Chapter 3 MATERIAL AND METHODS

3.1. Introduction

The study of flora consists of plant and situation of plant habitat. There are many definitions for word "flora". The word "flora" refers to the plants occurring within a given region. A Flora may contain anything from a simple list of the plants occurring in an area to a very detailed account of those plants. Floras are different from popular manuals in that they attempt to cover all of the plants, rather than only the most common or conspicuous ones. When a researcher wants to study Flora he/or she can understand many things from that. A Flora ahnost always contains scientific names, and it may also include common names, literature references, descriptions, habitats, geographical distribution, illustrations, flowering times, and notes. Less often, Floras includes such specialized information as data on plant chemistry, reproduction, chromosome numbers, and population occurrences. Sometimes, the plants are listed alphabetically, and sometimes they are represented within a classification system that indicates which plants are most similar or are thought to be related. Floras often also include devices called "keys" that enable the user to identify an unknown plant. Floristic elements are most often defined subjectively by grouping plant ranges into types based on descriptions provided by floras and manuals (McLaughlin and Bowers, 1990). By study of floristic traits of an area, it is possible to describe floristic province and phytogeographical regions. The delimitation of floristic provinces and sub-provinces is mainly based on the distribution boundaries of vascular plant species (Yurtsev, 1994) which is known as true plants. Hence, phytogeographical region can describe plant, flora and environmental of flora. It can give us the plant community's limitation of an area. Plant communities can be considered real entities even if their boundaries in space and time cannot always be detected (Van der Maarel, 1996). The fundamental problem of plant ecology is the understanding of the close relationship existing between the and its

57 environment. All phytoecological research is directly or indirectly aimed at achieving this still-distant goal. Actually, it is impossible to separate environment and vegetation as two entities (Billings, 1941). Then, it needs to distinguish vegetation, flora and envirormiental properties to better description of an area simultaneously. The flora element is influenced by climatic, edaphic and management conditions which affect vegetation characteristics. On the other hand, the studies of flora give us a view of plants in a given area while vegetation traits' studies lead us to distinguish the management activities on the given area. The flora study, nowadays, is done by genetics traits which is more powerful in terms of traditional study. It, however, is one of the most expensive approaches to study the flora of given area. Feuerer and Hawksworth (2007), for example, have studied the biodiversity of lichens based on Takhtajan's floristic regions of the and have shown that distinguished species of different regions on the basis of Molecular study and may be number of species in given regions be reduced. It, therefore, will well when genetic and Molecular analysis's instruments are cheaply employed by researchers to their research to better distinguish the flora, fauna, etc. Reviewing of the researchers studies' trend is always usable to carry out the current and future research and it gives us many views. The different approaches of study help to find the solution to the problems and sometimes, it leads to discover a new procedure to future studies. Hence, it is necessary to make apparent the methods of study. Some of them are as follows:

3.2. Flora of Alborz Mountain

The floristic studies on the Alborz Mountain were so rare till 1976 based on Jager's world map (Fig 3.1). After that, as it is given in the literature review, The Alborz Mountain is studied by some researchers who worked on just the Central Alborz. There are many Floras studies that have been carried by some botanists and phytoecologists like Buhse (1860-99), Bunge (1860), Ehlers (1980), Fischer (1981), Freitag (1975-85), Gilli (1939), Hedge (1968-78),

58 Zohary (1967-1975), Klein (1991), Rechinger (1963-2005), Bobek (1937- 1953), Bor (1970), Takhtajan (1973-1986), Breckle (1975-2004), etc. The Alborz Mountain is formed by some which is phytogeographically defined as a geographic area with a relatively uniform composition of plants species\ Hence, it is found the alpine vegetation and habitats in these areas. From west to east and low to upland, the flora and vegetation are ecologically changing. The present study can provide collection of information to better management of this ecosystem. Each ecosystem element can be noted by researchers who act in different fields. Comparative ecology, therefore, is one of the successfiil approaches that explains differences among individual species and try to find general patterns in the world of organism diversity (Krahulec et al, 1999). The two given areas from central and west Alborz are for this type. The flora was denoted into a spectrum of biological forms (Smith, 1913; Raunkiaer, 1934; Safaian, 2004) as described in the following classes: Phanerophytes have their dormant buds on branches which project freely into the air; they are the trees and Bushy trees. Their buds are more than 25 cm above surface ground. Chamaephytes includes plants with their buds or shoot-apices perennating on the surface of the ground or just above it (not exceeding 25 cm.), so that in countries with snow they are protected in winter. Most cushion shrubs belong to this category. Hemicryptophytes have their dormant buds in the upper crust of the soil, just below the surface; the aerial parts are herbaceous and die away in the critical period, so that they form an additional protection to the earth-buds. The perennating parts may be long or short, laterally extended or forming compact root-stocks, hence the group includes a large number of our native and hedgerow species, and many rosette or half-rosette species. This type may be subdivided to a considerable extent. Cryptophytes includes plants with their dormant parts subterranean in the case of geophytes with bulbs, rhizomes, tubers on stem and root, and root-buds. Another division is characterised by semi-aquatic dormant buds-helophytes and hydrophytes. The helophytes or marshplants do

1 - Online source: http://www.answers.com/topic/floristic-province

59 not include all so-called marsh species, but only such cryptophytes have their buds at the bottom of the water or in the subjacent soil. The hydrophytes (e.g. Nymphaea, Zostera, Hippuris, Elodea, Potamogeton) have either perermating rhizomes, etc., or winter-buds. Therophytes, or plants of the favourable season, live through the unfavourable season as seeds; hence they are annual plants. They are especially characteristic of deserts and of regions under high cuhivation. Plant species can be divided on the basis of life form into subclasses e.g. Annual, Biannual, Prennial species which can give us a frequency gradient in the study areas. Hence, the study area is also categorised by this classification.

3.2.1. Location of Study Area

Iran, which is mostly located in arid and semi arid region of the globe (Raziei et al, 2005), is high plateau so that study areas are located on the highest mountain range (Alborz Mt. range) of this country (Fig. 3.2). The geographical position of study areas are 50° 26'00.4"Hand 36° 48'52.8'N (Fig. 3.3) for the Javaherdeh site which it's equal-rectangular position for 4 comers in clockwise are ( 36° 45' 22" N, 50° 22' 21.4" E), ( 36° 53' 28.7" N, 50° 22' 17.5" E ), (36° 53' 31" N, 50° 30' 22.3" E ), and ( 36° 45' 24.2" N, 50° 30' 25.4" E ) (Fig. 3.4). And the geographical position for the Polour site also is 35° 55'37.9" N and 52° 03'13.2"E (Fig. 3.3) which it's equal-rectangular position for comers in clockwise are also (35°52'12.3"N, 51° 59'47.8" E), (36° 00'19.2" N, 5r59'55.1"E), (36° 00'14.9" N, 52° 07'54.4" E), and (35°52'0.8"N, 52° 07'47.4" E) (Fig. 3.5). Actually, the Javaherdeh site is located on west Alborz and the Polour site is on central Alborz.

3.2.2. Study in vegetation kingdom zone

It is long tune that the human has wished to describe the vegetation kingdom of earth basis of its vision and hypothesise. Many attempts are seen in the

60 books and articles. The most important of them are started by Martonne (1925). He recognized six main regions or groups of regions, as follows: Holarctic Region, Mediterranean Region, Desert Region of the Old World, Tropical and Intertropical Regions, Austral Temperate Regions, Antarctic Region.

The best of next attempt is that of Engler (1936), which, with slight modifications, is, in outline, as follows: I. NORTHERN EXTRA-TROPICAL REALM. A. Region. B. Subarctic Region. C. Central European Region. D. Mediterranean Region (including the Macaronesian Islands). E. Central Asiatic Region. F. Eastern Asiatic Temperate Region. G. Pacific N. American Region. H. Atlantic N. American Region. IL PALAEOTROPIC REALM. A. N. Afi-ican-Indian Desert Region. (Probably in part best relegated to the Mediterranean Regions in the broad sense.) B. Afi-ican and Region. C. S.W. Cape Region. D. S. Atlantic Island Region. E. Mascarene Region. F. Southern Indian Region. G. Monsoon Region. H. E. Chinese and S. Japanese Region. I. Sandwich Island Region. III. CENTRAL AND S. AMERICAN REALM. A. Central American Region. B. Tropical American Region.

61 C. Andine Region. D. Galapagos Region. E. Juan Fernandez Region. IV. AUSTRAL REALM. A. Southern S. American Region. B. Antarctic Region. C. Kerguelen Region. D. New Zealand Region. E. Australian Region. F. Island Region (, etc.). V. OCEANIC REALM.

On the basis of the categorisation of Good (1974), there are six floristic kingdoms flowering plants which include Boreal, Neotropical, Paleotropical, South African, Astralian, and Antarctic (Fig. 3.6). The six kingdoms are divided into tiiree subkingdoms, which are each subdivided into 35 units of vegetation regions. The latest complete categorisation is for Armen Takhtajan (1986), which builds his division based on Good's work. He divided the thirty five floristic regions into 152 units of floristic provinces which are schemed follow (Fig. 3.7 and 3.8): I. HOLARCTIC KINGDOM (HOLARCTICS) A. BOREAL SUBKINGDOM I. 1 Arctic 2 Atlantic Europe 3 4 lUyria or Balkan 5 Euxinus 0 6 0 7 Eastern Europe 8 9 Western Siberia

62 10 Altai-Sayan 11 Central Siberia 12 Transbaikalia 13 Northeastern Siberia 14 Okhotsk-Kamchatka 15 Canada incl. Great Lakes II. 16 17 -Hokkaido 18 Japan- 19 Volcano-Bonin 20 Ryukyu or Tokara-Okinawa 21 Taiwan 22 Northern China 23 Central China 24 Souhteastem China 25 Sikang-Yuennan 26 Northern Burma 27 Eastern Himalaya 28 Khasi- III. North American Atlantic Region 29 Appalachians 30 Atlantic and Gulf Coastal Plain 31 North American IV. Rocky Mountain Region 32 Vancouver 33 Rocky Mountains B. TETHYAN (ANCIENT MEDITTERRANEAN) SUBKINGDOM V. Macaronesian Region 34 35 36 Canaries

63 37 VI. Mediterranean Region 0 38 Southern Morocco 39 Southwestern Mediterranean 40 South Mediterranean 41 Iberia 42 Baleares 43 Liguria-Tyrrhenia 44 Adriatic 45 East Mediterranean 46 Crimea-Novorossijsk VII. Saharo-Arabian Region 47 48 Egypt-Arabia VIII. Irano-Turanian Region VIII A. Western Asiatic Subregion 49 Mesopotamia 50 Central Anatolia 51 Armenia- 0 52 Hyrcania IZI 53 Turania or Aralo-Caspia 54 Turkestan 55 Northern Baluchistan 56 Western Himalaya VIIIB. Central Asiatic Subregion 57 Central Tien Shan 58 -Tien Shan 59 60 Tibet C. MADREAN SUBKINGDOM IX. 61

64 62 63 Sonora 64 Mexican Highlands II. PALEPTROPICAL KINGDOM (PALOTROPICS) A. AFRICAN SUBKINGDOM X. Guineo-Congolian Region 65 Upper Guinea 66 Nigeria-Cameroon 67 Congo XI. Usambara-Zululand Region 68 Zanzibar-Inhambane 69 Tongoland-Pondoland XII. Sudano-Zambezian Region 70 Zambezi 71 Sahel 72 73 Somalia-Ethiopia 74 South Arabia 75 76 77 South Iran 0 78 Sindia XIII. Karoo-Namib Region 79 80 Namaland 81 Western Cape 82 Karoo XIV. St.HeIena and Ascension Region 83 St. Helena and Ascension B. MADAGASCAN SUBKINGDOM XV. Madagascan Region 84 Eastern Madagascar

65 85 Western Madagascar 86 Southern and Southwestern Madagascar 87 Comoro 88 Mascarenes 89 C. INDOMALESIAN SUBKINGDOM XVI. Indian Region 90 Ceylon () 91 Malabar 92 Deccan 93 Upper Gangetic Plain 94 Bengal XVII. Indochinese Region 95 South Burma 96 Andamans 97 South China 98 Thailand 99 North Indochina 100 Annam 101 South Indochina XVIII. Malesian Region 102 Malaya 103 104 Philippines 105 106 South 107 Celebes 108 Moluccas and West New Guinea 109 Papua 110 Bismarck Archipelago XIX. Fijian Region 111 New Hebrides

66 112 C. POLYNESIAN SUBKINGDOM XX. Polynesian Region 113 114 XXI. Hawaiian Region 115 Hawaii XXII. Neocaledonian Region 116 Neotropical Kingdom XXIII. Caribbean Region 117 Central America 118 119 Galapagos Islands XXIV. Region of the Guayana Highlands 120 Guayana XXV. Amazonian Region 121 Amazonia 122 XXVI. Brazilian Region 123 124 Central Brazilian Uplands 125 Chaco 126 Atlantic Brazil 127 Parana XXVII. Andean Region 128 Northern 129 Central Andes South African Kingdom XXVm. Cape Region 130 Cape

67 Australian Kingdom XXIX. Northeast Australian Region 131 North Australia 132 133 Southeast Australia 134 Tasmania XXX. Southwest Australian Region 135 Southwest Australia XXXI. Central Australian or Eremaean Region 136Eremaea Antarctic Kingdom XXXII. Fernandezian Region 137 Juan Fernandez XXXIII. Chile-Patagonian Region 138 Northern Chile 139 Central Chile 140 Pampas 141 142 Tierra del Fuego XXXIV. Region of the South Subantarctic Islands 143 Tristan-Gough 144 Kerguelen XXXV. Neozeylandic Region 145 Lord Howe 146 Norfolk 147 Kermadec 148 Northern New Zealand 149 Central New Zealand 150 Southern New Zealand 151 Chatham 152 New Zealand Subantarctic Islands

68 Zohaiy (1974) floristically divided tlie earth into 33 regions. Iran plateau is shortly affected by the followmg categorisation (Fig. 3.9 and 3.10): I. Holarctic Kingdom A. Boreal Subkingdom 1. Euro-Siberian region Pontic subregion Euxino-Hyrcanian province Hyrcanian subprovince B. Tethyan Subkingdom 2. Mediterranean region 3. Sahara-Arabian region 4. Irano-Turanian region Mesopotamia province Irano-Anatolian province Irano-Armenia subprovince Kurdestan-Zagrosian subprovince Central Iran subprovince Turanian province II. Paleotropic Kingdom Africa subkingdom 5. Sudanian region Sudano-Sahelian province Nubo-Sindian province

The phytogeographic units from least to greatest are the Subprovince (syn: District), Province (syn: Domain), Subregion, Region, Subkingdom, and Kingdom (Conard, 1933). They are defined on the basis of Criteria for phytochorion ranks as Takhtajan (1986) as shown below: Kingdom (syn: Realm): A grouping of regions, largely on the distinctness of their floristic history (Turrill, 1939) which is characterised by high levels of endemic families, sub-families and tribes and very high levels of species .

