Modeling temperature gradients across edges ... - Semantic Scholar

Forest Ecology and Management 117 (1999) 17±31

Modeling temperature gradients across edges over time in a managed landscape Sari C. Saundersa,*, Jiquan Chena, Thomas D. Drummerb, Thomas R. Crowc b

a School of Forestry and Wood Products, Michigan Technological University, Houghton, MI 49931, USA Department of Mathematics and Statistics, Michigan Technological University, Houghton, MI 49931, USA c USDA Forest Service, North Central Forest Experiment Station, Rhinelander, WI 54501, USA

Accepted 8 August 1998

Abstract Landscape management requires an understanding of the distribution of habitat patches in space and time. Regions of edge in¯uence 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 in¯uence can be de®ned 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 de®ned by different characteristics are not always concordant; the degree of spatial concurrence varies with time. Thus, edge effects are dif®cult 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 in¯uence (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 (r2>0.50) patterns of temperature at all eight times of the day. Model ®t was best at night (r20.97) and lowest in the afternoon (r20.50). Parameters for the models could be predicted from local, reference weather conditions. However, these linear relationships varied among parameters and with time of day (0.29r20.99). 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. # 1999 Elsevier Science B.V. All rights reserved. Keywords: Edge effects; Microclimate; Fragmentation; Temperature; Area of edge in¯uence (AEI); Depth of edge in¯uence (DEI)

*Corresponding author. Tel.: +1-906-487-3417; fax: +1-906-487-2915; e-mail: [email protected] 0378-1127/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved. PII: S0378-1127(98)00468-X

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S.C. Saunders et al. / Forest Ecology and Management 117 (1999) 17±31

1. Introduction Fragmentation changes the biogeographic nature of the landscape (i.e. the distribution of sizes, shapes and relative positions of habitat patches) decreasing both the quality and the quantity of original habitat. Fragmentation and the concomitant increase in the amount of edge habitat induce changes in both the abiotic (e.g. microclimate) and biotic (e.g. species composition and percent cover) environment. The distribution of these edges not only in¯uences species diversity, but has long-term implications for susceptibility of the landscape to disturbance and changes in management regime. For example, wild®re control is easier in landscapes with dispersed edges, i.e. relatively high heterogeneity (Franklin and Forman, 1987). However, harvesting that aggregates edges results in high temporal variability in patch size, and may thus provide more opportunities for species and gene dispersal (Wallin et al., 1994). Edges in¯uence both the ecosystem structure and function through their roles as sites of exchange of energy, materials, and organisms between patches (Hansen et al., 1988; Chen et al., 1996). At the ecosystem level, processes such as primary production and decomposition can be altered near edges relative to interior patch conditions (Chen et al., 1992). The composition and dynamics of plant communities are often in¯uenced through modi®cation of competitive relationships between individual plants and success of seedling establishment (Chen et al., 1992). Changes in abiotic conditions near forested edges can result in further structural alterations to tree architecture (Wales, 1972) or increases in basal area and stem density (Ranney et al., 1981; Williams-Linera, 1990). Biomass of exotic vegetation has been found to increase along forest edges in agricultural landscapes (Hester and Hobbs, 1992; Fraver, 1994) and a decrease in aboveground biomass of trees has been recorded in tropical forest fragments (Laurance et al., 1997). These alternations to the vegetative community can modify the composition and behavior of fauna, depending on the degree of contrast in vegetal structure and composition at the edge (Yahner, 1988; Noss, 1991). Increases in avian nest predation and parasitism have been associated with expansion of edge habitat in temperature forests (e.g. Wilcove et al., 1986; Donovan et al., 1995). Dispersal and foraging activity of

