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Titolo:
Modelling of rare plant species richness by landscape variables in an agriculture area in Finland
Autore:
Luoto, M;
Indirizzi:
Finnish Environm Inst, GIS & Remote Sensing Unit, FIN-00251 Helsinki, Finland Finnish Environm Inst Helsinki Finland FIN-00251 00251 Helsinki, Finland
Titolo Testata:
PLANT ECOLOGY
fascicolo: 2, volume: 149, anno: 2000,
pagine: 157 - 168
SICI:
1385-0237(200008)149:2<157:MORPSR>2.0.ZU;2-6
Fonte:
ISI
Lingua:
ENG
Soggetto:
DIVERSITY; COMMUNITIES; VEGETATION; PREDICTION; WILDLIFE; PATTERNS; HABITATS; ECOLOGY;
Keywords:
GIS; habitat; hotspot; semi-natural grassland; spatial modelling; surrogacy method;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Citazioni:
41
Recensione:
Indirizzi per estratti:
Indirizzo: Luoto, M Finnish Environm Inst, GIS & Remote Sensing Unit, Box 140, FIN-00251 Helsinki, Finland Finnish Environm Inst Box 140 Helsinki Finland FIN-00251 Finland
Citazione:
M. Luoto, "Modelling of rare plant species richness by landscape variables in an agriculture area in Finland", PLANT ECOL, 149(2), 2000, pp. 157-168

Abstract

A multivariate linear regression model is proposed for predicting and mapping rare vascular plant species richness in Finnish agricultural landscapesaccording to landscape variables. The data used in developing the model were derived from a floristic inventory from 105 0.5 km x 0.5 km grid squares. Using a stepwise multiple regression technique, four landscape variables were found to explain 71.8% of the variability in the number of rare plant species. The results suggest that the local 'hotspots' of rare plants (squares with greater than or equal to5 rare taxa) are mainly found in heterogeneous river valleys, where extensive semi-natural grasslands and herb-rich forests occur on the steep slopes. According to other similar studies, intermediate human disturbance increases the number of rare species in agricultural landscapes. It appears that empirical models based on landscape variables derived from digital maps can provide relatively accurate surrogates forextensive field surveys and fine-scale observations on the distributions of rare taxa in agricultural landscapes. Potential reasons for the performance of the model and the ecology and habitats of the species concerned are discussed.

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Documento generato il 10/04/20 alle ore 15:41:20