Patterns of tropical forest understory temperatures.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
23 Jan 2024
23 Jan 2024
Historique:
received:
28
06
2023
accepted:
02
01
2024
medline:
24
1
2024
pubmed:
24
1
2024
entrez:
23
1
2024
Statut:
epublish
Résumé
Temperature is a fundamental driver of species distribution and ecosystem functioning. Yet, our knowledge of the microclimatic conditions experienced by organisms inside tropical forests remains limited. This is because ecological studies often rely on coarse-gridded temperature estimates representing the conditions at 2 m height in an open-air environment (i.e., macroclimate). In this study, we present a high-resolution pantropical estimate of near-ground (15 cm above the surface) temperatures inside forests. We quantify diurnal and seasonal variability, thus revealing both spatial and temporal microclimate patterns. We find that on average, understory near-ground temperatures are 1.6 °C cooler than the open-air temperatures. The diurnal temperature range is on average 1.7 °C lower inside the forests, in comparison to open-air conditions. More importantly, we demonstrate a substantial spatial variability in the microclimate characteristics of tropical forests. This variability is regulated by a combination of large-scale climate conditions, vegetation structure and topography, and hence could not be captured by existing macroclimate grids. Our results thus contribute to quantifying the actual thermal ranges experienced by organisms inside tropical forests and provide new insights into how these limits may be affected by climate change and ecosystem disturbances.
Identifiants
pubmed: 38263406
doi: 10.1038/s41467-024-44734-0
pii: 10.1038/s41467-024-44734-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
549Subventions
Organisme : Academy of Finland (Suomen Akatemia)
ID : 319905
Organisme : Academy of Finland (Suomen Akatemia)
ID : 345472
Informations de copyright
© 2024. The Author(s).
Références
Hamilton, A. J. et al. Quantifying uncertainty in estimation of tropical arthropod species richness. Am. Nat. 176, 90–95 (2010).
pubmed: 20455708
doi: 10.1086/652998
Pillay, R. et al. Tropical forests are home to over half of the world’s vertebrate species. Front. Ecol. Environ. 20, 10–15 (2022).
pubmed: 35873358
doi: 10.1002/fee.2420
Raven, P. H. et al. The distribution of biodiversity richness in the tropics. Sci. Adv. 6, eabc6228 (2020).
Bennett, J. M. et al. The evolution of critical thermal limits of life on Earth. Nat. Commun. 12, 1198 (2021).
pubmed: 33608528
pmcid: 7895938
doi: 10.1038/s41467-021-21263-8
Sullivan, M. J. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368, 869–874 (2020).
pubmed: 32439789
doi: 10.1126/science.aaw7578
Vázquez, D. P., Gianoli, E., Morris, W. F. & Bozinovic, F. Ecological and evolutionary impacts of changing climatic variability. Biol. Rev. Camb. Philos. Soc. 92, 22–42 (2017).
Weiskopf, S. R. et al. Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Sci. Total Environ. 733, 137782 (2020).
Woodward, F. I. The impact of low temperatures in controlling the geographical distribution of plants. Philos. Trans. R. Soc. Lond. B, Biol. Sci. 326, 585–593 (1990).
doi: 10.1098/rstb.1990.0033
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).
pubmed: 32409476
doi: 10.1126/science.aba6880
Brown, J. H. Why are there so many species in the tropics? J. Biogeogr. 41, 8–22 (2014).
pubmed: 25684838
doi: 10.1111/jbi.12228
Cerezer, F. O. et al. Latitudinal gradient of termite diversity indicates higher diversification and narrower thermal niches in the tropics. Glob. Ecol. Biogeogr. 29, 1967–1977 (2020).
doi: 10.1111/geb.13167
Löffler, J. & Pape, R. Thermal niche predictors of alpine plant species. Ecology 101, e02891 (2020).
pubmed: 31509230
doi: 10.1002/ecy.2891
De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).
pubmed: 30936433
doi: 10.1038/s41559-019-0842-1
Haesen, S. et al. ForestTemp–Sub‐canopy microclimate temperatures of European forests. Glob. Change Biol. 27, 6307–6319 (2021).
doi: 10.1111/gcb.15892
De Frenne, P. et al. Forest microclimates and climate change: importance, drivers and future research agenda. Glob. Change Biol. 27, 2279–2297 (2021).
doi: 10.1111/gcb.15569
Kašpar, V. et al. Temperature buffering in temperate forests: comparing microclimate models based on ground measurements with active and passive remote sensing. Remote Sens. Environ. 263, 112522 (2021).
