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Title: Land-use history impacts spatial patterns and composition of woody plant species across a 35-hectare temperate forest plot
Land-use history is the template upon which contemporary plant and tree populations establish and interact with one another and exerts a legacy on the structure and dynamics of species assemblages and ecosystems. We use the first census (2010–2014) of a 35-ha forest-dynamics plot at the Harvard Forest in central Massachusetts to describe the composition and structure of the woody plants in this plot, assess their spatial associations within and among the dominant species using univariate and bivariate spatial point-pattern analysis, and examine the interactions between land-use history and ecological processes. The plot includes 108,632 live stems ≥ 1 cm in diameter (2,215 individuals/ha) and 7,595 standing dead stems ≥ 5 cm in diameter. Live tree basal area averaged 42.25 m 2 /ha, of which 84% was represented by Tsuga canadensis (14.0 m 2 / ha), Quercus rubra (northern red oak; 9.6 m2/ ha), Acer rubrum (7.2 m 2 / ha) and Pinus strobus (eastern white pine; 4.4 m 2 / ha). These same four species also comprised 78% of the live aboveground biomass, which averaged 245.2 Mg/ ha. Across all species and size classes, the forest contains a preponderance (> 80,000) of small stems (<10-cm diameter) that exhibit a reverse-J size distribution. Significant spatial clustering of abundant overstory species was observed at all spatial scales examined. Spatial distributions of A. rubrum and Q. rubra showed negative intraspecific correlations in diameters up to at least a 150-m spatial lag, likely indicative of crowding effects in dense forest patches following intensive past land use. Bivariate marked point-pattern analysis, showed that T. canadensis and Q. rubra diameters were negatively associated with one another, indicating resource competition for light. Distribution and abundance of the common overstory species are predicted best by soil type, tree neighborhood effects, and two aspects of land-use history: when fields were abandoned in the late 19th century and the succeeding forest types recorded in 1908. In contrast, a history of intensive logging prior to 1950 and a damaging hurricane in 1938 appear to have had little effect on the distribution and abundance of present-day tree species. Our findings suggest that current day composition and structure are still being influenced by anthropogenic disturbances that occurred over a century ago.  more » « less
Award ID(s):
1832210
NSF-PAR ID:
10312333
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
PeerJ
Volume:
10
ISSN:
2167-8359
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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Spreadsheet: biomass_corn, perennial grasses Description: Maximum aboveground biomass measurements from corn, switchgrass, miscanthus, native grass and restored prairie plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2.   Variate    Description year    year of the observation date    day of the observation (mm/dd/yyyy) crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” replicate    each crop has four replicated plots, R1, R2, R3 and R4 station    stations (S1, S2 and S3) of samplings within the plot. For more details, refer to link (https://data.sustainability.glbrc.org/protocols/156) species    plant species that are rooted within the quadrat during the time of maximum biomass harvest. See protocol for more information, refer to link (http://lter.kbs.msu.edu/datatables/36) For maize biomass, grain and whole biomass reported in the paper (weed biomass or surface litter are excluded). Surface litter biomass not included in any crops; weed biomass not included in switchgrass and miscanthus, but included in grass mixture and prairie. fraction    Fraction of biomass biomass_plot    biomass per plot on dry-weight basis (Grams_Per_SquareMeter) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying column biomass per plot with 0.01 3. Spreadsheet: biomass_poplar Description: Maximum aboveground biomass measurements from poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Note that poplar biomass was estimated from crop growth