Samuel D. Fuhlendorf and Fred E. Smeins. Texas A&M University, Department Rangeland ... Levin 1992). Analyses of spatial and temporal vari- ations in pattern ...
Landscape Ecology vol. 1 1 no. 2 pp 107-1 13 (1996) SPB Academic Publishing bv, Amsterdam
Spatial scale influence on longterm temporal patterns of a semi-arid grassland Samuel D. Fuhlendorf and Fred E. Smeins Texas A&M University, Department Rangeland Ecology and Management, College Station, TX 11843-2126, USA Keywords: scaling, temporal patterns, equilibrium, stability, succession, predictability, variability, grassland, savannah, chaos, ecological scale, vegetation dynamics
Abstract Longterm (45 years) temporal data were used to assess the influence of spatial scale on temporal patterns of a semi-arid west Texas grassland. Temporal basal area dynamics of common curlymesquite (Hilaria belangeri (Steud.) Nash) collected from permanent plots within two areas that were released from disturbance (longterm overgrazing and drought), were evaluated at two spatial scales (quadrat, site). Wiens (1989) proposed hypotheses to characterize the influence of scale on variability, predictability, and equilibrium. These hypotheses were tested for this grassland and temporal patterns observed were different for each spatial scale. The large scale (site) was characterized by low variation between units, high variation within units, high potential predictability, and possible movement toward a fluctuating but relatively stable or equilibria1 state. At the small scale (quadrat), variation between units was high, predictability low, and there was no indication of movement toward a stable state; chaotic behavior may be expressed at this scale although the length of the temporal record may not be sufficient to evaluate this phenomenon.
Introduction Ecological scale can be defined in terms of grain and extent (O'Neill et al. 1986). Grain refers to the resolution or sample unit size of a study and defines the lowest level of understanding, while extent defines the entire area of inference and determines the upper limits of understanding. In most studies extent is the primary characteristic described. When grain is not considered, however, many pattern-driving processes may not be properly understood. Ecological processes and patterns are best explained at an inherent scale for a certain characteristic across the entire landscape (Carlile et al. 1989). Over large temporal and spatial scales' the local environment appears in continuous drift with the changing macroclimate. On a smaller scale the macro-climate is mediated by site factors, and biot-
ic interactions become more important driving forces. Quantitative methods have been developed to determine proper scales for identification of scale-dependent ecological patterns (Sugihara and May 1990; Bedward et al. 1992; Cullinan and Thomas 1992). However, no proper scale exists to collectively describe population, community and landscape patterns (Greg-Smith 1964; Wiens 1989; Levin 1992). Analyses of spatial and temporal variations in pattern and the processes that drive them, studied across scale differences, could potentially cause contradictory interpretations. A hierarchical approach has been suggested to evaluate landscape dynamics at multiple scales (O'Neill et al. 1989, 1991; Klijn and de Haes 1994). Recently, several landscape ecological studies have reported the influences of spatial scale on spatial patterns (Turner 1990; Costanza and Maxwell 1994). Wiens (1989) developed hypotheses to
'For this paper, as in most ecological literature, large scale denotes large area or time and small scale denotes small area or time.
108 explain the influence of scale on variability, predictability, community dynamics, and equilibrium conditions. The goal of this investigation was to evaluate these hypotheses for longterm (45 years) temporal patterns at two spatial scales (quadrat, site) from a perennial grassland community which has been released from disturbance (heavy continuous grazing and severe drought).