Li, Y. et al. Local cooling and warming effects of forests based on satellite observations. Nat. Commun. 6, 6603 (2015).
pubmed: 25824529
doi: 10.1038/ncomms7603
Jucker, T. et al. Canopy structure and topography jointly constrain the microclimate of human‐modified tropical landscapes. Glob. Change Biol. 24, 5243–5258 (2018).
doi: 10.1111/gcb.14415
Macek, M., Kopecký, M. & Wild, J. Maximum air temperature controlled by landscape topography affects plant species composition in temperate forests. Landsc. Ecol. 34, 2541–2556 (2019).
doi: 10.1007/s10980-019-00903-x
Dobrowski, S. Z. A climatic basis for microrefugia: the influence of terrain on climate. Glob. Change Biol. 17, 1022–1035 (2011).
doi: 10.1111/j.1365-2486.2010.02263.x
Maclean, I. M., Mosedale, J. R. & Bennie, J. J. Microclima: an r package for modelling meso‐and microclimate. Methods Ecol. Evol. 10, 280–290 (2019).
doi: 10.1111/2041-210X.13093
Meineri, E. & Hylander, K. Fine‐grain, large‐domain climate models based on climate station and comprehensive topographic information improve microrefugia detection. Ecography 40, 1003–1013 (2017).
doi: 10.1111/ecog.02494
Pastore, M. A., Classen, A. T., D’Amato, A. W., Foster, J. R. & Adair, E. C. Cold‐air pools as microrefugia for ecosystem functions in the face of climate change. Ecology 103, e3717 (2022).
pubmed: 35388477
doi: 10.1002/ecy.3717
Camargo, J. L. C. & Kapos, V. Complex edge effects on soil moisture and microclimate in central Amazonian forest. J. Trop. Ecol. 11, 205–221 (1995).
doi: 10.1017/S026646740000866X
Geiger, R. & Bouyoucos, G. J. The climate near the ground. Am. J. Phys. 19, 192–192 (1951).
doi: 10.1119/1.1932763
Hay, J. E. & McKay, D. C. Estimating solar irradiance on inclined surfaces: a review and assessment of methodologies. Int. J. Sol. Energy 3, 203–240 (1985).
doi: 10.1080/01425918508914395
Jucker, T. et al. A research agenda for microclimate ecology in human-modified tropical forests. Front. For. Glob. Change 2, 92 (2020).
doi: 10.3389/ffgc.2019.00092
Lenoir, J., Hattab, T. & Pierre, G. Climatic microrefugia under anthropogenic climate change: implications for species redistribution. Ecography 40, 253–266 (2017).
doi: 10.1111/ecog.02788
Lembrechts, J. J., Nijs, I. & Lenoir, J. Incorporating microclimate into species distribution models. Ecography 42, 1267–1279 (2019).
doi: 10.1111/ecog.03947
Lembrechts, J. J. et al. SoilTemp: a global database of near‐surface temperature. Glob. Change Biol. 26, 6616–6629 (2020).
doi: 10.1111/gcb.15123
Haesen, S. et al. ForestClim—Bioclimatic variables for microclimate temperatures of European forests. Glob. Chang Biol. 29, 2886–2892 (2023).
Lawrence, D., Coe, M., Walker, W., Verchot, L. & Vandecar, K. The unseen effects of deforestation: biophysical effects on climate. Front. For. Glob. Change 5, 49 (2022).
doi: 10.3389/ffgc.2022.756115
Pincebourde, S., Murdock, C. C., Vickers, M. & Sears, M. W. Fine-scale microclimatic variation can shape the responses of organisms to global change in both natural and urban environments. Integr. Comp. Biol. 56, 45–61 (2016).
pubmed: 27107292
doi: 10.1093/icb/icw016
De Frenne, P. & Verheyen, K. Weather stations lack forest data. Science 351, 234–234 (2016).
pubmed: 26816367
doi: 10.1126/science.351.6270.234-a
Faye, E., Herrera, M., Bellomo, L., Silvain, J.-F. & Dangles, O. Strong discrepancies between local temperature mapping and interpolated climatic grids in tropical mountainous agricultural landscapes. PLoS ONE 9, e105541 (2014).
