Skip to main content
SpringerLink
Log in

Predicting Forest Microclimate in Heterogeneous Landscapes

  • Published:
Ecosystems Aims and scope Submit manuscript

Abstract

Forest microclimate plays an integral role in ecosystem processes, yet a predictive understanding of its spatial and temporal variability in heterogeneous landscapes is largely lacking. In this study, we used regression kriging (RK) to analyze the degree to which physiographic versus ecological variables influence spatio-temporal variation in understory microclimate conditions. We monitored understory temperature in 200 forest plots within a 274 km2 environmentally heterogeneous region in northern California (0.55 obs/km2). For each plot location, we measured four physiographic influences (elevation, coastal proximity, potential solar radiation, topographic wetness index) and three ecological drivers (forest patch size, proximity to forest edge, tree abundance). Temperature observations were aggregated to three time scales (hourly, daily, and monthly) to examine temporal variability in microclimate dynamics and its effect on spatial prediction. The obtained prediction models included both physiographic and vegetative effects, although the relative importance of individual effects varied greatly between the different models. Across time scales, elevation and coastal proximity had the most consistent physiographic effects on temperature, followed by the vegetative effects of forest patch size and distance to forest edge. RK captured significantly more landscape-scale variability in understory temperature than a regression-only approach with considerably better model performance at hourly and daily time scales than at a monthly scale. Using varied sampling density scenarios our results also suggest that predictive accuracy drops considerably at densities less than 0.34 obs/km2. This research illustrates how geospatial and statistical modeling can be used to distinguish physiographic versus ecological effects on microclimate dynamics and elucidates the spatial and temporal scales that these processes operate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

References

  • Asbjornsen H, Vogt KA, Ashton MS. 2004a. Synergistic responses of oak, pine and shrub seedlings to edge environments and drought in a fragmented tropical highland oak forest, Oaxaca, Mexico. For Ecol Manag 192:313–34.

    Article  Google Scholar 

  • Asbjornsen H, Ashton MS, Vogt DJ, Palacios S. 2004b. Effects of habitat fragmentation on the buffering capacity of edge environments in a seasonally dry tropical oak forest ecosystem in Oaxaca, Mexico. Agric Ecosyst Environ 103:481–95.

    Article  Google Scholar 

  • Ashcroft MB, Chisholm LA, French KO. 2008. The effect of exposure on landscape scale soil surface temperatures and species distribution models. Landsc Ecol 23:211–25.

    Article  Google Scholar 

  • Band LE, Peterson DL, Running SW, Coughlan R, Lammers J, Dungan J, Nemani RR. 1992. Forest ecosystem processes at the watershed scale: basis for distribution simulation. Ecol Model 56:171–96.

    Article  Google Scholar 

  • Bailey RG. 1980. Description of the ecoregions of the United States. : U.S. Department of Agriculture, Miscellaneous Publication No. 1391. 77p

  • Barbour MG, Billings WD, Eds. 2000. North American terrestrial vegetation. Cambridge: Cambridge University Press.

    Google Scholar 

  • Beers TW, Dress PE, Wensel LC. 1966. Aspect transformation in site productivity research. J For 64:691–2.

    Google Scholar 

  • Beven KJ, Kirkby MJ. 1979. A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69.

    Article  Google Scholar 

  • Bolstad PV, Swift L, Collins F, Regniere J. 1998. Measured and predicted air temperatures at basin to regional scales in the southern Appalachian mountains. Agric For Meteorol 91(3–4):161–76.

    Article  Google Scholar 

  • Burrough PA, McDonnell RA. 1998. Principles of geographic information systems. Oxford: Oxford University Press. p 333

    Google Scholar 

  • Chen J, Franklin JF, Spies TA. 1993. Contrasting microclimates among clearcut, edge, and interior of old growth Douglas-fir forest. Agric For Meteorol 63:219–37.

    Article  Google Scholar 

  • Chen J, Franklin JF, Spies TA. 1995. Growing season microclimatic gradients from clearcut edges into old growth Douglas-fir forest. Ecol Appl 5:74–86.

    Article  Google Scholar 

  • Chen J, Franklin JF. 1997. Growing season microclimate variability within an old-growth Douglas-fir forest. Clim Res 8:21–34.

    Article  Google Scholar 

  • Chen J, Saunders SC, Crow TR, Naiman RJ, Brosofske KD, Mroz GD, Brookshire BL, Franklin JF. 1999. Microclimate in forest ecosystem and landscape ecology. Bioscience 49:288–97.

