FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2589202402> ?p ?o ?g. }

• W2589202402 abstract "SUMMARY 1. Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often non-randomly distributed and geographically restricted. Although available SDM methods address some of these problems, the errors could be more directly and accurately modelled using a spatially-explicit approach. Software to implement spatial autocorrelation terms into SDMs are now widely available, but whether such approaches for inferring SDMs are an improvement over existing methodologies is unknown. 2. Here, within a simulated environment using 1000 generated species’ ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and Boosted Regression Trees, BRT) to a spatially-explicit Bayesian SDM method (Integrated Laplace Approximation, INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested whether any recommended methodological settings for all methods were further impacted by spatially non-random patterns in these data. 3. Spatially-explicit INLA was the most consistently accurate method, being most or equal most accurate in 5 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly, but when sampling points were randomly spread BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, spatial-INLA had a 4%-8% better in AUC score. Alternatively, when sampling points were restricted to a small section of the true range, all methods were on average 10-12% less accurate, with higher variation among the methods. None of the recommended settings for the different methods were found to be sensitive to clumping or restriction of data, except the complexity of the INLA spatial term. 4. INLA-based modelling approaches can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit INLA-based SDMs may be the better choice when modelling the spatial distribution of target species." @default.
• W2589202402 created "2017-03-03" @default.
• W2589202402 creator A5000176084 @default.
• W2589202402 creator A5026870621 @default.
• W2589202402 creator A5036967546 @default.
• W2589202402 creator A5048918035 @default.
• W2589202402 date "2017-02-06" @default.
• W2589202402 modified "2023-09-22" @default.
• W2589202402 title "Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data" @default.
• W2589202402 cites W1494196016 @default.
• W2589202402 cites W1568201516 @default.
• W2589202402 cites W1568936006 @default.
• W2589202402 cites W1747046542 @default.
• W2589202402 cites W1834040219 @default.
• W2589202402 cites W1862251502 @default.
• W2589202402 cites W1920817623 @default.
• W2589202402 cites W1979963867 @default.
• W2589202402 cites W1980193681 @default.
• W2589202402 cites W1984434323 @default.
• W2589202402 cites W1987964272 @default.
• W2589202402 cites W2001709920 @default.
• W2589202402 cites W2027264855 @default.
• W2589202402 cites W2036581019 @default.
• W2589202402 cites W2044769093 @default.
• W2589202402 cites W2045451950 @default.
• W2589202402 cites W2052583540 @default.
• W2589202402 cites W2057691392 @default.
• W2589202402 cites W2092135249 @default.
• W2589202402 cites W2100533358 @default.
• W2589202402 cites W2106565253 @default.
• W2589202402 cites W2110559441 @default.
• W2589202402 cites W2112315008 @default.
• W2589202402 cites W2119202692 @default.
• W2589202402 cites W2120055476 @default.
• W2589202402 cites W2123379755 @default.
• W2589202402 cites W2127367934 @default.
• W2589202402 cites W2129435498 @default.
• W2589202402 cites W2139416101 @default.
• W2589202402 cites W2144898279 @default.
• W2589202402 cites W2172138805 @default.
• W2589202402 cites W2262083583 @default.
• W2589202402 cites W2398228670 @default.
• W2589202402 cites W4250310729 @default.
• W2589202402 doi "https://doi.org/10.1101/105742" @default.
• W2589202402 hasPublicationYear "2017" @default.
• W2589202402 type Work @default.
• W2589202402 sameAs 2589202402 @default.
• W2589202402 citedByCount "0" @default.
• W2589202402 crossrefType "posted-content" @default.
• W2589202402 hasAuthorship W2589202402A5000176084 @default.
• W2589202402 hasAuthorship W2589202402A5026870621 @default.
• W2589202402 hasAuthorship W2589202402A5036967546 @default.
• W2589202402 hasAuthorship W2589202402A5048918035 @default.
• W2589202402 hasBestOaLocation W25892024021 @default.
• W2589202402 hasConcept C105795698 @default.
• W2589202402 hasConcept C106131492 @default.
• W2589202402 hasConcept C107673813 @default.
• W2589202402 hasConcept C124101348 @default.
• W2589202402 hasConcept C129848803 @default.
• W2589202402 hasConcept C140779682 @default.
• W2589202402 hasConcept C154945302 @default.
• W2589202402 hasConcept C159620131 @default.
• W2589202402 hasConcept C159985019 @default.
• W2589202402 hasConcept C192562407 @default.
• W2589202402 hasConcept C204323151 @default.
• W2589202402 hasConcept C31972630 @default.
• W2589202402 hasConcept C33923547 @default.
• W2589202402 hasConcept C41008148 @default.
• W2589202402 hasConcept C83546350 @default.
• W2589202402 hasConcept C9679016 @default.
• W2589202402 hasConceptScore W2589202402C105795698 @default.
• W2589202402 hasConceptScore W2589202402C106131492 @default.
• W2589202402 hasConceptScore W2589202402C107673813 @default.
• W2589202402 hasConceptScore W2589202402C124101348 @default.
• W2589202402 hasConceptScore W2589202402C129848803 @default.
• W2589202402 hasConceptScore W2589202402C140779682 @default.
• W2589202402 hasConceptScore W2589202402C154945302 @default.
• W2589202402 hasConceptScore W2589202402C159620131 @default.
• W2589202402 hasConceptScore W2589202402C159985019 @default.
• W2589202402 hasConceptScore W2589202402C192562407 @default.
• W2589202402 hasConceptScore W2589202402C204323151 @default.
• W2589202402 hasConceptScore W2589202402C31972630 @default.
• W2589202402 hasConceptScore W2589202402C33923547 @default.
• W2589202402 hasConceptScore W2589202402C41008148 @default.
• W2589202402 hasConceptScore W2589202402C83546350 @default.
• W2589202402 hasConceptScore W2589202402C9679016 @default.
• W2589202402 hasLocation W25892024021 @default.
• W2589202402 hasLocation W25892024022 @default.
• W2589202402 hasLocation W25892024023 @default.
• W2589202402 hasLocation W25892024024 @default.
• W2589202402 hasLocation W25892024025 @default.
• W2589202402 hasOpenAccess W2589202402 @default.
• W2589202402 hasPrimaryLocation W25892024021 @default.
• W2589202402 hasRelatedWork W1568936006 @default.
• W2589202402 hasRelatedWork W1838542636 @default.
• W2589202402 hasRelatedWork W1967755652 @default.
• W2589202402 hasRelatedWork W1969709262 @default.
• W2589202402 hasRelatedWork W2031113870 @default.
• W2589202402 hasRelatedWork W2045405291 @default.
• W2589202402 hasRelatedWork W2045594836 @default.

• next