Accounting for detection and immigration rates, the predicted maximum single-day populations of ruddy turnstones were 172%, 165%, and 129% of the observed counts for each year. 5. Synthesis and applications. Management and conservation plans for migratory species require abundance estimates that are near the true population size though they are difficult to obtain. Our study is the first empirical application of the generalized N-mixture model that incorporates temporal trends in immigration and estimates daily abundance of a staging unmarked migratory population. Correct estimation of population sizes and the environmental factors affecting them can aid the conservation prioritization of species and staging sites. Moreover, the use of generalized N-mixture models can improve our understanding of the environmental factors that shape migratory movements.
AN EXTENSION OF N-MIXTURE OCCUPANCY MODELS FOR COUNT DATA
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Bayesian N-mixture and occupancy modeling code for songbirds
<p>The proliferation of energy rights-of-way (pipelines and powerlines; ROWs) in the central Appalachian region has prompted wildlife management agencies to consider ways to use these features to manage and conserve at-risk songbird species. However, little empirical evidence exists regarding best management strategies to enhance habitat surrounding ROWs for the songbird community during stopover or breeding periods. We used a before-after-control-impact design to study cut-back border (linear tree cuttings along abrupt forest edges) harvest width (15 m, 30 m, and 45 m wide into the forest) and harvest intensity (14 m<sup>2</sup>/ha and 4.5 m<sup>2</sup>/ha basal area retention) prescriptions along ROWs and assessed their effects on mature forest and young forest songbird species and avian guilds (forest gap habitat, forest interior habitat, young forest habitat, and species of regional conservation priority) up to two years after treatment throughout West Virginia. Species richness during the spring stopover period initially decreased at one-year post-treatment but returned to pre-treatment levels by two-year post-treatment. Breeding season responses to cut-back border treatments varied across harvest width, harvest intensity, and time, but all responses of focal species abundance and guild richness were neutral or positive. Cut-back border harvest intensity had a stronger influence (i.e., more positive responses) than harvest width on breeding focal species abundances and guild richness. For harvest intensity, the more intense, 4.5 m<sup>2</sup>/ha retention treatment had a stronger influence (i.e., more positive responses) than the less intense, 14 m<sup>2</sup>/ha retention treatment. For harvest width, the narrowest treatment (15-m wide) had the strongest influence (i.e., more positive responses) of all width treatments, followed by the widest (45-m wide treatment) with the least influence from the 30-m wide treatment. Abundances and richness increased from pre-treatment to two-year post-treatment across all species and guilds that exhibited a response. These results suggest that cut-back borders increase breeding season habitat suitability along ROWs for the mature forest and young forest songbird community as well as for species of regional conservation priority in the short-term. These findings can aid development of management guidelines for the forest songbird community along abrupt forest edges of man-made habitat features in forest-dominated landscapes.</p>
Riparian land-cover data and model code for: Multiple-region, N-mixture community models to assess associations of riparian area, fragmentation, and species richness
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Data from: Estimating transient populations of unmarked individuals at a migratory stopover site using generalized N-mixture models
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Bayesian N-mixture and occupancy modeling code for songbirds
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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.