Luo et al., 2020 - Google Patents

Density estimation of unmarked populations using camera traps in heterogeneous space

Luo et al., 2020

Document ID
70724120690963183
Author
Luo G
Wei W
Dai Q
Ran J
Publication year
Publication venue
Wildlife Society Bulletin

External Links

Snippet

Camera traps are commonly used to monitor animal populations, but statistical estimators of density from camera‐trap data for species that cannot be individually identified are still in development, and few models take space use into account. We present a model to estimate …
Continue reading at wildlife.onlinelibrary.wiley.com (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/18Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202Market predictions or demand forecasting
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F2201/00Indexing scheme relating to error detection, to error correction, and to monitoring
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Luo et al. Density estimation of unmarked populations using camera traps in heterogeneous space
Royle et al. Hierarchical spatial models of abundance and occurrence from imperfect survey data
Jennelle et al. State‐specific detection probabilities and disease prevalence
Lichstein et al. Spatial autocorrelation and autoregressive models in ecology
Gopalaswamy et al. An examination of index‐calibration experiments: counting tigers at macroecological scales
Plumptre Monitoring mammal populations with line transect techniques in African forests
Chao et al. A new statistical approach for assessing similarity of species composition with incidence and abundance data
Richards et al. Distribution modelling and statistical phylogeography: an integrative framework for generating and testing alternative biogeographical hypotheses
Robertson et al. Getting the most out of atlas data
Schmucki et al. A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes
Dennis et al. Indexing butterfly abundance whilst accounting for missing counts and variability in seasonal pattern
Bellier et al. Simulation‐based assessment of dynamic N‐mixture models in the presence of density dependence and environmental stochasticity
Khatchikian et al. Geographical and environmental factors driving the increase in the Lyme disease vector Ixodes scapularis
Hefley et al. Correction of location errors for presence‐only species distribution models
Hanks et al. Reconciling multiple data sources to improve accuracy of large‐scale prediction of forest disease incidence
Sutherland et al. A multiregion community model for inference about geographic variation in species richness
Paquette et al. Explaining forest productivity using tree functional traits and phylogenetic information: two sides of the same coin over evolutionary scale?
Wright et al. Modelling misclassification in multi‐species acoustic data when estimating occupancy and relative activity
Stuber et al. How characteristic is the species characteristic selection scale?
Palencia et al. Innovations in movement and behavioural ecology from camera traps: Day range as model parameter
Broms et al. Accounting for imperfect detection in Hill numbers for biodiversity studies
Marion et al. Parameter and uncertainty estimation for process‐oriented population and distribution models: data, statistics and the niche
Matechou et al. Monitoring abundance and phenology in (multivoltine) butterfly species: a novel mixture model
Erickson et al. Accounting for imperfect detection in data from museums and herbaria when modeling species distributions: combining and contrasting data‐level versus model‐level bias correction
Mc New et al. Evaluating species richness: biased ecological inference results from spatial heterogeneity in detection probabilities