    Article  Google Scholar 

  • Clinton BD. 2003. Light, temperature, and soil moisture responses to elevation, evergreen understory, and small, canopy gaps in the southern Appalachians. For Ecol Manag 186(1–3):243–55.

    Article  Google Scholar 

  • Chuanyan Z, Zhongren N, Guodonga C. 2005. Methods for modeling of temporal and spatial distribution of air temperature at landscape scale in the southern Qilian mountains, China. Ecol Model 189:209–20.

    Article  Google Scholar 

  • Chung U, Seo HH, Hwang KH, Hwang BS, Choi J, Lee JT, Yun JI. 2006. Minimum temperature mapping over complex terrain by estimating cold air accumulation potential. Agric For Meteorol 137:15–24.

    Article  Google Scholar 

  • Daly C, Gibson WP, Taylor GH, Johnson GL, Pasteris P. 2002. A knowledge-based approach to the statistical mapping of climate. Clim Res 22:99–113.

    Article  Google Scholar 

  • Della-Bianca L, Dils RE. 1960. Some effects of stand density in a red pine plantation on soil moisture, soil temperature, and radial growth. J For 58(5):373–7.

    Google Scholar 

  • Dubayah R. 1994. Modeling a solar radiation topoclimatology for the Rio Grande River Basin. J Veg Sci 5:627–40.

    Article  Google Scholar 

  • Fridley JD. 2009. Downscaling climate over complex terrain: high fine-scale spatial variation of near-ground temperatures in a montane forested landscape (Great Smoky Mountains, USA). J Appl Meteorol Climatol 48:1033–49.

    Article  Google Scholar 

  • Gehlhausen SM, Schwartz MW, Augspurger CK. 2000. Vegetation and microclimatic edge effects in two mixed-mesophytic forest fragments. Vegetatio 147(1):21–35.

    Article  Google Scholar 

  • Geiger R. 1965. The climate near the ground. Cambridge: Harvard University Press. p 611

    Google Scholar 

  • Graham H. 2003. Confronting multicollinearity in ecological multiple regression. Ecology 84(11):2809–15.

    Article  Google Scholar 

  • Godefroid S, Rucquoij S, Koedam N. 2006. Spatial variability of summer microclimates and plant species response along transects within clearcuts in a beech forest. Plant Ecol 185(1):107–21.

    Article  Google Scholar 

  • Goovaerts P. 1997. Geostatistics for natural resources evaluation. Oxford: Oxford University Press. p 483.

    Google Scholar 

  • Harlow C, Burke E, Scott RL, Shuttleworth WJ, Brown C, Petti J. 2004. Derivation of temperature lapse rates in semi-arid southeastern Arizona. Hydrol Earth Syst Sci 8(6):1179–85.

    Google Scholar 

  • Hengl T, Heuvelink G, Rossiter DG. 2007. About regression-kriging: from equations to case studies. Comput Geosci 33:1301–15.

    Article  Google Scholar 

  • Klaasen W, van Bruegel PB, Moors EJ, Nieveen JP. 2002. Increased heat fluxes near a forest edge. Theor Appl Climatol 72:231–43.

    Article  Google Scholar 

  • Körner C. 2007. The use of ‘altitude’ in ecological research. Trends Ecol Evol 22(11):569–74.

    Article  PubMed  Google Scholar 

  • Krause P, Boyle DP, Bäse F. 2005. Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5:89–97.

    Google Scholar 

  • Lookingbill T, Urban D. 2003. Spatial estimation of air temperature differences for landscape-scale studies in montane environments. Agric For Meteorol 114:141–51.

    Article  Google Scholar 

  • Lundquist JD, Cayan DR. 2007. Surface temperature patterns in complex terrain: daily variations and long-term change in the central Sierra Nevada, California. J Geophys Res Atmospheres 112(11):D11124. doi:10.1029/2006JD007561.

    Article  Google Scholar 

  • Mahrt L. 2006. Variation of surface air temperature in complex terrain. J Appl Meteorol 45:1481–93.

    Article  Google Scholar 

  • Matejka F, Janous D, Hurtalova T, Roznovsky J. 2004. Effects of thinning on microclimate of a young spruce forest. Ekologia Bratislava 23:30–8.

