Matches in SemOpenAlex for { <https://semopenalex.org/work/W1549142192> ?p ?o ?g. }
- W1549142192 endingPage "117" @default.
- W1549142192 startingPage "102" @default.
- W1549142192 abstract "Abstract Aim Techniques that predict species potential distributions by combining observed occurrence records with environmental variables show much potential for application across a range of biogeographical analyses. Some of the most promising applications relate to species for which occurrence records are scarce, due to cryptic habits, locally restricted distributions or low sampling effort. However, the minimum sample sizes required to yield useful predictions remain difficult to determine. Here we developed and tested a novel jackknife validation approach to assess the ability to predict species occurrence when fewer than 25 occurrence records are available. Location Madagascar. Methods Models were developed and evaluated for 13 species of secretive leaf‐tailed geckos ( Uroplatus spp.) that are endemic to Madagascar, for which available sample sizes range from 4 to 23 occurrence localities (at 1 km 2 grid resolution). Predictions were based on 20 environmental data layers and were generated using two modelling approaches: a method based on the principle of maximum entropy (Maxent) and a genetic algorithm (GARP). Results We found high success rates and statistical significance in jackknife tests with sample sizes as low as five when the Maxent model was applied. Results for GARP at very low sample sizes (less than c. 10) were less good. When sample sizes were experimentally reduced for those species with the most records, variability among predictions using different combinations of localities demonstrated that models were greatly influenced by exactly which observations were included. Main conclusions We emphasize that models developed using this approach with small sample sizes should be interpreted as identifying regions that have similar environmental conditions to where the species is known to occur, and not as predicting actual limits to the range of a species. The jackknife validation approach proposed here enables assessment of the predictive ability of models built using very small sample sizes, although use of this test with larger sample sizes may lead to overoptimistic estimates of predictive power. Our analyses demonstrate that geographical predictions developed from small numbers of occurrence records may be of great value, for example in targeting field surveys to accelerate the discovery of unknown populations and species." @default.
- W1549142192 created "2016-06-24" @default.
- W1549142192 creator A5011486492 @default.
- W1549142192 creator A5031931817 @default.
- W1549142192 creator A5051871488 @default.
- W1549142192 creator A5082061648 @default.
- W1549142192 date "2006-09-27" @default.
- W1549142192 modified "2023-10-16" @default.
- W1549142192 title "ORIGINAL ARTICLE: Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar" @default.
- W1549142192 cites W1480376833 @default.
- W1549142192 cites W1551384531 @default.
- W1549142192 cites W1838542636 @default.
- W1549142192 cites W1897486692 @default.
- W1549142192 cites W1972082227 @default.
- W1549142192 cites W1972100933 @default.
- W1549142192 cites W1986643700 @default.
- W1549142192 cites W1994031665 @default.
- W1549142192 cites W1996320813 @default.
- W1549142192 cites W1996364967 @default.
- W1549142192 cites W2011601793 @default.
- W1549142192 cites W2019055691 @default.
- W1549142192 cites W2019980555 @default.
- W1549142192 cites W2041380827 @default.
- W1549142192 cites W2049626151 @default.
- W1549142192 cites W2052469867 @default.
- W1549142192 cites W2052489905 @default.
- W1549142192 cites W2053805369 @default.
- W1549142192 cites W2055764609 @default.
- W1549142192 cites W2063580659 @default.
- W1549142192 cites W2068211709 @default.
- W1549142192 cites W2070190147 @default.
- W1549142192 cites W2070262419 @default.
- W1549142192 cites W2077822176 @default.
- W1549142192 cites W2078666091 @default.
- W1549142192 cites W2079018504 @default.
- W1549142192 cites W2082159262 @default.
- W1549142192 cites W2084167491 @default.
- W1549142192 cites W2088626174 @default.
- W1549142192 cites W2089454337 @default.
- W1549142192 cites W2096152168 @default.
- W1549142192 cites W2102111039 @default.
- W1549142192 cites W2111954076 @default.
- W1549142192 cites W2112315008 @default.
- W1549142192 cites W2112776483 @default.
- W1549142192 cites W2114630657 @default.
- W1549142192 cites W2115268776 @default.
- W1549142192 cites W2118436877 @default.
- W1549142192 cites W2121510694 @default.
- W1549142192 cites W2121744618 @default.
- W1549142192 cites W2123337039 @default.
- W1549142192 cites W2123379596 @default.
- W1549142192 cites W2123880245 @default.
- W1549142192 cites W2124516299 @default.
- W1549142192 cites W2128506197 @default.
- W1549142192 cites W2129667088 @default.
- W1549142192 cites W2130695471 @default.
- W1549142192 cites W2135224306 @default.
- W1549142192 cites W2136017883 @default.
- W1549142192 cites W2139416101 @default.
- W1549142192 cites W2140263047 @default.
- W1549142192 cites W2140534668 @default.
- W1549142192 cites W2149507322 @default.
- W1549142192 cites W2151940493 @default.
- W1549142192 cites W2153026167 @default.
- W1549142192 cites W2157641482 @default.
- W1549142192 cites W2162348455 @default.
- W1549142192 cites W2163816695 @default.
- W1549142192 cites W2169600757 @default.
- W1549142192 cites W2169904462 @default.
- W1549142192 cites W2170473141 @default.
- W1549142192 cites W2179879290 @default.
- W1549142192 cites W40369460 @default.
- W1549142192 cites W4238380085 @default.
- W1549142192 doi "https://doi.org/10.1111/j.1365-2699.2006.01594.x" @default.
- W1549142192 hasPublicationYear "2006" @default.
- W1549142192 type Work @default.
- W1549142192 sameAs 1549142192 @default.
- W1549142192 citedByCount "2372" @default.
- W1549142192 countsByYear W15491421922012 @default.
- W1549142192 countsByYear W15491421922013 @default.
- W1549142192 countsByYear W15491421922014 @default.
- W1549142192 countsByYear W15491421922015 @default.
- W1549142192 countsByYear W15491421922016 @default.
- W1549142192 countsByYear W15491421922017 @default.
- W1549142192 countsByYear W15491421922018 @default.
- W1549142192 countsByYear W15491421922019 @default.
- W1549142192 countsByYear W15491421922020 @default.
- W1549142192 countsByYear W15491421922021 @default.
- W1549142192 countsByYear W15491421922022 @default.
- W1549142192 countsByYear W15491421922023 @default.
- W1549142192 crossrefType "journal-article" @default.
- W1549142192 hasAuthorship W1549142192A5011486492 @default.
- W1549142192 hasAuthorship W1549142192A5031931817 @default.
- W1549142192 hasAuthorship W1549142192A5051871488 @default.
- W1549142192 hasAuthorship W1549142192A5082061648 @default.
- W1549142192 hasConcept C102715595 @default.
- W1549142192 hasConcept C103215972 @default.
- W1549142192 hasConcept C105795698 @default.