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907 results for “Pine”
Data from: Nested polymerase chain reaction–based detection of Dothistroma septosporum, red band needle blight of pine, a tool in support of phytosanitary regimes
Red band needle blight is one of the most important foliar diseases of Pinus species and is of increasing international forest health and biosecurity concern. To provide a rapid identification technique for this pathogen in support of official control measures, a nested polymerase chain reaction–based diagnostic assay that employs species-specific primer sets has been developed. The assay is able to detect the presence of the pathogen direct from pine needles, irrespective of host species, to within 10 fg of target DNA, the equivalent of approximately 2–3 ascospores or hyphael cells.
Data for: Density dependence and spatial heterogeneity limit the population growth rate of invasive pines at the landscape scale
<p class="MsoBodyText"><span><span><span><span><span><span><span><span><span><span><span>Determining population growth across large scales is difficult because it is often impractical to collect data at large scales and over long timespans. Instead, the growth of a population is often only measured at a small, plot-level scale and then extrapolated to derive a mean field estimate. However, this approach is prone to error since it simplifies spatial processes such as the neighbourhood effects of density and dispersal. We present a novel approach that estimates how spatial processes derived from the effects of density and dispersal affect population growth between plot scales and landscape scales. The method is based on a scale transition theory and calculates a transition term to measure the spatial scaling of population growth, which we extend to unstable, expanding populations in order to assess whether landscape-scale population dynamics are different from those estimated at smaller spatial scales. We illustrate this approach using aerial imagery of eight locations in New Zealand experiencing non-native pine invasions. Analyses examined the dynamics at a plot scale (1 hectare) and compared this to estimates across entire landscapes (between 24 and 1600 hectares), in several cases for more than one time period. We used a Bayesian spatial random effects model to examine population growth and to account for neighbourhood effects and dispersal between plots in a rapidly changing system. </span></span></span></span></span></span></span></span></span></span></span></p> <p class="MsoBodyText"><span><span><span><span><span><span><span><span><span><span><span>We found that the estimates of the scale transition term were typically 10-25% of the mean field estimates, which led to mean field estimates of population growth extrapolated from plots being considerably higher than landscape estimates. The approach we have developed will not only have applications for predicting the populations' growth of invasive species, but also for studies examining the scaling of landscape-scale phenomena.</span></span></span></span></span></span></span></span></span></span></span></p>
FIGURE 7 in Description of the Yunnan shoot borer, Tomicus yunnanensis Kirkendall & Faccoli sp. n. (Curculionidae, Scolytinae), an unusually aggressive pine shoot beetle from southern China, with a key to the species of Tomicus
FIGURE 7. Tomicus yunnanensis sp. n., dorsal and lateral habitus of holotype.
FIGURE 3. T in Tomicus armandii Li & Zhang (Curculionidae, Scolytinae), a new pine shoot borer from China
FIGURE 3. T. armandii: (a) dorsal view, (b) lateral view, (c) antenna (170×, SEM).
FIGURE 10 in A new species of pine anole from the Sierra Madre del Sur in Oaxaca, Mexico (Reptilia, Squamata, Dactyloidae: Anolis)
FIGURE 10. Habitat at the type locality of Anolis peucephilus.
FIGURE 5 in A new species of pine anole from the Sierra Madre del Sur in Oaxaca, Mexico (Reptilia, Squamata, Dactyloidae: Anolis)
FIGURE 5. Head of holotype of Anolis peucephilus (SMF 96368). Scale bars equal 1.0 mm.
FIGURE 4 in A new species of pine anole from the Sierra Madre del Sur in Oaxaca, Mexico (Reptilia, Squamata, Dactyloidae: Anolis)
FIGURE 4. Holotype of Anolis peucephilus (SMF 96368). Scale bars equal 10.0 mm.
FIGURE 3. Cinara palaestinensis s in The sexuales of Cinara palaestinensis Hille Ris Lambers, the Aleppo pine aphid (Hemiptera: Aphididae Lachninae)
FIGURE 3. Cinara palaestinensis s.str.—apterous male: (a) ventral view, (b) antenna.
FIGURE 1. Cinara palaestinensis s in The sexuales of Cinara palaestinensis Hille Ris Lambers, the Aleppo pine aphid (Hemiptera: Aphididae Lachninae)
FIGURE 1. Cinara palaestinensis s.str.—oviparous female: dorsal view.
