Department of Botany Hazara University Mansehra 2015

DISTRIBUTION PATTERN AND CONSERVATION STATUS OF PLANTS ENDEMIC TO PAKISTAN IN HAZARA REGION

ABDUL MAJID

DEPARTMENT OF BOTANY HAZARA UNIVERSITY MANSEHRA 2015

HAZARA UNIVERSITY MANSEHRA

Department of Botany

DISTRIBUTION PATTERN AND CONSERVATION STATUS OF PLANTS ENDEMIC TO PAKISTAN IN HAZARA REGION

Abdul Majid

This research study has been conducted and reported as partial fulfilment of the requirements of Ph.D degree in Botany awarded by Hazara University Mansehra, Pakistan

Mansehra Monday, April 12, 2015

DISTRIBUTION PATTERN AND CONSERVATION STATUS OF PLANTS ENDEMIC TO PAKISTAN IN HAZARA REGION

SUBMITTED BY ABDUL MAJID PhD Scholar

RESEARCH SUPERVISOR PROF. DR. HABIB AHMAD (Tamgha-e-Imtiaz) Dean Faculty of Science Hazara University, Mansehra

CO-SUPERVISOR DR. HAIDER ALI Assistant Professor Centre for Plant Sciences & Biodiversity University of Swat, Swat

DEPARTMENT OF BOTANY HAZARA UNIVERSITY, MANSEHRA 2015

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CONTENTS Acknowledgements...... Abstract...... vi Chapter 1 ...... 1 1 INTRODUCTION...... 1 1.1 Endemism ...... 1 1.1.1 Endemism and rarity ...... 3 1.2 Distribution patterns of endemic plants ...... 4 1.3 Modelling distribution patterns of endemic taxa ...... 4 1.3.1 Working with distribution models ...... 5 1.3.2 Observations required for a good model ...... 6 1.3.3 Historical species data ...... 7 1.3.4 Bioclimatic data for modelling ...... 7 1.3.5 Maximum entropy model ...... 7 1.3.6 Ordination models ...... 9 1.3.7 Nonmetric multidimensional scaling (NMDS) ...... 9 1.4 Biodiversity loss and endemism ...... 10 1.5 Determining conservation status ...... 11 1.5.1 SDMs for assigning IUCN threat categories ...... 15 1.5.2 Pakistan's perspectives ...... 16 1.6 Study area ...... 17 1.6.1 Location ...... 17 1.6.2 Physical features ...... 18 1.6.3 Climate ...... 25 1.6.4 Vegetation types ...... 27 1.6.5 Population ...... 30 1.6.6 Flora and Fauna ...... 30 1.6.7 Protected areas ...... 31 1.7 Justification of the current study ...... 31 1.8 Aims and objectives ...... 31 2 Chapter 2 ...... 32 2.1 Historical literature survey ...... Error! Bookmark not defined. 2.2 Herbarium record analysis ...... 32 2.3 Field visits ...... 32 2.4 Preservation and identification ...... 33 2.5 Data organization for SDM ...... 33 2.6 Data analysis through SDM ...... 33 2.7 Associated species analysis ...... 34 2.8 Conservation status analysis...... 38 2.9 Habitat loss analysis ...... 39 2.10 Determination of endemic rich area ...... 39 3 Chapter 3 ...... 44 RESULTS ...... 44 3.1 General distribution ...... 44 3.1.1 Taxonomic distribution ...... 44 3.1.2 Distribution within plant habit and life form ...... 45 3.2 Species distribution modelling (SDM) ...... 45

3.2.1 Model performance ...... 45 3.2.2 Contribution of predictor variables ...... 46 3.3 Predicted distribution patterns ...... 49 3.3.1 Distribution along elevational gradient ...... 49 3.3.2 Distribution among land cover types ...... 50 3.3.3 Distribution among administrative units ...... 51 3.3.4 Distribution with in geological classes ...... 52 3.4 Endemics with highest and lowest occupancy ...... 53 3.5 Analysis of endemic associates ...... 53 3.5.1 Classification ...... 54 3.5.2 Ordination: ...... 67 3.6 Conservation status: ...... 76 3.6.1 Geographic Range ...... 76 3.6.2 Sub populations ...... 77 3.6.3 Population trends ...... 78 3.6.4 Habitat loss ...... 78 3.6.5 Threats ...... 80 3.6.6 IUCN threat categories ...... 80 3.7 Endemic rich areas ...... 87 3.8 Species specific conservation strategies ...... 87 4 Chapter 4 ...... 94 DISCUSSION ...... 94 4.1 General distribution ...... 94 4.1.1 Taxonomic distribution ...... 94 4.1.2 Life form and endemic distribution ...... 95 4.2 Distribution modelling ...... 95 4.2.1 Model performance ...... 96 4.2.2 Contribution of predictor variables ...... 97 4.2.3 Endemic with high distribution area ...... 100 4.2.4 Altitudinal gradients ...... 101 4.2.5 Distribution with in administrative divisions ...... 102 4.2.6 Geological classes and endemics distribution ...... 102 4.3 Analysis of endemic associates ...... 103 4.3.1 Classification ...... 103 4.3.2 Ordination ...... 105 4.4 Conservation status ...... 106 4.4.1 Geographic range ...... 106 4.4.2 Sub populations ...... 107 4.4.3 Habitat loss ...... 108 4.4.4 Threats ...... 110 4.4.5 IUCN threat categories ...... 112 4.5 Endemic rich areas ...... 114 4.6 Species specific conservation strategies ...... 115 4.7 CONCLUSION ...... 115 4.8 RECOMMENDATIONS ...... 116 4.9 REFERENCES ...... 118

