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Data from: A method that accounts for differential detectability in mixed samples of long-term infections with applications to the cas
负责人:
关键词:
2016-2017;chronic wasting disease;Rangifer tarandus
DOI:
doi:10.5061/dryad.q84p862
摘要:
1. Surveillance of wildlife diseases is logistically difficult, and imperfect detection is a recurrent challenge for disease estimation. Usi
Data from: Assessing species richness trends: declines of bees and bumblebees in the Netherlands since 1945.
负责人:
关键词:
DOI:
doi:10.5061/dryad.34tmpg4fm
摘要:
es of species are imperfect and many datasets were collected in an opportunistic manner. We need to improve our capabilities to assess richness trends usi
Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence
负责人:
Santostasi, Nina Luisa
关键词:
hidden Markov models capture-recapture hybridization Viterbi algorithm
DOI:
doi:10.5061/dryad.8g8r675
摘要:
d to any taxa, and it can be used to estimate absolute abundance and prevalence in a variety of cases involving imperfect detection and uncer
Data from: Estimating effects of species interactions on populations of endangered species
负责人:
关键词:
competition;Bombina variegata;Pelophylax ridibundus;Anurans (frogs and toads);Alytes obstetricans;biological invasions;Pelophylax lessonae;Epidalea calamita;statistics
DOI:
doi:10.5061/dryad.7gt4m
摘要:
, approaches using observational data would need to account for niche differences between species and for imperfect detection of individuals. To estimate
Data from: Models for assessing local-scale co-abundance of animal species while accounting for differential detectability and varied responses
负责人:
关键词:
co-occurrence;global change;Species Interactions;competition
DOI:
doi:10.5061/dryad.fn208
摘要:
tor while explicitly accounting for differential responses to environmental conditions. Our models also incorporate imperfect detection as well as abundance estimation er
Data from: Time to monitor livestock carcasses for biodiversity conservation and public health
负责人:
Mateo-Tomás, Patricia
关键词:
environmental policy integration human-mediated carcasses Natura 2000 vultures sanitary regulations large carnivores scavenging
DOI:
doi:10.5061/dryad.rt2vs67
摘要:
calculations, we show here the high levels of uncertainty in these estimates based on the imperfect information available regarding scavengers’ ecology.
Data from: Evaluating the target?tracking performance of scanning avian radars by augmenting data with simulated echoes
负责人:
关键词:
aeroecology;common terns;movement;radar ornithology;roseate terns;seabirds;target tracking
DOI:
doi:10.5061/dryad.3n5tb2rd2
摘要:
probabilities in turn allows the animal densities and fluxes to be corrected for imperfect detection. Despite their limitations, small scanning radars can track
Data from: Shifting up a gear with iDNA: from mammal detection events to standardized surveys
负责人:
关键词:
Borneo;Southeast Asia;tropical rainforest;conservation;biodiversity;Mammals;tropics;leeches;rainforest;eDNA;camera trap;iDNA
DOI:
doi:10.5061/dryad.4558p38
摘要:
ived mammalian detection events in a modern occupancy model that accounts for imperfect detection and compare the results with those from occupancy models parameter
Data from: Occupancy models for data with false positive and false negative errors and heterogeneity across sites and surveys
负责人:
关键词:
informative prior;imperfect detection;occupancy model;scenario;observation;phantom species;Mniotilta varia;detection probability;confirmed detection;hierarchical Bayesian model;Monte Carlo simulation
DOI:
doi:10.5061/dryad.t68v8
摘要:
d not have false positive errors. For the scenarios we expect to be most generally applicable, those with heterogeneity in occupancy and detection, the CACP
Data from: Identifying drivers of spatial variation in occupancy with limited replication camera trap data
负责人:
关键词:
occupancy model;Bayesian modeling;Serengeti;camera trap
DOI:
doi:10.5061/dryad.34kb373
摘要:
nt ecological quantities. These models account for imperfect detection using a latent variable to distinguish between true presence/absence and obser

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