both, invertebrate and vertebrate organisms are also in¯uenced by the microclimatic environment (e.g. Grubb, 1978; Wood and Samways, 1991; Wachob, 1996). Changes in structure, composition and function (i.e. processes) at edges primarily result from alteration of microclimate, following the structural modi®cation of the landscape. Microclimate, including solar radiation, moisture, wind, and temperature, is a primary driver of ecosystem and landscape-level processes such as photosynthesis, regeneration, plant growth, nutrient cycling, and decomposition (Geiger, 1965; Perry, 1994). Microclimatic variables are, however, differently affected by edges. Wind speed and solar radiation at edges are often intermediate to values measured within clearcuts and interior forests. However, for soil and air temperatures, moisture, and relative humidity, edge measurements are often more extreme than those within either patch due to stable air masses created at the edges (Kapos, 1989; WilliamsLinera, 1990; Brothers and Springarn, 1992; Geiger, 1965; Chen et al., 1995). Thus, examining the dynamics of individual as well as combined microclimatic conditions is critical for determining biological responses in the area of edge in¯uence (AEI; Chen et al., 1996). Rates of ecological transfer change at boundaries and are often abrupt relative to those within patches (Gosz, 1991). However, an ecotone, the zone de®ned by the set of spatially contiguous locations where the majority of variables show the highest rate of change (Fortin, 1994), may be characterized by sharp discontinuities in one direction but smooth gradients in other directions. The latter case appears to be more common (Fortin and Drapeau, 1995). Further, edges identi®ed from structural features do not always coincide with those de®ned by abiotic characteristics (Chen et al., 1996; Fortin et al., 1996; Saunders et al., in press). For example, the extent of edge de®ned by structure, such as the height of vegetation, will vary over successional time scales. Edge zones de®ned by abiotic features such as microclimate will shift in space diurnally and seasonally. Thus, not only is the rate of change of a variable at a boundary dif®cult to predict, but so is the location of maximum edge effect. The AEI, or transitional region between two community types, can be quanti®ed for speci®c structural or process variables by both its size and location


relative to the structural edge. This zone is unique relative to the patches it abuts and plays an important role in con®guring landscapes (Chen et al., 1996). However, the parameters characterizing an AEI for any edge change with time, the variable of interest, edge orientation, and topography (Chen et al., 1996). This edge zone is, thus, highly transient and parameters must be de®ned with respect to a speci®c ecological question. In an effort to better de®ne the AEI as a landscape element and determine the location of the maximum in¯uence on temperature relative to the structural edge, we examined the behavior of temperature across a forest-clearing boundary. We hoped to determine whether the behavior of temperature could be generalized (and thus modeled) for multiple time periods across the same edge in order to facilitate prediction and quanti®cation of edge effects during the growing season. A priori, we predicted that: 1. there would be more extreme temperatures near structural edges than within vegetation patches; 2. the horizontal positionofthemostextreme point(s) of temperature would be asymmetric with respect to the structural edge depending on time of day; and 3. the gradient of temperatures through the area of edge influence would change with (and could be predicted by) local, daily weather conditions. 2. Methods 2.1. Field site This study was conducted in the Washburn District of the Chequamegon National Forest, northern Wisconsin, USA (468300 ±468450 N, 91820 ±918220 W). The study area lies within subsection X.1, Bay®eld Barrens, of the Regional Landscape Ecosystems of Wisconsin (Albert, 1995). Soils are deep (30±90 m), loamy, glacial outwash sands with little organic materials, classi®ed as Psamments and Orthods. Underlying bedrock is Precambrian basalt, lithic conglomerate, sandstone, shale, and feldspathic to quartzose sandstone. Topography in the area ranges from level terraces to pitted outwash, formed by the melting of masses of glacial ice on which the sediments were deposited (Chequamegon National Forest, 1993; Albert, 1995).

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Our research was conducted within the pine-small block (PSB) eco-unit of the Chequamegon National Forest. These areas are delineated by the `desired future condition' of vegetation composition and structure de®ned by forest managers (Chequamegon National Forest, 1993) based on current understanding of the presettlement vegetation, forest habitat types, and ownership of the surrounding land. In the PSB unit, the overstory consists primarily of red pine (Pinus resinosa Ait.), planted during the 1940s, 1970s and 1980s, and jack pine (P. banksiana Lamb.), which has regenerated naturally. Species such as paper birch (Betula papyrifera Marsh.), red maple (Acer rubrum L.), trembling aspen (Populus tremuloides Michx.), big-toothed aspen (P. grandidentata Michx.), red oak (Quercus rubra L.) and scrub oak (Q. ellipsoidalis E.J. Hill) occur on account of natural successional dynamics and silvicultural activities. Historically, the PSB area was fragmented by frequent, naturally occurring ®res and burning by native Americans and early European settlers (Heinselman, 1981). Current management promotes early and midsuccessional species through harvesting in small patches of 16 ha. Species composition and structure are highly dynamic due to the frequent management activities conducted at the stand scale. It is, therefore, of interest to managers to acquire further information regarding the dynamics of abiotic and biotic impacts of this management regime across the landscape. 2.2. Temperature measurements We measured air temperature at the ground surface (surface temperature; Tsf,8C) every 5 m across the edge between a 6-year red pine (P1) and 50-year mixed pine stand (P2), to investigate diurnal and daily changes in temperatures across edges. This was one of the most abrupt transitions encountered during the broader scale study of the PSB eco-unit, and was thus expected to induce relatively distinct patterns in temperature compared with the low contrast edges between relatively similar patch types. We used two mobile microclimate stations to measure concurrently Tsf every 5 m from a point 60 m into P1 at the west end, to 60 m into P2 at the east end. The stations consisted of a Campbell Scienti®c CR10 datalogger coupled with an AM416 multiplexer (Campbell Scienti®c, Logan, UT), both housed in a cooler, with the