curves until the poplar was harvested in the winter of 2013-14. Variate    Description year    year of the observation method    methods of poplar biomass sampling date    day of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 diameter_at_ground    poplar diameter (milliMeter) at the ground diameter_at_15cm    poplar diameter (milliMeter) at 15 cm height biomass_tree    biomass per plot (Grams_Per_Tree) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying biomass per tree with 0.01 4. Spreadsheet: annual N leaching_vol-wtd conc Description: Annual leaching rate (kiloGrams_N_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_N_Per_Liter) of nitrate (no3) and dissolved organic nitrogen (don) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen leached and volume-wtd mean N concentration shown in Figure 3a and Figure 3b, respectively. Note that ammonium (nh4) concentration were much lower and often undetectable (<0.07 milliGrams_N_Per_Liter). Also note that in 2009 and 2010 crop-years, data from some replicates are missing.    Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) vol-wtd no3 conc.    Volume-weighted mean no3 concentration (milliGrams_N_Per_Liter) vol-wtd don conc.    Volume-weighted mean don concentration (milliGrams_N_Per_Liter) 5. Spreadsheet: summary_N leached Description: Summary of total amount and forms of N leached (kiloGrams_N_Per_Hectare) and the percent of applied N lost to leaching over the seven years for corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen amount leached shown in Figure 4a and percent of applied N lost shown in Figure 4b. Note the fraction of unleached N includes in harvest, accumulation in root biomass, soil organic matter or gaseous N emissions were not measured in the study. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) N unleached    N unleached (kiloGrams_N_Per_Hectare) in other sources are not studied % of N applied N lost to leaching    % of N applied N lost to leaching 6. Spreadsheet: annual DOC leachin_vol-wtd conc Description: Annual leaching rate (kiloGrams_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_Per_Liter) of dissolved organic carbon (DOC) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for DOC leached and volume-wtd mean DOC concentration shown in Figure 5a and Figure 5b, respectively. Note that in 2009 and 2010 crop-years, water samples were not available for DOC measurements.     Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 doc leached    annual leaching rates of nitrate (kiloGrams_Per_Hectare) vol-wtd doc conc.    volume-weighted mean doc concentration (milliGrams_Per_Liter) 7. Spreadsheet: growing season length Description: Growing season length (days) of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in the Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Date shown in Figure S2. Note that growing season is from the date of planting or emergence to the date of harvest (or leaf senescence in case of poplar).   Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation growing season length    growing season length (days) 8. Spreadsheet: correlation_nh4 VS no3 Description: Correlation of ammonium (nh4+) and nitrate (no3-) concentrations (milliGrams_N_Per_Liter) in the leachate samples from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data shown in Figure S3. Note that nh4+ concentration in the leachates was very low compared to no3- and don concentration and often undetectable in three crop-years (2013-2015) when measurements are available. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” date    date of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 nh4 conc    nh4 concentration (milliGrams_N_Per_Liter) no3 conc    no3 concentration (milliGrams_N_Per_Liter)   9. Spreadsheet: correlations_don VS no3_doc VS don Description: Correlations of don and nitrate concentrations (milliGrams_N_Per_Liter); and doc (milliGrams_Per_Liter) and don concentrations (milliGrams_N_Per_Liter) in the leachate samples of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data of correlation of don and nitrate concentrations shown in Figure S4 a and doc and don concentrations shown in Figure S4 b. 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  2. Abstract Questions