69 Region: The most fundamental phytogeographical division, based on existing major differences between plant communities and floristic composition fi*om region to region (Turrill, 1939) which is characterised by high levels of generic endemism (but not usually families unless mono-generic) and very high levels of species endemism. Provinces: A portion of a region based particularly on the occurrence of endemics and on statistically different assemblages of species and less on climax vegetational differences (Turrill, 1939) which is characterised by high levels of species endemism (but not usually genera unless monospecific) and assemblages of correlated families. Districts: it is characterised by high levels of infra-specific endemism. As Al-Nafie (2008) and Zohary (1973) indicated that in delineating a biogeographical region, reliable boundary lines are more based initially on the climatic zones than the taxa dominating these zones. Zohary (1973) has also discussed the diagnostic markers that delineate and characterise plant- geographical regions. According to him: 1-Each region should have a large number of endemics. 2- Each delineated region should have floristic stock where the proportion of endemics to the total number of species is high 3-Phytogeographical regions must also be speciation areas and centres of certain groups of taxa (centre of diversity). It is distinct from others. It can also be considered one of its main characteristics and markers. 4- The floral history and past geological events which might affect the floral composition in the region and make it distinct from others can also be considered one of its main characteristics and markers. 5-Regions might differ from one another in respect of being recipients or donors. Some regions can be regarded as a recipients (e.g. Saharo-Arabian) since they have collected their species from other regions (e.g. Irano- Turanian) that can be called donors. 6-Horizontal-vegetation units endemic to each region also play a greater role in drawing boundaries between neighbouring regions. Plant communities

70 differ markedly from one region to another although they might display many common species. 7-Although zonation complexes and higher elevation areas might suggest that plant-geographical regions are not uniform areas and plants in these areas might resemble those in adjacent regions, it should be indicated that the altitudinal zonation complex of each region is peculiar and is another characteristic of the region. Holarctic, which is also called to "North Temperate" (e.g. Ying et al, 1991), Eurasian-North American (e.g. Thome, 1972), or Circumpolar sensu lato, is the biggest area not only in the north hemisphere, but also on the earth. Its territory includes the most parts of north hemisphere, upper than 20° north latitude, whole of Europe, tropical section of North Africa, whole tropical section of Asia, and almost whole of . There are 60 families as exclusive families (Yousefi, 2007) in this unit. The Holarctic kingdom is divided into three subkingdoms include Boreal, Tethyan, and Madrean. Wildish areas of Iran are found on Boreal and Tethyan subkingdoms. Boreal subkingdom is the biggest subkingdom of Holarctic which is formed by several exclusive families and genera. Its flora is also rich in terms of two other subkingdoms. Takhtajan (1986) has divided this subkingdom into four regions that include Circumboreal, East Asia, North America, and Rocky Mountain regions. The Circumboreal region is the widest region m the boreal subkingdom and in the Holarctic kingdom, has almost flora of Europe, Asia, and some section of North America. Zohary (1973) has called the Euro-Siberian region one of the biggest regions of boreal subkingdom that it is approximately equal of circumboreal region of Takhtajan. Some areas of north of Iran, north of Turkey to East Asia are found in this region. The Floristic study in the present thesis is formed by Zohary (1975) and Takhtajan (1986) divisionalisations. Floristic regions and provinces are clarified by tick mark in the Takhtajan's method and it is also abstracted in the Zohary's approach. The flora element's lunitation was distinguished by Flora OrientaUs (Boissier, 1867-1888), Flora Iranica (Rechinger, 1963-2005),

71 Flora of Pakistan (Nasir et al, 1970-2003), Flora of Iraq (Townsend et al, 1966-1985), Flora of Iran (Parsa, 1978), Flora of Turkey and the East Aegean Islands (Davis, 1965-1985), Flora of the U.S.S.R (Komarov, 1968-1980) Illustrated guide of the genus Astragalus in Iran (Maassoumi, 1990, 1993), the annual Astragalus (L.) ( Maassoumi, 1986), The Genus Astragalus in Iran (Maassoumi, 1989, 1995, 2000, 2005), Plant vegetation herbage of Iran in Kew herbarium of London (Niaki, 1993), and many families books include Caprifoliaceae (Khatamsaz, 1995), Linaceae (Sharifiiia and Assadi, 1995), Dipsacaeae (Jamzad, 1993), Gentianaceae and Menyanthaceae (Khatamsaz, 1995), Violaceae (Khatamsaz, 1991), Plantaginaceae (Janighorban, 1995), Valerianaceae (Moussavi-AUashlou, 2001), Grossulariaceae (Assadi, 1998), Solanaceae (Khatamsaz, 1998), Iridaceae (Mazhari, 2000), Crassulaceae (Akhani, 2000), Guttiferae (Azadi, 1999), Saxifragaceae (Jamzad, 1995), Boraginaceae (Khatamsaz, 2002), Chenopodiaceae (Assadi, 2001), Plant Taxonomy (Mozaffarian, 1995), Papilionaceae (Vicieae) (Pakravan et al., 2000), Convolvulaceae (Nowroozi, 2002), Papilinoaceae(Astragalus I) (Maassoumi, 2003), Amaryllidaceae (Mahari, 2004), Plumbaginaceae (Assadi, 2005).

3.2.3. Study in vegetation regions: Zones of Irano-Turanian and Euro-Siberian

The Euro-Siberian and Irano-Turanian regions are dependent to the Holarctic kingdom. These regions have special characteristics which is necessary to know them. Because to recognize the Euro-Siberian and Irano-Turanian elements in the studies areas (Javaherdeh and Polour sites), these regions are introduced by their traits as follows: The Euro-Siberian region. The Euro-Siberian realm is the biggest and richest region in Iran. This region is terminologically known as the Hyrcano- Uxinian (Euxino-Hyrcanian) Province in Iran where Zohary (1974) has known it as one province of Pontic which has more extensive concept. It is bound on the north by polar areas and on the south by the Irano-Turanian and the Mediterranean regions, and by the Aralo-Caspian and Sino-Japanese

72 subregions. Rain and snow continue to fall in all seasons; the temperature fluctuates greatly; winters are usually severe, but relatively milder in the vicinity of seas and oceans. The flora of the European part of this region has been studied from north to south, especially broad-leaf plants. This region is divided into several sub-regions. One of them is the so-called Hyrcanian subregion (Ghahreman and Attar, 1999); the vegetation may, thus, be divided into plain associations, hillside associations, medium and high-ahitude mountain associations, Arasbaran , etc. Beech forests and plain associations, as two kinds of mentioned associations, have been most damaged by the inhabitants of the southern Caspian area, and dramatic alterations have occurred in this area's environment and ecosystem (Ghahreman and Attar, 1999). It is necessary to know that Euro-Siberian region based on Zohary's method is composed the Hyrcanian province in Iran, but Takhtajan (1986) doesn't consider this region (Circumboreal as an equal of Euro-Siberian) into the Iran, especially in north of Iran which behave like the Euro-Siberian region (Yousefi, 2007). Hence, in this research, the Euro-Siberian region is used by Zohary's approach.

The Irano-Turanian region. The region covers an area of about 3,452,775 ha as 90% of total Iran (Hedge and Wendelbo, 1978). It is situated in Iran, in Khorasan, Azarbaijan, Markazi and western Provinces (Heshmati, 2007). Despite topographic differences (plains high flatlands, mountains), this region has uniformity. It is characterized, among other things, by low rainfall and a long dry season. Temperature fluctuates sharply at different places in the region. Summer heat intensity is equal to that of the African Sahara, and winter cold is severer than in the Mediterranean region. Vital and vegetative activity comes almost to a standstill because of severe temperature fluctuation in two seasons, that is, because of cold weather and frost in the long winter time and due to the long period of dryness in summer-time. This region is vast; it is bounded by the Mediterranean and Euro-Siberian regions on the north, by the Sino-Japanese sub-region on the east, by the Saharo-Sindian

73 region on the south, and extends from the western coasts of the Pacific Ocean to the eastern coasts of the Atlantic. This region includes some sub-regions on account of its vast area and its topographic diversity. About 69% of the floristic elements of the country are distributed m it (Ghahreman and Attar, 1999). Unknown or little known plant species abound in this region, where have originated many species of the flora of Iran and a multitude of species of the world's flora. The region's flora includes many genera some of which with hundred of species. On the basis of Takhtajan (1986) and Zohary (1973) viewpoints, the territory of Irano-Turanian region is deferent. Takhtajan (1986) believes that it includes Central and Eastern Anatolia, the most parts of Syria, a section of eastern and southern Palestine, a small piece of Sinai Peninsula, a section of Jordan, northern part of Syria desert, Upper Mesopotamia, the most parts of Armenia mountains, the arid and semi-arid regions of southern and eastern Ultra Caucasia, Hyrcanian area (Talesh and its neighbour areas in long of 's coastal), Iran plateau except tropic desert, southern mounds of Hindu kush Mountain, southern slopes and western mounds of Himalaya toward of west to 83 degrees of eastern length, whole arid areas of south­ eastern of European to Gobi. He revealed that there are 25% of endemic species in this region. The Irano-Turanian region, on the basis of Zohary's viewpoint, consists of wide steppe and desert area of Sinai's Mountains, some section of Palestine, Syria's desert, central Anatolia, northeastern of Iraq, the most parts of Iran, Afghanistan and central Asia (kirgizstan, Tajikistan, ), Semi-desert plateau of Zangaro-Kazakstan, the altitudes of central Asia and high plateau of northern Africa. He also pointed out that there is 40% of endemic species in the Irano-Turanian region. Jamzad (2005) has given the number of exclusive species in different phytogeographical regions of Iran that Irano-Turanian region is the richest one (Table 3.1). Leonard (1993) has distinguished 52% of endemic species of Irano-Turanian elements in the Central Alborz.

74 Both scientists have divided the Irano-Turanian region into some sub- regions and provinces as mentioned 3.2.2 section. Zohary (1973) excludes the Hyrcanian subprovince as Euro-Siberian, whereas Takhtajan (1986) puts it in the Irano-Turanian region. He also revealed that Hyranian province has many taxa from circumboreal region, especially Euxinus-Caucasian province. The Irano-Turanian subregions comprise the following plant associations (Ghahreman and Attar, 1999): forests of sub-humid and semi-arid zones; juniperetums, Zagros quercetums, pistacietums, amygdaletums, pistacietums khinjuk, rhusetums, salicetums, populetums, as well as plant communites of central and hillside , including artemisietums, hultemietums, artemisieto-prosopietums, artemisieto-peganetums, artemisieto-euphorhiet- ums, pteropyretums, atraphaxetums, etc. It seems that Zohary viewpoint to divide the floristic region in Irano- Turanian region, especially in the Iran plateau, is more precise because its classification is used by some earlier researchers e.g. Baser (2002), Hamzaoglu (2006), Giicel et al, (2008).

3.2.4. Study in Armeno-Iranian province zone

A floristic province is a geographic area with a relatively uniform composition of plant species. Adjacent floristic provinces do not usually have a sharp boundary, but rather a soft one, a transitional area in which many species from both regions overlap (Takhtajan, 1986). Although Zohary (1973) considered Armeno-Iranian zone as separate province, Takhtajan (1986) introduced it as Armenia-Iran subprovince which its territory is eastern section of Anatolian plateau, the most highlands of Armenia, aridlands of southern ultra Caucasia, Zavand, Kopedt Dagh, the most parts of Iran (except south and westsouthem of tropical coastal areas and Hyrcanian area) and a section of Afghanistan. Zohary (1973), however, believes that the territory of this province includes Central and eastern Anatolia, the most areas of Iran (2/3 of area) and Afghanistan, and Baluchestan of Pakistan. The climate of this region is continental with warm and dry summer and cold

75 winter in the most areas (Frey and Probst, 1986). Rainfall occurs on winter and prime spring, mostly, in High Mountain (800-100 mm). Indicator vegetation form of this area is short in the mountain, salt-marsh in central zone, bushy-spiny shrub in uplands. There are more than 20 endemic genera (hedge and Wendelbo, 1978) in the Irano-Anatolian province where there is approximately heterogeneity area with a view to chorology feature (Takhtajan, 1986). Hence, it is divided into six subprovinces that include Armenia, Atropatan, Khorasan, Kurdistan-Zagros, Fars-Kerman, and Central Iran subprovince (Takhtajan, 1986) (see Fig. 3.7). Irano-Anatolian region is one of the most important areas as generative species and distribution of them in the Holarctic region, especially, in Alpine and mount zone (Frey and Probst, 1986).

3.2.5. Study in Hyrcanian province zone Takhtajan (1986) considered the Hyrcanian zone as a province of Irano- Turanian region which has different climate, edaphic, and phytochorion. Based on Heshmati (2007) view; Hyrcanian (south of Caspian Sea) Zone could be divided into three subdivisions on the basis of geographical situations which these subdivisions are (1) Alborz Range forest steppe, (2) Caspian Hyrcanian mixed forest and (3) Caspian lowland desert. Hyrcanian province is situated to the southeast of the region and south of the Caspian Sea, extending fromth e seashore to Alborz mountain slopes, and fromAstar a Moimtain pastures and Arasbaran in the west to Gorgan province (ancient Hyrcania), Golidagh and Golestan forests in the east. The Hyrcanian forests from Astara to Golidagh are 800 km long; their width varies between ca. 20 and ca. 70 km on the northern side of the Alborz range; their area, including that of Arasbaran forests, is about 3 million ha, a considerable part of which has, unfortunately, been destroyed or severely spoiled. The climate of the Hyrcanian zone on the southern Caspian shores is temperate and nearly subtropical. The annual rainfall decreases from west to east, with a maximum of 1850-1900 mm (or more) in Bandar-e AnzaH area, and a minimum of about 588 mm (or less) in the provmce of Gorgan; the relative humidity is

76 high but decreases in summer, and, for instance, in Gorgan it becomes as low as 65%, Average maximum temperature varies between 28 and 35 degrees C in the warmest month of the year, and average minimum temperature at different places fluctuates between 1.5 and 4 degrees, the xerothermic index is insignificant all over the southern Caspian coastal area; however, it increases from west to east (Ghahreman and Attar, 1999). As mentioned before, about 5% of the flora of Iran grow in the southern Caspian coastal region, and, so far as woody plants are concerned, include 80 tree species and over 50 shrubby ones. Dominated Life form in this area is evenness (Hedge and Wendelbo, 1978). Under the influence of climatic and edaphic factors, the Hyrcanian vegetation varies from west to east and from the seashore to high ahitudes, that is, from the coastal plam to 2,500-2,700m above sea level. Hyrcanian province' is presented by Zohary (1974) as Euxino-Hyrcanian province which is the sub-region of Pontic. Its territory includes northern mountain of Turkey, northern slops of Alborz Mt. (under 2500 m, Frey and Probst, 1986) and costal land of southern Caspian. South aspect of Alborz to 3000 m is located in Irano-Turanian region or Irano-Anatolian Province (Zohary, 1973) or Irano-Turkmenistan (Rechinger, 1986). Whereas, north aspect of Alborz (under 3000 m) is belongs to Euro-Siberian region (Klein, 1991). It is pointed out that flora elements of this province are 40% Euro- Siberian, 22% Mediterranean and Mediterranean-Euro-Siberian, 8% Irano- Turanian and remain for Plural region (Ghahreman and Attar, 1999). Takhtajan (1986), however, has pointed out that the territory of this province includes remained-relic forests in the southwestern of plain flat of Caspian Sea's coastal from Lankaran (in the souhteastem of ultra Caucasia) to Gillan and Mazandaran (in Iran), slopes of northeastern of Talesh mountains, the northern slopes of Alborz Mountains and its eastern tail. The flora of Hyrcanian province decreases from west to east line. This province is a relic forest which remained from relic mesophile forest from Tertiary (third era) (Takhtajan, 1986). There are some Mediterranean flora in the Hyrcanian

1 - Although Zohary has categorised it into Uxino-Hyracanian province, it is introduced as a province in this thesis for better comparison between two categorisations of Zohary and Takhtajan.

