New Technology for Estimating Seed Production of Moist‐Soil Plants

Gray, Matthew J.; Foster, Melissa A.; Peniche, Luis A. Peña

doi:10.2193/2008-468

Cited by 11 publications

(26 citation statements)

References 15 publications

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“…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).…”

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confidence: 99%

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

2011

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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.

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

2011

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“…However, performance outside of that region was reduced similar to previous models (Laubhan and Fredrickson , Gray et al , Stafford et al ). Gray et al () improved morphological models by streamlining the measurement process using desktop or portable scanners. Prediction models produced by Gray et al () depended on scanned seed‐head area instead of time‐consuming morphological measurements and retained robust predictive power ( r 2 > 0.91).…”

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“…Gray et al () improved morphological models by streamlining the measurement process using desktop or portable scanners. Prediction models produced by Gray et al () depended on scanned seed‐head area instead of time‐consuming morphological measurements and retained robust predictive power ( r 2 > 0.91). However, updated morphological models predict seed production rather than seed availability.…”

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“…Seed availability can be affected by decomposition, granivory, germination, and other factors prior to waterfowl accessing foods, including presence of an unknown biomass of seeds in the substrate remaining from previous growing seasons (Neely ; McGinn and Glasgow ; Nelms and Twedt ; Kross et al ; Foster et al ,). Additionally, current models have not been evaluated for annual changes in seed production using data collected across multiple years (Gray et al ).…”

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“…We evaluated effects of temporal variation and belowground seed density on seed production estimates from models developed by Gray et al (). We examined 1) previously published seed‐yield models from moist‐soil plants to assess effects of temporal variation in seed production on estimates; and 2) belowground biomass to adjust production estimates for foods actually available at the time of sampling.…”

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Temporally robust models for predicting seed yield of moist‐soil plants

Osborn

Hagy

McClanahan³

et al. 2017

Wildl. Soc. Bull.

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Rapid assessment of food production and subsequent availability is fundamental to evaluating wetland management practices and general habitat quality for waterfowl. Traditional methods of estimating food biomass (e.g., plot and core sampling) require considerable time, expertise, and cost. Rapid assessment models using plant measurements or scanned seed-head area have recently been adapted to predict seed production in moist-soil wetlands. We evaluated existing models of seed production and estimated benthic seed density with data collected during autumn 2011 in western Tennessee, USA, to improve prediction capability of seed availability for waterfowl. Generally, all models explained significant variation (r 2 ¼ 0.85-0.98) and accurately predicted seed production in moist-soil plants (r 2 ¼ 0.84-0.97). Belowground proportions of seed biomass and duck energy days differed across species relative to previously reported biomass estimates in moist-soil wetlands ( x ¼ 0.4-9.1%); thus, production estimates from models should be adjusted on a species-specific basis and the effect of belowground seeds on overall energetic carrying capacity estimates will vary with species composition of wetlands. We recommend use of updated most-soil rapidassessment models incorporating seed bank estimates to predict waterfowl food availability and evaluate management practices. Ó 2017 The Wildlife Society.

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Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through remote sensing

Byrd¹,

Lorenz²,

Anderson

et al. 2020

Ecological Applications

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California's Central Valley, USA is a critical component of the Pacific Flyway despite loss of more than 90% of its wetlands. Moist soil seed (MSS) wetland plants are now produced by mimicking seasonal flooding in managed wetlands to provide an essential food resource for waterfowl. Managers need MSS plant area and productivity estimates to support waterfowl conservation, yet this remains unknown at the landscape scale. Also the effects of recent drought on MSS plants have not been quantified. We generated Landsat-derived estimates of extents and productivity (seed yield or its proxy, the green chlorophyll index) of major MSS plants including watergrass (Echinochloa crusgalli) and smartweed (Polygonum spp.) (WGSW), and swamp timothy (Crypsis schoenoides) (ST) in all Central Valley managed wetlands from 2007 to 2017. We tested the effects of water year, land ownership and region on plant area and productivity with a multifactor nested analysis of variance. For the San Joaquin Valley, we explored the association between water year and water supply, and we developed metrics to support management decisions. MSS plant area maps were based on a support vector machine classification of Landsat phenology metrics (2017 map overall accuracy: 89%). ST productivity maps were created with a linear regression model of seed yield (n = 68, R 2 = 0.53, normalized RMSE = 10.5%). The Central Valley-wide estimated area for ST in 2017 was 32,369 ha (29,845-34,893 ha 95% CI), and 13,012 ha (11,628-14,396 ha) for WGSW. Mean ST seed yield ranged from 577 kg/ha in the Delta Basin to 365 kg/ha in the San Joaquin Basin. WGSW area and ST seed yield decreased while ST area increased in critical drought years compared to normal water years (Scheffe's test, P < 0.05). Greatest ST area increases occurred in the Sacramento Valley (~75%). Voluntary water deliveries increased in normal water years, and ST seed yield increased with water supply. Z scores of ST seed yield can be used to evaluate wetland performance and aid resource allocation decisions. Updated maps will support habitat monitoring, conservation planning and water management in future years, which are likely to face greater uncertainty in water availability with climate change.

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New Technology for Estimating Seed Production of Moist‐Soil Plants

Cited by 11 publications

References 15 publications

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

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

Temporally robust models for predicting seed yield of moist‐soil plants

Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through remote sensing

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