For multivariate analysis, data were z-score standardized and Euclidean distance matrices produced for each

parameter group. Permutational Multivariate Analysis of Variance (MANOVA) was used with GC# and site location as factors to determine if each category differed by stream and up and downstream of golf course facilities. Significant multivariate interactions were examined by trajectory analysis where the magnitude and direction of change for each stream and site location pair was explored ( Collyer and Adams, 2007). When interactions between stream and site location were not significant, multivariate post hoc tests selleck products were run to determine which streams differed. Multivariate categories for each sampling location were visualized with principle components analysis as biplots of components 1 and 2. Mantel and partial mantel tests and two block partial least squares were used to examine multivariate correlation between parameter groups. All statistical analyses were carried out in R 2.14.1 with the assistance of vegan and geomoph packages. Watershed area ranged for each sampling point from 10 to 93 km2. Anthropogenic land use (e.g., agriculture, development, tree plantations, etc.) ranged 48–78% among stream riparian zones (Table

1). The multivariate landscape group was Natural Product Library similar up and downstream of golf course facilities (Pillai’s Trace = 0.2, p = 0.914; Table 1; Fig. 2A). The landscape group significantly differed by stream (Pillai’s T = 16.9, p = 0.001). Post hoc comparison indicated that GC1 was only similar

to GC2 and GC5. The landscape of GC6 was Palmatine significantly different from GC2. The landscapes of GC2, GC3, and GC4 were similar ( Fig. 2A). Water quality among streams ranged from oligotrophic to eutrophic (Table 2). DOC ranged from 1.3 to 16.9 mg-C l−1 and was significantly lower downstream of golf courses (Wilcoxon’s paired test, p = 0.002; Fig. 3). SpCond, TDN, BACT, and BP were variable among sites but did not differ up and downstream of golf course facilities. TDP ranged from 4.1 to 44.1 μg-P l−1 and was significantly higher downstream of golf course facilities (Wilcoxon’s paired test, p = 0.023; Fig. 3). All together, the water quality group up and downstream of golf course facilities was similar (Pillai’s T = 0.2, p = 0.913), but significantly differed in water quality among streams (Pillai’s T = 14.3, p = 0.001; Fig. 2B). Post hoc comparison indicated that GC1 and GC2 were similar but significantly differed from the other streams, except between GC1 and GC5 which did not differ (p = 0.064). GC3, GC4, GC5, and GC6 had similar water quality. DOM ranged from strongly humic-like with features of terrestrial inputs (e.g., higher aromaticity (SUVA) and contributions of C2 and C3) to humic-like with features of microbial inputs (e.g.

Castellnou and Miralles (2009) further Obeticholic Acid detailed the industrial fire epoch by differentiating among five “generations of large wildfires” (Fig. 1), where a wildfire is defined

as an uncontrolled fire in an area of combustible vegetation that occurs in the countryside or a wilderness area. Both typological systems can be applied in most regions of the world. In this review paper we integrate these definitions for the first time in the long-term and recent forest fire history of the Alpine region. In fact, despite the considerable literature produced for specific areas, e.g., Conedera et al. (2004a), Carcaillet et al. (2009), Favilli et al. (2010), Colombaroli et al. (2013), no synthesis on historical, present and future fire regimes so far exists for the European Alpine region. The proposed approach additionally allows to insert the analyzed fire history in a more global context of ongoing changes as experienced also by other regions

of the world. To this purpose, the impact of the evolution of human fire uses, and fire suppression policies, on the fire regime and on the value of ecosystem services is presented; the potential influence of present and future fire management strategies on the cultural landscape maintenance, post-management forest ecosystems evolution, and the general landscape and habitat diversity is discussed. Looking at common traits in the worldwide fire regime trajectories, Pyne Wnt inhibitor (2001) identified three main fire epochs consisting of a pre-human phase driven by natural fire regimes, a successive phase dominated by land-use related anthropogenic fires, and a third phase resulting from the rise of industrial technology and the progressive banning of the use of fire in land management (Fig. Amobarbital 1): – First fire epoch: when the human population was too scarce and scattered to have a significant impact

on the fire regime and ignition sources were mostly natural (lightning and volcanoes). In this first fire epoch, fire became an important ecological factor along with climate fluctuations, influencing the selection of species life-history traits related to fire, e.g., Johnson (1996), Keeley and Zedler (2000), Pausas and Keeley (2009), and the evolution of fire-adapted and fire dependent ecosystems, e.g., Bond et al. (2005), Keeley and Rundel (2005), Beerling and Osborne (2006). Charcoal fragments stratified in alpine lakes and soils sediments have been used as proxy of fire activity in the European Alpine region (Ravazzi et al., 2005, Tinner et al., 2006 and Favilli et al., 2010). Early evidence of relevant fires in the Alps date back to interglacial periods during the Early Pleistocene (Ravazzi et al., 2005). However, due to multiple glaciations most of the Alpine stratigraphic record was eroded. Consequently, most fire regime reconstruction date-back to the Lateglacial-Holocene transition at around 15,000 cal. yrs BC (Favilli et al., 2010 and Kaltenrieder et al., 2010).