77 province which it is derived by Arctotertiary (Turgay flora) and Tropical (Poltava) elements that adjoined each other in long time (Kryshtovich, 1929).

3.2.6. Study of common species group between phytogeography regions

Biregional elements are species which are centred and distributed across two plant regions that are not separated by major climatic or topographic barriers (Al-Nafie, 2008). About nineteen percentage of Iran flora is formed by biregional or pluriregional which haven't univalent origin. Although these kinds of plants are tolerated in term of climate changes, they have essential requirements from edaphic factors. On the other hand, these flora are limited by edaphic than climate factor (Zohary, 1973). The most biregional flora of Iran belongs to Mediterranean- Irano-Turanian elements which are formed about 10 % (Zohary, 1974). It is investigated by comparison of one by one of two regions. For catching the best result to compare of two phytogeography regions, it is used similarity indices as are given follow: Floristic relationships between different regions (Peinado et al, 2005) of the studies areas were assessed the S0rensen (1948) similarity index:

I =—2^ 2a + b + c Where, a is the number of genera present in both assemblages under comparison; b is the number of genera present in the first assemblage and absent from the second; c is the number of genera present in the second assemblage and absent from the first. • The shnilarity of floristic composition in different region was also estimated using the Stogren-Radulescu coefficient, as another similarity index: X + Y-Z PSR = ^^ X + Y + Z Where, X is the number of species found in the first formation but absent from the second formation, Y is the number of species found in the second formation but absent from the first formation, and Z is the number of common species. The values of this coefficient vary from -1 to 1, indicating

78 similarity of species composition in the interval from -1 to 0 and difference in species composition in the interval from 0 to 1 (Shmidt, 1984). Moreover, the last similarity index is Krober's index (Zhang and Corlett, 2003): ^ 50C(A + B) ^"^ - AB Where, A is the number of taxa occurring in the first region; B, the number of taxa occurring in the second region; C, the number of taxa shared by both regions.

3.2.7. Study of Pluriregional species Pluriregional species grow in many regions and widely spread all over the world such as: Himalayan Mountain, Deccan plateau, , Namib Desert, and (Al-Nafie, 2008). The plants of pluriregional are found on special habitats which include Forests, prairies, alpine and sub- alpine habitats. Some plants of this group occupy especial habitats and they are purely seen in these habitats e.g. Euro-Siberian elements which are limited in the Hyrcanian territory. Some of elements are orophytes species which spread on the most areas. There are rarities of flora elements which are spread by anthropogenic and/or edaphic factors (Zohary, 1974). It is remarked that there are 1% from Euro-Siberian-Mediterranean, 2.5% from Euro-Siberian-Irano-Turanian, and 1.6% from Euro-Siberian-Irano-Turanian- Mediterranean in Iran (Zohary, 1973). Finally, there is another form of pluriregional elements which is called cosmopolitan species. Ubiquitous species or ubiquitarian plants form about 2.5% of Iran's flora (Zohary, 1974). It is, therefore, anticipated that there is some pluriregional elements in the study areas which need to be investigated on the basis of comparative surveying of two floristical region's lists.

79 3.2.8. Study of Endemic species The term 'endemic' in a botanical sense refers to species of restricted distribution (Mota et al, 2002). An endemic centre is a phytogeographical area which simultaneously has more than 50% the endemic species. Total number of the endemic species are more than 1000 species (Leonard, 1991) or it is stated the origin and source of endemic species (Klein, 1991). The knowledge obtained of the ecological requirements of the taxon and of the appropriate biotopes necessary for its successful occurrence and living might contribute to conservation of this unique endemic taxon (Kanka et al, 2008). Klein (1991) has pointed out that the endemic species in alpine zone of Alborz are mostly from Irano-Turanian Region or Irano-Anatolian province. He also revealed that endemism species' rate in Alborz is high (50.5%) which is more than Alps and close to the mountainous of Central Asia (48.5%). Hedge and Wendelbo (1978) have given some indicator species from Irano- Turanian as comparative between Iran and Turkey (Table 3.2). It shows that there are many species as native in the Irano-Turanian region which is extended to Turkey also. Coefficient of Jacard (1929) clarifies the endemism of an area as given formula:

JD=—xlOO ° S Which, JD is Jacard coefficient; G and S are Genus and species frequency, respectively. If there is ecologically high diversity, which frequency of the species in term of genera be more, than the Jacard coefficient consequently be less. Endemic taxa have investigated by authoritative references e.g. Flora Iranica (Rechinger, 1963-2005), Flora of Pakistan (Nasir et al, 1970-2003), Flora of Iraq (Townsend et al, 1966-1985), Flora of Iran (Parsa, 1978) and so on.

3.3. Vegetation of study areas

Vegetation is a complex phenomenon which, for various practical and academic reasons, deserves to be described and classified (Mucma, 1997). Existing vegetation communities for a potential vegetation type are

80 commonly arrayed to display successional pathways and disturbance relations (Jensen et al, 2000). Structure of Communities is the outcome of the habitat, environmental conditions and existing vegetation types. Community structure provides data about recognition and definition of different vegetation types; their mapping and the study of relationship betv^een plant species distribution and environmental control (Malik et al, 2007). Undoubtedly fires, and the other disturbance parameters, have dramatically altered the delicate balance between the arboreal vegetation of the slopes and the shrubby arctic-alpine vegetation of the summit (Whitney et al, 1982) which investigation of these communities need to comprehensive understanding of landscape and ecosystem ecology; as present thesis attempts to carry out it. In order to clarify the disturbance on upland grassland's vegetation, two areas were selected. It was expected that the Polour site located on central Alborz, is changed by human activities because of near distance to Tehran and other cities. It is, also, found that the central Iran's climate conditions have changed because of pass-way of mountainous. Forecasts, therefore, indicate that this area should have some change in the vegetation of grassland. The Javaherdeh site, however, is located on west Alborz. There are less disturbances factors in terms of the Polour site. Hence, they are selected to compare the grassland's vegetation condition by flora, present vegetation, and utilisation of these ecosystems' study as Danin et al, (1992) have similarly done by GIS and field work, After monitoring vegetation types, recognition of plants was carried out by previous studies' floristic list (e.g. Jouri, 1999) and if it is seen unknown plants, they were collected and transferred to herbarium of Azad Islamic University of Nour. The recognition of plants was done by Flora Iranica (Rechinger, 1963-2005), Cormohpytes of Iran (Ghahraman, 1988-1994), and Vegetation of Iran (Mobayen, 1975-1995).

3.3.1. Distinguish and determine vegetation types

Like any science, vegetation science uses classification to understand the laws of Nature, and organize knowledge (Peinado etal, 1998). Many attempts

81 to classify vegetation have been made; not only have these attempts been numerous: they have also been varied in their intent (Anderson, 1965). The vegetation classification is the most complicated issue on the research of vegetation (SONG and XU, 2003). The term "vegetation type" commonly means a well-defined and distinct aggregation of plant species, differentiated fi'om other aggregations by floristic composition (Looman, 1963). Vegetation types only characterised by dominance of species are often named association (Lepping and Daniels, 2006), plant community (Becking, 1957) or phytotype. There are several methods to describe the vegetation type in an area. These methods include physiognomic, floristic, ecologic, and syncretic approaches. Each method has some traits. Physiognomic method, for example, is used to classify the wide area's vegetation while floristic method is useful for small area. Although physiognomic system is complex and is not designed to reflect exact floristic composition (Howard, 1983), this method which is formed by life-form attribute (Mclntyre et al, 2000) and community stratification (Mucina, 1997) is a good method to clarify the type of a wide given area's vegetation (Kenoyer, 1929; Nichols, 1923). Then to reach a consistence of subjectivity and objectivity as much as possible for the vegetation classification (SONG and XU, 2003), it needs a syncretic method. Hence, it is used physiognomic-floristic method to obtain the vegetation type of the study areas, which have all kinds of life-form e.g. woody, grassy, and herbaceous groups, that it is possible to classify them on the basis of their appearance in the landscape (Kuchler, 1949).

3.3.2. Provide of Typology and others maps

A combination of detailed vegetation structure maps and inventory of a selection of characteristic and indicative species is a powerful tool for management evaluation (Provoost et al, 2004). The purpose of this map, besides providing a basic vegetation inventory, was to elucidate some vegetation-environment relationships in the study area (Komarkova and Webber, 1978). The follow procedures are adopted to provide typology maps for both study areas:

82 1) Recognition and distinction of plant types on aerial photo (1:50,000). Because of aerial photo's age (1952) is old, then fieldwork is necessary for correlation of reality and aerial photo which it is done by monitoring on both study areas about two weeks. 2) Determination of study area situation on 1:25,000-1:75,000 topographic maps and field limitation of study area on the basis of aerial photo's correction. Using different scales, maps were used for study areas' situation. The Javaherdeh site, for example, there are three sheets maps in scale 1:25,000 which couldn't easily match each other. Then, bigger scale like 1:75,000 was used 3) The data of field monitoring of vegetation type is transferred to Arc GIS v9.3 software to draw the GIS maps. All topographic maps are made in digit by contour lines traits in this software and is provided DEM map. After putting of vegetation type boundaries on this map, the minimum areas are eliminated by software. Finally, it is found a fi-esh eliminated map of vegetation type for both studies areas.

4) To show 3D position of study areas. Global Mapper version 10.0 software is employed. Worldwide elevation data (3-arc-second Resolation) is reachable by the internet for total of the earth. Then, longitude and latitude of study areas put in this connected software to Web. The limitation of study areas are extracted from this software in scale of 1:75,000 to 1:2,000,000 for the studies areas to wide area (e.g. Caspian Sea), respectively. Other maps (e.g. Geologic, physiographic, soil temperature and moisture regimes' maps, etc) have done as follows: • Hypsometric map of the study sites are provided by DEM map in scale 1:25000. Calculation of the area of each altitude class is obtained by ARC GIS v9.3. • Geologic map (1:100,000) is provided by Geology section of Ministry of Mine and Industries of Iran which it changed into 1:75,000 scale by using the Arc GIS software.

83 • Soil moisture and temperature maps of the study areas are provided by U.S. Dept. of Agriculture, Natural Resources Conservation service, and soil survey division in scale 1:100,000,000 for world soil resources are selected from world wide map as mentioned. • The profile maps of the Javaherdeh and Polour sites are provided by using Global Mapper versionlO.O software. • There are some general maps which are provided by online sources of web sites. The name of each map source has mentioned beyond the titles. • The maps were obtained by Arc GIS 9.3 software which includes Tin, Tingrid, Contour, Elevation, Slope, Aspect, Landform, Vegetation life form. Vegetation type (with cover density), Land unit. Soil moisture and temperature. Climatology, Isohyet, Isothermal, and Phytoclimatic map. The preparation processes are given below: No. map name Processes in Arc GIS 9.3 1 Arcmap\3DAnalyst\Create|Modify Tin\Create Tin Tin From Features 2 Tingrid Arcmap\3DAnalyst\Convert\Tin to Raster 3 Contour Tingrid\3DAnalyst\Surface Analysis\Contour 4 Elevation Tingrid\Reclassify\Convert\Raster to Features\ Eliminate\ Dissolve Tingrid\3DAnalyst\Surface Analysis\Slope\ Slope Reclassify\Convert\Raster to Features\Eliminate\ Dissolve Tingrid\3DAnalyst\ Surface Analysis\Aspect\ Aspect Reclassify\Convert\Raster to Features\Eliminate\ Dissolve Arc toolbox\Analysis Tools\Overly\ Intersect 7 Landform (Elevation, Slope and Aspect) 8 Vegetation Life Tingrid\Reclassify for vegetation Life Form form\Convert\Raster to Features\Eliminate\Dissolve 9 Vegetation Tingrid\Reclassify for vegetation Density Density\Convert\Raster to Features\Eliminate\Dissolve 10 7 and 8 Arc toolbox\Analysis Tools\Overly\ Intersect (7 and 8) 11 Land unit Tingrid\Reclassify for Land unit\Convert\Raster to Features\Eliminate\Dissolve 12 Soil Moisture Tingrid\Reclassify for Soil Moisture\Convert\Raster to

84 Features\Eliminate\Dissolve 13 Tingrid\Reclassify for Soil Temperature\Convert\Raster Soil Temperature to Features\Eliminate\Dissolve 14 Arc toolbox\Analysis Tools\Overly\ Intersect (8 and 12 8 and 12 and 13 and 13) 15 Tingrid\Reclassify for Climatology\Convert\Raster to Climatology Features\Eliminate\Dissolve 16 l.Arc Map\3D AnalystMnterpolate to RasterMnverse Isohyet Distance Weighted (IDW) 2.IDW\3DAnalyst\SurfaceAnalysis\Contour 17 l.Arc Map\3D AnalystMnterpolate to RasterMnverse Isothermal Distance Weighted (IDW) 2. IDW\3DAnalyst\Surface Analysis\Contour 18 Arc toolbox\Analysis Tools\Overly\ Intersect (16 and Phytoclimate 17 and 8) 19 Arc toolbox\Analysis Tools\Overly\ Intersect (7 and 7 and 18 18)

3.3.3. Determine of Stand area in each type

It is obvious that if it is wanted to provide a field data from vegetation, soil, etc, it is very difficult or impossible. A sample is controlled by cost, time, tools, and experts. Then it is important to collect the sample from each type which has different slopes, aspects, altitudes, soils and so on. The stand area is a solution way. Basis of defmition, the stand area is an area of given vegetation type which has all characteristics of the type (e.g. soil, topography, slope, ahitude, etc) in minimal scale so that the researcher can provide all required data from it. The location of stand area depends upon the selection of attributes and their variability within the management unit, the type of management and expected responses, the availability of sampling resources, and the purpose of the inventory or monitoring program. It is qualitatively possible to describe the type on the foundation of stand area's trait by using one sample. It is, however, quantitatively to describe the type dependent upon several samples from stand area as statistics method is recommended. In this case, the stand area was selected 1 to 4 ha (Neldner et al, 2005) for types to collecting field data for statistical analysis. Empirically, the stand area's

85 location is the centre of type where all types' characteristics have. It, however, is obtained in length of general slope of stand areas on both areas (Figs. 3.1 land 3.12).

