Modeling temperature gradients across edges over time in a managed landscape

• Published in: Forest ecology and management 1999-05, Vol.117 (1), p.17-31
• Main Authors: Saunders, Sari C., Chen, Jiquan, Drummer, Thomas D., Crow, Thomas R.
• Format: Article
• Language: English
• Subjects: Agricultural and forest climatology and meteorology. Irrigation. Drainage; Agricultural and forest meteorology; Agronomy. Soil science and plant productions; Area of edge influence (AEI); Biological and medical sciences; Climatic models of plant production; Depth of edge influence (DEI); Edge effects; Fragmentation; Fundamental and applied biological sciences. Psychology; General agronomy. Plant production; Generalities. Techniques. Climatology. Meteorology. Climatic models of plant production; Microclimate; Temperature
• ISSN: 0378-1127; 1872-7042
• DOI: 10.1016/S0378-1127(98)00468-X

Landscape management requires an understanding of the distribution of habitat patches in space and time. Regions of edge influence can form dominant components of both managed and naturally patchy ecosystems. However, the boundaries of these regions are spatially and temporally dynamic. Further, areas of edge influence can be defined by either biotic (e.g. overstory cover) vs. abiotic (e.g. microclimate) characteristics, or structural (e.g. vegetation height) vs. functional (e.g. decomposition rates) features. Edges defined by different characteristics are not always concordant; the degree of spatial concurrence varies with time. Thus, edge effects are difficult to generalize or quantify across a landscape. We examined temperature at eight times of the day across the edge between a clearing and a 50-year-old pine stand. We used simple, nonlinear equations to model and predict temperature gradients across this edge over time. The depth of edge influence (DEI) on temperature varied from 0 to 40 m, depending on the patch type and time of day. Two equations were required to model adequately ( r 2>0.50) patterns of temperature at all eight times of the day. Model fit was best at night ( r 2=0.97) and lowest in the afternoon ( r 2=0.50). Parameters for the models could be predicted from local, reference weather conditions. However, these linear relationships varied among parameters and with time of day (0.29≤ r 2≤0.99). Model validation was weak, with mean absolute percent error >10% for all day-time combinations. The models tended to underestimate DEI for both patch types, though edge depth was more accurately predicted in the closed-canopy stand than in the clearing. The difference between observed and predicted edge effects was highest at midday in the clearing and during the morning under closed canopy. The models predicted the location of peak temperature and the slope of temperature change (i.e. pattern of temperature variation) across the edge and the range of temperature better than actual values. We suggest that this approach may, therefore, be useful for characterizing edge dynamics if a wider range of local weather conditions could be monitored during initial data collection. The empirical evidence for temporal changes in position and intensity of abiotic edge effects emphasized the need to quantify these dynamics across time and space for sound planning at the landscape scale.