Seed size and seedling emergence: an allometric relationship and some ecological implications

Citation
Wj. Bond et al., Seed size and seedling emergence: an allometric relationship and some ecological implications, OECOLOGIA, 120(1), 1999, pp. 132-136
Citations number
30
Categorie Soggetti
Environment/Ecology
Journal title
OECOLOGIA
ISSN journal
00298549 → ACNP
Volume
120
Issue
1
Year of publication
1999
Pages
132 - 136
Database
ISI
SICI code
0029-8549(199907)120:1<132:SSASEA>2.0.ZU;2-6
Abstract
We develop a geometric model predicting that maximum seedling emergence dep th should scale as the cube root of seed weight. We tested the prediction b y planting seeds from 17 species ranging in weight from 0.1 to 100 mg at a variety of depths in a sand medium. The species were spread across 16 gener a and 13 families, all occurring in fire-prone fynbos shrublands of South A frica. Maximum emergence depth was found to scale allometrically with seed weight with an exponent of 0.334, close to the predicted value. We used the allometry to predict recruitment response to experimentally simulated vari ation in fire intensity. Five species with small (<2 mg) seeds and five wit h large (>10 mg) seeds were planted at less than or equal to 20-mm and 40-m m depths and exposed to low and high heat treatments and a control. The all ometric equation predicted that species with large seeds would be able to e merge from a depth of 40 mm but those with small seeds would not. Only 1% o f 481 seedlings from small-seeded species emerged from the 40-mm planting c ompared with 40% of 626 seedlings from the large-seeded group. The simulate d fire treatments killed seeds in shallow, but not deeper, soil layers. At simulated high fire intensities, seedling emergence was poor in small-seede d species but good in large-seeded species, with most seedlings emerging fr om the 40-mm planting depth. Seed size could be a useful general predictor of recruitment success under different fire intensities in this system. We suggest that allometric relationships in plants deserve wider attention as predictive tools.