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capability of recording temperatures over 80 m of the transect (40 m on each side of the cooler). Thermocouples were made from copper±constantan wire. Temperatures were measured every 20 s and averaged and recorded every 15 min. Data were downloaded in the ®eld every 3±5 days using a portable laptop computer. We left the stations in place for 10 days, beginning Julian day 165 (June 14), 1995. Surface temperatures were monitored across the same edge with consistent methodology in 1996, beginning Julian day 157 (June 6). We maintained two reference stations throughout the sampling periods in 1995 and 1996, one in a closed canopy pine/oak stand in the Moquah Research Natural Area (REFC), and one in the open pine barrens (REFO). These reference data were used to develop simple, linear regressions to estimate missing data points along the transect during the study period. At both of these reference stations, we measured air temperatures (8C) at 0, 0.5, 1.0, and 2.0 m above the ground. Skies were characteristically clear, with scattered cloud and brief thundershowers by mid-afternoon throughout the monitoring period in 1995. Weather conditions were similar in 1996, with only scattered cloud cover and no prolonged periods of precipitation. Replicate edges, in terms of orientation, height and composition of the closed canopy stand, and age of the clearcut were not available within this landscape. However, we present this study as a descriptive example of the complexity of edge effects and an exploration of the modeling approach. 2.3. Depth of edge influence The gradients of surface temperatures across the edge were examined at each of the eight speci®ed times during the day: 0500, 0800, 1100, 1400, 1700, 2000, 2300, and 0200 h. Only six out of the 10 days monitored (Julian days 165±170) were examined due to large data gaps associated with equipment failure from power ¯uctuations and damage by bears. Three-term moving averages were taken of all temperatures (i.e. the temperature at any location was averaged with that measured 5 m to the west and 5 m to the east) to try to minimize in¯uences of extremes of shading and microtopography on temperature along the transect.

To determine the distance into either patch that was edge in¯uenced, we calculated the following: 1. the mean temperature within patch 1, P1x , and patch 2, P2x ; 2. the absolute difference between these means, Dx jP1x ÿ P2x j; 3. 5% of this difference, 0:05 Dx ; and 4. the mean of each patchthe 5% difference between the means, e.g. P1x 0:05 Dx . The distances from the edge into each patch which had temperatures outside the range calculated in Step 4 were considered the depths of edge influence (DEI; Chen et al., 1996) at the 5% level for that time and day (Fig. 1). 2.4. Model development We modeled temperature patterns for the same eight times of the day used to examine depth of edge in¯uence. The three-term moving averages of temperature were scaled between 0 and 1 for model development (Fig. 2). Distance along the transect was also scaled from 0 to 1, with the edge between patches at 0.5, implicitly incorporating patch type into the model (points at distances 0.5 were in patch type 1, the six-year red pine plantation, and points at distances >0.5 were in patch type 2, the 50-year mixed red-jack pine plantation). We scaled both temperature and distance during model testing for computation purposes, to ensure that all functions (e.g. ln values) were de®ned. We ®rst determined a best ®t equation for temperature pattern across the edge for each of the eight times during the six days in 1995. We tested a series of seven curves which were ¯exible enough, with modi®cation of their parameter values, to accommodate the multiple patterns seen across times. This minimized the number of equations required to model all time periods. The initial models and their parameter values were chosen based on visual comparisons of observed and predicted values, and minimization of least squares in Quattro Pro spreadsheets. The initial parameter estimates were then used in derivative-free, nonlinear regressions in SAS (unix version) to obtain the ®nal parameter values for each time on each day. Final models were chosen to


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Fig. 1. As an example of estimation of depth-of-edge influence (DEI), we calculated the following: (1) the mean temperature within patch 1, P1x , and patch 2, P2x ; (2) the absolute difference between these means, Dx jP1x ÿ P2x j; (3) 5% of this difference, 0:05 Dx ; and (4) the mean of each patchthe 5% difference between the means, e.g. P1x 0:05 Dx . The distances from the edge into each patch which had temperatures outside the ranges calculated in Step 4 were defined as the DEIs (5) at the 5% level.

minimize mean square error (MSE) and autocorrelation among residuals, and to maximize r2, calculated as: 1 ÿ SSE=CoTSS

(1)

where SSE is the error sum of squares and CoTSS the corrected total sum of squares. Two ®nal models were chosen to model temperature across the edge. The best ®t model for temperatures at 0200, 0500, 1400, and 1700 h was: f x 1 2 x 3 x2 ÿ1

