FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3100404430> ?p ?o ?g. }

• W3100404430 abstract "Abstract Environmental factors interact with internal rules of population regulation, sometimes perturbing systems to alternate dynamics though changes in parameter values. Yet, pinpointing when such changes occur in naturally fluctuating populations is difficult. An algorithmic approach that can identify the timing and magnitude of parameter shifts would facilitate understanding of abrupt ecological transitions with potential to inform conservation and management of species. The “Dynamic Shift Detector” is an algorithm to identify changes in parameter values governing temporal fluctuations in populations with nonlinear dynamics. The algorithm examines population time series data for the presence, location, and magnitude of parameter shifts. It uses an iterative approach to fitting subsets of time series data, then ranks the fit of break point combinations using model selection, assigning a relative weight to each break. We examined the performance of the Dynamic Shift Detector with simulations and two case studies. Under low environmental/sampling noise, the break point sets selected by the Dynamic Shift Detector contained the true simulated breaks with 70-100% accuracy. The weighting tool generally assigned breaks intentionally placed in simulated data (i.e., true breaks) with weights averaging >0.8 and those due to sampling error (i.e., erroneous breaks) with weights averaging <0.2. In our case study examining an invasion process, the algorithm identified shifts in population cycling associated with variations in resource availability. The shifts identified for the conservation case study highlight a decline process that generally coincided with changing management practices affecting the availability of hostplant resources. When interpreted in the context of species biology, the Dynamic Shift Detector algorithm can aid management decisions and identify critical time periods related to species’ dynamics. In an era of rapid global change, such tools can provide key insights into the conditions under which population parameters, and their corresponding dynamics, can shift. Author Summary Populations naturally fluctuate in abundance, and the rules governing these fluctuations are a result of both internal (density dependent) and external (environmental) processes. For these reasons, pinpointing when changes in populations occur is difficult. In this study, we develop a novel break-point analysis tool for population time series data. Using a density dependent model to describe a population’s underlying dynamic process, our tool iterates through all possible break point combinations (i.e., abrupt changes in parameter values) and applies information-theoretic decision tools (i.e. Akaike’s Information Criterion corrected for small sample sizes) to determine best fits. Here, we develop the approach, simulate data under a variety of conditions to demonstrate its utility, and apply the tool to two case studies: an invasion of multicolored Asian ladybeetle and declining monarch butterflies. The Dynamic Shift Detector algorithm identified parameter changes that correspond to known environmental change events in both case studies." @default.
• W3100404430 created "2020-11-23" @default.
• W3100404430 creator A5046616085 @default.
• W3100404430 creator A5069443586 @default.
• W3100404430 date "2019-01-17" @default.
• W3100404430 modified "2023-09-23" @default.
• W3100404430 title "The Dynamic Shift Detector: an algorithm to identify changes in parameter values governing populations" @default.
• W3100404430 cites W1681390718 @default.
• W3100404430 cites W1983401758 @default.
• W3100404430 cites W1987402617 @default.
• W3100404430 cites W1987464952 @default.
• W3100404430 cites W1992147353 @default.
• W3100404430 cites W2005711658 @default.
• W3100404430 cites W2006959528 @default.
• W3100404430 cites W2010541785 @default.
• W3100404430 cites W2046258683 @default.
• W3100404430 cites W2057791186 @default.
• W3100404430 cites W2067430903 @default.
• W3100404430 cites W2074096899 @default.
• W3100404430 cites W2078353730 @default.
• W3100404430 cites W2083771789 @default.
• W3100404430 cites W2085187101 @default.
• W3100404430 cites W2100891518 @default.
• W3100404430 cites W2102892532 @default.
• W3100404430 cites W2103948957 @default.
• W3100404430 cites W2105827100 @default.
• W3100404430 cites W2111798644 @default.
• W3100404430 cites W2112499642 @default.
• W3100404430 cites W2115434827 @default.
• W3100404430 cites W2117166867 @default.
• W3100404430 cites W2120328339 @default.
• W3100404430 cites W2122219241 @default.
• W3100404430 cites W2122774926 @default.
• W3100404430 cites W2126010503 @default.
• W3100404430 cites W2139220821 @default.
• W3100404430 cites W2143510389 @default.
• W3100404430 cites W2149189573 @default.
• W3100404430 cites W2152168289 @default.
• W3100404430 cites W2156623230 @default.
• W3100404430 cites W2162136094 @default.
• W3100404430 cites W2162335786 @default.
• W3100404430 cites W2168828467 @default.
• W3100404430 cites W2169715400 @default.
• W3100404430 cites W2172388050 @default.
• W3100404430 cites W2178047388 @default.
• W3100404430 cites W2346421095 @default.
• W3100404430 cites W2562810418 @default.
• W3100404430 cites W2592732652 @default.
• W3100404430 cites W2592927802 @default.
• W3100404430 cites W2604998546 @default.
• W3100404430 cites W2610632796 @default.
• W3100404430 cites W2765802082 @default.
• W3100404430 cites W2803550381 @default.
• W3100404430 cites W2867976305 @default.
• W3100404430 cites W2922639658 @default.
• W3100404430 cites W3100575704 @default.
• W3100404430 cites W3125328255 @default.
• W3100404430 cites W4253228525 @default.
• W3100404430 doi "https://doi.org/10.1101/522631" @default.
• W3100404430 hasPublicationYear "2019" @default.
• W3100404430 type Work @default.
• W3100404430 sameAs 3100404430 @default.
• W3100404430 citedByCount "0" @default.
• W3100404430 crossrefType "posted-content" @default.
• W3100404430 hasAuthorship W3100404430A5046616085 @default.
• W3100404430 hasAuthorship W3100404430A5069443586 @default.
• W3100404430 hasBestOaLocation W31004044301 @default.
• W3100404430 hasConcept C105795698 @default.
• W3100404430 hasConcept C111919701 @default.
• W3100404430 hasConcept C11413529 @default.
• W3100404430 hasConcept C115961682 @default.
• W3100404430 hasConcept C121332964 @default.
• W3100404430 hasConcept C126691448 @default.
• W3100404430 hasConcept C1276947 @default.
• W3100404430 hasConcept C140779682 @default.
• W3100404430 hasConcept C143724316 @default.
• W3100404430 hasConcept C144024400 @default.
• W3100404430 hasConcept C149923435 @default.
• W3100404430 hasConcept C151730666 @default.
• W3100404430 hasConcept C154945302 @default.
• W3100404430 hasConcept C183115368 @default.
• W3100404430 hasConcept C24890656 @default.
• W3100404430 hasConcept C2779343474 @default.
• W3100404430 hasConcept C2908647359 @default.
• W3100404430 hasConcept C33923547 @default.
• W3100404430 hasConcept C41008148 @default.
• W3100404430 hasConcept C76155785 @default.
• W3100404430 hasConcept C81917197 @default.
• W3100404430 hasConcept C86803240 @default.
• W3100404430 hasConcept C94915269 @default.
• W3100404430 hasConcept C98045186 @default.
• W3100404430 hasConcept C99498987 @default.
• W3100404430 hasConceptScore W3100404430C105795698 @default.
• W3100404430 hasConceptScore W3100404430C111919701 @default.
• W3100404430 hasConceptScore W3100404430C11413529 @default.
• W3100404430 hasConceptScore W3100404430C115961682 @default.
• W3100404430 hasConceptScore W3100404430C121332964 @default.
• W3100404430 hasConceptScore W3100404430C126691448 @default.
• W3100404430 hasConceptScore W3100404430C1276947 @default.
• W3100404430 hasConceptScore W3100404430C140779682 @default.

• next