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    Location

    Tropical rain forest vegetation, lowlands of Papua New Guinea.

    Methods

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    Results

    The spatial distribution of common species, and all stems collectively, was aggregated in the secondary forest plot but not different from random in the primary forest plot. Diameter and height were also strongly spatially auto‐correlated in the secondary forest plot but not in the primary forest plot. Conspecific aggregations were more common in the secondary forest plot. Finally, the secondary forest plot was characterized by the presence of diversity‐repelling species and lower diversity than the primary forest plot, where diversity‐accumulating species were present.

    Conclusions

    We attribute the weaker autocorrelation of tree size in the primary forest to the development of size hierarchies throughout the course of stand aging. The conspecific aggregation and low local diversity within the secondary forest plot are likely caused by dispersal limitation during a brief period of establishment after disturbance. The higher local diversity of the primary forest can be explained by the reduction of species aggregation through increased mortality of conspecifics. This is caused by strong intraspecific competition, supporting the spatial segregation hypothesis (interspecific spatial segregation).

     
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  3. Abstract

    Measuring and modelling the shape of tree stems is a fundamental component of forest inventory systems for both commercial and biological purposes. The change in diameter of the stem along its length (a.k.a. 'taper') is one of the most important and widely used means of predicting tree stem volume. Until recently, the options for obtaining accurate estimates of stem taper and developing stem taper models have been limited to measurements of felled trees or the use of optical dendrometers on standing live trees. Here, we tested both a tripod-mounted terrestrial laser scanner (TLS; a Focus 3D 120 of FARO Technologies, Inc., Lake Mary, FL, USA), and a mobile laser scanner (MLS; the ZEB1 of the GeoSLAM Ltd, Nottingham, UK) to measure tree diameters at various heights along the stem of 20 destructively harvested broadleaf and needleleaf species using the outer hull modelling method, for the purpose of developing individual-tree and species-specific taper models. Laser scanner specifications were a major factor determining stem taper measurement accuracy. The longer-range, low beam divergence TLS could estimate stem diameter to an average of 15.7 m above ground (about 79 per cent of the canopy height), while the shorter-range high beam divergence MLS could estimate an average of 11.5 m above ground (about 45 per cent of the canopy height). Stem taper error increased with respect to height above ground, with the TLS providing more consistent and reliable diameter measurements (root mean square error (RMSE) = 1.93 cm; 9.57 per cent) compared with the MLS (RMSE = 2.59 cm; 12.84 per cent), but both methods were nearly unbiased. We attribute ~60 per cent of the uncertainty in stem measurements to laser beam diameter and point density, showing positive and negative correlations, respectively. MLS was unable to converge on the two tested taper models but was found to be an efficient means of easily sampling diameters at breast height (DBH) and reconstructing stem maps in simple forest stands with trees greater than ~10 cm DBH. TLS provided precision stem diameter measurements that allowed for the creation of similar taper models for three out of the four study species. Future work should focus on evaluating MLS systems with improved specifications (e.g. beam divergence and range), since these instruments will likely lead to dramatic improvements in reliable estimates of forest inventory parameters, in line with the current TLS technology.

     
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  4. Abstract

    Tree death due to lightning influences tropical forest carbon cycling and tree community dynamics. However, the distribution of lightning damage among trees in forests remains poorly understood.

    We developed models to predict direct and secondary lightning damage to trees based on tree size, crown exposure and local forest structure. We parameterized these models using data on the locations of lightning strikes and censuses of tree damage in strike zones, combined with drone‐based maps of tree crowns and censuses of all trees within a 50‐ha forest dynamics plot on Barro Colorado Island, Panama.

    The likelihood of a direct strike to a tree increased with larger exposed crown area and higher relative canopy position (emergent > canopy >>> subcanopy), whereas the likelihood of secondary lightning damage increased with tree diameter and proximity to neighbouring trees. The predicted frequency of lightning damage in this mature forest was greater for tree species with larger average diameters.

    These patterns suggest that lightning influences forest structure and the global carbon budget by non‐randomly damaging large trees. Moreover, these models provide a framework for investigating the ecological and evolutionary consequences of lightning disturbance in tropical forests.

    Synthesis. Our findings indicate that the distribution of lightning damage is stochastic at large spatial grain and relatively deterministic at smaller spatial grain (<15 m). Lightning is more likely to directly strike taller trees with large crowns and secondarily damage large neighbouring trees that are closest to the directly struck tree. The results provide a framework for understanding how lightning can affect forest structure, forest dynamics and carbon cycling. The resulting lightning risk model will facilitate informed investigations into the effects of lightning in tropical forests.

     
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  5. Abstract Background

    Snags, standing dead trees, are becoming more abundant in forests as tree mortality rates continue to increase due to fire, drought, and bark beetles. Snags provide habitat for birds and small mammals, and when they fall to the ground, the resulting logs provide additional wildlife habitat and affect nutrient cycling, fuel loads, and fire behavior. Predicting how long snags will remain standing after fire is essential for managing habitat, understanding chemical cycling in forests, and modeling forest succession and fuels. Few studies, however, have quantified how fire changes snag fall dynamics.

    Results

    We compared post-fire fall rates of snags that existed pre-fire (n= 2013) and snags created during or after the fire (n= 8222), using 3 years of pre-fire and 5 years of post-fire data from an annually monitored, 25.6-ha spatially explicit plot in an old-growthAbies concolor–Pinus lambertianaforest in the Sierra Nevada, CA, USA. The plot burned at low to moderate severity in the Rim Fire of 2013. We used random forest models to (1) identify predictors of post-fire snag fall for pre-existing and new snags and (2) assess the influence of spatial neighborhood and local fire severity on snag fall after fire. Fall rates of pre-existing snags increased 3 years after fire. Five years after fire, pre-existing snags were twice as likely to fall as new snags. Pre-existing snags were most likely to persist 5 years after fire if they were > 50 cm in diameter, > 20 m tall, and charred on the bole to heights above 3.7 m. New snags were also more likely to persist 5 years after fire if they were > 20 m tall. Spatial neighborhood (e.g., tree density) and local fire severity (e.g., fire-caused crown injury) within 15 m of each snag barely improved predictions of snag fall after fire.

    Conclusions

    Land managers should expect fall rates of pre-existing snags to exceed fall rates of new snags within 5 years after fire, an important habitat consideration because pre-existing snags represent a wider range of size and decay classes.

     
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