(2)

and the ®nal model chosen for temperatures at 0800, 1100, 2000, and 2300 h was: f x 1 eÿe

ÿ 2 3 x

4 :

(3)

where x is the distance from the edge and f(x) the temperature. We then plotted parameter values for all days at a particular time of day against eight measures of daily temperature conditions at the open (REFO) and closed (REFC) canopy reference stations: REFO minimum, REFO maximum, REFO average, REFO range, REFC minimum, REFC maximum, REFC average, and REFC range. We calculated linear regressions to predict the change in each parameter at a particular time of day from whichever reference variable had the highest correlation, and graphically appeared to have the strongest linear association with a parameter. We

developed linear models of parameter dynamics with changes in daily reference temperatures for models at all times of the day (Table 2). To examine the range of edge conditions that might be expected over the course of one month of the growing season, we used the REFO summary temperature data to determine parameters for equations to predict temperature (scaled from 0 to 1) at all eight times of the day for Julian days 171±197 (20 Jun±16 Jul 1996). We predicted the minimum (edgemin) and range (edgerange) of temperatures across the entire 120-m transect from temperatures at the reference stations during these days, using linear regressions of these same relationships determined for Julian days 165±170. These values of edgemin and edgerange were then used to convert temperatures from their scaled to unscaled values (Fig. 3): Tunscaled Tscaled edgerange edgemin

(4)

We examined the validity of the model using temperatures measured from 0 to 65 m across the same edge during Julian days 157±160 in 1996. We used the same procedures described earlier to determine: (a) equation parameters; (b) scaled, predicted temperatures; (c) edgemin and edgerange; and (d) ®nal, unscaled predicted temperatures for these four days at all eight points in time. Mean absolute error (MAE; Mayer and Butler, 1993), calculated as

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Fig. 2. Scaled temperature (8C) at eight times of the day for six days (Julian days 165±170) across the edge between a six-year (P1) and a 50year (P2) mixed red and jack pine stand in Chequamegon National Forest, WI. The structural edge between the two patches is at 0 m, points 0 m in P2.

MAE jyi ÿ ^yi j=n

(5)

where n16, yi is the observed temperature at point i and ^yi is the predicted temperature at point i, and the mean absolute percent error: MA%E 100 jyi ÿ ^yi j=jyi jn

(6)

were used to compare predicted with observed temperatures across the edge and to compare the predictability of temperature among times of the day. MAE, being in the same units as the data, allows for assessment of the ecological utility of the model, whereas MA%E provides a relative (and therefore comparative) measure of error among times and days.

3. Results 3.1. Temperature gradients across the edge Surface temperature at the edge was intermediate to temperature in the two patches during the night and early morning (2000, 2300, 0200, 0500, and 0800 h). However, temperature peaked at the edge, i.e. was higher than the mean in either the clearing (P1) or forest (P2) during midday (1100, 1400, and 1700 h). This dynamic situation was retained by the model (Figs. 3 and 4; compare Figs. 4a and b) throughout the day, except at 1100 h (compare Figs. 2 and 3). The model simulated the shift in location of the peak


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Fig. 3. Temperature (8C) across the edge between a six-year (P1) and a 50-year (P2) mixed red and jack pine stand predicted by the models for each of eight times of the day on Julian days 171±197, 1995. Different lines indicate different days. Points 0 m are in P2.

temperature relative to the edge through the midday time period; from just west of the edge (in P1) at 1400 h to just east of the edge (in P2) at 1700 h (compare Figs. 2 and 4). 3.2. Depth of edge effect The depth-of-edge in¯uence at the 5% level varied from 0 to 40 m for P1 (the young pine stand) and 5 to 35 m for P2 (the 50-year mixed pine stand) when comparing among all days and times simultaneously

(Fig. 5). Comparing the variability in DEI at each time of day across the six days, P1 was more variable among days at all times except 0500 h when P2 was more variable (5±35 m vs. 5±25 m for P1). Maximum DEI for a given time was the greatest for P1 except at 0500 h (25 m vs. 35 m for P2), 1700 h (5 m vs. 5 m for P2), and 2000 and 2300 h (15 m for both P1 and P2). In general, P1 was more edge-in¯uenced in the morning and afternoon, whereas P2 was more edge-in¯uenced in the evening from 1700 to 2300 h (Fig. 5).

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Fig. 4. Scaled temperature (8C) (A) observed and (B) predicted by the model at eight times of the day on Julian day 165, 1995. Measurements cross the edge between a clearcut and a 50-year mixed red and jack pine stand in Chequamegon National Forest, WI, at 0 m distance.