    Google Scholar 

  • McDonald RI, Urban DL. 2005. Forest edges and tree growth rates in the North Carolina Piedmont. Ecology 85:2258–66.

    Article  Google Scholar 

  • Meentemeyer V. 1978. Macroclimate and lignin control of litter decomposition rates. Ecology 59:465–72.

    Article  CAS  Google Scholar 

  • Meentemeyer RK, Rank NE, Anaker BL, Rizzo DM, Cushman JH. 2008. Influence of land-cover change on the spread of an invasive forest pathogen. Ecol Appl 18(1):159–71.

    Article  PubMed  Google Scholar 

  • Minasny B, McBratney AB, Whelan BM. 2005. VESPER version 1.62. Australian Centre for Precision Agriculture, McMillan Building A05, The University of Sydney, NSW 2006. (http://www.usyd.edu.au/su/agric/acpa).

  • Nash JE, Sutcliffe JV. 1970. River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–90.

    Article  Google Scholar 

  • Newmark W. 2005. Diel variation in the difference in air temperature between the forest edge and interior in the Usambara Mountains, Tanzania. Afr J Ecol 43:177–80.

    Article  Google Scholar 

  • Odeh IOA, McBratney AB, Chittleborough DJ. 1995. Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging. Geoderma 67(3):215–26.

    Article  Google Scholar 

  • Pepin N, Kidd D. 2006. Spatial temperature variation in the Eastern Pyrenees. Weather 61:1–11.

    Article  Google Scholar 

  • Pohlman CL, Turtonand SM, Goosem M. 2009. Temporal variation in microclimatic edge effects near powerlines, highways and streams in Australian tropical rainforest. Agric For Meteorol 149(1):84–95.

    Article  Google Scholar 

  • Ritter E, Dalsgaard L, Eirthorn KS. 2005. Light, temperature and soil moisture regimes following gap formation in a semi-natural beech-dominated forest in Denmark. For Ecol Manag 206(1–3):15–33.

    Article  Google Scholar 

  • Saunders SC, Chen J, Crow TR, Brosofske KD. 1998. Hierarchical relationships between landscape structure and temperature in a managed forest landscape. Landsc Ecol 13:381–95.

    Article  Google Scholar 

  • Thornton PE, Running SW, White MA. 1997. Generating surfaces of daily meteorological variables over large regions of complex terrain. J Hydrol 190:214–51.

    Article  Google Scholar 

  • Turner MG, Chapin FS. 2005. Causes and consequences of spatial heterogeneity in ecosystem function. In: Lovett GM, Jones CG, Turner MG, Weathers KC, Eds. Ecosystem function in heterogeneous landscapes. New York: Springer. p 9–30.

    Chapter  Google Scholar 

  • Waring RH, Running SW. 1998. Forest ecosystems: analysis at multiple scales. 2nd edn. San Diego: Academic Press. p 370.

    Google Scholar 

  • Webster R, Oliver MA. 2007. Geostatistics for environmental scientists. Chichester: John Wiley & Sons. p 330.

    Book  Google Scholar 

  • Xu M, Chen J, Brookshire BL. 1997. Temperature and its variability in the oak forests of Southeast Missouri’s Ozarks. Clim Res 8(3):209–23.

    Article  Google Scholar 

  • Xu M, Chen J, Qi Y. 2002. Growing-season temperature and soil moisture along a 10 km transect across a forested landscape. Clim Res 22:57–72.

    Article  Google Scholar 

  • Xu M, Qi Y, Chen JQ, Song B. 2004. Scale-dependent relationships between landscape structure and microclimate. Plant Ecol 173(1):39–57.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Agronomy, University of Cordoba, P.O. Box 3048, 14080, Cordoba, Spain

    T. Vanwalleghem

  2. Center for Applied Geographic Information Science, Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, North Carolina, 28223, USA

    R. K. Meentemeyer

Authors
  1. T. Vanwalleghem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. K. Meentemeyer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. K. Meentemeyer.

Additional information

Author contributions

Meentemeyer conceived the study, collected the data, and wrote the paper; Vanwalleghem analyzed data, contributed new methods and models, and wrote the paper.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Vanwalleghem, T., Meentemeyer, R.K. Predicting Forest Microclimate in Heterogeneous Landscapes. Ecosystems 12, 1158–1172 (2009). https://doi.org/10.1007/s10021-009-9281-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10021-009-9281-1

Keywords

Search

Navigation