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2,399 results for “fragmenter”
ExtendedData Fig. 6 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 6 | Driversoffragmentationsensitivitywithnatural disturbances. Resultsof Bayesianphylogeneticmixedeffectmodelspredicting fragmentationsensitivityfor 1564 birdpopulations (n = 1034 species). Populationswereclassifiedasfragmentationsensitiveiftheywereidentifiedas 'Forest-core' by BIOFRAG. Restrictedanalysisassignedfragmentationsensitivity onlyto 'Forestspecialists' (a); Expandedanalysisassignedfragmentation sensitivitytoboth ' Forestspecialist' and ' Forestassociated' species (b; see Methods).Bayesianposteriordistributionisshownabovetheline;effectsiZe estimateswithcredibleintervals (CI) belowtheline (68%: thickerrorbars; 95%: thinerrorbars).HigheffectsiZesindicateapositiveassociationwith fragmentationsensitivity;loweffectsiZesindicateanegativeassociation. Finchandhawksilhouettesindicatethatbothmodelswererunonacomplete sample. Historicaldisturbanceisabinaryvariable (1/0) calculatedusingnatural disturbance (forexamplefires,storms & glaciation) layersonly.
ExtendedData Fig. 2 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 2 | Thelatitudinalgradientinaveragedispersallimitation ofbirdassemblages.Datapoints (colouredbylevelofhistoricaldisturbance) showthecommunitymeanvaluesforavianassemblagessampledat 31 study landscapesmappedin Fig.1. Theoverallgradientisnotexplainedbylandscape disturbancehistory.Absolutelatitudeisthecentroidlatitudeofallsampling pointsineachstudylandscape.Mean dispersallimitationisthenegative (thatis inverse) hand-wingindex (nHWI) averagedacrossallspeciesintheassemblage; nHWIislogarithmicallyscaled (log(1/HWI)) forvisualiZation.Statisticsarefroma linearmodelwith Gaussianerrors;purplelineshowsmodelfit (R2 = 0.44); shaded regionshowsthestandarderrorof theregressioncoefficient.
Fig. 3 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
Fig. 3 | Fragmentationsensitivityincreaseswithdispersallimitationinbird assemblages.a, Variationin fragmentationsensitivity anddispersalability plottedonaconsensusphylogenetictree.Eachbranchrepresentsagenus (n = 441), withdataattipsaveragedacrossfamilies (n = 115) forvisualiZation. Branchcoloursindicatedispersallimitation (leastdispersivespeciesin red); tipcoloursshowtheproportionoffragmentation-sensitivespeciesineach family (expandedanalysis;mostsensitivein yellow).b, Datapoints (coloured bylevelofhistoricaldisturbance) aremeansfor 31 studylandscapes.Foreach assemblage,fragmentationsensitivityisassignedtoforest-corespecies withhighforestdependency (Restrictedanalysis),andmeandispersal limitationisthenHWIaveragedacrossallspecies;nHWIislogarithmically scaled (log(1/HWI)) forvisualiZation.StatisticsarefromageneraliZedlinear modelwithquasi-binomialerrors;purplelineshowsmodelfit (R2 = 0.180); shadedregionshows 95% confidenceintervals.Boxplotsin b showthesame distributionswithmedianvalue,interquartilerangeandwhiskerstoextreme values (outliersaredatapoints>1.5× quartiles).Resultsfortheexpandedsample areshownin ExtendedData Fig. 4.
Fig. 2 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
Fig. 2 | Globalpatternsoflandscapedisturbanceanddispersallimitation. a, Thepresenceofnatural oranthropogenichistoricaldisturbancesrecorded ineachgridcell.Naturaldisturbancepressures (bright red) includemajorfires, stormsandglaciation;theseeventshavetypicallypersisted forlongerperiods oftimeandmaycausecompleteremovalofforestbiota.Anthropogenicforest loss (palered) representsmore recentdisturbancethatoftenalterscomposition oflocalassemblageswithoutcompleteeradication.b, VariationinnHWI averagedacross speciesoccurringineachgridcell,rangingfromlow (blue) to high (red) dispersallimitation.Dispersallimitationdataarecalculatedfrom measurementsof 10,562 birdspecies,logarithmicallyscaledforvisualiZation (log(1/HWI)).Yellowdotsshowstudylandscapes (21 from BIOFRAG;10 from additionalsampling).Gridcellsin a and b are 2.5 arcminutes. c,d, Hypothetical relationships:extinctionfilterspredictthatfragmentationsensitivityis negativelyassociatedwithhistoricaldisturbance (c), whiledispersal-related mechanismspredictthatfragmentationsensitivityispositivelyassociated with dispersallimitation (d).