Fig. 5. Depth-of-edge influence (5% DEI) for observed temperatures from the edge into adjacent patches, six-year (P1) and 50-year (P2) mixed red and jack pine stands in Chequamegon National Forest, WI. DEI is calculated as the width of a temperature zone falling outside values 5% of the mean for each patch type (see Fig. 1).

3.3. Model fit and validity

times, and exceeded 100% for many day-time combinations. Patterns of predicted and observed temperatures were consistent, though actual values differed widely (Fig. 7). For example, the maximum difference between values of observed and predicted temperatures for any day-time combination across an edge in 1996 was 17.988C. The average difference was 9.068C (SD4.248C). However, the maximum and average differences between ranges in observed and predicted temperatures were substantially lower; 13.408 and 4.058C (SD3.378C), respectively. The depth-of-edge effect predicted by the model at each time during days 171±197, was similar to that observed for days 165±170 (used for model development) (Fig. 8). However, the predicted edge in¯uence was closer to observed values for the forest patch than for the clearcut. The model tended to underestimate depth-of-edge in¯uence for both patches. Observed and predicted values differed most at midday (1100 and 1400 h) for P1, the six-year pine stand, and in the

Model ®t varied diurnally, being the best at night (average r20.92 and 0.97 for 2000 and 2300 h, respectively) and worst in mid-afternoon (average r20.50 at 1400 h; Table 1, Fig. 6). All parameters, regardless of time of day, were more closely associated with the open canopy reference data (REFO) than with measurements at the closed canopy reference station (REFC), except for 4 which, at 1700 h, showed the strongest relationship ± though a relatively weak one ± with REFC minimum temperature (r20.29; Table 2). However, there was little consistency among times of day or among parameters for any given time of day in the variable which was most useful for predicting parameter change. Contrary to expectation, the lowest mean absolute and percent errors calculated during model validation occurred early in the morning and during the middle of the day (0800, 1100, 1400, and 1700 h; Table 3). However, MA%E was still >10% for all days and

Time (h)

Julian days

Equation

Range in parameter estimates 1 min

0200 0500 0800 1100 1400 1700 2000 2300 a

165±170 165±170 165±169 165±169 165±169 164±169 164±169 165±169

1ÿ(SSE/CoTSS).

2 ÿ1

f x 1 2 x 3 x 4 f x 1 2 x 3 x2 ÿ1 4 f x 1 eÿexp ÿ 2 3 x 4 f x 1 eÿexp ÿ 2 3 x 4 f x 1 2 x 3 x2 ÿ1 4 f x 1 2 x 3 x2 ÿ1 4 f x 1 eÿexp 2 3 x 4 f x 1 eÿexp 2 3 x 4

6.14 5.59 0.58 0.62 2.74 31.52 0.61 0.74

2

3

Average r2 a

4

max

min

max

min

max

min

max

10.49 22.64 0.71 0.71 6.47 101.30 0.75 0.87

ÿ22.88 ÿ62.23 4.95 10.47 ÿ22.04 ÿ347.78 ÿ17.81 ÿ9.02

ÿ12.71 ÿ11.40 8.23 16.46 ÿ7.60 ÿ95.19 ÿ6.34 ÿ5.57

7.74 6.88 ÿ21.67 ÿ26.96 8.95 74.72 12.53 11.56

13.8 45.50 ÿ13.04 ÿ16.66 24.14 302.14 32.73 17.61

ÿ0.05 ÿ0.10 0.18 0.04 0.00 0.11 0.22 0.10

0.08 0.91 0.21 0.05 0.23 0.24 0.29 0.24

0.81 0.82 0.84 0.85 0.50 0.80 0.92 0.97


Table 1 Final equations chosen to estimate temperature f(x) from the distance to the edge (x) between a six-year and a 50-year pine plantation over seven days in Chequamegon National Forest, WI. Temperature and distance were scaled from 0 to 1 for model development

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morning (0500 and 0800 h) for P2, the 50-year pine stand. 4. Discussion

Fig. 6. Coefficients of determination (r2) for nonlinear models predicting temperature from distance to the edge between two adjacent patches, a 6-year and a 50-year pine stand (Table 2), at eight times of the day on Julian days 164±170, 1995.

