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Canopy structure and topography jointly constrain the microclimate of human‐modified tropical landscapes

Jucker, Tommaso, Hardwick, Stephen R., Both, Sabine, Elias, Dafydd M.O., Ewers, Robert M., Milodowski, David T., Swinfield, Tom, Coomes, David A.·2018·Global Change Biology

Abstract

Local‐scale microclimatic conditions in forest understoreys play a key role in shaping the composition, diversity and function of these ecosystems. Consequently, understanding what drives variation in forest microclimate is critical to forecasting ecosystem responses to global change, particularly in the tropics where many species already operate close to their thermal limits and rapid land‐use transformation is profoundly altering local environments. Yet our ability to characterize forest microclimate at ecologically meaningful scales remains limited, as understorey conditions cannot be directly measured from outside the canopy. To address this challenge, we established a network of microclimate sensors across a land‐use intensity gradient spanning from old‐growth forests to oil‐palm plantations in Borneo. We then combined these observations with high‐resolution airborne laser scanning data to characterize how topography and canopy structure shape variation in microclimate both locally and across the landscape. In the processes, we generated high‐resolution microclimate surfaces spanning over 350 km 2 , which we used to explore the potential impacts of habitat degradation on forest regeneration under both current and future climate scenarios. We found that topography and vegetation structure were strong predictors of local microclimate, with elevation and terrain curvature primarily constraining daily mean temperatures and vapour pressure deficit (VPD), whereas canopy height had a clear dampening effect on microclimate extremes. This buffering effect was particularly pronounced on wind‐exposed slopes but tended to saturate once canopy height exceeded 20 m—suggesting that despite intensive logging, secondary forests remain largely thermally buffered. Nonetheless, at a landscape‐scale microclimate was highly heterogeneous, with maximum daily temperatures ranging between 24.2 and 37.2°C and VPD spanning two orders of magnitude. Based on this, we estimate that by the end of the century forest regeneration could be hampered in degraded secondary forests that characterize much of Borneo's lowlands if temperatures continue to rise following projected trends.