pubmed: 25141212
pmcid: 4139370
doi: 10.1371/journal.pone.0105541
Houspanossian, J., Nosetto, M. & Jobbágy, E. G. Radiation budget changes with dry forest clearing in temperate Argentina. Glob. Change Biol. 19, 1211–1222 (2013).
doi: 10.1111/gcb.12121
Meier, R., Davin, E. L., Swenson, S. C., Lawrence, D. M. & Schwaab, J. Biomass heat storage dampens diurnal temperature variations in forests. Environ. Res. Lett. 14, 084026 (2019).
doi: 10.1088/1748-9326/ab2b4e
Dawson, T. E. et al. Nighttime transpiration in woody plants from contrasting ecosystems. Tree Physiol. 27, 561–575 (2007).
pubmed: 17241998
doi: 10.1093/treephys/27.4.561
Jordan, D. & Smith, W. Energy balance analysis of nighttime leaf temperatures and frost formation in a subalpine environment. Agric. For. Meteorol. 71, 359–372 (1994).
doi: 10.1016/0168-1923(94)90020-5
Rosado, B. H., Oliveira, R. S., Joly, C. A., Aidar, M. P. & Burgess, S. S. Diversity in nighttime transpiration behavior of woody species of the Atlantic Rain Forest, Brazil. Agric. For. Meteorol. 158, 13–20 (2012).
doi: 10.1016/j.agrformet.2012.02.002
Zeppel, M. J. et al. Interactive effects of elevated CO
pubmed: 21616926
doi: 10.1093/treephys/tpr024
Lejeune, Q., Davin, E. L., Guillod, B. P. & Seneviratne, S. I. Influence of Amazonian deforestation on the future evolution of regional surface fluxes, circulation, surface temperature and precipitation. Clim. Dyn. 44, 2769–2786 (2015).
doi: 10.1007/s00382-014-2203-8
Mildrexler, D. J., Zhao, M. & Running, S. W. A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests. J. Geophys. Res. Biogeosci. 116, G03025 (2011).
Baker, J. C. A. & Spracklen, D. V. Climate benefits of intact Amazon forests and the biophysical consequences of disturbance. Front. For. Glob. Change 2, 47 (2019).
Iida, S. I. et al. Evapotranspiration from the understory of a tropical dry deciduous forest in Cambodia. Agric. For. Meteorol. 295, 108170 (2020).
Christina, M. et al. Importance of deep water uptake in tropical eucalypt forest. Funct. Ecol. 31, 509–519 (2017).
doi: 10.1111/1365-2435.12727
Germon, A., Laclau, J.-P., Robin, A. & Jourdan, C. Deep fine roots in forest ecosystems: Why dig deeper? For. Ecol. Manag. 466, 118135 (2020).
doi: 10.1016/j.foreco.2020.118135
Maeda, E. E. et al. Evapotranspiration seasonality across the Amazon Basin. Earth Syst. Dyn. 8, 439–454 (2017).
doi: 10.5194/esd-8-439-2017
Hardwick, S. R. et al. The relationship between leaf area index and microclimate in tropical forest and oil palm plantation: Forest disturbance drives changes in microclimate. Agric. For. Meteorol. 201, 187–195 (2015).
pubmed: 28148995
pmcid: 5268355
doi: 10.1016/j.agrformet.2014.11.010
Laurance, W. F. et al. The fate of Amazonian forest fragments: a 32-year investigation. Biol. Conserv. 144, 56–67 (2011).
doi: 10.1016/j.biocon.2010.09.021
Maeda, E. E. et al. Shifts in structural diversity of Amazonian forest edges detected using terrestrial laser scanning. Remote Sens. Environ. 271, 112895 (2022).