3.3.4. Sampling

Sampling procedure is both more accurate and less time consuming to obtain (Kilbum, 1966). Plants are easy to sample and measure; they do not run away (Austin, 1999). Many sampling scales have been introduced into vegetation analysis, most of them of a semi-quantitative nature, thus aimed at estimations rather than precise measuring or counting (Mucina et al, 200). Ideally, estimates of the effectiveness of the sampling should be robust to community structure and sampling design (Keating at al, 1998). The sampling was carried out on the stand areas of types on the basis of statistical estimation of sampling volume. Anyone studying the phytosociological literature observes a considerable volume of syntaxonomic names, including countless numbers of synonyms and homonyms, and is often faced with inconsistencies in the application of these names to particular plant communities. Nomenclatural stability is urgently required, to avoid fiirther confiision and allow easy and correct usage of syntaxonomic names by applied vegetation ecologists such as foresters, agriculturalists, and nature conservationists (Weber et al, 2000). The nomenclature is, therefore, drawn from A Dictionary of Iranian Plant Names (Mozaffarian, 1998). • The slope was measured by a clinometer (Suunto Optical Reading Clinometer, Model no. PM-51360PC) as degrees for each sampling position which is changed to percentage to use for the Arc GIS software and statistical methods e.g. CCA and DCA approaches. • Aspect was determined with a compass and reported here as the nominal categories: North, South, East, West, North west, North east. South west, South east which they were changed to contiguous data, as quantitative data, using follow equation (McCune et al, 2002):

86 l-COS(e-45) Where, 9 is amount of aspect in terms of 360 degrees. Then it is used into the statistical methods e.g. CCA and DCA. • The soil sample: o Sampling: The soil sample was randomly collected (five samples) within each stand area in plots, bulked and sealed in plastic bags (Plate 3.1, photo 3.15 f-h). Each bag was labelled by date, GPS position, vegetation type, Plot number, sample number, altitude, aspect, and slope. The soil samples were collected using a steel trowel to a depth of approximately 15 cm, after brushing aside any surface litter. Because of shallow depth and gravelled, sometimes, soil, it is impossible to excavate more than 15 cm. Then samples were dried on air-dried condition (30 - 40°C) for one week. Nitrogen concentration and particle size distribution (percentage gravel, sand and silt and clay) were determined on samples dried at 105°C for 48 hours in a forced draught oven. Dried samples were sieved (2 mm mesh) for the determination of nitrogen concentration. Ca and Mg (meq/1), and Organic Carbon (%) are obtained by Titration method (Dewis and Freitas, 1984). o Analysis: Analysis Soil reaction (PH) and electrical conductivity (EC) were determined on three replicate 1:5 soil water extracts after shaking for 1 hour. Soil reaction was measured using a PH Meter-Metrohm model 691 Swiss; electrical conductivity (ds/m) was measured with Conductometer-Metrohm model 644 Swiss. Organic content, which is obtained by Valkli black system (Aliehyaee and Behbehanizadeh, 1993), was estimated from loss-on-ignition determined by heating oven-dried soil to 550°C for 3 hours in a muffle ftimace. The difference in weight before and after muffling is expressed as a percentage of dry weight. N (Nitrogen), P (phosphate), and K (Kalium) are obtained by Kjeltec Auto 1030 Analyzer, Spectrophotometers (Novaspec II, England), and Plamephotometers (Coming) systems, respectively.

87 Although there is a field approach to determine the soil texture (Fig. 3.13), laboratory method is more accurate than it is obtained by Hydrometer method (Page et al, 1982). Particle size distribution was based on the scale of U.S. Bureau of soils (cited in Kiff, 1973): particles <100 |xm were classified as silt and clay, collectively. Particles >100 nm and <2 mm was classified as sand; gravel >2 mm. 50 g of soil was dispersed using a 5% w/v sodium pyrophosphate solution for an estimate of the particle size distribution (percentage gravel, sand, and silt and clay). The solution was then poured through a weighted 100 ^m mesh sieve and dried at lOS^C for 24 hours. Percentage silt and clay was estimated as the difference between the initial 50 g and the oven-dried weight of soil remaining in the sieve (Le Brocque, 1995). The residue was then sieved through 2 mm and 100 ^m sieves (gravel and sand fi"actions, respectively) using a vibration sieve shaker (2" mm) and weighed. All fi-actionsar e expressed as a percentage of initial dry weight (50 g).

3.3.4.1. Sampling size

The optimum sample size for grassland sampling depends on the attribute being described, the size of plants present, and the scale of spatial patterns within the vegetation. Sometimes the size selected in a rangeland inventory or monitoring program is determined by convention or past practices. In fact, it is important to continue using sample units of the same size for repeated measurements (Kent and Croker, 1992). Therefore, sample size must be carefully considered in the planning stages because of its critical role in determining sample accuracy and sample precision. Then, to accede to best sample size, it was employed the minimal area (Cain, 1932, 1938; modified by Hopkins, 1957; Cain and Castro, 1959) as quadratic form which is obtained 1 m^ in (Plate 3.1, photo 3.1.a, b, d) to 0.5 m^ in grassland so that Eddleman et al, (1964) have reported that the smallest sized plots of lOOcm^ are useful for alpine vegetation. The sample size depends upon the choosing of properties for the purpose of the inventory

88 or monitoring program in the management unit (Bureau of Land Management, 1996). Hence, it was carried out on stand area as reference of monitoring, Another vegetation survey method is plotless method (Yarranton, 1966) which is divided to line transect (Kinsinger et al, 1960; Ripley et al, 1963; Jurasinski and Beierkuhnlein, 2006), step-point method (Evans and Love, 1957), and Point-frame method (Heady and Rader, 1958) (Fig. 3.17), which are used to different objectives so that they are methods of obtaining estimates of plant characteristics by measuring the different traits (Melville and Welsh, 2001). In this method, the sample area is reduced to a line along which the length of canopy projection is measured (Brun and Box, 1963). This method was also employed in the stand area which was big area or it was very long shape form.

3.3.4.2. Sampling volume of types

Sampling precision is influenced by manipulating sample volume in a manner that considers vegetation patterns, so that more variability is encompassed within sample units rather than among sample units (Mueller-Dombois and EUenburg, 1974). Larger sample units reduce sample variance and usually generate data that more closely follow a normal distribution. However, these advantages are offset by several other factors. First, the volume of the sample units must more than double to reduce sample size by 50%, causing an increase in the total area sampled (Daubenmire, 1968). Second, the smaller sample volume suggested by a lower sample variance would decrease the precision of the sample, due to its effect on the t-value when calculating confidence intervals (Bonham, 1989). Third, sample units with a large area are difficult to methodically count or estimate, which increases the possibility of bias. Finally, the logistical constraints of time and resources generally permit fewer samples to be taken using large sample units, presenting problems of achieving an adequate dispersal of sample units across the site (Cook and Stubbendieck, 1986). In practice, the trade-off between fewer, large sample units and many smaller sample units depends on the time taken

89 to measure attributes at each sample unit and the time needed to travel to and locate the additional sample units. There is a difference between qualitative and quantitative inventory of vegetation. The sample volume will be one plot or transect in each stand area as homogeneity area, when it is seen on the basis of qualitative view. Quantitative inventory, however, needs more volume of plots and transects to calculate in statistical approaches. In the studies area, it was, therefore, employed statistical formula (Valizadeh and Moghaddam, 2006) to receive the sampling volume as follow:

p X X n Where, A'^ is the number of requirement sample volume, n is initial sample from the stand area, / is calculated by T-student table from statistical books, p is the p-value amount that it was considered here, 0.05, x is average of a data set, and s^ is variance amount which is calculated by follow formula:

s':=- a- n-1 The symbol n can be calculated in term of weight, percentage or frequency of dominated species in a given stand area.

3.3.4.3. Sampling system

Random and systematic sampling is generally used in sampling vegetation. The regular placement of quadrats along transect is an example of systematic samplmg (Fig. 3.14). Random sampling, however, follows the chanceful phenomenon which gives to all recordable field data as an equal chance. Random selection of sample units is an underlying assumption of most statistical inference techniques, because it ensures that the sample unit selection is free from personal bias and not confounded by possible spatial patterns within the vegetation (Daubenmire, 1968). Therefore, a major advantage of adopting random sampling is that data sets can be compared

90 using conventional statistical inference techniques that estimate the sample mean and its precision. Each random sampling has two dimensions; the place of point and the aspect of moving to place another point. Place or distance of points can be obtained by the random table from statistical books or from calculation machines. As an assumption, we can choose two digits of number columns as distance of between points. To movement, hence, the random sampling was applied in the stand area based upon geographic azimuth in which the firs point was selected by random number from random table. After sampling of the first point, next point was established clockwise with n degree (see follow) from first with a given distance on the basis of random table's number, next point with degree n from second point with a given distance, etc to 360 degree as a complete cycle. In connection with sampling volume section, there are some sampling amount on the basis of formula (here I call it m). Hence, the azimuth degree will be clear as follow assumed formula: n = 360 -w For instance, if we accept 15 as sampling volume, then n (=24°) is the degree for each random point from geographic north.

3.3.4.4. Sampling time

The time of sampling is also so important to record all phenomena of given area. Season to season fluctuations in vegetation attributes represent the key consideration when deciding what time of year to conduct rangeland inventory or monitoring programs. To prevent seasonal differences from confounding interpretations, comparisons between inventory data from various sites or monitoring data from a single site over a number of years, should be confined to the same season so that key species are at a similar level of maturity. Also, it is possible to select vegetation attributes that are relatively insensitive to seasonal fluctuations, such as basal cover, to minimize the effects of sampling during different seasons (Smith and Ruyle, 1991). The best time of the year to sample depends upon the objectives of the

91 program and the selection of attributes to be measured. For example, regular monitoring to evaluate stocking rates by determining utilization levels are best conducted at the end of the grazing season. If species composition is identified as a monitored attribute, more accurate data collection is possible by coinciding field work with the period when plants are flowering and identifiable (McCarran and Cole, 1993). The time of sampling, therefore, was done in three years (from May to end of September of 2005, 2006, and 2009) at three times (May-Jun, July, August-September) for covering of phonological stages of plants in the both areas.

3.3.5. Data recording of sampling units

There are many traits of the vegetation type which can record as sample. Canopy Cover, Frequency, Density, production of plants, biomass or phytomass, tillage, and so on as biotic factors. Soil texture, soil properties, soil temperature and moisture regimes, ahitude, aspect, and so on are abiotic factors as record. In this research, canopy cover, frequency, production of plants were employed to obtain the rangeland condition and trend and rangeland capacity as biotic factors and soil texture, EC, PH, N, P, K, Mg, Ca, and Organic matter of soil, ahitude, aspect, and slope were recorded in each stand area. Plants are the basic of nature research in the terrestrial ecosystems. Life form classifications until now tended to consider the plant as a unit (Halloy, 1990). The most part of plant, which is analysed, is canopy or crown cover. Canopy cover describes the area represented by the vertical projection of plant foliage onto the ground. Canopy cover is determined as the perimeter of the plant at its widest horizontal plane. It generally assumes that small gaps within the foliage are included and an average crown perimeter is imagined to smooth irregular edges (Daubenmire, 1968). Cover is among the most widely used measures of abundance of plant species because it is not biased by the size or distribution of individuals (Cain, 1950; Floyd and Anderson, 1987).

92 Cover methods quantify vegetative communities in only 2 dimensions (Thome et al, 2002). There are several tools to record the canopy cover. Plot (Plate 3.1, photo 3.1 b), quadrat (Plate 3.1, photo 3.1 a,d), line transect, step-point method (Evans and Love, 1957) (Fig. 3.15 left and right, respectively) are some of the important tools to record. Not they only record the vegetation cover, but they also record frequency, density, litter, soil cover, stone cover, and tillage of the given area. These methods are a transect (criterion) for recording of the vegetation and soil changing m a given habitat. Quadrat method is used to record the cover as square shape. There is several cover estimation which are deffirent in classes levels (Table 3.3). Elzinga et al, (1998) have revealed that Daubenmire (1959) and the Braun- Blanquet (1965) systems are probably the most commonly used and the other systems split the lowest classes into even finer units. Daubenmire method can monitor canopy cover, frequency, and composition by canopy cover. It is also applicable to a wide, variety of vegetation types as long as the plants do not exceed waist height. The detail of procedure is given by Daubenmire (1959) and Coulloudon et al, (1999). The technique involves visually designating one of the 6 cover classes (Table 3.3) to each quadrat. Each species within the quadrat is usually assessed separately. Canopy cover is typically considered, simply because ground cover and basal cover are difficult to estimate if obscured by other plants (Cook and Stubbendieck, 1986). Originally, 20 cm x 50 cm quadrats were used, but sample imit size and sample unit shape should be selected to suit the vegetation patterns at the site (Bureau of Land Management, 1996). It was, however, used quadrat 1 m x 1 m in this research as it was obtained by minimal area's method which is mentioned above. Once sampling is completed, species cover can be estimated by multiplying the number of times a class was recorded by the midpoint of that cover class, adding the results for each class, and calculating an average by dividing by the total number of quadrats sampled (Daubenmire, 1959).

93 The Step-Point Method of Sampling is a useful method to record the cover. It involves making observations along transect at specified intervals, using a pin to record cover as a "hits." It measures cover for individual species, total cover, and species composition by cover. This method is best suited for use with grasses and forbs, as well as low shrubs. (Coulloudon et al, 1999). It was used sometimes in the grassland when the one is moving (Plate 3.1, photo 3.l.e). The Line Intercept method (Plate 3.1, photo 3.1.c) consists of horizontal, linear measurements of plant intercepts along the course of a line (tape). It is designed for measuring grass or grass-like plants, forbs, shrubs, and trees. The following vegetation attributes are monitored with this method (Elzinga e/^/, 1998): • Foliar and basal cover • Composition (by cover) The figures 3.15 and 3.16 are shown how the step-point and line intercept methods work. • production of plants: Plant production, which is known phytomass as useable to animal, is useable to calculate the rangeland capacity. Direct methods to determine phytomass typically involve sampling procedures using a sample unit with defined boundaries (i.e., some type of quadrat), so that biomass can be expressed relative to a known area. In this way, the biomass values obtained fi"om a small area are subsequently converted to a more conventional scale, such as kilograms/hectare or pounds/acre (Bonham, 1989). In order to obtain the weight of useable for animal, it was employed quadrat, although we have many methods to receive it, as sample unit. It was employed quadrat method to record the weight of plants. In each quadrat, after record all recordable phenomena, grasses and forbs were cut fi-om 1 cm above of surface ground. It was the cutting of current growth of shrubs or bushy trees' branches. Cutting operation was carried on whole plants which were inside the quadrat (see Fig. 3.17). It was separately done to each species. Cut parts were put into a papery pocket with label of quadrat. Then the samples were transferred to laboratory

94 to dry them. Their palatability was determined by using of Rangeland Plants' Code (Technical office of rangeland, 1980 and Safaian and Shokri, 2004). They were aerially dried within one week with 35°C of weather condition. They were weighted species by species after one week with Analytical balance (0.001 g) model WT6002, China. It was weighted on the basis of Class I, II, and III as palatability rank for each quadrat. Phytomass can be also obtained by weight estimation which needs to experienced experts.