Fig. 1 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
Fig. 1 | Hypothesespredictingthedistributionoffragmentation-sensitive species. Thetoppathway (a) illustrateshow ' extinctionfilters' linkedto historicaldisturbances (forexample, fireandanthropogenicforestloss) canbenon-random,removingspeciestraitsassociatedwithsensitivityto disturbanceandretainingmoreresilientsurvivors.Tropicalbirdcommunities thathavelargelyavoidedseverehistoricaldisturbancetheoreticallycontain morespecieswithdisturbance-sensitivetraits (suchaspoordispersaland ecologicalspecialiZation),accentuatingtheimpactsofforestfragmentation (b). Backgroundturnoverofspecies,shownin (b) butpresentinallpathways, israndomwithrespecttodisturbance-sensitivetraits.Adifferentmechanism involvestheevolutionofflightadaptationstocopewithseasonalfluctuations intemperatureandresources (including vegetation,insects,flowersand fruits).In birds, thepredominantadaptationtoseasonalityinvolvesincreased mobility (fromlocaldispersaltolong-distancemigration),sohighly seasonal communitieslackdispersal-limitedspecies,potentiallyincreasingtheir resiliencetoforestfragmentation (c) incomparisonwithclimaticallystable regions (b). Relativespeciesrichnessisshownbythenumberofbirdsilhouettes inthecommunity.
ExtendedData Fig. 5 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 5 | Driversoffragmentationsensitivitywith Anthropogenicdisturbances.Resultsof Bayesianphylogeneticmixedeffect modelspredictingfragmentationsensitivityfor 1564 birdpopulations (n = 1034 species).Populations wereclassifiedasfragmentationsensitiveif they wereidentifiedas 'Forest-core' by BIOFRAG. Restrictedanalysisassigned fragmentationsensitivityonlyto 'Forestspecialists' (a); Expandedanalysis assignedfragmentationsensitivityto both 'Forestspecialist' and 'Forest associated' species (b; see Methods). Bayesianposteriordistributionisshown abovetheline;effectsiZeestimateswithcredibleintervals (CI) belowtheline (68%: thickerrorbars;95%:thinerrorbars).HigheffectsiZesindicateapositive associationwithfragmentationsensitivity;loweffect siZesindicateanegative association.Finchandhawksilhouettesindicatethatbothmodelswererunon acompletesample.Historicaldisturbanceisabinaryvariable (1/0) calculated usinganthropogenicdisturbance (forestloss) only.
ExtendedData Fig. 1 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 1 | Correlationbetweendisturbanceandlatitude. Study landscapesexposedtohighlevelsofhistoricaldisturbance (n = 16 landscapes; red) tendtobefoundathigherlatitudesthanlandscapesexposedtolowerlevels ofhistoricaldisturbance (n = 15 landscapes;blue).Disturbancelevelisestimated fromglobalmapsof majorhistoricaldisturbance (forexamplefire,glaciation). Absolutelatitudeisthecentroidlatitudeof allsamplingpointsineachstudy landscape.Boxplotsshowthemedian, interquartilerangeandwhiskers extendingtoextremevalues.Statisticsshowresultsof two-sided Wilcoxon ranksum testindicatingthatdisturbanceandlatitudearecorrelated (without accountingforspatialauto-correlation).
ExtendedData Fig. 4 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 4 | Correlationbetweenfragmentationsensitivityand dispersallimitationinbirds. Datapoints (colouredbylevelofhistorical disturbance) arecommunitymeanvaluesforavian assemblagesat 31 study landscapesmappedin Fig.1. Foreachassemblage,fragmentationsensitivity isassignedtospecieswith 'Forest-core' habitatpreferenceandeitherahigh or mediumforestdependency (Expandedanalysis).Meandispersallimitationis thenegative (thatisinverse) hand-wingindex (nHWI) averagedacross allspecies intheassemblage;nHWIislogarithmicallyscaled (log(1/HWI)) forvisualiZation. StatisticsarefromageneraliZedlinearmodelwithquasi-binomialerrors;purple lineshowsmodelfit (R2 = 0.270); shadedregionshows 95% confidenceintervals. Adjacentboxplotsshowthesamedistributionwithmedianvalue,interquartile range,andwhiskerstoextremevalues (outliersaredatapoints>1.5x quartiles).
ExtendedData Fig. 3 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 3 | Correlationbetweenfragmentationsensitivityand latitudeinbirds. Datapoints (colouredbylevelofhistoricaldisturbance) are communitymeanvaluesforavianassemblagesat 31 studylandscapesmapped in Fig.1. Foreachassemblage,fragmentationsensitivityisassignedto (a) Forest-specialistspecieswith 'Forest-core' habitatpreference (Restrictedanalysis),and (b) Forest-associatedspecieswith ' Forest-core' habitatpreference (Expanded analysis).Absolutelatitudeistheabsolutecentroidlatitudeofallsampling pointsineachstudylandscape.StatisticsarefromgeneraliZedlinearmodels withquasi-binomialerrors;purplelineshowsmodelfit (Restrictedanalysis:R2 = 0.2559, Expandedanalysis:R2 = 0.3208); shadedregionshowsthe 95% confidence intervals.
