Ceška, A. & H. Roemer. 1976: Ceška-Roemer Program for Identifying Species-Relevé Groups, Manuscript. 14 pp.Google Scholar
Dale, M.B. & L., Quadraccia. 1973. Computer assisted tabular sorting of phytosociological data. Vegetatio 28: 57–73.Google Scholar
Holzner, W. & F., Stockinger. 1973. Der Einsatz von Elektronenrechnern bei der pflanzensoziologischen Tabellenarbeit. Oesterr. Bot. Z. 121: 303–309.Google Scholar
Holzner, W., M.J.A., Werger & G.A., Ellenbroek. 1978. Automatic classification of phytosociological data on the basis of species groups. Vegetatio 38: 157–164.Google Scholar
Lieth, H. & G.W., Moore. 1971. Computerized clustering of species in phytosociological tables and its utilization for field work. In: G.P., Patil, E.C., Pielou & W.E., Waters (eds.), Statistical Ecology, Vol. 1, p. 403–422. London, Fenn. State Univ. Press.Google Scholar
Louppen, J.M.W. & E.van der, Maarel. 1979. Clusla. Vegetatio 40: 107–115.Google Scholar
Maarel, E.van der. 1979. Transformation of cover-abundance data Vegetatio 39: 97–115.Google Scholar
Maarel, E.van der, J.G.M., Janssen & J.M.W., Louppen. 1978. TABORD, a program for structuring phytosociological tables. Vegetatio 38: 143–156.Google Scholar
Orlóci, L. 1978 Multivariate analysis in vegetation research. Junk, The Hague, 2nd ed., 451 pp.Google Scholar
Orlóci, L. 1980. An algorithm for predistive erdination. In: E. van der Maarel (ed.). Advances in vegetation science: Classification and ordination Vegetatio 42: 000-000.Google Scholar
Spatz, G. & J., Siegmund. 1973. Eine Methode zur tabellarischen Ordination, Klassifikation und ökologischen Auswertung von pflanzensoziologischen Bestandesaufnahmen durch den Computer. Vegetatio 28: 1–17.Google Scholar
Streng, R. & Schönfelder. 1978. Ein heuristisches Computer-Programm zur Ordnung pflanzensoziologischer Tabellen. Hoppea, Denkschr, Regensb. Bot. Ges. 37: 407–433.Google Scholar
Wedekind, H., 1972. Datenorganisation. Walter de Gruyter, Berlin, New York. 2. Aufl. 271 pp.Google Scholar
Wildi, O., P.H. Fewster & L. Orlóci, 1978: Management and multivariate analysis of large data sets in vegetation research. Manuscript. London, Ont., Canada.Google Scholar
Anthropogenic impacts have been documented across more than three-quarters of the Earth’s ice-free terrestrial surface [1,2], and the global intensification of land use threatens biodiversity in human-impacted areas [3,4]. Multifunctional landscapes comprised of natural and semi-natural ecosystems set in the context of intensive land use can address multiple goals of production and conservation [5,6,7], but severe degradation can overcome ecosystem resilience and reduce ecosystem stability and function through catastrophic state shifts .
In degraded environments, conservation of native biodiversity and restoration of ecosystem function often requires some degree of ecosystem restoration to a less-degraded state. As such, managers must often determine the causes of ecological degradation before proceeding with restoration. When degradation factors are clear and controllable, restoration can be managed agronomically and proceeds linearly towards a restoration objective, such as pre-settlement conditions . For example, restoring native grassland from cropland agriculture typically involves managing for maximal native species diversity and minimal non-native species by planting a diverse mix of native species and removing exotic species [10,11].
Restoration of intensively cultivated areas is fairly straightforward, but the best course of restoration action for sites degraded by multiple factors can be elusive. Specific sources of degradation might not be readily identifiable because they result from multiple causative factors that vary spatially and temporally, and the response to restoration might be non-linear and unpredictable [12,13,14]. Land-use history, recent disturbance, and invasive species create legacies with long-term impacts on plant community composition that strongly resist change toward the desired restoration outcome [12,15,16]. Restoration and conservation under such conditions require an ecological approach using the initial extent of floristic degradation to identify appropriate restoration action and outcomes [9,17].
We construct vegetation states to describe floristic degradation in nine old-field grassland tracts in south-central Iowa and north-central Missouri, USA. This study was prompted by a clear need to identify patterns of floristic degradation that influence other taxa in these grasslands [18,19,20]. We use multivariate analyses to categorize floristic degradation into vegetation states with plant species data, a common approach in rangelands worldwide [21,22,23]. We also use soil organic carbon (SOC) as an indicator of biophysical degradation because both cultivation and severe grazing have been shown to reduce SOC content .
We report the relationship between native and exotic plant species abundance, grazing history, and a regionally-ubiquitous invasive plant species, the Eurasian, cool-season grass, tall fescue (Schedonorus phoenix (Scop.) Holub). Tall fescue is one of the most economically-important grasses in eastern North America since its introduction in the 1940’s , but tall fescue has since been shown to reduce native plant species richness, fire spread, and habitat quality in grassland [26,27,28,29,30].
We predicted that patterns of plant community composition (i.e., vegetation states) correspond to patterns in three factors of degradation: tall fescue abundance, grazing history and soil organic carbon. To inform restoration action, we use a state-and-transition framework [31,32] to discuss potential degradation and restoration pathways with respect to conservation in working grassland landscapes.