Materials and methods Data were collected at the Texas A&M University Agricultural Experiment Station located 56 km south of Sonora, Texas on the southwestern edge of the Edwards Plateau (Hatch et al. 1990). Since 1918 annual precipitation has averaged approximately 600 mm. Rainfall has a spring/fall bimodal distribution and is highly variable with frequent droughts; a major drought occurred during the period 1950 to 1956. Soils are shallow (15 to 30 cm) and are formed from limestone substrates. They are Tarrant stony clays and classified as Lithic Haplustolls. Limestone fragments and slabs outcrop frequently across the area. Landscape patterns are heterogeneous with gentle slopes which produce patterns of shallow soil divides, boulder limestone outcrops, and low areas with relatively deep, continuous soils. The vegetation is a mosaic of juniper and oak clusters interspersed within a matrix of mid and short grasses (Smeins et al. 1976; Smeins and Merrill 1988). Two, contiguous 16 ha ungrazed exclosures were used to analyze the influence of scale on temporal dynamics. Prior to 1948, the exclosures had a history of heavy continuous use by domestic herbivores. Since then, domestic herbivores have been excluded. In 1948, 3 lines were established along the long axis of each exclosure, and 12 permanently marked 30.5 x 30.5 cm (originally measured as 1 ft2) quadrats were established on each line. The quadrats were evenly spaced and placed at least 4 m from the nearest woody species and where sufficient soil existed for herbaceous plant growth to occur. Basal diameter of all individuals of all perennial grasses were measured in each of the 72 quadrats for each year until 1965. Since 1965, intervals between sampling became sporadic with additional measurements taken in 1968, 1982, 1984, and 1992. Basal diameters were used to cal-
culate circular basal area of each species within each quadrat. Common curlymesquite (Hilaria belangeri (Steud.) Nash) is a short, stoloniferous, perennial sod-grass that tends to increase in abundance with grazing and is the dominant herbaceous species. When grazing is eliminated the general trend is for this species to be replaced by taller bunchgrasses such as Bouteloua curtipendula (Michx.) Ton: and Eriochloa sericea (Scheele) Munro. Smeins and Merrill (1988) reported that curlymesquite contributed 68% or more of the basal area of perennial grasses prior to the establishment of the grazing exclosures in 1948. By 1982 common curlymesquite contributed only 19%. Analysis in the current study was limited to curlymesquite because of its abundance throughout the duration of this investigation and its significance as a successional indicator. Large scale dynamics (extent) were analyzed by summing the 36 individual quadrats in each exclosure and comparing that to individual quadrats dynamics (grain) to evaluate scaling influences. Thirty-five ofthe 72 quadrats were not evaluated at the small scale. Since 1948, some of the quadrats had become covered by woody species such as Juniperus ashei Buchholz. Cover of a quadrat by woody species can greatly alter the herbaceous vegetation. Hence, these quadrats were removed from small-scale analysis to omit this confounding effect. This omittion of the 35 quadrats resulted in the analysis of 37 units at the small scale. Correlation analysis (Turner et al. 1992) and analysis of variance was conducted to quantify differences between dynamic patterns at each scale. Correlations of the summed basal area of common curlymesquite between the two exclosures across years (1948- 1992) were used to evaluate large scale dynamics. Correlations of the basal area of common curlymesquite for all combinations (n=668) of the 37 individual quadrats against one another over time (1948-1992) were used to evaluate small scale dynamics. Analysis of variance was used to identify any significant differences between units within each scale and to assess the significance of variation in annual precipitation. Differences between correlation analysis, analysis of variance and interpretation of plots at each scale were used to explain differences in variability, predictability, and dynamic patterns across scales.
109
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Fig. 2. Number of individual quadrats (N=37) without common curlymesquite across time.
Results Influence of spatial scale on temporal dynamics of common curlymesquite was determined through correlations between the large scale units (2 exclosures) and for all combinations of appropriate small scale units (37 quadrats). The large scale is a sum of individual quadrats so the measurements at each scale are not independent of each other. Lack of independence enhances the illustration of scaling influence because the data analyzed for both scales are identical. Common curlymesquite has generally decreased in percent composition in each of the exclosures through time. At this large scale, absolute values have varied significantly across years but shown little or no consistent positive or negative trends (Fig. 1). However, when small scale units are analyzed (n=37), the number of quadrats without common curlymesquite increased from 0 in 1949 to 22 (out of 37) in 1992 (Fig. 2). Only two quadrats contained this species through the entire time frame. Although common curlymesquite has recently become locally limited, the few remaining plants have sufficiently high absolute basal areas to maintain levels at the large scale. The pattern has changed from many small plants within many quadrats to a few large plants in a few quadrats, which seems to reflect a general pattern of grasses when under grazed verses ungrazed conditions (Butler and Briske 1988). Correlations between large scale units over time indicated a strong positive relationship (r=0.9 1)
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Fig. 3. Relationship between the two large scale units (16 ha exclosures) over time where each point represents the sum of the basal area (cm2) of common curlymesquite for all quadrats within each unit for a given year.