• Rangeland condition: Rangelands, as habitat, have different characteristics which researchers are surveying them from first of starting of rangeland science. Rangeland condition, rangeland trend, and capacity of rangeland are basically concepts which have many methods to inventory. Range condition, however, describes an evaluation of the current status of rangeland vegetation (Holechek et al, 1995). Condition assessments provide the framework to register information obtained by range inventories on the basic status of existing vegetation, and to gauge changes or range trend through monitoring (Dyksterhuis, 1949). In addition, range condition is used as a guide to ensure sustainable land use, to determine carrying capacity and adjust stocking rates, to identify potential responses to range improvement programs such as brush control or reseeding, and to evaluate the best locations of fences and water facilities to improve utilization within a pasture (Pieper and Beck, 1990). In earlier days of rangeland management, range condition was a general term describing the status of resources at a site with particular reference to livestock grazing. Today, range condition usually carries a specific cormotation, reflecting current status of the vegetation and soils occupying a site in comparison to the site potential expected if the climax vegetation was present (Adams et al, 1995). Therefore, an initial and critical step in evaluating range condition is to classify range sites to determine site potential. Hence, it was used Daubenmire method (Daubenmire, 1968) to determine the rangeland condition. I this method, there are 6 factors as basic description indicators of rangeland condition as follow:

95 a) Vegetation cover percentage 20 point b) Soil conservation percentage 20 point c) Plant composition 20 point d) Production percentage from climax 15 point e) Vigour and regeneration 15 point f) frequency of litter and humus 10 point

The rangeland condition will be obtained by adding of above points. Its range will be from 0 to 100. The details of classification mentioned of factors are given follow: a) Vegetation cover b) Soil conservation Cover percentage point Soil conservation percentage point 100 20 100 20 75-99 16-19 75-99 17-19 50-74 10-15 60-74 12-16 25-49 4-9 40-59 6-11 10-24 1-3 20-39 1-5 <10 0 <20 0 c) Plant composition^ Composition percentage (class ) point > 80% class I, < 10% class III 18-20 > 60% class I, < 10% class III 14-17 <15% class I, < 60% class II, > 25% class III 9-13 < 10% class I, < 30% class II, class III between 25-80% 4-8 Class III between 80-95% 1 -3 > 95 class III 0

1 - This section is impractical in term of rangeland condition of Iran. Then, it's modified by Bassiri (2000) as fallow: ^_[(IxlO)+(IIx5) + (IIIxl)]-100 B^nSA 28 Where, A is raw point of plant composition on the basis of 0 to 25, B is final point of plant composition based upon 0 to 20, and I, II, III are percentage of classes of species palatability. 2 - It means the palatability of species based of class I, very palatable; class II, medium palatable; and class III, unpalatable (Technical office of rangeland, 1980)

96 d) Production percentage from climax Percentaee of Droduction froi]n climax pomt 90-100 15 65-89 11-14 35-64 6-10 <10 0 e) Vigour and regeneration* f) frequency of litter and humus Regeneration of dominate class point Percentage of frequency point Class! 15 100 10 Class I, II 13 80-99 9-10 Class II, I 11 70-80 8-9 Class II 9 60-70 7-8 Class II, III 7 50-60 6-7 Class III, II 5 40-50 5-6 Class III 3 30-40 4-5 It isn't seen plant regeneration 0 20-30 3-4 10-20 2-3 5-10 1-2 0-5 0-1

The aggregate of point, which is obtained by 6 factors, compared with table 3.4 classes' scores. The field sheet of this method is given in the table 3.5. This method is useful to humid and semi humid regions with dominated cover of herbaceous and shrubs form.

Another method, which was applied to determine the rangeland condition, is Range Value method (Safaian and Shokri, 2002). This method is so flexible to grassland or (Sabetpour et al, 2004). This method can clarify the rangeland condition and capacity on the basis of palatability index and canopy cover's area. Plants species of vegetation type are initially recognised based upon of Range Value's methodology. Palatability index of species is

1 - It is modified by Bassiri (2000) for rangeland condition of Iran. 2 - this item is modified by Mesdaghi (2004)

97 determined by the role and importances of species founded upon observational and empirical view, ecologic condition, vigour and growth rate, and selectivity by animal (Safaian and Shokri, 1996) which this index is divided into follow classes:

Species palatabilitv condition point Very palatable species 9-10 Palatable species 6-8 Medium palatable species 3-5 Slight palatable species 1-2 Unpalatable species 0

Alfalfa {Medicago sativa L.), white clover {Trifolium repense L.), orchard grass {Dactylis glomerata L.), and sheep's fescue (Festuca ovina L.) are known as very palatable species. Mountain trefoil {Trifolium montanum L.), medic burr {Medicago polymorph L.), Brome {Bromus tomentesus Trin.), Brome {Bromus tomentellus Boiss.), chewing red fescue {Festuca rubra L.), mountain holy clover {Onobrychis cornuta L.), asparagus {Astragalus spp.) are known as a palatable species. The medium palatable species include Persian melic grass {Melica persica Kunth), perennial rye grass {Lolium prenne L.), and Iranian dandelion {Taraxacum Iranicum V. Soaest). The slight palatable species are also comprised the Iranian sage of Jerusalem {Phlomis persica Boiss.), mountain Pimpinella {Pimpinella tragium Vill.), nettle {Urtica dioica L.), Unpalatable species, which are known in this method, are finally comprised the coralwort plants {Cardamine spp.), and centaury {Centaurea spp.) (More palatability index is in table 3.6).

Another trait, which is considered in this method, is occupied area of canopy cover as the most important factor that can protect the soil surface and product phytomass to animal. The coefficient of this factor is determined by the canopy cover's diameter which is divided into follow subclasses:

98 Diameter of canopy cover (in cm) point 0-5 1 6-10 2 11-15 3 16-20 4 21-25 5 26-50 6 50-75 7 >75 8

Frequencies of plants are determined by a pin which hits to species on the 50 m transect. The distance of hits on transect is regularly 50 cm to 50 cm. It is; however, depending on the life-form of vegetation. If it is shrubland, then it needs a transect with 100 m or 200 m length that the hit distance is 1 or 2 m from each hit-point, respectively (Mahdavi et al, 2005). The transect length will be 30 m or 50 m, on the basis of intensity of canopy cover, in the grassland habitat which hit distance is 30 cm or 50 cm, respectively (Jouri et al, 2009). The other biotic or abiotic frequencyar e also determined by the hit of pin on the phenomena include soil, litter, and stone. Their point will be clear by the star (•) symbol (table 3.7). The calculation of rangeland condition is obtained by follow formula:

v.p.s.=-y ^xlOO xIS xR.V

Where, V.P.S is the rangeland condition's score, K is the maximum point which the plant species are got (it is 10), «, is frequencyo f plant /, N is the total of plant frequency, IS is the value of rangeland plant or palatability index, and R. V is the percentage of vegetation cover of each stand area or vegetation type which it is determined by follow formula (Jouri et al, 2009): R.V = 100 - 5] (soil + stone + litter)hits The rangeland capacity is directly determined by the amount of V.P.S as follow given formula: Rangeland capacity = V.P.S x 0.02

99 When the V.P.S point determine, then the class of rangeland condition will give the using by follow classes:

Class of rangeland condition point Excellent > 51 Good 39-50 Medium 26-38 Fair 13-25 Very fair 0-12

This method was used in the study areas when the landscape of vegetation type was uniform. On the basis of method methodology, one transect is enough to each stand area. For statistical approach, it needs 5 transects in each stand area. Therefore, it was used 5 transects in each stand area of vegetation type.

• Rangeland condition trend: Range trend refers to the change in the status of resources at a site detected by monitoring and is usually expressed as improving, declining, or stable. It originally pertained to any goal defined by management such as changing vegetation cover by adjusting stocking rates or grazing practices (Bonham, 1989). The general association of range trend with data describing any vegetation attribute in a monitoring program is still theoretically valid, but today the term carries a more specific interpretation relating to the comparison of consecutive assessments of range condition in a monitoring program (Holechek etal, 1995). Therefore, weaknesses in methods to assess range condition are manifested in the evaluation of range trend. Most important, range trend is an ecological assessment relating current species composition to that perceived as the climax vegetation at a site, and without connotations to the goals of management. For example, improving range trends usually reflect more desirable conditions for livestock production and watershed stability, but

100 could have undesirable consequences to the habitat of wildlife species that require a high proportion of forbs in their diet. These interpretations are better indicated from other concepts such as the desired plant community or resource value ratings (Ratliff, 1993). Because land resources must be monitored for an extended period to assess range trend, apparent trend is sometimes appraised from data describing site conditions collected at a single point in time. Apparent trend is determined by ranking soil and water criteria believed to reflect the probable changes in resource status, including the presence of unpalatable species, plant vigour, and soil surface conditions. Although a subjective appraisal relying on professional judgement, apparent trend provides land managers with useful information to guide short-term tactical decisions (Tueller and Blackburn, 1974). Site responses to climate and management must be distinguished to credibly interpret range trends and apparent trends. Repeated records based on quantitative comparisons over a long period provide the best indication of overall trends due to management, but such intensive monitoring strategies can be impractical in many situations. Even then, range frend assessments can be insensitive to detect early phases of improvement or deterioration, when masked by the yearly fluctuations in species composition caused by variable precipitation and temperature patterns. Although evaluation of range trends should be restricted to data collected at the same site to avoid the possible confounding by site variability, exclosures can act as valuable comparison areas to provide supplementary evidence describing the effect of current weather conditions (West et al, 1994). Nowadays, the experts of rangeland use modem technical tools to clarify the rangeland trend that it includes satellite image, and digital photo. There are several methods to determine the rangeland trend e.g. permanent quadrat, permanent fransect, and trend balance. If there is a reference site or exclosure site, it can possible to use permanent quadrat and transect. The trend balance, however, is the applied method to rangeland which hasn't any exclosure area. To using of the trend balance method, it should initially obtain the rangeland condition as it is

101 before expressed. Palatability class is also considered. In this method, vegetation and soil attributes on the basis of follow acts (Mesdaghi, 2004):

Appearance of regressive signals (modified by Mesdaghi, 2004) ^ A) In the plant: 1- there is a grazing-line in the bushy trees and higher than it, the offshoot ^ , can grow 2- there isn't compacted branches in the shrubs on the basis of overgrazing 2 1 3- the plant is made dry by overgrazing and unable to regenerate 3 1 4- it isn't seen the new vigour of all kinds of plants in recent year 2 3 5- it is seen generic fair in the plant growth on the basis of overgrazing 3 3 6- there isn't any age-class balance (new seedlings and mid-age plant) 2 2 7- Some unpalatable plants are grazed, too. 2 2 B) In the soil: 1- there is bare ground by overgrazing or specific condition of ecologic 1 1 2- there is profound wash with steep walls and without plant cover 3 3 3- if there is piece stone on the surface soil, it is from erosion of soft soil of 1 0 surface 4- formation of new sediment without plant cover is more intensive which 2 2 there isn't regeneration opportunity 5- if there is any slight colour of subsurface soil, it is caused by soil washing 2 0 6- it is seen the dust on air when there is wind or move the animal 1 1 7- soft soil and sand are seen bottom of shrub by the wind erosion 2 1 8- it is seen a altitude difference the soil of aroimd of shrubs or between them 2 2 9- it is seen hillock behind the perennial species on the shelvy amplitudes 2 1 10- the root and basal of all kinds of plants is visible. 3 0 11- it is seen old soil profile on the stones of area. 2 1 12- the water of stream is muddy 1 1 13- there are many micro-terrace on the shelvy amplitudes 1 1 total 3399 26 The algebraic summation of positive and negative points will determine the rangeland trend. If the gathering of points (two columns) is more than +3, then the rangeland trend is positive. If it be less than -3, then the trend is negative. Between two numbers (+3 to -3), the trend is constant. This method was employed in the studies areas.

102 • Rangeland capacity: Rangeland capacity (or range capacity) is referred to as "carrying capacity" or "grazing capacity" which has an ecologic and management concept. There are many definitions which have been mentioned as the carrying or grazing capacity's concepts. The concept of carrying capacity is wider than grazing capacity. Carrying capacity is an ecological concept of a given area or management unit. Each field of science has a definition of carrying capacity. Kashiwai (1995), for example, in ecology, is known the carrying capacity as a classic concept of population ecology. The carrying capacity of a range (land) is the number of animals that can be grazed without undue harm to the soils and vegetation (Vallentine, 1990). It is always assimied that when soils and plants are imharmed, animals on the range are likewise unharmed, but this is not always correct. Nutrition may be limiting for animal welfare on otherwise properly grazed ranges. In range livestock systems, it and the sometimes synonymous grazing capacity have often referred to the number of animals which an area of land can support without degradation of plant or soil resources (Scamesshia, 1990). Heady (1975) went on to state that optimum carrying capacity expresses the most profitable levels of all products and services, while optimum grazing capacity suggests the most profitable stocking rate. If livestock stocking level is optimized to describe a livestock carrying capacity, livestock carrying capacity is then defined as the optimum stocking level to achieve specific objectives given specified management options (Scamesshia, 1990). Grazing capacity is considered to be the average number of animals that a particular range or ranch will sustain over time (Gah et al, 2000). In term of research objective, which has focused on management of land on the basis of managing of livestock, it is accepted the grazing capacity as land management's tool. There are many methods to obtain the grazing capacity. Workman and MacPherson (1973) attempted to calculate the grazing capacity in yearlong based upon algebraic method, although they discussed three previous methods. In this research, it was applied the weight method of grazing capacity as follow formula (Moghaddam, 1998):

103 ,^^^, R.Fxarea(ha) AU.M = AUxR.D.FxM Where, R.F is abstracted of Reachable Forage of rangeland (dry matter), AU is Animal Unit, R.D.F is dry forage of rangeland which animal can daily use it. The amount of this is 1.7 Kg (in Iran) for one Animal Unit per day, M is during of grazing which is here is month, and AU.M is the amount of nutrient or forage which can hold one AU per month (Range and Watershed Management Dictionary, 2001). R.F is calculated by follow formula: R.F = RU.F(or P.P) x yield (of species) Where, P. UF is Proper Use Factor' (as it also is know Allowable Use') (as percentage), P.P is plant palatability (as percentage). Both of proper use factor or plant palatability can use to calculate the rangeland forage. It, however, is depend on the amount of P.U.F or P.P. so that each amount is less, then it is accepted. For example, if percentages of proper use factor and plant palatability are 56% and 45%, respectively, then it will be calculate the 45 percentage because this amount can protect the species without any future harsh. Yield is the total amount of species production.

• Diversity, richness and evenness of sites: In each site, there are many species with different cover percentage and density in which it can be a judgeship criterion. In order to obtain diversity, richness, and evenness of site, it was used quadrat as an area which can count the number of plants, kind of them, and the cover percentage of plants. There are many diversity indices which is given the diversity of a given area. Shannon's diversity index (Shannon and Weaver 1949) is one of them that is reliable to calculate the diversity. It, therefore, is employed to calculate the diversity of each quadrate and consequently the site.

H' = -X(Pi)(lnPi) i=l

1 - As definition, it is a limitation of use of a given species in which the species should have enough reservation to pass the harsh weather condition and able to grow next vegetational stage (Moghaddam, 1998)

104 Were, s is number of species, / is the code of species, In is natural logarithm, andp is a species proportion which is calculated by follow equation:

n Where,«, is the number of people in the / species, n is the total people in each sampling unit. The range of H' is 0 to 4.5 in which the end of range is shown the most diversity of the study site. Richness is another index which is demonstrated the amount of species in a given area. There are two formulas (Margalef, 1985; menhinick, 1964; cited by Ludwing and Reynolds, 1988) that calculate the richness. In this research, richness is obtained by using the Margalef (1985) formula as follow: R = Azl ln(n) Where, s is the number of species, n is the number of people of a species, bi is the natural logarithm. The range of acceptance of richness index is from 0 to c» in which the most number is shown the most richness. And finally, to calculate the evenness, it was used Hill (1973) index as follow formula:

^ e"'-l Where, e is neperian number. The range of acceptance of evenness index is from 0 to 00 so that the most number is shown the most richness.