Data - Prescribed fire in longleaf pine ecosystems: fire managers' perspectives on priorities, constraints, and future prospects
<p><strong>The following information describes the data coded in the corresponding database as it relates to survey question responses. This dataset includes all data used to produce graphs in:</strong></p><p>Kupfer, J.A., Lackstrom, K., Grego, J.M., Dow, K., Terando, A.J., and Hiers, J.K. 2022. Perspectives on prescribed fire management in longleaf pine ecosystems: Current constraints and future prospects. <i>Fire Ecology</i> 18, 27. <a href="https://doi.org/10.1186/s42408-022-00151-6">https://doi.org/10.1186/s42408-022-00151-6</a>..</p><p><strong>Part I: Criteria for Prioritizing Burn Sites (Columns A-S)</strong></p><p><strong>Question #1: "For the characteristics listed below, please rank your top 3 criteria for determining whether a site has a high priority for burning." Scale: 1 = highest, 2 = next; 3 = next; NULL = not in top 3. Columns K-S indicate simply whether a criterion was chosen (1) or not (0), regardless of rank.</strong></p><p>A. Participant ID</p><p>B. TimeSinceBurnRk = Time since the last fire </p><p>C. FuelReductionRk = Burn to reduce fuels</p><p>D. EcoHealthRk = Burn to improve ecosystem health</p><p>E. TimberRk = Improving timber</p><p>F. FirebreaksRk = Presence of firebreaks</p><p>G. TandERk = Burn to assist T&E species</p><p>H. WUIrk = Proximity to Wildland Urban Interface</p><p>I. ExoticInvasiveRk = Prioritizing due exotics / invasives</p><p>J. OtherCritRk = Other criteria not included above</p><p>K. TimeSinceBurn = Time since the last fire (1 = cited in any order; NULL = not in top 3)</p><p>L. FuelReduction = Burn to reduce fuels (same ranking as previous)</p><p>M. EcoHealth = Burn to improve ecosystem health (same ranking as previous)</p><p>N. Timber = Improving timber (same ranking as previous)</p><p>O. Firebreaks = Presence of firebreaks (same ranking as previous)</p><p>P. TandE = Burn to assist T&E species (same ranking as previous)</p><p>Q. WUI = Proximity to Wildland Urban Interface (same ranking as previous)</p><p>R. ExoticInvasive = Prioritizing due exotics / invasives (same ranking as previous)</p><p>S. OtherCrit = Other criteria not included above (same ranking as previous)</p><p> </p><p><strong>Part II: Longleaf Pine Burning Frequency (T-U)</strong></p><p><strong>Question #3: "Recommendations for the frequency of prescribed burns depend on the local conditions, including: 1) the diversity of sites with longleaf ecosystems, 2) variation in the suite of understory species that define the habitat, and 3) the 'historic' fire return interval. How often, on average, should longleaf pine stands in your unit(s) be burned?</strong></p><p>T. RecentBurnFreq: 1 = < every 2 yrs, 2 = every 2-4 yrs, 3 = every 4-5 yrs, 4 = > every 5 yrs </p><p><strong>Question #4. In practice, how often, on average, have longleaf pine units in your area been burned over the past 10 years?</strong></p><p>U. CompBurnFreq: -1 = less frequently than previous answer; 0 = as frequently as previous answer; 1 = more frequently than previous answer (the response was compared to that from Question 3 and scored comparatively).</p><p> </p><p><strong>Part III: Current legal, institutional, and managerial constraints (V-AB)</strong></p><p><strong>Question #6. In this section we would appreciate your help in better understanding how specific factors constrain or limit your ability to conduct prescribed burns. Do the following factors pose constraints to prescribed burning at your units? Scale: 1 = not a constraint; 2 = sometimes a constraint; 3 = commonly a constraint; -9999 = Not applicable. (note: #NULL! = no response)</strong></p><p>V. ConstrPublic: Burning constrained by public concerns </p><p>W. ConstrWUI: Burning constrained by nearby development </p><p>X. ConstrRisk: Burning constrained by concerns about liability, career, etc.</p><p>Y. ConstrPartners: Burning constrained by partnerships and agreements</p><p>Z. ConstrIncent: Limited incentives or organizational pushback</p><p>AA. ConstrLegal: Legal constraints (e.g. NEPA, etc.)