(Fig. 3). When basal area in one exclosure changed, the other exclosure responded in a similar manner. Analysis of variance showed no significant differences between the two large scale units within any year (p=0.137). Though there was substantial variation across time, lack of significant differences and high correlation indicate synchronous movement between units over time. Correlations between individual quadrats over time were highly variable but in general weak (r=O. 187, sd=0.283, n=688) (Table 1). Comparisons of paired quadrats with essentially the same beginning basal area in 1949 illustrates the poor relationship of dynamics between these initially
110 Table I . Mean and distribution of correlation coefficients between all pairwise combinations of 37 individual quadrats. Correlation class (r)
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similar small scale units (Fig. 4). Initial dynamics of pairs of similar quadrats indicates a general decrease in basal area associated with the drought of the early 1950’s. However, there is no indication of sustained synchronous movement between small scale units across time, in fact their behavior actually appears divergent. Analysis of variance indi-
cates highly significant differences (p=O.OOOl) in basal areas of individual quadrats (small scale) for each year and across all years. At the large scale, analysis of variance indicated a highly significant influence of annual precipitation on basal area (p=O.OOOl). Basal area of common curlymesquite was not significantly different across years (p=0.4217) indicating no overall trend at the large scale. However, a year by precipitation interaction was significant (p=O.OOOl) which indicates that if variation caused by precipitation is accounted for, some successional changes (decreases) in absolute basal area of curlymesquite become evident across time.
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Fig. 4. To illustrate low correlations and divergent behavior at the small scale, four representative pairs of individual quadrats are shown. Each graph represents a quadrat pair selected for their high similarity in initial basal area in 1949 (shown as *). Each point represents the basal area (cm2) of common curlymesquite in two quadrats for a year and the line illustrates their trajectory and relationship through time.
111 Table 2. Influence of different spatial scales on interpretation of temporal dynamics of a grassland community dominated by common curlymesquite. Scale
Variability Between-unit Within-unit Potential predictability Probability of equilibrium Probability of chaos Extent of driving process
Large (exclosure)
Small (quadrat)
Low High High Possible Minimal Large
High Unknown Low Minimal Possible Small (variable)
Discussion Spatial and temporal scale of observation are important in interpreting the patterns that best describe the dynamics of this semi-arid perennial grassland (Table 2). During the time frame of this study many processes occurred that contributed to the variation at both scales. While no attempt was made to determine specific driving forces, the system has been externally influenced since 1949 by: 1) release from a long history of continuous overgrazing, 2) a severe drought from 1951 to 1956, 3) increase in woody plant density and cover, and 4) successional replacements (Smeins and Merrill 1988). Wiens ( 1989) hypothesized that spatial variance between units will generally decrease with an increase in unit of measurement. Large scale (16 ha exclosure) analysis indicated synchronous movement between units which is demonstrated by a high correlation and no significant differences between units for each year (Fig. 1). The similarity between the two units indicates low variances between units, because much of the heterogeneity is within and averages out at the large scale. At the small scale (quadrats), correlations between units were low and analysis of variance indicated significant differences between pairs of individual units (Fig. 4). This illustrates the high spatial variance between sampling units. Since no lower level measurements were taken within-unit variation can not be determined. Potential predictability is inversely related to between-unit variability, and therefore, also varies
with differences in scale. An increase in scale of observation, transforms unpredictable, unrepeatable individual cases to entities with regular behavior that allow generalizations (Levin 1992). Small scale observations in this study are highly variable and divergent in their behavior, that is, the trajectory of two quadrats with the same starting point in 1949 became increasingly different through time. Large spatial scale prediction requires longterm temporal data since at the large scale important driving processes occur at low frequencies over long time intervals. Small or local scale processes usually occur more frequently (Wiens 1989), which indicates the importance of more frequent samples to determine driving forces at this level of analysis. Measurements would have to be made more frequently to determine important driving processes and maintain accurate predictions at the scale of individual quadrats since these