3.3.6. Analysis of vegetation parameters

There are many techniques which can interpret the vegetation gradients. Plants species, on the basis of environmental conditions, gather the common area which they prefer and balance it. Assessments of environmental pattern have been obtained by concerning of study on ecological adaptation of species (Anderson, 1967). Then the most powerfijl analysis of vegetation is investigation of the species distribution from each other, first of all, and than in term of environments components (Choler, 2001). Quantitative techniques, which are stabled on the basis of statistical techniques, are the best approach

105 to interpret the vegetation demography. Various groups of species could well distinguish different patterns in the data (Dale, 1988). Classification and ordination of vegetation, nowadays, are a component of interpretation of a given area. The value of classification and ordination lies in their use as tools in helping to provide useful information from a particular situation; they are tools of convenience and both approaches can be, and indeed are, appropriate in certain circumstances (Anderson, 1965). Classification remains partly an art to which the ecologist's experience and understanding may contribute much (Gauch and Whittaker, 1981). The ordinations provided a meaningful description of floristic variation (Witkowski and O'Connor, 1996). Ordination is the primary quantitative method for studying both types of structure (Wagner and Helene, 2004). Ordinational technique will indicate the possibility of classification if such a possibility does indeed exist, and it can be further argued that ordinational techniques are likely to provide more ecologically valuable information than classificatory procedures (Anderson, 1965). Ordination techniques use abundance or presence-absence data of species, and oflen environmental data, in order to reveal various aspects of community structure, such as ecological gradients and relationships between species and their environment (De'ath, 1999). All ordinations are similar in objective; they aim to reduce the dimensionality of the original data matrix into few dimensions which explain most of the variance presents (Pamell and Waldren, 1996). There are some techniques in ordination that can give us the resuhs of fieldwork into visional view which clustering analysis and principal components analysis are the most applied methods. The characterization of homogeneous plant communities is one of the principal aims in phytosociology, phytogeography, phytoclimatology, and landscape ecology. Clustering methods represent a useful approach to this problem (Sardinero, 2000). Some researchers have believed that gradient analysis aims to explain the differences in species composition in a biotic community observed at different sampling locations (Whittaker, 1957; Ter Braak, 1986; ter Braak and Etienne 2003). Cluster Analysis, in the first instance, seeks to identify clusters

106 of points in space (Edwards and Cavalli-Sforza, 1965). Many researches have worked on cluster analysis, as the indirect gradient analysis technique (Hill, 1974), to determine the vegetation structures. For example, Casazza et al, (2008) have applied cluster analysis to investigate of ecological and historical factors on species distribution pattern on Alps. Principal components analysis is a widely-used ordination method as being of potential use in ecology. The method examines a sum of squares and cross-products matrix, and working in this Euclidean space performs eigenanalysis to summarize linear trends of variation (Kenkel and Orloci, 1986), although Principal Components Analysis (PCA) often involutes opposite ends of a coenospace, producing results that may be difficult to interpret (Karadzic and Popovic, 1994). In this research, the technique of correspondence analysis was used to identify gradients in floristic data between the community types and to distinguish the important classes of vegetation in the studies areas. To calculation of distance between clusters, Wards' method (minimum variance clustering. Ward, 1963; Gauch, 1982) was employed. In this method a cluster algorithm begins by treating each sample as a cluster with a single member. In an iterative fashion like clusters are then joined until only a single, composite cluster remains (Shupe, 2005). Euclidean distance was considered to distance index. Its resuh is interpreted as dendrogam which can divide vegetation community into sub-association. Principal component analysis, which was invented in the first of 19th century (Pearson, 1901), has been used widely in all areas of ecology and systematics (Jolliffe, 2002). It reduces the large number of inter-related variables to a few axes summarizing most of the variability (linear combinations of the original variables, principal components) (Speckman et al, 2005). The method is based on maximization of the variance of linear combinations of variables. Successive components are constructed to be uncorrelated with previous ones (Leps, and Smilauer, 1999). Often most of the variation can be summarized with only a few components, so data with many variables can be displayed effectively on a two- or three-dimensional

107 graph that uses the components as axes. If the original variables were not measured on the same scale, the analysis should be performed on standardized variables by the use of the correlation matrix rather than the variance-covariance matrix (Ter Braak and Prentice, 1988). With the variance-covariance matrix, the eigenvalues and percent of eigenvalues are equal to the variances of the components and the percent of variance explained by the components. These interpretations not hold for analyses using the correlation matrix (James and McCulloch, 1999). PC A, as indirect gradient analysis (Ter Braak, 1994), can describe the floristical data without any environmental factor's impact so that the environmental impact is emerged after analysis in the interpretation stage of data (Hill, 1979). Hence, the ordination of studies vegetation was also done by principal component analysis as it is mentioned the procedure stage. In order to understand the present management's operation, the rangeland condition's scores were investigated by correlation coefficient and to show which item of vegetation is influenced the rangeland condition; it was used regression model as follow formula (Mesdaghi, 2004): Yi=Po+P,X,+p,X,,+... + Pp_jXip., Where, Po is the intercept, p, j p_, are the line gradient, Xj, jj jp., are the variables which are independent variables, i is the number of phenomena, p is the maximum predictable variable, and Y is the dependent variable. The amount of correlation between variables (dependent and independent) was determined by correlation coefficient of Pearson (as known r symbol). The amount of dependent changes by independent variables was evaluated the using of coefficient of determination (as known r^ symbol). When there are more than two independent variables, then there is an additive effect which the variables influence each others as multicoUinnearity affects. Hence, it shifts the main direct line to indirect which is incorrect to predict the dependent variable's behaviour. There are some solution to solve the problem include applying the ridge regression, stepwise applying (Mesdaghi, 2004). Variance inflation factor (VIF) is a discrimination approach to distinguish weather there is any multicoUinnearity between

108 variables or not. VIF calculation is found the most applied statistical books, e.g. Ezekiel and Fox, 1959; Kennedy and Gentle, 1980; Steel et al, 1997; Zar, 1999; and Weisberg, 2005; Bihamta and Zare Chahouki, 2008) as follow formula: 1 VIF = 1-r.^ Where, fjMs the multidiscrimination coefficient, / is the number of independent variables. If there is completely linear regression, then minimum VIF tends toward to 1 number. If there is multicollinnearity affects, then VIF trend to maximum as toward to number 10 (Neter et al, 2000). The independent variables, in this research, were cover percentage, litter percentage, slope, latitude, and aspect, grasses, forbs, and shrubs covers percentages. Rangeland condition's scores, diversity, richness, and evenness index were as dependent variables.

3.3.7. Analysis of Environmental parameters

Vegetation has tenacious relationship with environmental factors which can be described by plant ecology or phytosociology. Quantitative phytosociology (Mason, 1960) under some conditions gradual environmental transitions result in gradual shifts in the species comprising plant communities (Bliss, 1963) can be considered to vegetation communities. Collective information from the correlations between phytosociological gradients and environmental factors (Jakupi et al, 2008) led to the conclusion that the distribution of species on the slopes, for example, is controlled by moisture and heat regimes of the soil layers in contact with the vegetation (Ayyad and Dix, 1964). Correlation of species with particular environmental variables (Haridasan et al, 1996), as a quantitative analysis (Nimis, 1989), has much more potential as a tool for explanation of plant distribution (Goodall, 1970). In order to show correlation of species with environmental factors, direct gradient analysis is the best method which can applicably describe this relationship. Detrended correspondence analysis (DCA) ordmation (Ter

109 Braak and Prentice, 1988) was initially used to characterise sample locations (Gauch and Whittaker, 1981; Zhang and Oxley, 1994) and calculated the gradient length. If the gradient length be more than 3 (Haghiyan et al, 2009) or 4 (Jongman et al, 1994; Yang et a/, 2007) then it is recommended to apply another ordination technique (Ejmaes, 2000) e.g. Canonical Correspondence Analysis which is called direct gradient analysis (Ter Braak, 1986). Canonical Correspondence Analysis (CCA), developed by ter Braak (1986,1987a) as an extension of Correspondence Analysis (CA), approximates unimodal responses of the species to environmental gradients (Makarenkov and Legendre, 2002). CCA is currently one of the most popular ordination techniques in community ecology (Shiu, 2004). CCA is the technique that selects the linear combination of environmental variables that maximizes the dispersion of the species scores. This gives the first CCA axis. The second and further CCA axes also select linear combinations of environmental variables that maximize the dispersion of the species scores, but subject to the constraint of being uncorrected with previous CCA axes (Brown et al, 1993). As many axes can be extracted as there are environmental variables. CCA is therefore 'restricted correspondence analyses in the sense that the site scores are restricted to be a linear combination of measured environmental variables (Shiu, 2004). The analysis of CCA was done to 999 run which in all CCA runs, rare communities were downweighted (Ter Braak, 1986), and the community scores are weighted averages of the site scores scaled by the square root of the eigenvalue (Ter Braak and Schaffers, 2004). Significance testing of axes using Monte Carlo permutation methods (Leps and Hadincova, 1992, Kalos and Whitlock, 2004). There are three levels to investigate those using DCA and CCA methods which include topographic, edaphic, and climatic levels. Hence, as the number of stands increases, there comes a point where stand ordination becomes cluttered and difficult to interpret (Huschle and Hironaka, 1980, Ter Braak and Schaffers, 2004). Therefore it was separately analysed to better distinguish the most important of each level.

110 3.3.8. Bioclimatic aspects of study areas Understanding the current distribution of vegetation and its interaction with climate regularity is important for predicting its future change (Femandez- Cancio et al, 2007; Blinova, 2008). Practical bioclimatic evaluation studies are an important contribution to the spatial shaping of the landscape (Durlo, 2003). The problem of vegetation zonality is approached with the assumption that the leading role in zonal differentiation belongs to climatic factors that could be indicated by different peculiarities of vegetation (Nakamura et al., 2007). Spatial patterns of climate variability, vegetation and topography are fairly similar. This is a good indication that climate and vegetation in this strong topographic gradient is determined by the altitude (Pineda-Martinez et al, 2007). Different phytogeographical groups appear to be responding to different climatic signals (Safford, 2007). Hence, climate-vegetation classifications are based on correlations between geographic patterns of selected climatic and vegetation parameters on the present landscape (Lenihan and Neilson, 1993). Therefore, bioclimatic or, better view, Phytoclimatic (Garcia Lopez and Allue Camacho, 2006) assessment can give us a local and global view to manage the vegetation and forecast the future trend of landscape's vegetation features. The Javaherdeh site is climatically located on west Alborz which is influenced by Mediterranean and local's meteoric fronts. Then its condition is fluctuated by these fronts, especially on fall and winter seasons. The Polour site, however, is climatically influenced by central Iran's fronts and Mediterranean front. Then its climatic zonation has a little difference to the Javaherdeh site. Then it might be seen grassland condition in the Javaherdeh site, on the basis of climate and altitude condition, whereas the Polour site should has the flora elements of central Iran which it is investigated in next chapter based upon precipitation, temperature and gradients changes of vegetation in term of climate.

Ill 3.3.8.1. Determine of areas climate by Emberger's method

Although there are many methods, which can give us the climate condition of given area, Emberger's method is one of the applied methods in climatology. Hence, the studies areas' climate was determined by Emberger's method as follow formula (Alizadeh, 2004).

v2 2000p

Where, P is precipitation, M is average of maximum temperature (°K), m is average of minimum temperature (°K), Q is the Emberger's coefficient. Minimum and maximum temperatures were changed into Kelvin degree as follow formula: T., =273 + e<°=>

The areas climates were drawn into Embrothermic index on the basis of average minimum temperature and Q.

3.3.8.2. Determine of Isothermal and Isohyets maps

The lack of climate data on the upland is the most problem for researcher which it is also comprised this research. Hence, it needs to use the neighbour's stations data to obtain the study area's gap data. There are several methods in this case. Kriging, which is a linear interpolation that allows predictions of unknown values in the study area based on information from measurements made at sample locations (Ewers and Pendall, 2008), one approach that makes distrust data. Statistical methods, e.g. regression method, is suitable, if the distance of study area is less than 70 Km (Ziaii and Behnia, 2002), The studies areas, unfortunately, don't have any station. Then it was calculated based upon the mentioned subject. First of all, based of spatial analysis of Arc GIS software, the 70 Km radian was determined for two areas. The data of temperatures (Maximum and minimum) and precipitation of stations, which include synoptic, climatology, and data logger, were collected from meteorology administrative and Power ministry's stations. The stations, which were more than 70 Km

112 distance, were omitted. Because the studies areas were more than 1400 m altitude, then the stations below 500 m from free sea were also omitted (see tables 2.3 to 2.10). Simple linear regression was separately employed to determine the correlation between altitude and precipitation and/or maximum and minimum temperatures as follow formula: Y = a + bX Where, Y is dependent variable (e.g. temperatures or precipitation), a is constant number, b is line's gradient, and X is independent variable which here is altitude. If r and r^were more than 65 percent as empirical judgment of natural phenomena, then the equation is suitable to determine the given object e.g. temperatures and precipitation. The significance of correlation was determined by F ratio (Fishers' ratio) (Vittinghoff et al, 2005). The predict equations of two studies areas are given in the tables 3.8 and 3.9 which are strongly demonstrated a robust correlation between dependent and independent variables. Based upon the equations, each line of contour was consisted three climatic traits; include precipitation, maximum and minimum temperatures. The valuable lines were transferred into Arc GIS software and the isothermal and isohyet's maps were obtained. Determination of the Soil temperature and moisture regimes were given by manual of land classification for irrigation (Mahler, 1979), Soil Taxonomy (Soil Survey Staff, 1975, and 1999), Soil Survey Manual (Soil Survey Staff, 1993), Soil Survey Laboratory Methods Manual (Soil Survey Laboratory Staff, 1992), and Land Evaluation (University Ghent, LT.C, 1991). The method of survey was given by Franklin Newhall method (Soil Survey Staff, 1975) and its improved approach (Van Wambeke, 1987). Although the scientific procedure is very difficult to firm areas, Franklin Newhall method attempts to solve this problem. On the basis of this method, it can possible to estimate the soil moisture regime by using of climatic traits include precipitation amount and average of month's temperature and also latitude and altitude. It also is known that basis of atmosphere temperature; we can determine the soil temperature by adding 1 degree of Celsius to atmosphere temperature (Azarshab, 2002). While the

113 average difference between summer and winter's temperature degree become more than 5° C, then the soil temperature regime can be distinguished by follow categories (Bybordi, 1993):

Summer temp. -Winter temp. (°C) Kind of regime Less than zero Perjelic 0-8 Cryic Less than 8 with semi-hot summer Frigid 8-15 Mesic 15-22 Thermic >22 Hyperthermic

3.3.9. Management features

Management practises in rangeland, grassland, forestland, and so on are the tools which can make ability to landman (landuse manager) for better utilising the land. It, in rangeland management, includes grazing strategies, balancing of rangeland capacity, grazing intensity, recovery of native and palatable plants, etc. Because of be length of ecological serai for both biotic and abiotic factors in a given area, the management can not balance its objectives on the basis of time in given period. Hence, capability-ecological evaluation of land as an efficient tool, which is defined the estimation of probable use of human fi^om land for different landuse objectives include agriculture, rangeland management, forest management, park management, aquaculture, improving of industry cities or villages on the basis of agriculture, industry, and commerce and goods criterions (Makhdoum, 2009), can give the manager to better conduct of land. Process of ecological capability's evaluation of land (or environment), in order to landuse planning, includes three sections; 1) recognition of ecologic resources, 2) immobilizing of data and analysis of them, 3) evaluation and classification of land (Makhdoum, 1988). Ecological resources can ordinarily divide into two classes include sustainable and unsustainable resources.

114 Stones, landforais and geomorphology of lands, soils, and vegetation are the sustainable resources which are constant if it does not happen sudden phenomena include systemic, tectonic, volcanic and human disturbances. Unsustainable resources are not constant in the time and spatial. Variations of these resources happen fast without impacts of human and natural's forces. Unsustainable resources are included climate, water resources, and animals (Makhdoum, 2009). Recognition of sustainable resources is obtained by different maps as information layers include altitude, slope, aspect, landform unit, hydrographic, geomorphology and lithology, soils and sediment, vegetation type, life-form of vegetation, and animal habitats maps (Makhdoum, 1988). In order to obtain a capability-ecological evaluation's map, there are some stages (Makhdoum, 2009) which are given below:

Mapping of landform units: landform is included natural units of land which each unit is formed by climatic, weathering, sediment and evolutionary deposits factors (Way, 1987). Therefore, soils, which are formed in the one unit of landform and on the given parent material (stone) in same condition, are similar each other with similar-common physical traits. Hence, the landform determines its soil and vegetation (Smith, 1982). With knowing of landform, it can be recognizable the soil and vegetation of a given area and can be realizable the capability of land to different usages. On the other view, each landform unit forms with unique climate's conditions (Way, 1978). Regarding that, it can be pointed out that each landform unit demonstrates a macro-ecosystem. There are many parameters which exhibit the landform unit includes condition of natural streams, slope and its tension, macro and micro peaks, altitude, first and second aspect, and width profile of gullies. Slope, altitude, and aspect, however, are more important than the other. The landform, unit in Iran, is obtained by integration of slope, altitude, and aspect maps.

