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468 results for “deciduous”
An Isotopic Approach to Partition Evapotranspiration in a Mixed Deciduous Forest at the University of Michigan Biological Station, Pellston, MI (2017)
Transpiration (T) is perhaps the largest fluxes of water from the land surface to the atmosphere and is susceptible to changes in climate, land use and vegetation structure. However, predictions of future transpiration fluxes vary widely and are poorly constrained. Stable water isotopes can help expand our understanding of land–atmosphere water fluxes but are limited by a lack of observations and a poor understanding of how the isotopic composition of transpired vapour (δT) varies. Here, we present isotopic data of water vapour, terrestrial water and plant water from a deciduous forest to understand how vegetation affects water budgets and land–atmosphere water fluxes. We measured subdiurnal variations of δ18OT from three tree species and used water isotopes to partition T from evapotranspiration (ET) to quantify the role of vegetation in the local water cycle. We find that δ18OT deviated from isotopic steady‐state during the day but find no species‐specific patterns. The ratio of T to ET varied from 53% to 61% and was generally invariant during the day, indicating that diurnal evaporation and transpiration fluxes respond to similar atmospheric and micrometeorological conditions at this site. Finally, we compared the isotope‐inferred ratio of T to ET with results from another ET partitioning approach that uses eddy covariance and sap flux data. We find broad midday agreement between these two partitioning techniques, in particular, the absence of a diurnal cycle, which should encourage future ecohydrological isotope studies. Isotope‐inferred estimates of transpiration can inform land surface models and improve our understanding of land–atmosphere water fluxes.
Aboveground biomass and nitrogen allocation of ten deciduous southern Appalachian tree species at the Coweeta Hydrologic Laboratory in 1997
Allometric equations were developed for mature trees of 10 deciduous species at the Coweeta Hydrologic Laboratory in western North Carolina, U.S.A. These equations included the following dependent variables: stem wood mass, stem bark mass, branch mass, total wood mass, foliage mass, total biomass, foliage area, stem surface area, sapwood volume, and total tree volume. High correlation coefficients (R2) were observed for all variables versus stem diameter, with the highest being for total tree biomass, which ranged from 0.981 for Oxdendrum arboreum to 0.999 for Quercus coccinea. Foliage area had the lowest R2 values, ranging from 0.555 for Quercus alba to 0.962 for Betula lenta. When all species were combined, correlation coefficients ranged from 0.822 for foliage area to 0.986 for total wood mass, total tree biomass, and total tree volume. Species with ring versus diffuse/semiring porous wood anatomy exhibited higher leaf area with a given cross-sectional sapwood area as well as lower total sapwood volume. Liriodendron tulipifera contained one of the highest foliar nitrogen concentrations and had consistently low branch, bark, sapwood, and heartwood nitrogen contents. For a tree diameter of 50 cm, Carya spp. exhibited the highest total nitrogen content whereas Liriodendron tulipifera exhibited the lowest.
Figure 31 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 31. Monthly changes in mean biomass in each row of C. (C.) udumalpetense per month per m2 (Vertical line indicates SE).
Figure 15 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 15. Showing the monthly fluctuations of total population and temperature in Rows I, II and III.
Figure 14 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 14. Showing the monthly fluctuations of male, female and nymph population, temperature and humidity in Row III.
Figure 11 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 11. Showing the month wise vertical distribution of total Ctenolepisma (C.) udumalpetense population.
Figure 10 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 10. Showing the month wise vertical distribution of Ctenolepisma (C.) udumalpetense nymphal population.
Figure 8 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 8. Showing the vertical distribution of total population of Ctenolepisma (C.) udumalpetense in each row.
Figure 6 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 6. Showing the mean of total male, female and nymph of Ctenolepisma (C.) udumalpetense in three rows with standard error.
Figure 5 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 5. Showing the relative density of Ctenolepisma (C.) udumalpetense (Male, Female, Nymph) in each month in the forest floor of Trimurti Dam roadside, Tamil Nadu.
Figure 4 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 4. Showing the relative density of Ctenolepisma (C.) udumalpetense (Male, Female, Nymph) in each row in the forest floor of Trimurti Dam roadside, Tamil Nadu.
Figure 7 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 7. Showing the month wise mean density of male, female and nymph population of Ctenolepisma (C.) udumalpetense.
Figure 9 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 9. Showing the month wise vertical distribution of adult Ctenolepisma (C.) udumalpetense population.
Figure 16 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 16. Showing the monthly fluctuations of total population and humidity(%) in Rows I, II and III.
Figure 12 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 12. Showing the monthly fluctuations of male, female and nymph population, temperature and humidity in Row I.