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FIGURE 1 Piecewise structural equation models relating variation in (a) annual mean temperature ( T mean) and vapour pressure deficit (VPDmean) and (b) mean maximum daily temperature ( T max) and vapour pressure deficit (VPDmax) to canopy structural and topographic metrics. The models also capture the effects of logging pressure and land -use intensity on maximum canopy height ( H max), as well as the covariation between H max and plant area index (PAI) -which was explicitly accounted for by taking the residuals of a regression relating PAI to H max as a predictor of temperature and VPD in the models. Dark grey arrows denote positive relationships, while light ones correspond to negative associations. The width of the arrows reflects the strength of the pathway and is proportional to the standardized path coefficient (reported in Appendix S3). A dashed arrow was used to represent the interaction effect between aspect and H max. Only pathways that were significant to p ≤ 0.05 are shown. R 2 values are reported for each endogenous variable. Additionally, panels (b) and (d) provide a breakdown of how much of the variance in T mean, VPDmean, T max and VPDmax was explained by topographic metrics, canopy structural metrics and (in the case of VPD) by temperature [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 1 Piecewise structural equation models relating variation in (a) annual mean temperature ( T mean) and vapour pressure deficit (VPDmean) and (b) mean maximum daily temperature ( T max) and vapour pressure deficit (VPDmax) to canopy structural and topographic metrics. The models also capture the effects of logging pressure and land -use intensity on maximum canopy height ( H max), as well as the covariation between H max and plant area index (PAI) -which was explicitly accounted for by taking the residuals of a regression relating PAI to H max as a predictor of temperature and VPD in the models. Dark grey arrows denote positive relationships, while light ones correspond to negative associations. The width of the arrows reflects the strength of the pathway and is proportional to the standardized path coefficient (reported in Appendix S3). A dashed arrow was used to represent the interaction effect between aspect and H max. Only pathways that were significant to p ≤ 0.05 are shown. R 2 values are reported for each endogenous variable. Additionally, panels (b) and (d) provide a breakdown of how much of the variance in T mean, VPDmean, T max and VPDmax was explained by topographic metrics, canopy structural metrics and (in the case of VPD) by temperature [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF
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FIGURE 4 Variation in (a) elevation, (b) maximum canopy height, ( c ) mean annual temperature, (d) mean maximum daily temperature, (e) mean annual vapour pressure deficit and (f) mean maximum daily vapour pressure deficit across the Stability of Altered Forest Ecosystems (SAFE) landscape at 50 × 50 m resolution. Panels (c -f) correspond to predicted values obtained from the regression models described in the main text and illustrated in Figure 1 [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 4 Variation in (a) elevation, (b) maximum canopy height, ( c ) mean annual temperature, (d) mean maximum daily temperature, (e) mean annual vapour pressure deficit and (f) mean maximum daily vapour pressure deficit across the Stability of Altered Forest Ecosystems (SAFE) landscape at 50 × 50 m resolution. Panels (c -f) correspond to predicted values obtained from the regression models described in the main text and illustrated in Figure 1 [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF
FIGURE 5 Areas of the SAFE landscape where mean maximum daily vapour pressure deficit (VPDmax) is predicted to exceed hypothesized transpiration thresholds (12 and 15 hPa) for tree seedlings under current (dark red) and future climate conditions (light red). Results for two different IPCC representative concentration pathways (RCP) are shown: the RCP4.5 emission mitigation scenario (a and b) and the RCP8.5 business -as -usual scenario (c and d). The percentage of 50 × 50 m pixels exceeding VPDmax thresholds is given in brackets for each scenario. The black contour lines mark the current extent of oil -palm plantations within the SAFE landscape, as well as areas dominated by short scrubby vegetation that develops in the aftermath of logging and of bare ground [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 5 Areas of the SAFE landscape where mean maximum daily vapour pressure deficit (VPDmax) is predicted to exceed hypothesized transpiration thresholds (12 and 15 hPa) for tree seedlings under current (dark red) and future climate conditions (light red). Results for two different IPCC representative concentration pathways (RCP) are shown: the RCP4.5 emission mitigation scenario (a and b) and the RCP8.5 business -as -usual scenario (c and d). The percentage of 50 × 50 m pixels exceeding VPDmax thresholds is given in brackets for each scenario. The black contour lines mark the current extent of oil -palm plantations within the SAFE landscape, as well as areas dominated by short scrubby vegetation that develops in the aftermath of logging and of bare ground [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF
FIGURE 6 Systematic differences between ALS -derived understorey microclimatic conditions and WorldClim2 gridded climate surfaces. Panel (a) shows the probability density distribution of mean annual temperature values predicted from ALS -derived canopy structural and topographic metrics across the Stability of Altered Forest Ecosystems (SAFE) landscape (as mapped in Figure 4c) and those obtained from the WorldClim2 database for the same area. Panel (b) illustrates how -for a given point within the SAFE landscape -the difference between these two temperature surfaces changes according to the maximum canopy height of that pixel. Large black circles correspond to mean difference values (± 1 standard deviation) calculated at 2 m height intervals. Panels (c and d) show these same patterns for maximum temperature [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 6 Systematic differences between ALS -derived understorey microclimatic conditions and WorldClim2 gridded climate surfaces. Panel (a) shows the probability density distribution of mean annual temperature values predicted from ALS -derived canopy structural and topographic metrics across the Stability of Altered Forest Ecosystems (SAFE) landscape (as mapped in Figure 4c) and those obtained from the WorldClim2 database for the same area. Panel (b) illustrates how -for a given point within the SAFE landscape -the difference between these two temperature surfaces changes according to the maximum canopy height of that pixel. Large black circles correspond to mean difference values (± 1 standard deviation) calculated at 2 m height intervals. Panels (c and d) show these same patterns for maximum temperature [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF

