FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2114060135> ?p ?o ?g. }

• W2114060135 endingPage "1469" @default.
• W2114060135 startingPage "1455" @default.
• W2114060135 abstract "Aim To test statistical models used to predict species distributions under different shapes of occurrence–environment relationship. We addressed three questions: (1) Is there a statistical technique that has a consistently higher predictive ability than others for all kinds of relationships? (2) How does species prevalence influence the relative performance of models? (3) When an automated stepwise selection procedure is used, does it improve predictive modelling, and are the relevant variables being selected? Location We used environmental data from a real landscape, the state of California, and simulated species distributions within this landscape. Methods Eighteen artificial species were generated, which varied in their occurrence response to the environmental gradients considered (random, linear, Gaussian, threshold or mixed), in the interaction of those factors (no interaction vs. multiplicative), and on their prevalence (50% vs. 5%). The landscape was then randomly sampled with a large (n = 2000) or small (n = 150) sample size, and the predictive ability of each statistical approach was assessed by comparing the true and predicted distributions using five different indexes of performance (area under the receiver-operator characteristic curve, Kappa, correlation between true and predictive probability of occurrence, sensitivity and specificity). We compared generalized additive models (GAM) with and without flexible degrees of freedom, logistic regressions (general linear models, GLM) with and without variable selection, classification trees, and the genetic algorithm for rule-set production (GARP). Results Species with threshold and mixed responses, additive environmental effects, and high prevalence generated better predictions than did other species for all statistical models. In general, GAM outperforms all other strategies, although differences with GLM are usually not significant. The two variable-selection strategies presented here did not discriminate successfully between truly causal factors and correlated environmental variables. Main conclusions Based on our analyses, we recommend the use of GAM or GLM over classification trees or GARP, and the specification of any suspected interaction terms between predictors. An expert-based variable selection procedure was preferable to the automated procedures used here. Finally, for low-prevalence species, variability in model performance is both very high and sample-dependent. This suggests that distribution models for species with low prevalence can be improved through targeted sampling." @default.
• W2114060135 created "2016-06-24" @default.
• W2114060135 creator A5056426839 @default.
• W2114060135 creator A5062379523 @default.
• W2114060135 date "2007-05-16" @default.
• W2114060135 modified "2023-10-12" @default.
• W2114060135 title "Predicting species distributions: a critical comparison of the most common statistical models using artificial species" @default.
• W2114060135 cites W1513618424 @default.
• W2114060135 cites W1710732412 @default.
• W2114060135 cites W1838542636 @default.
• W2114060135 cites W1964837462 @default.
• W2114060135 cites W1969921504 @default.
• W2114060135 cites W1981665165 @default.
• W2114060135 cites W1983406956 @default.
• W2114060135 cites W1983436597 @default.
• W2114060135 cites W2003689602 @default.
• W2114060135 cites W2009313389 @default.
• W2114060135 cites W2009499611 @default.
• W2114060135 cites W201274257 @default.
• W2114060135 cites W2013382385 @default.
• W2114060135 cites W2015628143 @default.
• W2114060135 cites W2017512049 @default.
• W2114060135 cites W2020185435 @default.
• W2114060135 cites W2025450401 @default.
• W2114060135 cites W2028669920 @default.
• W2114060135 cites W2035205798 @default.
• W2114060135 cites W2038393238 @default.
• W2114060135 cites W2046345384 @default.
• W2114060135 cites W2052779929 @default.
• W2114060135 cites W2055394180 @default.
• W2114060135 cites W2061666069 @default.
• W2114060135 cites W2078666091 @default.
• W2114060135 cites W2079018504 @default.
• W2114060135 cites W2088443024 @default.
• W2114060135 cites W2091258481 @default.
• W2114060135 cites W2096152168 @default.
• W2114060135 cites W2101299555 @default.
• W2114060135 cites W2104960492 @default.
• W2114060135 cites W2112315008 @default.
• W2114060135 cites W2115268776 @default.
• W2114060135 cites W2115317524 @default.
• W2114060135 cites W2120160157 @default.
• W2114060135 cites W2121510694 @default.
• W2114060135 cites W2122680804 @default.
• W2114060135 cites W2123337039 @default.
• W2114060135 cites W2123494897 @default.
• W2114060135 cites W2124516299 @default.
• W2114060135 cites W2133439825 @default.
• W2114060135 cites W2139857790 @default.
• W2114060135 cites W2140534668 @default.
• W2114060135 cites W2142763255 @default.
• W2114060135 cites W2147423506 @default.
• W2114060135 cites W2153459256 @default.
• W2114060135 cites W2155115177 @default.
• W2114060135 cites W2155475871 @default.
• W2114060135 cites W2161548576 @default.
• W2114060135 cites W2161793075 @default.
• W2114060135 cites W2164575016 @default.
• W2114060135 cites W2167827350 @default.
• W2114060135 cites W2168213791 @default.
• W2114060135 cites W2169536548 @default.
• W2114060135 cites W2178556939 @default.
• W2114060135 cites W4211131047 @default.
• W2114060135 cites W4243871771 @default.
• W2114060135 cites W4301861531 @default.
• W2114060135 doi "https://doi.org/10.1111/j.1365-2699.2007.01720.x" @default.
• W2114060135 hasPublicationYear "2007" @default.
• W2114060135 type Work @default.
• W2114060135 sameAs 2114060135 @default.
• W2114060135 citedByCount "206" @default.
• W2114060135 countsByYear W21140601352012 @default.
• W2114060135 countsByYear W21140601352013 @default.
• W2114060135 countsByYear W21140601352014 @default.
• W2114060135 countsByYear W21140601352015 @default.
• W2114060135 countsByYear W21140601352016 @default.
• W2114060135 countsByYear W21140601352017 @default.
• W2114060135 countsByYear W21140601352018 @default.
• W2114060135 countsByYear W21140601352019 @default.
• W2114060135 countsByYear W21140601352020 @default.
• W2114060135 countsByYear W21140601352021 @default.
• W2114060135 countsByYear W21140601352022 @default.
• W2114060135 countsByYear W21140601352023 @default.
• W2114060135 crossrefType "journal-article" @default.
• W2114060135 hasAuthorship W2114060135A5056426839 @default.
• W2114060135 hasAuthorship W2114060135A5062379523 @default.
• W2114060135 hasConcept C102715595 @default.
• W2114060135 hasConcept C103215972 @default.
• W2114060135 hasConcept C105795698 @default.
• W2114060135 hasConcept C114289077 @default.
• W2114060135 hasConcept C129848803 @default.
• W2114060135 hasConcept C134306372 @default.
• W2114060135 hasConcept C151956035 @default.
• W2114060135 hasConcept C154945302 @default.
• W2114060135 hasConcept C170964787 @default.
• W2114060135 hasConcept C185933670 @default.
• W2114060135 hasConcept C18903297 @default.
• W2114060135 hasConcept C33923547 @default.
• W2114060135 hasConcept C41008148 @default.

• next