FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2964081765> ?p ?o ?g. }

• W2964081765 abstract "Abstract Population monitoring is important for investigating a variety of ecological questions, and N‐mixture models are increasingly used to model population size ( N ) and trends (λ) while estimating detectability ( p ) from repeated counts within primary periods (when populations are closed to changes). Extending these models to dynamic processes with serial dependence across primary periods may relax the closure assumption, but simulations to evaluate models and inform effort (e.g., number of repeated counts) typically assume p is constant or random across sites and years. Thus, it is unknown how these models perform under scenarios where trends in p confound inferences on N and λ, and conclusions regarding effort may be overoptimistic. Here, we used global positioning system data from greater sage‐grouse ( Centrocercus urophasianus ) to inform simulations of the detection process for lek counts of this species, and we created scenarios with and without linear annual trends in p . We then compared estimates of N and λ from hierarchical population models either fit with single maximum counts or with detectability estimated from repeated counts (dynamic N‐mixture models). We also explored using auxiliary data to correct counts for variation in detectability. Uncorrected count models consistently underestimated N by >50%, whereas N‐mixture models without auxiliary data underestimated N to a lesser degree due to unmodeled heterogeneity in p such as age. Nevertheless, estimates of λ from both types of models were unbiased and similar for scenarios without trends in p . When p declined systematically across years, uncorrected count models underestimated λ, whereas N‐mixture models estimated λ with little bias when all sites were counted repeatedly. Auxiliary data also reduced bias in parameter estimates. Evaluating population models using scenarios with systematic variation in p may better reveal potential biases and inform effort than simulations that assume p is constant or random. Dynamic N‐mixture models can distinguish between trends in p and N , but also require repeated counts within primary periods for accurate estimates. Auxiliary data may be useful when researchers lack repeated counts, wish to monitor more sites less intensively, or require unbiased estimates of N ." @default.
• W2964081765 created "2019-07-30" @default.
• W2964081765 creator A5003501456 @default.
• W2964081765 creator A5005383713 @default.
• W2964081765 creator A5050798825 @default.
• W2964081765 creator A5066462927 @default.
• W2964081765 date "2019-07-01" @default.
• W2964081765 modified "2023-10-11" @default.
• W2964081765 title "The importance of simulation assumptions when evaluating detectability in population models" @default.
• W2964081765 cites W1581101741 @default.
• W2964081765 cites W1846287699 @default.
• W2964081765 cites W1857913039 @default.
• W2964081765 cites W1929724705 @default.
• W2964081765 cites W1965718955 @default.
• W2964081765 cites W1982512880 @default.
• W2964081765 cites W1984536023 @default.
• W2964081765 cites W1985717371 @default.
• W2964081765 cites W2026259401 @default.
• W2964081765 cites W2029478484 @default.
• W2964081765 cites W2034522602 @default.
• W2964081765 cites W2048057357 @default.
• W2964081765 cites W2063523103 @default.
• W2964081765 cites W2068487188 @default.
• W2964081765 cites W2089269859 @default.
• W2964081765 cites W2096382322 @default.
• W2964081765 cites W2096534982 @default.
• W2964081765 cites W2098006724 @default.
• W2964081765 cites W2107146381 @default.
• W2964081765 cites W2109076860 @default.
• W2964081765 cites W2109749951 @default.
• W2964081765 cites W2128482065 @default.
• W2964081765 cites W2135207953 @default.
• W2964081765 cites W2136145847 @default.
• W2964081765 cites W2138913269 @default.
• W2964081765 cites W2155115177 @default.
• W2964081765 cites W2157289208 @default.
• W2964081765 cites W2171346236 @default.
• W2964081765 cites W2177401494 @default.
• W2964081765 cites W2300821254 @default.
• W2964081765 cites W2313388432 @default.
• W2964081765 cites W2317548762 @default.
• W2964081765 cites W2333482802 @default.
• W2964081765 cites W2336812860 @default.
• W2964081765 cites W2347052062 @default.
• W2964081765 cites W2538404274 @default.
• W2964081765 cites W2566997354 @default.
• W2964081765 cites W2603275110 @default.
• W2964081765 cites W2731844551 @default.
• W2964081765 cites W2748793224 @default.
• W2964081765 cites W2765179244 @default.
• W2964081765 cites W2766185785 @default.
• W2964081765 cites W2776750609 @default.
• W2964081765 cites W2789575653 @default.
• W2964081765 cites W2791860237 @default.
• W2964081765 cites W2792849877 @default.
• W2964081765 cites W2793864561 @default.
• W2964081765 cites W2802115038 @default.
• W2964081765 cites W2867826667 @default.
• W2964081765 cites W2898094583 @default.
• W2964081765 cites W2913452413 @default.
• W2964081765 cites W2963375044 @default.
• W2964081765 cites W4237298697 @default.
• W2964081765 cites W4245399135 @default.
• W2964081765 doi "https://doi.org/10.1002/ecs2.2791" @default.
• W2964081765 hasPublicationYear "2019" @default.
• W2964081765 type Work @default.
• W2964081765 sameAs 2964081765 @default.
• W2964081765 citedByCount "14" @default.
• W2964081765 countsByYear W29640817652019 @default.
• W2964081765 countsByYear W29640817652020 @default.
• W2964081765 countsByYear W29640817652021 @default.
• W2964081765 countsByYear W29640817652022 @default.
• W2964081765 countsByYear W29640817652023 @default.
• W2964081765 crossrefType "journal-article" @default.
• W2964081765 hasAuthorship W2964081765A5003501456 @default.
• W2964081765 hasAuthorship W2964081765A5005383713 @default.
• W2964081765 hasAuthorship W2964081765A5050798825 @default.
• W2964081765 hasAuthorship W2964081765A5066462927 @default.
• W2964081765 hasBestOaLocation W29640817651 @default.
• W2964081765 hasConcept C105795698 @default.
• W2964081765 hasConcept C126322002 @default.
• W2964081765 hasConcept C144024400 @default.
• W2964081765 hasConcept C149782125 @default.
• W2964081765 hasConcept C149923435 @default.
• W2964081765 hasConcept C168743327 @default.
• W2964081765 hasConcept C2908647359 @default.
• W2964081765 hasConcept C33923547 @default.
• W2964081765 hasConcept C52079815 @default.
• W2964081765 hasConcept C71924100 @default.
• W2964081765 hasConcept C95190672 @default.
• W2964081765 hasConceptScore W2964081765C105795698 @default.
• W2964081765 hasConceptScore W2964081765C126322002 @default.
• W2964081765 hasConceptScore W2964081765C144024400 @default.
• W2964081765 hasConceptScore W2964081765C149782125 @default.
• W2964081765 hasConceptScore W2964081765C149923435 @default.
• W2964081765 hasConceptScore W2964081765C168743327 @default.
• W2964081765 hasConceptScore W2964081765C2908647359 @default.
• W2964081765 hasConceptScore W2964081765C33923547 @default.
• W2964081765 hasConceptScore W2964081765C52079815 @default.
• W2964081765 hasConceptScore W2964081765C71924100 @default.

• next