Analysis and convening of data: in order to mapping or to showing of resources of a given area or catchments, it is used the parameters of

115 ecological resources on the map. This method is called Unit or Environmental Unit in Iran (Makhdoum, 1988; Adhamie Mojarad, 1990). In this method, ecosystem boundary is obtained by the each one of sustain-ecological resources in which in each obtained ecosystem, homogeneity and resemblance of sustain-ecological resources are acquired. Process of analysis and convening of data in order to mapping of environmental units is obtained by follow steps: First step: correlation and assimilation of landform units' maps with soil type's maps in order to providing of the first basic map of environmental unit. Second step: correlation and assimilation of basic map one into vegetation types' maps in order to providing of the second basic map of environmental unit. Third step: correlation and assimilation of second basic map into vegetation covers (or density) percentages in order to providing of final map of environmental unit. Fourth step: providing and regulation of tables of environmental units (ecological-sustainability characteristics). Analyzer can construe to the capability-ecological evaluation of a given area. Capability-ecological evaluation is the middle stage of land planning process (Basinski, 1985). In fact, the land evaluation is first-basic step to obtain the best suitable usage of land and management system on the basis of physical, biological, and socio-economical results of specified-analyzed area. The analysis and classification of land (environmental unit) in Iran obtain from comparison of ecological traits of each environmental unit with ecological model of considered usage. The ecological models of Iran are classified as Range management and agriculture, aquaculture, envirormient's conservation, tourism (include extensive and intensive outdoor recreation), rural, urban, and industrial improving (Makhdoum, 2009). If there are two or more usages for each unit, then it is suitable to choose the best option fi"omthem . This process is called determination of preference between different usages which, in fact, is the utilisation programming of land or land plaiming (Langdalen, 1975; Ive and

116 Cocks, 1986). In this section, two objectives should precisely be considered; 1) conservation of environment, and 2) logic providing of human requirements. Hence, the best tools are different ecologic-recognisable databases to handle of range management goal on land. Geographical information system (GIS) is on the most powerful tool to obtain and handle different data layers. In this case, all mentioned layers with the other maps input the ARC GIS 9.3 software on the basis of below processes: step Layer Name Make Processes in Arc GIS 9.3 1 Elevation Tingrid\Reclassify\Convert\Raster to Features\Eliminate\Dissolve Slope Tingrid\3DAnalyst\Surface Analysis\Slope\ Reclassify\Convert\ Raster to Features\ Eliminate\ Dissolve Aspect Tingrid\3DAnalyst\ Surface Analysis \Aspect\ Reclassify\Convert\Raster to Features\Eliminate\Dissolve 4 Landform unit Arc toolboxVAnalysis Tools\Overly\ Intersect (Elevation, Slope & Aspect) 5 First Environmental Unit Arc toolboxVAnalysis Tools\Overly\ Intersect (Landform & Soil Type) 6 Second Environmental Unit Arc toolboxVAnalysis Tools\Overly\ Intersect (First Environmental Unit & Vegetation Type) Final Environmental Unit Arc toolboxVAnalysis Tools\Overly\ Intersect (Second Environmental Unit & Vegetation Density) Ecological Capability of Final Environmental Unit\ Arc Range management plan toolbox\ Data management tools\ Generalization Dissolve

117 All maps include ecological capability and proposal-preference utilisation maps are obtained as step 8. There is an additional explanation here that Projection System is one way to obtain the altitudinal models for phenomena. It is generally made by conical cylindrical logic which is based on mathematical calculations. The projection system of Iran's maps is basically provided by Universal Transverse Mercator (UTM). Each 6° (6 degree) in UTM system is located into one zone. Hence, 60 zones are for the earth. Some country maybe located on one or less or more zones which Iran is located on zones 38, 39, 40, and 41. The studies areas are located on zone 39. The used map in this research is provided by Survey Organization of Army on the basis of World Geodetic System (Wgs) 84 (Hassaniye Pak, 1998). There are three options to obtain the spatial layers include coordinate system, projections system, and ellipsoid system (Sanjari, 1999) which in this research is used the coordinate system, UTM, and ellipsoid (WGS 84) system in the Zone 39 N (North hemisphere)

3.4. Statistical Methods and softwares

It is not far from reality that the statistics approaches are as resolvents of sciences which they help to researchers to find the reality of phenomena on the basis of minimum data. It is long time which statistical methods have served the human especially in nature studies (Singh and Chalam, 1937). Starting of computer as a personal instrument and many kinds of softwares, which can solve many problems, the statistical procedures have also found new attitudes. The specification of data for classification by computer can enhance objectivity (Sokal, 1974), if the statistical methods joint the computers' software. Hence, in this research. Cluster Analysis, Principal Component Analysis, Detrended Canonical Analysis, and Correspondence Canonical Analysis were carried out using PC-Ordination version 4.7 statistical software (McCune and Mefford, 1999). Variance inflation factor (VIF) was determined by Minitab v. 15.1 software. The multicoUinnearity effect was also investigated by Systat v. 12.0 software. The regression equation was carried out by Spss 17.0 (Spss Co., 2008). Calculating of

118 rangeland condition's scores and Emberger's coefficient were done by Excel 2003. The similarity indices, diversity, richness, and evenness were calculated by PAST v. 1.9 (Hammer and Harper, 2006) and Ecological Methodology software (Krebs, 1999).

119 6C

OJ e-t "o 2 CJ ab /^•v rsi r! u, ^ & t/i CJ S3 l-i O cs 53 o U-i •*-• 3 O ^ (/3! ;-i T3 •»-< 1) <^ O 1/1 3 wl o 4-* o C/l T3 ^^ C Tt cS 0> 13 hO C ea c3 C/3 ^ ;^ O rt 3 u IT C/a2 ^ •T3 ^ o a, 3 O oC r~ 3 >-. OS t4-l J3 ^^ o T3 »i (1> W es 60 •(-> cd f> i> •4-* &U ^^ c(L> VO - o D, '^

5 -•-» 6i) o «^-< :eS •!-< o »-^ c n o o ^ ts -o S oo ;-i •*-> (Tt o (U

JC ^—s -•-» -o m 3 . >-. i-i m

I I-

Bi_2

Javaherdeh 3D Elevation

Polour 3D Elevation

t^ Meters 0 700 1,400 2,800 4,200

Fig. 3.2: 3D elevation's position of the Javaherdeh (up) and Polour (down) sites relevant to Alborz Mt. range and Mazandaran Province (red boundary) ^^^ i^fc^Sm^^^*"^^ • Javaherdeh Site <^ inn

ElevatioKn Value ^ m High : M50 (m) m

Low : 1450 (m) .„

Polour Site Dnc O

Elevatjon Value I High: 4210 (m) m

, Law: 2410(m)

Caspian Sea

JaMilit'i«kh J>ite

•i^ Polour Sitt^ hi 1:75,000 1 V,.,.,, I 700 1.400 2.800 4.200

Fig. 3.3: The geographical position of the Javaherdeh (left) and Polour (right) sites relevant to Alborz Mt. range and Mazandaran Province (red boundary) 4081000

407K0O0

4075000

4072000

4O«M0O -4O«9O0O

(^ Legend J)

\J^ Boundary '~\-^ River CCS Border Stream 2^ Sparse (Forest) Topography line £3 Dense (Forest) /\/ Cliff C^' Rural Path A Peak

Fig. 3.4: The equal-rectangular position of the Javaherdeh sites relevant to Alborz Mt. range and Mazandaran Province (red boundary) S92M»''" snvT. " 1

^Q Boundary Topography Line River ^^ Buildings Flood ^B Active Mine ' Fencing ^ Post = by Path Peak 1:75,000 1 MPtPn. I I I Bushy Tree 0 700 1,400 2,800 4,200

Fig. 3.5: The equal-rectangular position of the Polour sites relevant to Alborz Mt. range and Mazandaran Province (red boundary)

-•2 2 I.Euro-Siberian region, II. Eastern Asiatic Region Hi. Irano-Turanian region, IV. Mediterranean region, VI. Saharo-sendian, VII. Sudanian region, NU-SI. Nubo-Sindian Fig. 3.9: Map of floristic regions (Source: Zohary, 1973)

I.A. Euxinian-Hyrcanian province, l.B. Mediterranean region, I.C. Saharo-Arabian Region Subregion, I.Dl. Mesopotamia province, I.D2. irano-Anatolian province, I.D3. Turanian province, II.Al: Sahel-sudanian province, II.A2. Nubo-sindian province Fig. 3.10: Map of floristic region and provinces of west Asia (Source: Zohary, 1974) 45<>U00 446IIIHI 448MIII 4M(HH) L. l_ u u

r- 446000

C3 Boundary ( Hsplaii Sea X Gps Position Elevation Class (m) ( ) 1450-1650 CD 2650-2S50 ( I 1650-1850 f 1 2850-3050 ^B 1850-2050 ( I 3050-3250 ( ) 2050-2250 ^1 3250-3450 /^l ( 1 2250-2450 p~1 3450-3650 1:75.0001 M^.^ I (~1 2450-2650 0 700 1,400 2,800 4.200

Fig. 3.11: Position of sampling into the stand areas in the Javaherdeh site 5')IK)l)ll 594*111(1 59millU (.(I2ltltll 1 1 1 1 r + 4- + + + + +

- + + + r ^ § & i + + + PIS 9^^^ f" + itf + P + r ^^^ 1( IK-h i^ w

1 1 1 1 1 1 r

{^) Boundary X Gps Position Elevation Class (m) ( 1 2400-2600 ( 1 3400-3600 B 2600-2800 CD 3600-3800 Q 2800-3000 ( 1 3800-4000 [ 1 3000-3200 ( ) 4000-4200 1:75,0001 vw^ | [ I 3200-3400 0 700 1,400 2,800 4,200

Fig. 3.12: Position of sampling into the stand areas in the Polour site Place approximately 25 grams In palm. Add water dropwise and knead the soil to break Add dry soil to soak down all aggregates. Soil is at the proper <^ up water consistency when plastic and moldable, like moist putty.

^ I is the soil too dry? j c[)> Is the soil too wet? cj>( Sand j ball when squeezed? No^ ^ No No V^ Yes <^ Place ball of soil between thumb and forefinger, gently push the soil with the thumb, squeezing it upward into a ribbon. Form a ribbon of uniform thickness and width. Allow the ribbon to emerge and extend over the forefinger, breaking from its own weight. 0 Does the soil form a ribbon? ^ 0 Yes Does soil make a weak Does soil make a Does soil make a strong ribbon less than 1 inch ribbon 1 inch long ribbon 2 inches or longer long before breaking? No before breaking? No before breaking? ^Yes OYes ^Yes C Excessively wet a small pinch of soil in palm and rub with forefinger. J <> 0 ^ Does soil Does soil Does soil feel very feel very feel very gritty? gritty? gritty? 0 No

Fig. 3.13: The field way's flowchart to receive to the soil texture (Source: Herrick et al, 2005) 50-1 In 45H • 40 H • 35-^ in 30-^ • 25- 1 1

20- u 15- • 10- • 5- n 0- Fig 3.14: A systematic sample often Im x Im quadrats along a 50m transect. The 2m mark is randomly selected to be the beginning point within the first 5 m segment. The remaining quadrats are then placed at 5m intervals after that (at 7m, 12m...47m) (source: Elzinga et al, 1998) distance of intercept transect

total transect length

.ti -C ^

^

±i ^ ^

^^r ^o V^^/V^»^/ '-'l-^O -.*-C• A<^ ' II Y CN \ u-> lo II .'^ F V i2 ^^ c Vj^ o JMM^ Q_ \V^\/ t-i distance e \>L K o ^^\Sy ^ ^^5^^^ CIJ 1 JsaB^^ _t3 UJ ^^f f- f £ w ^ distance f distance a+b+c+d+e+f s %cover •• c VJ total transect length I ^p ^^ c3^ Fig 3.15: Line (left) and Point (right) transect method of measuring cover (source: Elzinga et al, 1998) aerial co/er

Point-frame (Coulloudon et al, 1999), when each Basal cover compared to aerial cover (source: pin hits to a foliar, litter, stone, or bare soil, it Elzinga et al, 1998), actually there are two cover. calculates as a hit. This method is generally used It is depend on study goal. Rangeland condition inside of plots (Jouri, 2006). method, for example, uses both cover to record. Whereas in ground cover, it uses just aerial cover (Jouri, 2006)

Point 1 Point 2 Points 1 and 2 show the first two points on a line. In Point 1, the pin flag is touching dead fescue, live bluegrass, clover, live fescue, litter and a rock. Record fescue only once, even though it intercepts the pin twice. In Point 2, the flag touches fescue, then touches litter and finally the fescue plant base (source: Herrick et al, 2005) Fig. 3.16: Tools and recording of vegetation cover in the rangeland Record weights of all plants within the vertical projection of the quadrat even though the base is not within the quadrat.

I, ' I'/ >"'

1 / '.III 11' OJI, ,' / I "<

I l' ' '/

Do not record weights of portions of plants outside the vertical projection of the quadrat even though the base is within the quadrat Fig. 3.17: A diagram of plants estimation inside the quadrate (Source: Coulloudon e/a/, 1999) Table 3,1: the number of exclusive species in different phytogeographical regions of Iran (source: Jamzad, 2005) Ave. No. of exclusive species on Vegetation region Total No. of exclusive species million hectare Irano-Turanian 1452 14 Hyrcanian 115 12.5 Saharo-Sindian 52 1.14 Pulural region 108 Total of country 1727 10.46

Table 3.2: Some indicator species from Irano-Turanian as comparative between Iran and Turkey (Source: Hedge and Wendelbo, 1978) Frequency of species Common Frequency of Endemism uenera Iran Turkey species Iran Turkey Acantholimon 84 26 7 80 54 Allium 77 - 22 23 - Cousinia 192 38 6 83 68 Dionysia 24 2 1 88 50 Ferula 23 17 3 43 48 Gypsophila 28 49 8 44 56 Nepeta (annual) 20 1 1 65 - Scorzonera 51 39 12 41 44 Tragopogon 29 18 4 55 33

Table 3.3: Cover estimation classes recommended by various authors (Source: Elzinga etal, 1998) Braun-Blanquet Dabenmire Domin-Krakina class EcoData Bailey & (1965) (1959) (Shimwell, 1972) (Jensen et al., 1994) Poulton (1968) - 0-1% (+) - Very small Solitary 1-5% (c) 0-1% 1 Small 1-5% 1-5% Seldom 6-15% 2-5% 2 6-25% 6-25% Very scattered 16-25% 6-25% 3 26-50% 26-50% 1-4% 26-35% 26-50% 4 51-75% 51-75% 6-10% 36-45% 51-75% 5 > 75% 76-95% 11-25% 46-55% 76-95% 6 96-100% 26-33% 56-65% 96-100% 7 34-50% 66-75% 8 51-75% 76-85% 9 76-90% 86-95% 10 91-100% 100%

Table 3.4: Classification of Rangeland Condition by Daubenmire method (Source: Daubenmire, 1968) Class Point Excellent 88-100 Good 70-87 Fair 50-69 Weak 30-49 Very weak 11-29 Non-utilizable 0-10 Table 3.5: A sample of the field data sheet of Daubenmire method (Modified by Jouri,2006) PC is palatable class, Ave. is average, and phytomass is obtained by weight estimation.

