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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).

opencc-by-4.0May 2023View details →

Supplementary Documents for Manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating'

This file contains all the supplementary material for the manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating' sent to Radiation Measurements on 8/20/2023.

Fig. 3 in Dispersion Pattern of Giant Cicada (Hemiptera: Cicadidae) in a Brazilian Coffee Plantation

Fig. 3. Schematic representation of the experimental area with capture, release, and recapture points of Quesada gigas adults marked with colored synthetic nail enamels.The numbers inside the recapture points represent the sequential order of recapture.

Fig. 1 in Dispersion Pattern of Giant Cicada (Hemiptera: Cicadidae) in a Brazilian Coffee Plantation

Fig. 1. Location of the coffee plantation (Coffea arabica) used for the experiments of Quesada gigas dispersion and mating and oviposition behaviors. State of Minas Gerais (MG), Brazil.

Fig. 5 in Dispersion Pattern of Giant Cicada (Hemiptera: Cicadidae) in a Brazilian Coffee Plantation

Fig. 5. Time course recapture of Quesada gigas marked in green and released on 17 October 2017 until the end of recaptures on 13 November 2017. Recapture rates represent the percentage of recaptured of males and females in defined points throughout the area of study.

Fig. 2 in Dispersion Pattern of Giant Cicada (Hemiptera: Cicadidae) in a Brazilian Coffee Plantation

Fig. 2. (A) Frontal view of the sound trap employed to capture Quesada gigas in the experimental area. Sound transmitter; Blanched fabric (2.0 × 1.5 m) positioned for insect landing. (B) Back view of the sound trap. Sound transmitter; Blanched fabric (2.0 × 1.5 m) positioned for insect landing; Battery, 12 volts; CD player and sound amplifier.

opencc-zeroAug 2023View details →

dryad32/100

SAMC Model Inputs from: Predicting dispersal and conflict risk for wolf recolonization in Colorado

The colonization of suitable yet unoccupied habitat due to natural dispersal or human introduction can benefit recovery of threatened species. Predicting habitat suitability and conflict potential of colonization areas can facilitate conservation planning. Planning for reintroduction of gray wolves (Canis lupus) to the U.S. state of Colorado is underway. Assessing which occupancy sites minimize the likelihood of human-wolf conflict during dispersal events and seasonal movements is critical to the success of this initiative. We used a spatial absorbing Markov chain (SAMC) framework, which extends random walk theory and probabilistically accounts for both movement behavior and mortality risk, to compare the viability of potential occupancy sites (public lands >500 km2 to minimally meet wolf pack range area). The SAMC framework produced spatially explicit predictions of wolf dispersal, philopatry, and conflict risk ahead of recolonization prior to reintroduction efforts. Our SAMC model included: 1) movement resistance based on terrain, roads, and housing density; 2) mortality risk and potential conflict (absorption) based on livestock presence, social tolerance, land ownership, and state boundaries; and 3) site fidelity based on habitat quality. Using this model, we compared 21 public land units by deriving predictions of: A) relative survival time outside each site, B) intensity of use and retention time within each site, and C) the probability of use on adjacent public lands. We also predicted and mapped potential conflict hotspots associated with each site. Among the units assessed, a complex of USFS Wilderness areas near Aspen, chiefly the Hunter-Fryingpan and Collegiate Peaks Wilderness areas, had the best overall rankings when comparing predictions of each metric. The area balances high-quality, well-connected habitat with relatively low livestock density and high social tolerance. Synthesis and applications: Our findings highlight the utility of the SAMC framework for assessing colonization areas and the capacity to identify locations for effective proactive management, especially of conflict-prone species. The flexibility of the SAMC framework enables predicting likely areas of philopatry and human-wildlife conflict using spatially-explicit metrics which can improve the success of conservation translocations and management of species with changing geographic extents.

opencc-by-4.0Aug 2023View details →

Source data for Ordouie, E. et al. Differential phase-diversity electrooptic modulator for cancellation of fiber dispersion and laser noise.

The experimental data and primary simulation results.

opencc-by-4.0Sep 2023View details →

Shear wave velocity inversion based on dispersion characteristics of seabed Scholte wave in deep water

Simulated displacement records at the seabed with three different water depths using spectral element method.

opencc-by-4.0Sep 2023View details →

Biomimetic Dispersive Solid-Phase Microextraction as a Novel Concept for High-Throughput Estimation of Human Oral Absorption of Organic Compounds

Statistical data of the fourteen in-vitro MLR models evaluated for the prediction of the effective permeability of organic compounds across the human intestine, presented in Table S1 and Table 2 of the article Analytical Chemistry, 95, 2023, 13123−13131.   (DOI: 10.1021/acs.analchem.3c01749)