The area of edge in¯uence is a highly transient landscape element in both, space and time. Our results indicate that the total, thermal transitional zone between a clearing and forest stand can vary in width by as much as 45 m over the course of a day. For any one time of day across a one-week period, the same temperature zone can vary up to 40 m depending on daily weather conditions (Fig. 5). These DEI values are smaller than those reported in the U.S. Paci®c Northwest, where edge in¯uences can reach 3±4 tree heights (Chen et al., 1995), but larger than those for

Table 2 Best linear predictors of parameters in models of temperature change across an edge between six-year and a 50-year pine plantations. The independent variables tested were daily minima (min), maxima (max), ranges (range) and averages (x) from microclimate reference stations in open canopy (REFO) and closed canopy (RFEC) conditions Time (h) 1 0200 0500 0800 1100 1400 1700 2000 2300

r2

Best predictor of model parameter

REFO REFO REFO REFO REFO REFO REFO REFO

2 x range range range range max min x


3 x range max x range max range max


4 x range max x range max range max


range range min range range min min x

1

2

3

4

0.52 0.86 0.95 0.58 0.81 0.78 0.81 0.53

0.58 0.87 0.68 0.99 0.85 0.80 0.89 0.73

0.67 0.88 0.66 0.99 0.89 0.82 0.90 0.72

0.98 0.92 0.59 0.88 0.60 0.29 0.42 0.67

Table 3 Mean absolute error, MAE, (mean absolute percent error, MA%E) for model of temperature during three days across the edge between a sixyear and a 50-year plantation, Chequamegon National Forest, WI. N16 for all day-time combinations Time (h)

0200 0500 0800 1100 1400 1700 2000 2300 Mean Standard deviation

MAE (MA%E) Julian day 157

Julian day 158

Julian day 159

9.75 (>100) 9.88 (>100) 3.09 (28) 5.7 (30) 8.16 (39) 5.63 (45) 10.70 (92) 7.86 (70) 7.60 2.61

6.08 (>100) 16.86 (>100) 3.61 (30) 6.81 (40) 5.97 (53) 9.31 (>100) 8.67 (89) 10.1 (>100) 8.43 4.00

14.89 (>100) 5.22 8.41 3.35 3.32 12.55 16.61 9.19 5.53

(49) (49) (14) (15) (91) (>100)

Mean

Standard deviation

10.24 13.37 3.97 6.97 5.83 6.09 10.64 11.52

4.43 4.94 1.11 1.36 2.41 3.02 1.94 4.55


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Fig. 7. Observed and predicted (during model validation) temperatures across the edge between a six-year (P1) and a 50-year (P2) mixed pine stand on Julian day 159, 1996.

the tropics (Kapos, 1989) and eastern hardwoods (Matlack, 1993). Open areas have larger DEIs and are more variable over most times of the day. By extension, the area of interior conditions for any one patch type is also extremely variable. Ecologists and managers will have to consider the shifting nature of these ecological conditions relative to structural elements of the landscape when calculating habitat availability within reserve networks and managed landscapes. These results support previous work demonstrating the variability of depth-of-edge in¯uence. Empirical

measurements of the edge in¯uence on air temperature in forest remnants vary from 15 m (Williams-Linera, 1990), 24 m (Matlack, 1993), 20±60 m (Kapos, 1989) and up to 180 m (Chen et al., 1995), depending on methodology and the ecosystem being studied. Estimates of DEI are in¯uenced by the orientation of the edge, the time of day (Lovejoy et al., 1986; Chen et al., 1995) and size of clearings and remnant forest patches (Kapos, 1989). East±west edges in the temperature zone would be expected to exhibit more extreme edge effects than those edges with a northerly orientation, due to the lower amount of solar radiation at the latter

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Fig. 8. Average depth-of-edge influence (DEI) calculated from oberved and predicted temperature values (Julian days 165-170, 1995) into adjacent patches; six-year and 50-year mixed red and jack pine stands in Chequamegon National Forest, WI. DEI was calculated as the width of the zone where temperatures fell outside the mean for a patch5% the difference between the means of the two patches (see Fig. 1).

(Kapos, 1989). In the northern hemisphere, southfacing edges, in particular, receive the greatest amount of solar radiation, resulting in altered air temperatures up to 230 m from clearcut edges into old-growth Douglas-®r forests (Chen et al., 1995). Examining the pattern of temperature change from an edge into both the adjacent patches provides a more comprehensive understanding of the dynamics of these effects than an estimation of environmental