Range site: altitude: Slope: Date: Range Type: Aspect: Examiner: Quadrats life form PC Species Names 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Ave. 1 o 1 SUM 1

•fi o 1

SUM

Shrub

SUM

Bushy Tree

SUM Sum of vegetation cover percentage Basal Area o O Litter •a § Biological Crust 2 O Rock or Gravel 6^ Bare Ground Soil conservation Annual Production I class Phytomass Aimual Production 11 class Annual Production III class regeneration of Plant, I, II, III slope percentage of quadrat GPS Position *** •• * «» ******** * *

B CO * * * * * * * * »**

CO 000

•S o a^QLialcualpLicua^cucuPucucua^cucuOHCucuPHCuCMOLiCLiCLiCu

ftT ^ ^ ^ ^ ^ ^ ^ ^ SaoQaooQa uuuouv>yv>y S eg

&> 6) 6) &> &) II ^ nil I1 . 111 11 1M l ^ ^^QH S 3 Q Q I I - S I- - !!lll II OOOC5!iOOOOO^«^'^<^K

8 «S a w e 4) s et a u c a 'S

"rt -fa* > S 5 S S S S S ^ ^ ^ ^ ^ ^ Qj "$ "6 "S: "S; "C "§ "5 Jd ^ &, '-••4 *»** •*»* »»•** •*»* •»** **»* ^ '^ ^ ^ ^ '^ ^ .. (S »-| m eg g a '-rJr«^'^>nvor--ooo\ •*•*» • * »*•• • • « «

*** * * * * * * * * * * * * * * *

CO oooovovo^vomtNOO^'^ffn^ooooo'o^tNtN oom«o

•= o m J eg I I I S i ^ o» a, (u I 9<>. «> U I (U 5§=§"*« "•^ ^'^ § & S> S> S> .IIS .3I .S §1 S S 5 9 o Q a •ts .ts -is •a y .§.§ I ^ ^ ^ Illliptiii S S S is* IT* 8 f.rtt 5 S S 6 S S LJ t^ i,^ s^ f^ 1^ *^ f^ t^ IW V3 V3 ^ ^ ^ t*^ f^ (^ ^ ^ ^ 1^ O O W

« a 09 o S« « a CO O "o CQ OQ . .Q '!? 1^-i »3 .1111 1 I o a "Si o

1^ Ik ^ to to "^ "^ "^

csr^^CNf<^<^c^<^^^^ofnr<^rnf<^•^T^TJ•rJ••^^•«tTt^^l/-»^o>r>ln^/^lOlrl « »»»•»•«• • •• *»•

•^ ^ * * * * * ••«*» » * • • • •

(» ir>m>r>iri»r>ir>ir>«r>nir>ir>>r>

•= o ou cucuoia; Q-aicualPua; n-cC D.aD-a,< a,aa;^a;^

I ii S Si Si ^ Si Si Si Si Si &J 6\ 6> 6i bj 6j fi) I et a a Q 0 ?^ 8^ e^ e^ >3 9 9 9 Q Q S S 3 9 O i ^5i ^ s> s> «> a 9 .§ .§ .§ .§ .§ .§ .§ .§ .§ .§ .§ .§ .§ .§ .§ .§ «j .a -a -a -a ^ g. g. jg jg g §"1 ^ 5* ?• r» r*" •*• *• ** *• "^ **• § II

a M (M O « s et a w C 'S a .Si '3

c o § § § o QQQCJOQQQQ So 00 bp go ^ III 60 =0 1>5 I en "^ "^ "^ to I 5 p « « • * ***** * * * *

I ** *** •«•««* * *****

S fSVOVO — — — •^0000^<*1

•ts o

5^ S? I 3SJ S3i I I S itt IJ .1 lists'5^III I s § § § l^^^^s s ^ i ^s's'll s B s s B

!i C5 ;D ::i ^ b ;5 O 0 0 U Q U U U U C O O'O'Cb b 1^ O O O O o q

o « a o s n a w C '5 CO

o1=1 o en aaa^ OOOOOOOOOO — — —1^ — — -H p 000000O\asOsOsOsO\O>OsO>O\ ****«'« ied

o *»•••• •••««»»«* »** » ***** CL,

cn ^^^•^roofn<^coocscs(N(S

OQ ... • . . . .

S i I §i I I I %> %> V 111 II 5 5 3 J*** .*• .** ^ ^ ^ ^ a 2 s s «>5 «<5 «<3 - C C C o o o o 2 O O ^ C^ CX III' &< Si Si Si ^ C^ 6, Q, r r r UUCjOa^G^OOUOU'JUUO ,* •« ,o

w a e e « O a C w u o «a V a .Sa *3 I «5 I e?«ifi.S 9 i

a a a •-• ^ §J §5 fe I -3 -3 'S S o S S S .» o ^ S, 6, p" S^ S' S^ g ill Sc i §ci Ss i pSu S S § S cn aj aj a> ?s: -c ;«: •« ,o ^o ^o ^C" U U U

CO (N — •ooooooooomS®®®'^®®'^^'^'^^®®®''**^'*

J2 S CQ

i ^

E 11 ^ > Q fe a> liii II ^ I .§ S 8 S S ? S I i :s -§ -§ 3 § § i^ W ^ V U ^^ ^^^ •> C3 •mi.

S

a (A IM O « S « a u C

oc u » "3 § S ^ !" '•3 •"* '£• 3 &i en 0

o * * * * * * * * * * •*«•*» * * ON

yi -^(NTTCN-^tN — — OOOOOO^-^-'i-'^'^S^*^^'^®®^'^®''^

•t! O a;^

s S S

(J O U b b ^6^ iiil!}l

tn 'o PQ u a u w o s n CO o e a OQ 'o w OQ e I. a s -g .22 a 3 'u JS •5 « o ^ 8 OQ III •I § o ^K >o> Ca s Q S o o o o o o o I -^ t i vq >"C ««;>*; »S! "S; «s; ill •?"i I 3 y y S b SP 5? a a ^S" ,§" ^S' ^i' ^S* ^S."3 ',< M hq te) bj tq h; l^q k Ct, tt, (U I I r~r--r~-oooooooooooooooooooooNOsOsO\a^osOvO\CT\CNOOOOOOO »* •• ••«» »* «••»•*••• * * •* -I" o * * • « * * * OH

M ^•^CNCNOJCNCSCS—''-OOOO — — — OOOOOOOTr"<*Tt — Tf'^

t2 6 Okcuali^ ^oL. o-cuouolok ci.aa;oL;^aIpt;<;

^ i i ^ I a> JO ^^ &5 ^ "3 Q a ^^ 9) I «) «> V ^ ^ ^ Q Q .1 3 3 U >^ &> 5 S ^ v$> <^ v^ G gill

t I i

a o o

CO

p CS

o »•• •*••• * * • • * * * « * * *

n>n«omvo^vorivoi-fSo>r»iOf«^

c2 E CQ •S O J cfc

Rj fti 6j ft) ^ s Q a Q Q Q QSi ^Q SQi - ^ K) K> 5fi) 9^ 9.^ «0 JO &j ft) ft) ftj &> ^ ^ ^ ^ ^ § § § II I •c: •c >; <; -c: II Ci, tx ty SL CL O O -^J 3" g' 3* 3^ 3" e^ O U a, 0;; ^>3 mil I fill a, a, a, a, a, .2 .£ 5 S tS t5 o « < ^

8 s a Me « S « B w C '•C ou u CO ou rn I 2 l(N

-^

o * * * * * * * » • « * * • « *

(/) ooOTft-r-t-»r~r~r»ioooovovofnromir>

c^ 0^ ^ cC CL, p^ p^ Q.ci,CL;cu<<

^ 4> «) ^ s ^ ^ &> <3 CJ Q ^ ^ ^ i .^ Q Q Q I ^ «> V ^ %> V U S:> U U « CJ CS §1^ H ^ ^ ti:§ ^l tlllllll.§ .§ l.§ iliiiii ill

w V & M «M o s et a w e a *S

o o en

I ^vovor~r--r~r-t—r--r-r-r-»r-ooooooooooooooooooooosONONO\osONOs e rM(N

o • ««•«••»•• •***•

c» vovc,«^«^-^,^^«^Hr«^cnr»iroir>r^0'^m

!l a^

s

S5i ^3 4> V ^ U •S-S ^ t* ^ •*» .1 •5. "IN* •V^ *^ -<5 -Q -O 5J 2 5 III 11 .^ ill ^ O "^l ^ ^ J J OR,a.,ft,Oa5ft^D(5ft-a:; b ;:i b ejoo

I

I u ^ VI i k^ t n •a ^ tig 1 I 5 5 11 *» 2 o o ill R R K II flit § a, a, ft, a, a, a, ft, III! I 2 OsO\0\0000000000-^'—1--'—1-^'— -^«^-'—'(S

-^ o **** ** * * ** • « • •

en mm'^rOTrro^'*ror<^Tfvovorovovoinu->r«immfnm'^

it! o a^a^cucualsucueuftlcu do^ cu d^ d.^ doi a^ft^o^pLjalaiGifcCucufc

i S I J ^ e^ i a 5 iiti O O 9 S^ ^ ^ ^ ^ ^ ^

8 '3 V a tn e s A a u e -•a8 "S III o a &. c a u ^^^ S .Q .Q .Q .0 ^! ^ ,£ .S ^ Q Q a a en CO Co 05 03 I r^eoo\©-<(Sr»^Tj-vr»sor~-eoosO-^cscnTi-»r>sor-ooo\o — tsm'*vr»vo H I cd ******* ***»»»

•^ O * * * * * **«**« *****

CA Soooooo^^^'^ — --mvovoooOfor

CQ < <

3 S S 5 5 s> s> 60 ^ go be I .1 i S3 ? 9 !3 *••• lllllfls s s G o j .0 .Q .Q M s § C«3 «<3 ?5 6, ^ •§ I ^ •^ (^ c^ Co t^ c^ Q S 0 0 0 I0 0 1^ (iO § § 8 r r s g e o o u u u u

u a o u PQ •S V c X u s PQ « e u u -•a8 .a o *s CQ J •§ 5 a "5.2 !=. /nil o o § §) §3 §) S) S •| I' s' s" s' § rn Q Q Q <3 Q 03 &Q Co (a Co Co Co Co Co Co Co OO Co &0 I • * » » • * •

^ ^ **««' **«* ***«* **«

Ui OOOcN'—iTf^^ror<^^^^vDm'—'<—iO^v^'^'0^^2'^'^'®®^^

p^' CLI o^ o^ o<' o^ o^ o,' o^ al o^ o^ al ON' •< OH' o^ < cu OH* eu < ai &! &.' < ^ ci-cu pui

S § R ^ §1 ^ I (U tu 4> «> fa s ^ oS 5o5 .eo .so .co .o .o .o . ^ ^. ^ « » Q, a,:5: ^ <: ^s «cs: >: C R Q .Q ^.« •2 .Q '*>*§ S Q S I Si-S •© -Cl Q ^^^ 3^^ll •-4 ^ ^

V a e s « a w a o • pal w «3

o o <6 en •Nf) 'fiif* •*••** *>^ **^ *»^ a> Co oa fi; fi; ^ ^ fi; fi;

I 2 OOOOOOONO\ONO\OSOSOSO\OSONOOOOOOOOOO — -H^^,-.^ — n1 c>d i-» *'»'» ** ****'»**» ***** * § >i 73

a00 O • * CL, * * * *

mTr(Nr<^mmmrnm<^ VOvOt-~vO

•s o a; o. •< cu fC PL^ cu ^ - ^ ^ ^ . - OQ " - &; cu &; PL^ O. a. a a, < •< D, ol

a s> s> s>I ^ a 5 r* IIIIIIIF^^i .§ ^I ? •? •? •? •? •? •? •? •? •? -r •?•?§>§> 9> $ Fi nil II r? a, a, a, O

S?

< i2 w ;§ a 9 o u E m u c/l Vi V) b U k. e I C/l o 3 X c^S 3 3 O CQ s CQ S • c o a U S o ffi PQ . PQ O S CO So 3 73 CO .S ^.-2 .sa X .2:2 2 o I o « CQ u % I CQ >• ^ '^ S fc.CQ, I S Si S-g o Vi U I I I CQ 3 E S M« 11 en sssss p ^ [— ^ 1 r 1 o S! o s ? ? 9 9 c 00 00 $ •* n 5 « « « $ •A •A n- ; 7 ? 9 m V 5 3 5 & ^ ^ 5 5 u ? ? § ? 9< 0^ A «*1 en R •a «0 00 00 00

r- r-

so i^ s S o X 1 >o M /^ U g o f^ fn m m Id ^ 1 A O o o u o 4> 0\ 0^ fi VO a ts ^ 00 00 C4 ^ (>l N M M o o o a » 0\ ON » ^1 eo 00 00 00 TJ - 1) r- 1- r^ u - o « « « « Ml «n •A Wl V s ^ ^ - t3 +-" 1 r- r- c^ c- •^ s •fl U 1 s u i 1- J 1 S 1 tA b 8 s A O r- M m •V •rt « r- 00 0\ o M M 2 trt « r- 00 o\ 00 0\ O «S «A 00 E - a M a ^ f\ 1*1 (*> ? u u 1 - 9 Table 3.8: the predict equations of climatic condition for the Javaherdeh site a: minimum temperature, b: maximum temperature, c: average temperature, d: precipitation, R: correlation coefficient, R^: coefficient of determination, F: Fishers ratio, and Sig.: significant level

Equation R R^ F Sig.p T..=7.841-0.00194H 0.949 0.901 463.54 0.000 T ,=20.128-0.001H 0.898 0.806 42.44 0.000 max T . =13.984-0.00147H 0.965 0.931 687.009 0.000 ave. P''=1013.047-0.241H 0.704 0.496 50.146 0.005

Table 3.9: the predict equations of climatic condition for the Polour site a: maximum temperature, b: minimum temperature, c: average temperature, d: precipitation, R: correlation coefficient, R^: coefficient of determination, F: Fishers ratio, and Sig.: significant level

Equation R R^ F Sig.F T .=22.7-0.0034H 0.893 0.813 234.67 0.000 max T.,=11.12-0.0046H 0.915 0.874 76.54 0.000 mm T c= 19.93-0.004H 0.868 0.759 342.76 0.000 ave. P"* =998.78-0.205 H 0.895 0.801 152.767 0.000 PLATE 3.1

^^^^B^^^^^^ atssstii*-. ."SfciSH j^Sifflia| ^H^HKki^Ht^

WP f^Si^ K^""^ '& K ^^

a) Quadrate 1 m^ the Javaherdeh site b) Quadrate 4 m' in shrubland, the Javaherdeh site r^j^^BBB^^H

^^b ^S9[^^^^^^^^H PP 11

d) Quadrate 1 m , the Po\our site

f) Collecting of soil sample, the Javaherdeh site

I Collecting of soil sample, the Polour site h) Collecting of soil sample, the Javaherdeh site Photo 3.1: collection field data from habitats in the studies areas