</p><p>AB. ConstrOther: Other constraints</p><p><strong> </strong></p><p><strong>Part IV: Environmental and resource constraints (AC-AJ)</strong></p><p><strong>Question 7: Please indicate how often these factors constrain prescribed burning in your management unit during the longleaf pine dormant season and growing season. Scale: 1 = not a constraint; 2 = sometimes a constraint; 3 = commonly a constraint; -9999 = Not applicable. (</strong><i><strong>note: #NULL! = no response</strong></i><strong>)</strong></p><p>AC. ConstrDormWx: Inappropriate weather conditions during dormant season</p><p>AD. ConstrDormFuel: Concerns about fuel loads during dormant season</p><p>AE. ConstrDormAQ: Air quality / smoke management issues during dormant season</p><p>AF. ConstrDormRes: Shortage of resources (personnel, money, equip) during dormant season</p><p>AG. ConstrGrowWx: Inappropriate weather conditions during growing season</p><p>AH. ConstrGrowFuel: Concerns about fuel loads during growing season</p><p>AI. ConstrGrowAQ: Air quality / smoke management issues during growing season</p><p>AJ. ConstrGrowRes: Shortage of resources (personnel, money, equip) during growing season</p><p> </p><p><strong>Part V: Seasonal Weather Constraints (AK-BD)</strong></p><p><strong>Question 8: For weather conditions during the dormant season and growing season, please check the boxes for all factors which are common constraints on prescribed burning. (scale: 1 = factor selected, #NULL! = factor not selected).</strong></p><p>AK. ConstrHiTDorm: High temperature is a common constraint during the dormant season</p><p>AL. ConstrHiTGrow: High temperature is a common constraint during the growing season</p><p>AM. ConstrLowTDorm: Low temperature is a common constraint during the dormant season</p><p>AN. ConstrLowTGrow: Low temperature is a common constraint during the growing season</p><p>AO. ConstrHiRHDorm: High relative humidity is a common constraint during the dormant season</p><p>AP. ConstrHiRHGrow: High relative humidity is a common constraint during the growing season</p><p>AQ. ConstrLowRHDorm: Low relative humidity is a common constraint during the dormant season</p><p>AR. ConstrLowRHGrow: Low relative humidity is a common constraint during the growing season</p><p>AS. ConstrHiWindDorm: High winds are a common constraint during the dormant season</p><p>AT. ConstrHiWindGrow: High winds are a common constraint during the growing season</p><p>AU. ConstrLowWindDorm: Low winds are a common constraint during the dormant season</p><p>AV. ConstrLowWindGrow: Low winds are a common constraint during the growing season</p><p>AW. ConstrDaysRainDorm: # of days since last rain is a common constraint during the dormant season</p><p>AX. ConstrDaysRainGrow: # of days since last rain is a common constraint during the growing season</p><p>AY. ConstrDroughtDorm: Drought conditions are a common constraint during the dormant season</p><p>AZ. ConstrDroughtGrow: Drought conditions are a common constraint during the growing season</p><p>BA. ConstrAtmoDispDorm: Low atmospheric dispersion is a common constraint during the dormant season</p><p>BB. ConstrAtmoDispGrow: Low atmospheric dispersion is a common constraint during the growing season</p><p>BC. ConstrTransWindDorm: Adequate transport winds are a common constraint during the dormant season</p><p>BD. ConstrTransWindGrow: Adequate transport winds are a common constraint during the growing season</p><p><strong> </strong></p><p><strong>Part VI: Expectations for future changes in burning constraints due to climate change and urban growth (BE-BW)</strong></p><p><strong>Question 9: To what extent do you think climate change currently affects your management decisions? </strong></p><p>BE. CurrClimChange. Scale: 0 = not at all; 1 = a little; 2 = moderately; 4 = a great deal; -9999 = don't know. #NULL! = no response.</p><p><strong>Question 10: To what extent do you think urban growth currently affects your management decisions? </strong></p><p>BF. CurrUrbanGrowth: Scale: 0 = not at all; 1 = a little; 2 = moderately; 4 = a great deal; -9999 = don't know. #NULL! = no response.</p><p><strong>Question 12:</strong> <strong>To what extent do you think future climate change (e.g., increasing temperature, more intense rainstorms, and/or extreme weather events) will affect your prescribed burning decisions? </strong>. <strong>Scale for all variables: 0 = not at all; 1 = a little; 2 = moderately; 3 =a great deal; -9999 = don't know. #NULL! = no response.