Fig. 5 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
Fig. 5 | Predictorsofdispersallimitationinbirds. Resultsshownareoutputs ofphylogeneticgeneraliZedleast-squaresmodelspredictingdispersal limitation (nHWI) acrossallbirdspeciessampled,includinglong-distance migrants (swallowimage,dark bars;n = 1,034), onlyresidentspeciesandshort distance/partialmigrants (thrushimage,medium bars;n = 921) oronlyresident species (pittaimage, palebars;n = 858). Panelspresentthreesetsofmodels withincreasingcomplexity:aunivariatemodelwithsinglepredictor (a,d), and multivariatemodelswithtwo (b,e) andthree (c,f) predictors.Eachpredictoris calculatedatthespecieslevelbyaveragingacrosslandscapeswhere eachspecies ispresent.Disturbance (red) isthelocalbinarydisturbancescore,latitude (yellow) istheabsolutelatitudeof thelandscapecentroids andseasonality (blue) isthestandarddeviationof meanmonthlytemperaturevalues.a–c, Effect-siZeestimateswith 95% confidenceintervals;anegativeeffectindicates reduceddispersallimitation (thatis,increaseddispersalability).R2 and Akaike informationcriterion (AIC) valuesarecalculatedforfullsamplemodelsonly. d–f, Proportionof independentvariationexplainedbyeachmodelcovariate, calculatedusinghierarchicalpartitioning.
Fig. 4 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
Fig. 4 | Dispersallimitation (nHWI) explainsvariationinfragmentation sensitivity.Resultsof Bayesianphylogeneticmixed-effect modelspredicting fragmentationsensitivityforall 1,564 birdpopulations (n = 1,034 species). Populationswereclassifiedasfragmentationsensitiveiftheywereidentifiedas 'Forest-core' by BIOFRAG. Restrictedanalysisassignedfragmentationsensitivity onlyto 'Forestspecialists' (a); Expandedanalysisassignedfragmentation sensitivitytoboth ' Forestspecialist' and ' Forestassociated' species (b; see Methods).Bayesianposteriordistributionisshownabovetheline;effect-siZe estimateswithcredibleintervals (CI) arebelowtheline (thickerrorbars,68%; thinerrorbars,95%).HigheffectsiZesindicateapositiveassociationwith fragmentationsensitivity;loweffectsiZesindicateanegativeassociation. Historicaldisturbanceisabinaryvariable (1/0) calculatedusingalldisturbance layers (forestloss,glaciation,stormsandfires).
Fig. 5. Proposed fragmentation scheme for 2-aminobenzoyl O in Chemotaxonomic investigation of Apocynaceae for retronecine-type pyrrolizidine alkaloids using HPLC-MS/MS
Fig. 5. Proposed fragmentation scheme for 2-aminobenzoyl O-β-D-apiofuranosyl-(1 → 6)- β-D-glucopyranoside (m/z 432.15 when protonated) in positive ion mode ESI to m/z 300.11, 138.06, and 120.04 fragments.
The joint effects of forest habitat area and fragmentation on Dung beetles
<p>Habitat loss and habitat fragmentation usually occur together, at the same time and place. However, while there is consensus that habitat loss is the preeminent threat to biodiversity, the effects of fragmentation are contentious. Some argue that habitat fragmentation is not bad for biodiversity, and even that it is good. Generally, the studies that find no harm or positive outcomes of fragmentation invariably assume that it is independent of habitat loss. However, dissociating the effects of habitat fragmentation from habitat loss is questionable because the two are essentially coupled. Accordingly, we evaluated how forest area and fragmentation (via edge effects) influenced dung beetles<em> per se</em>, and through their effects on the abundance of mammals, using structural equation modeling (SEM). Dung beetles are very sensitive to forest habitat loss and fragmentation, and to changes in the abundance of mammals on which they depend for dung. Our study area was in the Tana River, Kenya, where forest fragments are depauperated of mammals except for two endemic species of monkeys. We mapped 12 forests, counted the resident monkeys, and sampled 113,955 beetles from 288 plots. Most of the 87 species of beetles found were small tunnellers. After implementing a fully latent Structural Regression SEM, the optimal model explained a significant 26% of the variance in abundance, and 89% of diversity. The main drivers of beetle abundance were positive, direct, effects of forest area and number of monkeys, and negative edge effects. The main drivers of diversity were the direct effects of the beetle abundance, indirect effects of forest area and abundance of mammals, and indirect negative edge effects. Thus forest area, fragmentation (via edge effects), and the number of monkeys jointly influenced the abundance and diversity of the beetles directly and indirectly.</p>
Fig. 4 in The isolation, structure and fragmentation characteristics of natural truxillic and truxinic acid derivatives in Abrus mollis leaves
Fig. 4. Plausible MS/MS fragment ion structures of truxillate forms (1, 5–7) and their precursors (9, 10) during the fragmentation process in negative and positive ion mode. It showed that the main fragmentation pattern of truxillate configurations was symmetric dissociation.
