unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance

By Ian J. Fiske, R. Chandler. 2011

2,252 citations recorded by the source corpus.

Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientific questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mechanisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unified modeling interface. The R package unmarked provides such a unified modeling framework, including tools for data exploration, model fitting, model criticism, post-hoc analysis, and model comparison.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/0165ff8dce9dbbe037c93e218f5461ef74cb490b

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Citations in the corpus

Black, Grey and Watch Lists of alien species in the Czech Republic based on environmental impacts and management strategy (2016, methodology): “Results of the PCA were included in occupancy models (MacKenzie et al. 2003, 2006) using the occu function of the unmarked package, version 0.10-4 (Fiske and Chandler 2011).”
Next‐generation monitoring of aquatic biodiversity using environmental DNA metabarcoding (2016, methodology): “All statistical analyses were performed using R.3.0.2 (R Core Team 2014) and the package ‘UNMARKED’ (Fiske & Chandler 2011).”
Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data (2015, methodology): “© 2014 John Wiley & Sons Ltd Both occupancy models were run using the package unmarked in R 3.0 (Fiske & Chandler 2011; R Development Core Team 2013).”
Occupancy patterns and niche partitioning within a diverse carnivore community exposed to anthropogenic pressures (2013, methodology): “We used camera trapping and dynamic occupancy modeling (Fiske and Chandler, 2011; MacKenzie, 2003; MacKenzie et al., 2006) to examine the distribution of a diverse carnivore guild across a multiple human use landscape in Kenya’s southern Rift Valley.”
Detecting small changes in populations at landscape scales: a bioacoustic site-occupancy framework (2019, methodology): “9–2 (Fiske and Chandler, 2011) only fits dynamic occupancy models parameterized with the initial site occupancy rate, extinction, and colonization, and does thus not directly allow for the testing of trends in site occupancy rates over time.”
Should we consider individual behavior differences in applied wildlife conservation studies (2017, methodology): “Individual heterogeneity in detection and capture can be accounted for in two widely used platforms formodeling population parameters viamark-recapture data and include PledgerMixtureModels and Individual RandomEffects Models in Program MARK (White and Burnham, 1999) and Behavioral Response (Mb), In”
A hierarchical model combining distance sampling and time removal to estimate detection probability during avian point counts (2014, background): “Availability and perceptibility can be highly influenced by weather, observer ability, habitat, species, seasonal date, and time of day, among other factors (Alldredge et al. 2007c, Fitzpatrick et al. 2009).”
Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities (2017, methodology): “For instance, many of the models reviewed are implemented in the R-package ‘unmarked’ (Fiske and Chandler 2011), and/or in the programs PRESENCE (Hines 2014) and MARK (White and Burnham 1999).”
Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions (2021, methodology): “We used the R package unmarked to fit single- season occupancy models (Fiske & Chandler, 2011).”
Improving distribution data of threatened species by combining acoustic monitoring and occupancy modelling (2016, methodology): “We used the detection/non-detection matrix generated after the validation of the classified data to fit single-season occupancy models using the package Unmarked in R (Fiske & Chandler 2011).”
Shared resources between giant panda and sympatric wild and domestic mammals (2015, positive): “All occupancy modelling was conducted using the ‘‘unmarked’’ package in R (Fiske and Chandler, 2011).”
Advances and applications of occupancy models (2014, background): “…relatively easy to collect, and the availability of various free software packages has contributed to proliferation of the use of occupancy models (e.g. Program PRESENCE, Hines 2006; Program MARK, White & Burnham 1999; the R package unmarked, Fiske & Chandler 2011; OpenBUGS, Lunn et al. 2009).”
Responses of invasive predators and native prey to a prescribed forest fire (2017, methodology): “To estimate survey-specific α, we ran a single-season occupancy model for the before period and another single-season occupancy model for the after period, using the R package unmarked (Fiske and Chandler 2011).”