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Seed and Soil Dynamics in Shrubland Ecosystems

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Seed and Soil Dynamics in Shrubland Ecosystems Predicting Seedling Emergence Using Soil Moisture and Temperature Sensors Jeffrey R. Taylor Bruce A. Roundy Phil S. Allen Susan E. Meyer Abstract: Hydrothermal time models are often used to predict seed Stipa capensis) provide important forage for grazing ani- germination rates. In this study, soil water potential data from mals. In the Great Basin Desert, United States of America, three resistance-type sensors (Colman cells, Watermark brand introduced downy brome (Bromus tectorum) can invade and sensors, and Delmhorst gypsum blocks) and from a time-domain disrupt native perennial communities (Billings 1994; reflectometry (TDR) probe (Campbell Scientific 615) were input into D’Antonio and Vitousek 1992). a hydrothermal time model to predict seedling emergence in a Successful germination and seedling establishment in growth chamber experiment for six desert grass species, including desert ecosystems is often limited by species-specific seed Brachypodium distachyon, Bromus fasciculatus, Crithopsis germination responses to near-surface soil water potentials delilianus, and Stipa capensis from the Negev Desert, and natural- and temperatures. Frasier (1987) reported that seedling ized downy brome (Bromus tectorum) and bottlebrush squirreltail survival for five warm-season desert grasses depended upon (Elymus elymoides) from the Great Basin Desert. Seeds were sown the number of seedlings that successfully emerged after in a structureless sandy loam soil that was irrigated approximating initial wetting, the number of nongerminated and viable 0.58, 1.25, 1.50, and 2.00 times field capacity volumetric water seed that remain, and the time interval between wet and dry content and allowed to dry in a growth chamber programmed to periods. For example, the ability of Lehmann lovegrass simulate spring temperatures in Provo, UT. Colman cells and (Eragrostis lehmanniana) to retain a viable seedbank dur- Delmhorst gypsum blocks indicated more rapid soil surface drying ing erratic summer rainfall in the Sonoran Desert grassland than did Watermark sensors and TDR probes. Inputs provided by allows it to establish better than some native grasses that the sensors correctly predicted seedling emergence of these rapidly germinate most of their seeds after initial rains and are then germinating grasses when water potential was high, but incorrectly subject to seedling desiccation (Abbott and Roundy 2003). predicted emergence when rapidly decreasing water potentials Seed germination models using soil matric potential (Y) inhibited emergence. These results suggest that more accurate and temperature data can be used to accurately predict measurement or prediction of seed zone water potentials may be germination (Allen and others 1999; Bradford 1990; Finch- necessary before hydrothermal time models can effectively predict Savage and Phelps 1993; Roman and others 1999). seedling emergence in the field. Gummerson (1986) was the first to combine water potential and temperature data to model germination (hydrothermal time). Hydrothermal time has since been used to success- fully model germination for a variety of species (Allen and Introduction ____________________ others 1999; Christensen and others 1997; Finch-Savage and Phelps 1993; Roman and others 1999). Annual and perennial grasses influence arid land plant Electric resistance sensors and time-domain reflectom- communities by affecting biomass production, soil erosion, etry (TDR) probes are used to estimate soil moisture condi- plant and herbivore interactions, and fire frequency. In the tions (Amer and others 1994; Baker and Allmaras 1990; Negev Desert, Israel, annual grasses (such as Brachypodium Collins 1987; Roundy and others 1997, 2001). Various sen- distachyon, Bromus fasciculatus, Crithopsis delilianus, and sors have been compared to see how they respond to changes in soil moisture. Topp and Davis (1985) compared TDR and gravimetric sampling at various depths within the top meter of soil and found both techniques produced similar results. In: Hild, Ann L.; Shaw, Nancy L.; Meyer, Susan E.; Booth, D. Terrance; Collins (1987) compared gypsum blocks with Colman cells McArthur, E. Durant, comps. 2004. Seed and soil dynamics in shrubland and found that Colman cells had poor sensitivity in dry soils ecosystems: proceedings; 2002 August 12–16; Laramie, WY. Proceedings RMRS-P-31. Ogden, UT: U.S. Department of Agriculture, Forest Service, as well as large differences among individual sensor resis- Rocky Mountain Research Station. tances. When comparing gypsum blocks and Watermark Jeffrey R. Taylor and Susan E. Meyer are, respectively, Range Technician and Ecologist at the Shrub Sciences Laboratory, Rocky Mountain Research sensors, Hanson and others (2000) reported that gypsum Station, Forest Service, U.S. Department of Agriculture, Provo, UT 84606, blocks did not indicate reductions in soil water matric U.S.A., e-mail: [email protected] and [email protected]. Bruce A. Roundy and potential until the surrounding media fell below a certain Phil S. Allen are, respectively, Professor, Department of Integrative Biology, and Associate Professor, Department of Plant and Animal Sciences, Brigham moisture threshold. In addition, they found that Watermark Young University, Provo, UT 84602, U.S.A. 140 USDA Forest Service Proceedings RMRS-P-31. 