Predicting Seed Yield of Moist-Soil Plants

Gray, Matthew J.; Kaminski, Richard M.; Weerakkody, Govinda J.

doi:10.2307/3802843

Cited by 19 publications

(24 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conservationists often manage seasonal wetlands to maintain diverse, early‐succession plant communities that produce abundant seeds, tubers, and aquatic invertebrates for waterfowl and wetland wildlife (e.g., moist‐soil management; Fredrickson and Taylor 1982, Smith et al 1989). Scientists estimate annual seed production in dewatered wetlands or uplands in summer and autumn by measuring standing seed crops or vacuuming seeds from the soil surface (Gray et al 1999 a , b ; Gray et al 2009; Naylor et al 2005; Penny et al 2006). Alternatively, core samplers are commonly used to estimate seed abundance after seeds have dropped from panicles, when seeds may be present beneath the soil or water surface, and when food availability may have changed from the time of seed production and sampling (Murkin et al 1994; Gray et al 1999 a , b ; Stafford et al 2006; Kross et al 2008).…”

mentioning

confidence: 99%

“…Scientists estimate annual seed production in dewatered wetlands or uplands in summer and autumn by measuring standing seed crops or vacuuming seeds from the soil surface (Gray et al 1999 a , b ; Gray et al 2009; Naylor et al 2005; Penny et al 2006). Alternatively, core samplers are commonly used to estimate seed abundance after seeds have dropped from panicles, when seeds may be present beneath the soil or water surface, and when food availability may have changed from the time of seed production and sampling (Murkin et al 1994; Gray et al 1999 a , b ; Stafford et al 2006; Kross et al 2008). However, scientists often use small‐diameter coring devices (≤15 cm) due to laborious processing methods, which results in sampling of small proportions of habitats and subsequent extrapolation of abundance estimates and potential error associated with the core sampling process (Loesch et al 1994, Manley et al 2004, Stafford et al 2006, Soulliere et al 2007, Kross et al 2008).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Estimation and correction of seed recovery bias from moist‐soil cores

2011

Self Cite

View full text Add to dashboard Cite

Scientists estimate seed abundances to calculate seasonal carrying capacities and assess wetland management actions for waterfowl and other wildlife using soil core samples. We evaluated recovery of known quantities of moist‐soil seeds from whole and subsampled experimental core samples containing 12 seed taxa representing small, medium, and large size classes. We recovered 86.3% (SE = 1.8) of all seeds added to experimental cores; 8.3% (SE = 1.2) of seeds were destroyed during the sieving process and 5.4% (SE = 1.2) were not recovered by observers. Recovery rates varied by seed size, but not seed quantity or disproportionate ratios of seed‐size classes. Overall seed recovery rates were similar between subsampled (${\bar {x}}$ = 81.2%, SE = 3.6) and whole–processed core samples (${\bar {x}}$ = 86.3%, SE = 1.8). We used recovery rates to generate size‐specific, taxon‐specific, and constant correction factors and applied each to actual core sample data. Size‐specific correction factors increased seed mass estimates in the Mississippi Alluvial Valley (${\bar {x}}$ = 10.1%, SE = 0.32), upper Midwest (${\bar {x}}$ = 21.2%, SE = 0.61), and both regions combined (${\bar {x}}$ = 15.7%, SE = 0.51) differently, as seed composition in core samples varied regionally. We suggest scientists consider using size‐specific correction factors to account for seed recovery bias in core samples because these factors may be applied to a variety of taxa and produced similar mass estimates as taxon‐specific correction factors. However, if data from core samples are unavailable at the resolution of seed size classes, we suggest increasing seed mass estimates by 16% to account for seed recovery bias. © 2011 The Wildlife Society.

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

Estimation and correction of seed recovery bias from moist‐soil cores

2011

Self Cite

View full text Add to dashboard Cite

show abstract

“…Predictor variables included number of dots for the dotgrid method and area (cm 2 ) for scanners; the response variable was mass of seed per plant. We used simple linear regression to build prediction models for each plant species and method, combining years for more robust parameter estimates (Myers 1990, Gray et al 1999b). We used weighted least-squares regression for red sprangletop (desktop model) and fall panicum (all models), because plots of residuals against predicted values revealed nonconstant variance (Myers 1990).…”

Section: Methodsmentioning

confidence: 99%

“…Prediction models have been used to estimate seed production of moist-soil plants (Laubhan 1992). The first studies developed multiple linear-regression models to predict seed yield using multiple plant morphological measurements (e.g., plant ht, inflorescence diam; Laubhan and Fredrickson 1992, Gray et al 1999b, Sherfy and Kirkpatrick 1999. Waterfowl biologists were reluctant to use these models because measuring multiple plant parts was tedious and time consuming.…”

mentioning

confidence: 99%

New Technology for Estimating Seed Production of Moist‐Soil Plants

2009

Self Cite

View full text Add to dashboard Cite

Waterfowl biologists estimate seed production in moist‐soil wetlands to calculate duck‐energy days (DEDs) and evaluate management techniques. Previously developed models that predict plant seed yield using morphological measurements are tedious and time consuming. We developed simple linear regression models that indirectly and directly related seed‐head area to seed production for 7 common moist‐soil plants using portable and desktop scanners and a dot grid, and compared time spent processing samples and predictive ability among models. To construct models, we randomly collected approximately 60 plants/species at the Tennessee National Wildlife Refuge, USA, during September 2005 and 2006, threshed and dried seed from seed heads, and related dry mass to seed‐head area. All models explained substantial variation in seed mass (R2< 0.87) and had high predictive ability (R2predicted < 0.84). Processing time of seed heads averaged 22 and 3 times longer for the dot grid and portable scanner, respectively, than for the desktop scanner. We recommend use of desktop scanners for accurate and rapid estimation of moist‐soil plant seed production. Seed predictions per plant from our models can be used to estimate total seed production and DEDs in moist‐soil wetlands.

show abstract

“…Laubhan and Fredrickson (1992) predicted seed yield of moist-soil plants in Mississippi using phytomorphological measurements and multiple linear regression. Using the same method, Gray et al (1999) found inconsistency in predictor variables, suggesting model-site specifi city. With moistsoil management in Texas, availability of seeds as food for animals could be enhanced (Haukos and Smith 1996).…”

Section: Availability Of Foodmentioning

confidence: 99%