</strong></p><p>BG. ClimChangeShort: Effects over the next 5-10 years</p><p>BH. ClimChangeMed: Effects over the next 10-30 years</p><p>BI. ClimChangeLong: Effects over the next 30-50 years</p><p><strong>Question 13:</strong> <strong>To what extent do you think future urbanization patterns (e.g., changes in the wildland urban interface, loss of habitat to restore longleaf pine ecosystems) will affect your prescribed burning decisions? Scale for all variables: 0 = not at all; 1 = a little; 2 = moderately; 3 =a great deal; -9999 = don't know. #NULL! = no response.</strong></p><p>BJ. UrbChangeShort: Effects over the next 5-10 years</p><p>BK. UrbChangeMed: Effects over the next 10-30 years</p><p>BL. UrbChangeLong: Effects over the next 30-50 years</p><p><strong>Question 15: Thirty years from now, which of these constraints do you think will be the most significant constraints to your use of prescribed burning? Please select up to 5 constraints. Scale: 1 = selected; #NULL! = selected</strong></p><p>BM. FutConstrWx: Inappropriate weather conditions (1 = cited in any order; NULL = not in top 5)</p><p>BN. FutConstrFuel: High fuel loads (same scale as previous)</p><p>BO. FutConstrAQ: Air quality issues, including smoke management (same scale as previous)</p><p>BP FutConstrRes: Shortage of resources (personnel, money, equipment) (same scale as previous)</p><p>BQ. FutConstrPublic: Avoiding public objections or concerns over burning (same scale as previous)</p><p>BR. FutConstrWUI: Residential or other development in or near burn areas (same scale as previous)</p><p>BS. FutConstrRisk: Risk aversion (liability, career, political repurcussions) (same scale as previous)</p><p>BT. FutConstrAgree: Challenges posed by agreement and partnerships (same scale as previous)</p><p>BU FutConstrIncent: Limited incentives, institutional history (same scale as previous)</p><p>BV. FutConstrLegal: Legal constraints (same scale as previous)</p><p>BW. FutConstrOther: Other constraints</p><p> </p><p><strong>Part VII: Responder Demographics (BX)</strong></p><p>Respondent provided state and management unit information. Scale 1 = yes; 0 = no.</p>
Data supporting "Aerobic methane production in Scots pine shoots is independent of drought or photosynthesis"
<p>Data supporting our New Phytologist publication "Aerobic methane production in Scots pine shoots is independent of drought or photosynthesis"</p>
Supplementary material 1 from: Medina-Villar S, Pérez-Corona ME, Herrero A, Cruz-Alonso V, Carro-Martinez N, Andivia E (2024) The alien conifer Cupressus arizonica can outcompete native pines in Mediterranean mixed forests under climate change. NeoBiota 92: 211-231. https://doi.org/10.3897/neobiota.92.116634
Can exotic tree species outcompete native ones in Mediterranean mixed forests under climate change
Fig. 2 in Biogeography and evolution of the screw-pine genus Benstonea Callm. & Buerki (Pandanaceae)
Fig. 2. – BEAST maximum clade credibility tree of Pandanaceae. Bayesian posterior probabilities (BPP) and 95 % highest posterior density (HPD) interval on nodes are displayed. See main text for more details on clades.
Fig. 4 in Biogeography and evolution of the screw-pine genus Benstonea Callm. & Buerki (Pandanaceae)
Fig. 4. – Biogeographical areas used for the Lagrange analysis of Benstonea Callm. & Buerki displayed on an elevation map retrieved from the WorldClim database [http://www.worldclim.org/current]. Dispersal events inferred by the Lagrange analysis are also indicated. See legend of Fig. 3 for more details on biogeographical areas.
Fig. 3 in Biogeography and evolution of the screw-pine genus Benstonea Callm. & Buerki (Pandanaceae)
Fig. 3. – Biogeographical scenario of Benstonea Callm. & Buerki inferred using the DEC model implemented in Lagrange and displayed on the BEAST maximum credibility clade tree. Abbreviations for the biogeographical areas: A. India and Indochina (the limit of this area is set at the Isthmus of Kra; PARNELL, 2013), B. Sunda shelf (including Peninsular Malaysia, Sumatra and Java, but excluding the Philippines, which are here included in the Wallacea region), C. Borneo, D. Wallacea (including Sulawesi, the Philippines and the Mollucas Islands) and E. the Sahul shelf (including New Guinea and Northern Australia) and the Pacific islands (here the Solomon islands and Fiji). Please see figure 4 for more details on the geographical boundaries of the biogeographical areas. Other abbreviations: P: peripheral isolate; V: vicariance; D: dispersal event.