2004 Predicting Seedling Emergence Using Soil Moisture and Temperature Sensors Taylor, Roundy, Allen, and Meyer sensors were more responsive in drier soils. In a comparison thermal time assumes that matric potential has no effect (in between Colman cells, TDR, and gravimetric sampling, other words, it has a value of 0): Amer and others (1994) found that Colman cells tended to TT = (0– )(T–T )t indicate higher soil moisture content and exhibited more Yb(g) b (g) variation, but were more effective at measuring shallow which can be simplified to: depths because they measure moisture at points. A more TT = (– )(T–T )t thorough and simultaneous comparison among available Yb(g) b (g) sensors in soils with widely fluctuating soil water potentials The TT equation and the HT equation incorporate the would better determine their ability to provide meaningful same parameters with the exception of soil water potential. data to predict seedling emergence. This can be useful for germination modeling when there is abundant soil moisture but an accurate Y measurement is Hydrothermal Time unavailable. Nondormant seeds germinate after being exposed to suit- able temperatures and water potentials for an adequate Methods _______________________ period of time (Allen and others 1999; Bradford 1995; Finch- We had three objectives in this study. First, compare the Savage and Phelps 1993; Gummerson 1986; Roman and drying response of four different soil moisture sensors near others 1999). Hydrothermal time can be described by the the soil surface with different irrigation treatments. The equation (Gummerson 1986): sensors we used were TDR probes (Campbell Scientific, Inc., HT = (Y–Yb(g))(T–Tb)t(g) Logan, UT), Watermark brand sensors (Irrometer Co., Inc., Riverside, CA), Delmhorst gypsum blocks (Delmhorst In- At the time of germination: strument Co., Towaco, NJ), and Colman cells (Soiltest Inc., qHT = (Y–Yb(g))(T–Tb)t(g) Lake Bluff, IL). Second, use soil moisture data from the different sensors as driving variables in a hydrothermal time HT is the amount of hydrothermal time that has accumu- model for 11 grass seed collections from the Negev Desert, lated (expressed as MPa- -hours or days). The amount of ∞ Israel, and the Great Basin, United States of America. Third, hydrothermal time required for germination is (the qHT compare predicted and observed emergence for these 11 amount of HT that satisfies the requirement for radicle grass seed collections. emergence). Y is the water potential of the soil, and Yb(g) is the minimum or base water potential for (g) fraction of the seeds to germinate for a given population. T is the tempera- Soil Moisture Release Curve ture of the soil, and T is the minimum seed germination b We used a structureless, nonsaline (electrical conductiv- temperature. The time required for g fraction of the seeds to ity 0.4 mmhos cm–1), sandy loam (74.8 percent sand, 15.2 germinate is t . (g) percent silt, and 8.5 percent clay) for this experiment. Water Hydrothermal time analysis requires that data be probit content of six soil samples was measured at 0.01, 0.033, 0.1, transformed (to linearize the typical cumulative time course 0.3, 0.7, and 1.5 MPa using pressure plate Soil Moisture for germination of a particular collection). Transformed data Equipment Co. extractors. The relationship between volu- are used to compare germination time differences among the metric water content and matric tension was modeled using various fractions of the seed population. Base soil water log-linear regression (double-log) focusing on three data matric potentials ( ) are assumed to vary normally within Yb points in the crucial area: the population. The Yb(g) of a given germination fraction –14.3195 4.74341 from a population of seeds with a known Yb(50) can be Y = e / x calculated using a formula: where Y is soil water matric tension (MPa) and x is volumet- Yb(g) = Yb(50) + (sYb x prob(g)) ric water content (fig. 1). where Yb(g) is the minimum or base water potential for g fraction of the seeds to germinate from a given population, Sensors is the standard deviation of the base water potential, and sYb We buried three resistance-type sensors and one TDR prob is the probit conversion of fraction g. Although the (g) probe in plastic trays. Colman resistance cells were indi- model can algebraically calculate for any fraction, Yb(g) vidually calibrated for drying soil water content (Roundy fractions beyond 2 standard deviations of the mean are less and others 1997) to reduce the effects of sensor variability, likely to be normally distributed. while standard calibration curves were used for Delmhorst gypsum blocks (CSI 1983), Watermark sensors (CSI 1996a), Thermal Time and TDR probes (CSI 1996b). Resistance cells or blocks were all 2 cm in height or diameter and were buried at a depth of We adapted the thermal time (TT) equation from the 1 cm (zone of measurement 1 to 3 cm).
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