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Cites

Research works printed but not linked: 73

  1. 10.1016/j.jag.2017.10.009 · DOI
  2. 10.2307/1931468 · DOI
  3. 10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2 · DOI
  4. 10.1002/ece3.3460 · DOI
  5. 10.1038/nclimate3071 · DOI
  6. 10.1016/bs.aecr.2017.12.005 · DOI
  7. 10.1371/journal.pone.0069679 · DOI
  8. 10.1017/S026646740000866X · DOI
  9. 10.1073/pnas.1605365113 · DOI
  10. 10.1111/j.1523-1739.2009.01338.x · DOI
  11. 10.2307/1313612 · DOI
  12. Clarke A., 2017, Principles of thermal ecology: Temperature, energy and life, pp.
  13. Coomes D. A., 2018, Airborne laser scanning of natural forests in New Zealand reveals the influences of wind on forest carbon, Forest Ecosystems, 5, 1, 10.1186/s40663-017-0119-6 · DOI
  14. 10.1073/pnas.1311190110 · DOI
  15. 10.1126/science.351.6270.234-a · DOI
  16. 10.1111/1365-2664.13023 · DOI
  17. 10.1371/journal.pone.0076296 · DOI
  18. 10.1111/j.1365-2486.2010.02263.x · DOI
  19. 10.1029/2007JG000632 · DOI
  20. 10.1038/s41467-017-02810-8 · DOI
  21. 10.1186/s12898-017-0141-0 · DOI
  22. 10.1371/journal.pone.0058093 · DOI
  23. 10.1038/ncomms7836 · DOI
  24. 10.1098/rstb.2011.0049 · DOI · Paper page
  25. 10.1371/journal.pone.0105541 · DOI
  26. 10.1002/joc.5086 · DOI · Paper page
  27. 10.1126/sciadv.1501392 · DOI
  28. 10.1038/srep32017 · DOI
  29. 10.1371/journal.pone.0101654 · DOI
  30. 10.1088/1748-9326/11/11/114022 · DOI
  31. 10.18637/jss.v017.i01 · DOI · Paper page
  32. Hardwick S. R., 2015, Interactions between vegetation and microclimate in a heterogeneous tropical landscape, Imperial College London, 138, pp
  33. 10.1016/j.agrformet.2014.11.010 · DOI
  34. Hijmans R. J., 2016, raster: Geographic data analysis and modeling. R package version, 2.5–8.
  35. 10.1111/j.1744-7429.1999.tb00135.x · DOI
  36. IPCC, 2014, IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Pachauri RK, Meyer LA), 151
  37. Jones C. D., 2003, Uncertainty in climate‐carbon‐cycle projections associated with the sensitivity of soil respiration to temperature, Tellus Series B, 55, 642
  38. 10.5194/bg-15-3811-2018 · DOI
  39. 10.1111/ele.12964 · DOI
  40. 10.1111/gcb.12750 · DOI
  41. 10.14358/PERS.80.9.863 · DOI · Paper page
  42. 10.1111/j.1365-2435.2008.01514.x · DOI
  43. 10.1016/j.agee.2015.01.027 · DOI
  44. 10.1111/2041-210X.12512 · DOI
  45. 10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2 · DOI
  46. 10.1111/ecog.02788 · DOI
  47. Lindeman R. H., 1980, Introduction to bivariate and multivariate analysis, 444
  48. 10.1098/rspb.2013.2236 · DOI
  49. 10.1088/1748-9326/aaa4ff · DOI · Paper page
  50. 10.1111/nph.15027 · DOI · Paper page
  51. 10.1029/2009JD013493 · DOI · Paper page
  52. 10.1093/treephys/25.10.1283 · DOI
  53. 10.1016/j.rse.2016.01.014 · DOI
  54. 10.1111/gcb.12257 · DOI
  55. R Core Development Team., 2016, R: A language and environment for statistical computing
  56. 10.1111/gcb.14068 · DOI
  57. 10.1111/gcb.13973 · DOI
  58. 10.1111/btp.12355 · DOI
  59. 10.1016/j.biocon.2015.02.018 · DOI
  60. 10.1111/gcb.13914 · DOI
  61. 10.1002/ece3.3262 · DOI
  62. 10.1016/j.foreco.2005.12.019 · DOI
  63. 10.2307/2259061 · DOI
  64. 10.1111/j.1461-0248.2012.01864.x · DOI
  65. 10.1002/ecs2.1797 · DOI
  66. 10.1088/1748-9326/aa6f97 · DOI
  67. 10.1038/ncomms15531 · DOI
  68. 10.1007/s13595-017-0628-z · DOI
  69. 10.1098/rstb.1999.0528 · DOI
  70. 10.1093/treephys/tpq015 · DOI
  71. 10.1111/1365-2435.12351 · DOI
  72. 10.1111/nph.12321 · DOI
  73. 10.1016/j.rse.2012.02.001 · DOI

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Metadata

Authors
Jucker, T., Hardwick, S. R., Both, S., Elias, D. M. O., Ewers, R. M., Milodowski, D. T., Swinfield, T., & Coomes, D. A.
Affiliations
  • Forest Ecology and Conservation group, Department of Plant Sciences University of Cambridge Cambridge UK
  • Blackett Laboratory, Department of Physics Imperial College London London UK
  • School of Environmental and Rural Science University of New England Armidale NSW Australia
  • Lancaster Environment Centre Lancaster University Lancaster UK
  • Imperial College London Ascot UK
  • School of GeoSciences University of Edinburgh Edinburgh UK
Venue
Global Change Biology
Publisher
Wiley
Published
2018-09-23
Volume
24
Issue
11
Pages
5243-5258
ISSN
  • 1354-1013
  • 1365-2486
Cited by
281
References
3
Language
en
Funders
Natural Environment Research Council (NE/K016377/1)

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