change into remnant forest alone. Previous empirical ± and modeling (e.g. Chen et al., 1993) ± studies have focused on alterations of the microclimate in residual forest patches. For landscapes that are managed in many small patches of different cover types or silvicultural regimes, this does not provide a complete picture of the biophysical implications of edge creation. Williams-Linera (1990) monitored conditions across edges into both pasture and patches of tropical forest in Panama, concluding that the greatest change in maximum temperature (one measure of depth-ofedge in¯uence) was between 0.7 and 10.5 m into the pasture and 2.5 m to 15 m into the forest. Raynor (1971) also recorded temperatures at different distances into both forest and clearing. However, successive sampling points were too far apart (at least 20 m) speci®cally to determine the depth-of-edge in¯uence. Modeling work by Chen et al. (1996) estimated edge effects of different intensities on both sides of a clearcut-forest edge based on hypothetical patterns of six different variables. For air temperature, their projections varied from 40 to 210 m into forest remnants and 50 m to 210 m into the clearcuts, depending on the time of day and edge orientation. Total zones of edge in¯uence thus reached up to 280 m in width. Our results also indicated that these patterns varied among times, with edge in¯uences ranging from 10 to 60 m in width, over ®ve days and eight times of the day. Murcia (1995) noted that the lack of general consensus regarding patterns of edge effects could stem from poor replication, interactions of multiple edge effects on the landscape, and a simpli®ed perception of edge effects. Although we were unable to replicate our measurements along this transect on account of the heterogeneity of management patches, our results

Table 4 Daily reference temperatures for time period of model development, model predictions, and model validation of temperature gradients across a 6±50-year pine plantation edge in Chequamegon National Forest Reference variable

REFO average REFO maximum REFO minimum a

Julian days 165±170, 1995. Julian days 171±197, 1995. c Julian days 157±160, 1996. b

Model development a

Model preductions b

Model validation c

Min (8C)

Max (8C)

Min (8C)

Max (8C)

Min (8C)

Max (8C)

20.87 35.88 3.62

35.88 45.52 18.54

12.99 16.65 3.55

28.64 45.15 16.37

12.57 23.99 ÿ1.91

14.39 31.03 7.31


nonetheless indicate that determination of DEI based on average daily temperatures or temperatures measured at one point in time, may present a simplistic view of the nature of the edge impacts. In some cases, one could erroneously conclude that there are no edge effects on the variable of interest due to averaging of these in¯uences over the time scale of study. This further emphasizes the importance of choosing a temporal period and scale relevant to the biological process or species under investigation; conclusions will differ among studies with different temporal resolutions of analysis. The dynamic nature of temperature at patch margins and of edge effects, in general, make it necessary to develop methods for predicting these changes over time. However, these dynamics also provide a challenge when building a generalized model of edge in¯uences. Although our model provided limited predictive power across the time period examined, some results suggest that this modeling approach is useful for describing temperature patterns. The variation in temperature that was explained by distance to edge, using our original set of equations, was greater than 80% for all times of the day except 1400 h when r2 dropped to 0.50 (Fig. 6, Table 1). Further, there were consistently strong relationships between model parameters and local, daily weather conditions (Table 2). The limited range of weather conditions represented during the time period used for original model development may have restricted the predictive use of the model. Reference temperatures during the growing season for which we predicted edge dynamics (Julian days 171±197) and during the period used to validate the model (Julian days 157±160, 1996) frequently fall outside the ranges of average, maximum, and minimum temperatures used to develop our original, predictive equations for model parameters (Julian days 165±170, 1995; Table 4). However, the general pattern of temperature across the edge appeared consistent across the growing season. Thus, if a wider range of daily weather conditions was used during model development, predictions of temperature later in the summer should be improved. Validation of the model indicated a high level of error. Two of the 24 day-time combinations, Julian day 150 at 1400 and 1700 h, were within the range of an `acceptable' 10% MAE (Mayer and Butler, 1993). Interestingly, error levels were lower for all three days

29

during late morning and early afternoon; although evening and night temperatures were expected to show the least deviation from observed values based on initial r2 calculations for the model (Table 1; Fig. 6). The weak validation and unexpected pattern in errors with time may be an indication of changing edge dynamics with successional growth in subsequent years. Further, the date of initiation and growth rate of spring vegetation probably created structural differences at the edge between years; 1995 was relatively hot and dry compared to 1996 which was a cool, wet year (®eld observations). The differences in daily, local temperatures between 1995 (model development) and 1996 (model validation; Table 4) also suggest that climate±vegetation relationships may have differed between the years. Microtopography has a strong in¯uence on temperature at this ®ne (5 m) spatial scale. This variation could be further reduced through averaging to a larger spatial resolution to improve prediction of general trends across the edge. Alternatively, the observed dynamics which approached a bimodal pattern (e.g. Fig. 2: 0500; 1100; and 1700 h) could be edge, rather than microtopographically, induced and one could consider modeling more of this variation. Microtopography was similar at all points within P1 (¯at to concave), and there were no slash piles or rocks to act as heat sinks. Other researchers have found similar bimodal patterns but also given them little attention (e.g. Kapos, 1989; Hester and Hobbs, 1992). The patterns and ranges, if not actual values, of temperatures predicted by our models were similar to those recorded in subsequent years (Fig. 7). A more accurate prediction of 4 in the two models would contribute most to reduction of the difference between observed and simulated values. This further supports the need for a larger data set encompassing extreme weather conditions to clarify whether a generalized modeling approach can be used to examine edge effects at this temporal and spatial scale. Although our modeling approach may be useful for examining dynamics across multiple edges in a landscape, the equations used here are unlikely to be generalizable; each edge should be modeled independently. Effects of patch (and transition) type on temperature vary among patches as they do among time periods for a single edge type. For example, a preliminary examination of other edges within this