Ewers, R. M. et al. A large-scale forest fragmentation experiment: the Stability of Altered Forest Ecosystems Project. Philos. Trans. R. Soc. B: Biol. Sci. 366, 3292–3302 (2011).
doi: 10.1098/rstb.2011.0049
Pacifici, M. et al. Assessing species vulnerability to climate change. Nat. Clim. Change 5, 215–224 (2015).
doi: 10.1038/nclimate2448
Vinod, N. et al. Thermal sensitivity across forest vertical profiles: patterns, mechanisms, and ecological implications. N. Phytol. 237, 22–47 (2023).
doi: 10.1111/nph.18539
De Kort, H. et al. Pre‐adaptation to climate change through topography‐driven phenotypic plasticity. J. Ecol. 108, 1465–1474 (2020).
doi: 10.1111/1365-2745.13365
Graae, B. J. et al. Stay or go–how topographic complexity influences alpine plant population and community responses to climate change. Perspect. Plant Ecol. Evol. Syst. 30, 41–50 (2018).
doi: 10.1016/j.ppees.2017.09.008
Chen, J. et al. Microclimate in forest ecosystem and landscape ecology: variations in local climate can be used to monitor and compare the effects of different management regimes. BioScience 49, 288–297 (1999).
doi: 10.2307/1313612
Frey, S. J., Hadley, A. S. & Betts, M. G. Microclimate predicts within‐season distribution dynamics of montane forest birds. Divers. Distrib. 22, 944–959 (2016).
doi: 10.1111/ddi.12456
King, R. B. Temperature‐induced multi‐species cohort effects in sympatric snakes. Ecol. Evol. 12, e8601 (2022).
Miller, D. A. et al. Quantifying climate sensitivity and climate-driven change in North American amphibian communities. Nat. Commun. 9, 3926 (2018).
pubmed: 30254220
pmcid: 6156563
doi: 10.1038/s41467-018-06157-6
Schurr, F. M. et al. How to understand species’ niches and range dynamics: a demographic research agenda for biogeography. J. Biogeogr. 39, 2146–2162 (2012).
doi: 10.1111/j.1365-2699.2012.02737.x
Wild, J. et al. Climate at ecologically relevant scales: a new temperature and soil moisture logger for long-term microclimate measurement. Agric. For. Meteorol. 268, 40–47 (2019).
doi: 10.1016/j.agrformet.2018.12.018
Zellweger, F., De Frenne, P., Lenoir, J., Rocchini, D. & Coomes, D. Advances in microclimate ecology arising from remote sensing. Trends Ecol. Evol. 34, 327–341 (2019).
pubmed: 30651180
doi: 10.1016/j.tree.2018.12.012
Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).
CGLS. Copernicus Global Land Service. Providing Bio-Geophysical Products of Global Land Surface. https://land.copernicus.eu/global/themes/vegetation (2022).
Sexton, J. O. et al. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. Int. J. Digit. Earth 6, 427–448 (2013).
doi: 10.1080/17538947.2013.786146
Wu, J., He, J. & Christakos, G. Quantitative Analysis and Modeling of Earth and Environmental Data: Space-time and Spacetime Data Considerations (Elsevier, 2021).
Dale, M. R. & Fortin, M.-J. Spatial Analysis: A Guide for Ecologists (Cambridge University Press, 2014).
Cressie, N. The origins of kriging. Math. Geol. 22, 239–252 (1990).
doi: 10.1007/BF00889887
Cressie, N. Statistics for Spatial Data (John Wiley & Sons, 2015).
Lamorey, G. & Jacobson, E. Estimation of semivariogram parameters and evaluation of the effects of data sparsity. Math. Geol. 27, 327–358 (1995).
doi: 10.1007/BF02084606
Ij, H. Statistics versus machine learning. Nat. Methods 15, 233 (2018).
doi: 10.1038/nmeth.4642
Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).
doi: 10.1007/BF00058655
Liashchynskyi, P. & Liashchynskyi, P. Grid search, random search, genetic algorithm: a big comparison for NAS. Preprint at https://arxiv.org/abs/1912.06059 (2019).
Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. & Gräler, B. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ 6, e5518 (2018).
pubmed: 30186691
pmcid: 6119462
doi: 10.7717/peerj.5518
van den Hoogen, J. et al. A geospatial mapping pipeline for ecologists. Preprint at BioRxiv https://doi.org/10.1101/2021.07.07.451145 (2021).
Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).
pubmed: 32917875
pmcid: 7486894
doi: 10.1038/s41467-020-18321-y
Ismaeel, A. & Maeda, E. E. Tropical Forest Microclimate Maps. national Finnish Fairdata services ( https://www.fairdata.fi/en/ ). https://doi.org/10.23729/dd3de08e-39a1-46b0-b28a-7bc577b6c914 (2023).