Figure 32 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 32. Monthly changes in biomass (mg dry wt./m2) of male, female and nymph population of C. udumalpetense.
Comparative physiology of canopy tree leaves in evergreen and deciduous forests in lowland Thailand
<p><span>Three major forest types in lowland Thailand and its adjacent parts in Southeast Asia are mixed deciduous forest (MDF), dry dipterocarp forest (DDF) and dry evergreen forest (DEF). We report the leaf physiology of canopy trees in these forests. The leaf mass-based photosynthetic rates (<em>A</em><sub>max</sub>), stomatal conductance (<em>G</em><sub>max</sub>) and photosynthetic nitrogen use efficiency were significantly different between the deciduous forests (MDF and DDF) and the evergreen forest (DEF). The canopy trees of MDF with thick, eutrophic soils had the highest intrinsic water use efficiency (<em>A</em><sub>max</sub>/<em>G</em><sub>max</sub>) among the forest types. Forest-to-forest variations in leaf mass area were related to different nutrient use strategies (less vs. more conservative) associated with different soil nutrients rather than with leaf phenology/longevity. In the interspecific variations within each forest, <em>A</em><sub>max</sub> in MDF and DEF was limited by foliar phosphate, whereas that in DDF was limited by foliar nitrogen. The close association between leaf physiology and soil properties suggests that climate change and increasing human impacts will disrupt this association, leading to forest degradation and dysfunction.</span></p>
Comparative physiology of canopy tree leaves in evergreen and deciduous forests in lowland Thailand
<p>The major forest types in lowland Thailand and its adjacent parts in Southeast Asia with the distinct dry season are the mixed deciduous forest (MDF), dry dipterocarp forest (DDF) and dry evergreen forest (DEF). We report the first comprehensive data set in leaf physiology of canopy trees in these three forest types and clarify adaptive functional differences of woody plants among three forests. Unlike temperate forests, the forest variations in leaf mass per area (LMA) were related to nutrient use strategies (less vs. more conservative) associated with soil nutrients rather than with leaf phenology (evergreen vs. deciduous). In the interspecific variations within each forest, <em>A<sub>max</sub></em> in MDF and DEF was limited by foliar phosphate, whereas that in DDF was limited by foliar nitrogen. The close association between leaf physiology and soil properties suggests that climate change and increasing human impacts will disrupt this association, leading to forest degradation and dysfunction.</p>
Canopy gaps facilitate upslope shifts in montane conifers but not in temperate deciduous trees in the Northeastern United States
<p>Many montane tree species are expected to migrate upslope as climate warms, but it is not clear if forest canopy gaps, which can facilitate tree seedling recruitment, serve as an important mechanism driving tree species range shifts. Patterns of tree seedling establishment can inform us about early stages of tree species migrations and are critical to examine in the context of global climate change.</p> <p>We contrasted elevational distributions of tree seedlings both within and outside of forest canopy gaps with the distributions of conspecific adults and saplings across the deciduous-coniferous ecotone on ten mountains in four states of the northeastern United States. We tested if seedling distributions of four dominant tree species (<em>Abies balsamea, Picea rubens, Acer saccharum</em>, and<em> Fagus grandifolia</em>) were shifted upslope of conspecific adult and sapling distributions. We also examined if this shift was facilitated by canopy gaps and what environmental drivers affected species distributions.</p> <p>There was limited seedling recruitment of dominant tree species at the temperate-coniferous ecotone, which we attributed to (i) an observed downslope shift of seedling distributions of the low-elevation deciduous species (<em>Acer saccharum, Fagus grandifoli</em>a) and (ii) an upslope shift in seedling distributions of the high-elevation conifers (<em>Abies balsamea, Picea rubens</em>). The upslope shift of conifer seedlings contrasts with our previous research at these sites which observed downslope shifts of sapling distributions in <em>Picea rubens</em>, suggesting that seedlings may be responding to more recent climate warming. Canopy gaps in high-elevation conifer forests facilitated these upslope shifts by promoting conifer seedling recruitment. However, gaps at lower elevations did not play a significant role in seedling recruitment or the observed downslope shifts of the dominant deciduous species. Climate was the dominant predictor of adult tree distributions whereas both climate and soil were important predictors of seedling distributions.</p> <p><strong><em>Synthesis</em></strong>. Our study illustrates that tree seedlings have the potential for monitoring the early stages of tree species migrations, and particularly so in canopy gaps in high-elevation conifer forests. Further, we stress that species range shifts are sensitive to local scale heterogeneity in light availability (i.e., canopy gaps) and other non-climatic factors.</p>
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
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