30


management area indicated that patch type explained signi®cant variation only at 2300 h for an edge between a seven-year red pine plantation and a recent clearcut, and only at 1400 h across the edge from a 60year-old pine plantation to a clearcut. For other edges, the shape of temperature change between the two patches remained similar among all times of the day and could probably be modeled with a single equation. The size of the patches will also affect temperature dynamics across edges. When a landscape is divided into relatively more, smaller patches, less of the landscape reaches interior conditions and zones of edge in¯uence are a more-dominant landscape feature. Kapos (1989) determined that 1-ha forest patches exhibited narrower depths-of-edge in¯uence for temperature than did larger, 100-ha patches. Management patches on the landscape in this study averaged only 200 m in width and probably exhibit narrow and relatively unstable zones of edge in¯uence compared to larger stands. The relatively small patch size also makes it dif®cult to isolate the dynamic caused by one edge from in¯uences of nearby edges of different types. This modeling approach may, therefore, provide more predictive power in landscapes where management is conducted at broader spatial scales. Future studies should assess the consistency of these edge dynamics as size, shape, and position of patches change within the landscape. Conservation managers and planners of protected areas must work with residual patches in fragmented landscapes across which the ¯ows of energy, nutrients, water and species can be highly altered from the nonmanaged state (Saunders et al., 1991). Dynamic edge in¯uences have implications for predicting vegetation and faunal responses to management activities and impacts of reserve boundaries on the internal dynamics of remnant landscapes. In actively managed areas or naturally patchy landscapes, information on effective edge widths will be critical for determining the area of stands available for species dependent on interior habitat conditions (Harris, 1984; Matlack, 1993). The location and nature of microclimatic gradients created across edges will not always be discernible from structural features on the landscape. This study provided empirical support for previous investigations of hypothetical landscapes that suggested the highly ephemeral nature of ecotones.

Depending on the variable, edge effects will vary in width and location across times of the day. The dynamic nature of non-structural edges at ®ne spatial and temporal scales poses a challenge for developing generalized models of temperature patterns. However, these dynamics also highlight the need for intensive study of multiple edge types under a wide variety of weather conditions. A generalized model of temperature dynamics de®ned by the distance to and type of edge, would provide valuable information on landscape patterns for conservation planning and management. Acknowledgements We thank John Vucetich for input into the empirical model and members of the Landscape Ecology and Ecosystem Science group at MTU for comments on drafts of the manuscript. Kimberley Brosofske and Paul Crocker assisted with data collection in the ®eld. This work was supported by cooperative agreement No. 23-94-12 between the Research Branch of the USDA Forest Service, North Central Experiment Station and Michigan Technological University (MTU), USDA competitive grant NRI No. 97-35101-4315, and an MTU Academic Women's Caucus grant to Sari C. Saunders. References Albert, D.A., 1995. Regional Landscape Ecosystems of Michigan, Minnesota, and Wisconsin: A working map and classification. USDA-For Ser Gen Tech NC-178, North Central Forest Experiment Station, St. Paul, MN. Brothers, T.S., Springarn, A., 1992. Forest fragmentation and alien plant invasion of central Indiana old-growth forests. Conserv. Biol. 6, 91±100. Chen, J., Franklin, J.F., Lowe, J.S., 1996. Comparison of abiotic and structurally defined patch patterns in a hypothetical forest landscape. Conserv. Biol. 10, 854±862. Chen, J., Franklin, J.F., Spies, T.A., 1992. Vegetation responses to edge environments in old-growth Douglas-fir forests. Ecol. Appl. 2, 387±396. Chen, J., Franklin, J.F., Spies, T.A., 1993. An empirical model for predicting diurnal air-temperatue gradients from edge into oldgrowth Douglas-fir forest. Ecol. Model. 67, 179±198. Chen, J., Franklin, J.F., Spies, T.A., 1995. Growing-season microclimatic gradients from clearcut edges into old-growth Douglas-fir forests. Ecol. Appl. 5, 74±86.

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