Species in the grass family (Poaceae) have caused some of the most damaging invasions in natural ecosystems, but plants in this family are also among the most widely used by humans. Therefore, it is important to be able to predict their likelihood of naturalisation and impact. We explore whether plant height is of particular importance in determining naturalisation success and impact in Poaceae by comparing naturalisation of tall-statured grasses (TSGs; defined as grass species that maintain a self-supporting height of 2 m or greater) to non-TSGs using the Global Naturalised Alien Flora database. We review the competitive traits of TSGs and collate risk assessments conducted on TSGs. Of the c. 11,000 grass species globally, 929 qualify (c. 8.6%) as TSGs. 80.6% of TSGs are woody bamboos, with the remaining species scattered among 21 tribes in seven subfamilies. When all grass species were analysed, TSGs and non-TSGs did not differ significantly in the probability of naturalisation. However, when we analysed woody bamboos separately from the other grasses, the percentage of TSGs that have naturalised was 2–4 times greater than that of non-TSGs for both bamboos and non-bamboo groups. Our analyses suggest that woody bamboos should be analysed separately from other TSGs when considering naturalisation; within the ≥ 2 m height class they do not naturalise at the same rate as other TSGs. Rapid growth rate and the capacity to accumulate biomass (a function of height) give many TSGs a competitive advantage and allow them to form monospecific stands, accumulate dense and deep litter mats, reduce light availability at ground level, and alter fire and nutrient-cycling regimes, thereby driving rapid ecosystem transformation. While the height distribution in grasses is continuous (i.e. no obvious break is evident in heights), the 2 m designation for TSGs defines an important functional group in grasses that can improve predictive modelling for management and biosecurity.

Tall-statured grasses: a useful functional group for invasion science

BRUNDU, GIUSEPPE ANTONIO DOMENICO;CICATELLI, Angela;Guarino, Francesco;
2018-01-01

Abstract

Species in the grass family (Poaceae) have caused some of the most damaging invasions in natural ecosystems, but plants in this family are also among the most widely used by humans. Therefore, it is important to be able to predict their likelihood of naturalisation and impact. We explore whether plant height is of particular importance in determining naturalisation success and impact in Poaceae by comparing naturalisation of tall-statured grasses (TSGs; defined as grass species that maintain a self-supporting height of 2 m or greater) to non-TSGs using the Global Naturalised Alien Flora database. We review the competitive traits of TSGs and collate risk assessments conducted on TSGs. Of the c. 11,000 grass species globally, 929 qualify (c. 8.6%) as TSGs. 80.6% of TSGs are woody bamboos, with the remaining species scattered among 21 tribes in seven subfamilies. When all grass species were analysed, TSGs and non-TSGs did not differ significantly in the probability of naturalisation. However, when we analysed woody bamboos separately from the other grasses, the percentage of TSGs that have naturalised was 2–4 times greater than that of non-TSGs for both bamboos and non-bamboo groups. Our analyses suggest that woody bamboos should be analysed separately from other TSGs when considering naturalisation; within the ≥ 2 m height class they do not naturalise at the same rate as other TSGs. Rapid growth rate and the capacity to accumulate biomass (a function of height) give many TSGs a competitive advantage and allow them to form monospecific stands, accumulate dense and deep litter mats, reduce light availability at ground level, and alter fire and nutrient-cycling regimes, thereby driving rapid ecosystem transformation. While the height distribution in grasses is continuous (i.e. no obvious break is evident in heights), the 2 m designation for TSGs defines an important functional group in grasses that can improve predictive modelling for management and biosecurity.
2018
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4718730
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