Data from: Molecular signatures of reticulate evolution within the complex of European pine taxa
<p>Speciation mechanisms, including the role of interspecific gene flow and introgression in emergence of new species, are the major focus of evolutionary studies. Inference of taxonomic relationship between closely related species maybe challenged by past hybridization events, but at the same time it may provide new knowledge about mechanisms responsible for the maintenance of species integrity despite interspecific gene flow. Here, using nucleotide sequence variation and utilizing a coalescent modelling framework, we tested the role of hybridization and introgression in the evolutionary history of closely related pine taxa from <i>Pinus mugo</i> complex and <i>P. sylvestris</i>. We compared the patterns of polymorphism and divergence between taxa and found great overlap of neutral variation within <i>P. mugo</i> complex. Our phylogeny reconstruction indicated multiple instances of reticulation events in the past, suggesting an important role of interspecific gene flow in the species divergence. The best fitting model revealed <i>P. mugo</i> and <i>P. uncinata</i> as sister species with basal <i>P. uliginosa</i> and asymmetric migration between all investigated species after their divergence. The magnitude of interspecies gene flow differed greatly, and it was consistently stronger from representatives of <i>P. mugo</i> complex to <i>P. sylvestris</i> than in the opposite direction. The results indicate the prominent role of reticulation evolution in those forest trees and provide genetic framework to study species integrity maintained by selection and local adaptation.</p>
Fig. 4 in Benstonea Callm. & Buerki (Pandanaceae): characterization, circumscription, and distribution of a new genus of screw-pines, with a synopsis of accepted species
Fig. 4. – Infructescences and details of stigmas of species of Benstonea Callm. & Buerki. A. Benstonea affinis (Kurz) Callm. & Buerki; B. Benstonea gibbsiana (Martelli) Callm. & Buerki; C. Benstonea ornata (Solms) Callm. & Buerki; D. Benstonea pachyphylla (Merr.) Callm. & Buerki. [Photos: M. W. Callmander]
Fig. 4 in Benstonea Callm. & Buerki (Pandanaceae): characterization, circumscription, and distribution of a new genus of screw-pines, with a synopsis of accepted species
Fig. 4. – Infructescences and details of stigmas of species of Benstonea Callm. & Buerki. A. Benstonea affinis (Kurz) Callm. & Buerki; B. Benstonea gibbsiana (Martelli) Callm. & Buerki; C. Benstonea ornata (Solms) Callm. & Buerki; D. Benstonea pachyphylla (Merr.) Callm. & Buerki. [Photos: M. W. Callmander]
Thwaites and Pine Island Glacier change 2016-2021
<p>Visualization of <a href="https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1">Sentinel</a> 1 radar images using <a href="https://matplotlib.org/cmocean/">cmocean</a> ice colourmap, similar to the approach used by <a href="https://www.pnas.org/content/117/40/24735/tab-figures-data">Lhermitte</a>. Data from <a href="https://www.nasa.gov/mission_pages/Grace/index.html">GRACE</a> was processed by <a href="https://data1.geo.tu-dresden.de/ais_gmb/index.html#grid">TU Dresden</a>. Funded by Rijkswaterstaat KPP. </p>
Effects of time since invasion and control actions on a coastal ecosystem invaded by non-native pine trees
<p><span>Invasive non-native trees cause structural and functional changes in plant communities, which tend to </span><span>increase over time since invasion. Native vegetation responses after control operations provide important information for restoration.</span></p> <p><span>We evaluated the effects of time since invasion and of pine control on plant community structure and on functional traits in a </span><span>coastal open ecosystem in southern Brazil</span><span>. We compared richness, diversity, abundance and cover of woody and non-woody native plant species, as well as species composition and community-weighted means (CWM)</span> <span>based on functional traits (</span><span>dispersal syndrome, fruit type, maximum height, and shade tolerance) of plant communities</span><span>, in four conditions: a non-invaded area, an area where pines were controlled (managed area), an area of recent invasion, and an area invaded longer ago.</span></p> <p><span>Woody species abundance, richness and diversity declined over time since invasion. However, while abundance recovered to the point of not differing from the non-invaded condition in areas where pines were controlled, species diversity and richness were lower in the managed area than in the area that was never invaded. The effects of pine invasion on richness and diversity of non-woody plants did not increase over time, but plant cover progressively diminished. </span></p> <p><span>Woody and non-woody species composition varied between the four conditions. Species composition similarity was lower between conditions for non-woody than for woody species. CWM differed between the older invasion and the other conditions, determined especially by native plant height and shade tolerance. Taller plants and m</span><span>ore shade tolerant native species were exclusively sampled in the older invasion.</span></p> <p><span>Synthesis and application. Pine invasion reduced species abundance, plant cover, richness, and diversity, altering the composition of plant community. The escalation of negative temporal effects of pine invasion was observed on the composition of woody and non-woody species and on functional traits. Although pine control favored the natural regeneration of non-woody species, diversity of woody species in the area submitted to pine control was lower than in the non-invaded condition. Restoration activities are therefore required to increase woody species diversity. These results provide relevant guidance for the restoration of coastal ecosystems following invasive pine control. </span></p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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