Fig. 2. X in The isolation, structure and fragmentation characteristics of natural truxillic and truxinic acid derivatives in Abrus mollis leaves
Fig. 2. X-ray structure of compound 1, the displacement ellipsoids are drawn at the 50% probability level. Another molecular compound 1, H2O molecules, CO molecule and Ca atoms are not shown. H atoms are shown as small spheres of arbitrary radii.
Fig. 5 in The isolation, structure and fragmentation characteristics of natural truxillic and truxinic acid derivatives in Abrus mollis leaves
Fig. 5. Plausible MS/MS fragment ion structures of truxinate forms (2–4, 8) during the fragmentation process in the positive and negative ion mode. It showed that the main fragmentation pattern of truxinate configurations was complex and asymmetrical.
Neutral model data from "Fragmentation mitigates biodiversity loss immediately after habitat destruction"
<p>Raw community data from the manuscript "Fragmentation mitigates biodiversity loss immediately after habitat destruction." The folder contains text files of raw community data from the neutral model. Filenames contain the parameter values used in the simulation of that community. In the text files, each number is a different species and its position in the vector indicates its x, y coordinate in the 2D map. See <a href="https://github.com/cmsmith91/fragmentation/blob/main/python_code/neutral_mod-amarel15june2021.py">code</a> in the manuscript github repository. </p>
HglT fragments of heterocytous cyanobacteria
<p>Assembled <em>hglT</em> gene fragments obtained via PCR and direct sequencing using primer set 2 (Fw1 mix B + Rv1, Supplementary table 2) on the heterocytous cyanobacterial cultures listed in Table 2, Supplementary Table 8 and shown in Figure 5 (sequences in blue) in Pérez Gallego et al. 2023 </p> <p>Nucleotide sequences were obtained via Sanger sequencing and were processed using Geneious Prime (v 2023.0.4). Sequences were trimmed using an error probability limit of 0.01. Potential heterozygous bases in single reads were identified using a 50% peak similarity cutoff, peak detection height was set at 10%. When available, the consensus sequence was obtained by aligning forward and reverse reads with Geneious assembler using the highest sensitivity settings. When appropriate, sequences belonging to the pCR™4-TOPO™ vector (Invitrogen, Carlsbad, CA, USA) were identified and removed.</p> <p>To generate the phylogenetic trees sequences were aligned using MAFFT (v7.407) with L-INS-i iterative refinement method (Katoh and Standley, 2013) and poorly aligned regions were removed using trimAl (Capella-Gutiérrez et al., 2009). Phylogenetic trees were built using IQ-tree (v1.6.7) and its in-built nucleotide substitution model finder (Kalyaanamoorthy et al., 2017), using 1000 replicates to perform SH-like approximate likelihood ratio test (SH-aLRT) (Guindon et al., 2010) and 1000 bootstrap replicates. </p>
Data and source code of "Refining intra-patch connectivity measures in landscape fragmentation and connectivity indices"
<p>Data and source code related to the article "Refining intra-patch connectivity measures in landscape fragmentation and connectivity indices".</p> <ul> <li>The script "generate_artificial_landscapes.R" was used to produce the artificial landscapes located in the folder "ARTIFICAL_LANDSCAPES". The package rflsgen was used to produce these landscapes.</li> <li>The script "evaluation_artificial_landscapes.R" was used to evaluate these artificial landscapes, in accordance with what is presented in the article. It relies on the intra R package also introduced in the article.</li> <li>The script "evaluation_real_landscape.R" was used to evaluate the Koniambo massif landscape (New Caledonia), according to what is presented in the article. It also relies on the intra R package.</li> </ul>
Fragment of a vase with narrow neck
Fragment of a vase with narrow neck. Argaric culture. Early-Middle Bronze Age (2300-1600 BC). Inventory number: PG.2000.1495 Find this object in the museum's online catalog [Carmentis](https://www.carmentis.be:443/eMP/eMuseumPlus?service=ExternalInterface&module=collection&objectId=198538&viewType=detailView) Source: Objaverse 1.0 / Sketchfab
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