Emergence Patterns of Rare Arable Plants and Conservation Implications

Joel Torra; Frank Forcella; Jordi Recasens; Aritz Royo-Esnal

doi:10.3390/plants9030309

Emergence Patterns of Rare Arable Plants and Conservation Implications

Plants (Basel). 2020 Mar 1;9(3):309. doi: 10.3390/plants9030309.

Authors

Joel Torra¹, Frank Forcella², Jordi Recasens¹, Aritz Royo-Esnal¹

Affiliations

¹ Department of Hortofruticultura, Botànica i Jardineria, Agrotecnio, Universitat de Lleida. Alcalde Rovira Roure 191, 25198 Lleida, Spain.
² Department of Agronomy and Plant Genetics, College of Food, Agricultural and Natural Resources Sciences (CFANS), University of Minnesota, St Paul, MN 55108, USA.

Abstract

Knowledge on the emergence patterns of rare arable plants (RAP) is essential to design their conservation in Europe. This study hypothesizes that is possible to find functional groups with similar emergence patterns within RAP with the aim of establishing management strategies. Seeds of 30 different species were collected from Spanish arable fields and sown under two tillage treatments: (a) 1 cm depth without soil disturbance to simulate no-till, and (b) 1-10 cm depth with soil disturbance every autumn to simulate tillage to 10 cm depth. Two trials were established; the first trial being maintained for three seasons and the second for two seasons. Relative emergence in autumn, winter and spring was calculated each season. Afterwards, multivariate analysis was performed by K-means clustering and Principal Component Analysis to find groups of RAP species with similar emergence patterns. Four RAP groups were defined, and each was based on its main emergence season: autumn, winter, spring, or autumn-winter. Tillage treatment and the year of sowing had little effect on emergence patterns, which were mostly dependent on environmental factors, particularly temperature and rainfall. Therefore, conservation strategies could be designed for each of these RAP functional groups based on emergence patterns, rather than on a species-by-species basis.

Keywords: Calinski–Harabasz criterion; K-means; Principal Component Analysis (PCA); burial depth; clustering; multivariate analyses; no-tillage; soil disturbance; tillage.