responses are out of phase with one another and driven by processes different from those operating at the large scale. The concept of natural systems becoming equilibrial has traditionally dominated ecology. Synchronous movement between units at the large scale across time indicates a tendency of the system to maintain some level of metastability with a driving process (probably climate) (Fig. 1). At the small scale, however, variations occur much more rapidly. An individual quadrat can go from relatively high basal area one year to zero the next with no indication of the cause from climatic data (Fig. 4). The stability or equilibrium of a community is arranged in numerical scales that form a nested hierarchy (Rahel 1990). A community is most stable when the absolute abundance remains relatively constant over time, as illustrated by the large scale of this study. The least stable condition occurs when even the presence and absence of component species are unpredictable over time and where local extinctions and recolonization are common, similar to the individual quadrat analysis where common curlymesquite no longer appeared in 60% of the units in 1992 and was only recorded in two quadrats across all years. Wiens (1989) stated that any behavior at small scales could produce much different behavior at larger scales. It is evident that the large scale is less variable than the small scale and any judgements of stability or equilibrium depend upon the scale of analysis.
112 Recent discussions of dynamics have focused less on linear equilibrial based theory and more on non-linear behaviors, such as chaos (Gleick 1987). Chaos is a deterministic behavior that appears stochastic, and demonstrates extreme sensitivity to initial conditions. It is difficult to identify in natural systems because long term data sets are required (Schaffer and Kot 1985). The possibility of chaotic behavior is predicted on: 1) the presence of strange attractors and, 2) divergent properties where systems with the same starting point lead to extremely different results (Hastings et al. 1993). Tilman and Wedin (1992) identified traits similar to chaotic behavior in a perennial grass population but failed to identify either of these characteristics. Other scientists claim that few, if any, plant populations would be expected to express chaotic behavior (Crawley 1990). Although our data were collected over a long time frame relative to most ecological studies, identification of strange attractors requires much larger and longer term data sets. Phase-space plots of these data only indicate a lack of point attractors and little or no cyclic behavior. Even if we accept the presence of strange attractors it does not always indicate chaotic behavior. Systems must also be divergent, where similar starting points lead to exponentially different results over time. Predictability may exist over a short time but deteriorates over larger temporal scales. Correlation of large scale units (r=0.91) identifies a lack of divergence at this spatio-temporal scale (Figs. 1 and 3). Large units maintained very similar values throughout the entire time frame which eliminates the application of chaotic behavior. However, individual quadrats compared with other quadrats with similar starting points appear to be highly divergent and unpredictable over time. According to this data, if strange attractors could be identified for individual quadrats or small scales, this could illustrate chaotic behavior. Identification of chaotic behavior is inconclusive but could exist at the small scale. Large scale patterns are often driven by physical environmental or climatic variations, while at local scales driving processes are frequently biotic interactions, such as competition (Menge and Olson 1990). No attempt was made to determine specific driving forces at each scale, but it is obvious that
different processes are important at each scale. Patterns at the large scale are driven by sufficiently large processes to influence both units synchronously, such as climate or release from herbivory. Patterns of individual quadrats are driven by processes small enough to influence units independently, such as biotic interactions and topoedaphic features. However, during the drought of the 1950’s the large scale influence of the climate became harsh enough to cause consistent and major decreases in basal area of small scale units, which indicates that pattern driving processes can change over time. Many of the hypotheses by Wiens (1989) are apparent for this perennial grassland community (Table 2). The large scale is characterized by low variation between units, high variation within units, high predictability, and possible movement toward a relatively stable or equilibrial state although fluctuating with precipitation. At the small scale, variation between units is high, predictability is low, there is no indication of movement toward a stable state and chaotic behavior could occur. Multiple scales of evaluation enhance the interpretation of longterm temporal patterns for this semi-arid grassland and generally agree with the tenets developed by Wiens (1989).
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