ABSTRACT

This dissertation communicates the results of scientific endeavour regarding the distribution pattern and conservation status of endemic plants of Pakistan distributed in Hazara region (about 20,000 Km2 area) of Northern Pakistan. The study provides a scientific database for developing conservation strategies for threatened endemic taxa of the area. The endemic taxa were determined through a survey of the available literature and herbarium specimens. Organized field surveys were performed during the years 2011-2014 for recording the ground realities and monitoring stations for the taxa conecerned. Actual and potential areas of the distribution of a taxa were thoroughly investigated and locations were georeferenced along the complete field data. Sample of each taxon was properly tagged, identified and preserved. The marked localities were repeatedly surveyed for monitoring fluctuations and decline in a population. Distribution patterns, ecological niche and potential habitats were analysed using Species Distribution Model, Maxent. Twenty Four topoclimatic variables including geology and land cover were used as predictor variables. Distribution of floral associates of endemic taxa were analysed using Twinspan and Nonmetric

Detrended Scaling (NMDS). Statistical analyses of predicted habitat maps and floral associates were performed using statistical packages in R. Conservation status of the endemic taxa was determined following IUCN Red List Categories and Criteria (2001,

2012) and guidelines for regional application. Habitat loss was estimated by analysing sixteen days MODIS time series images of thirteen years i.e. 2001-2013, using GIS program IDRISI Selva. Hot spots were identified by calculating the sum of all prediction maps using raster calculator in Arc GIS. Analyses of the generated data revealed that

vii endemics were distributed among 28 families 51 genera and 71 species. Boraginaceae and Ranunculaceae were richest families each with 8 endemic taxa followed by

Rosaceae (5), Apiaceae (4) and Gentianaceae (4). Genus Pseudomertensia was the largest genus having 8 endemic taxa. Dominant life form among endemics was hemicryptophyte (60) followed phenarphyte (8) and therophyte (2). Highest number were herbs (63) followed by shrubs (7) and trees (1). Maxent AUC range was lying within excellent range (0.92-0.98). Among topographic factors geology was proved to be highly influencial factor affecting the distribution of 39 taxa followed by elevation (28) and land cover (27). Among climatic factors, precipiation of the coldest months was highest dominating factor affecting 31 taxa followed by Mean Diurnal Range (26), mean temperature of the coldest months, temperature annual range (9) and precipitation seasonality (9). While analysing the distribtuon pattern along elevational gradient, highest endemic taxa were found between mid ranges (2000m and 3500m) and lower number was noted at extreme upper and lower ranges. Among the land cover types, highest number of endemics were found in dense temperate mixed forests and dense temperate coniferous forests while least number was found in temperate sparse coniferous forests and subtropical scrub forests and wetlands. With respect to administrative divisions, Mansehra District contained highest endemics followed by

Batagram and Kohistan. Among geological classes highest taxa were distrbuted in

Proterozoic metaclastic and metasedimentry rocks followed by Proterozoic Cambrian

Quartizite and Mezozoic Metasedimentry rocks. Endemic associates were broadly classified into five communities i.e Oxalis-Adiantum-Cymbopogon community,

Justicia-Acacia-Cymbopogon community, Trifolium-Pinus-Viburnum community,

Valeriana-Salix-Abies community and Poa-Kobresia-Pseudonaphalium community.

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NDVI revealed that habitat loss was occurring at alaroming rate with 18.3% agricultural extenstion and reduction of forest at 14.7%. Conservation status of endemics revealed that large number of endemics were confined to small geographic ranges with few populations facing multiple threats. The IUCN criteria placed Androsace hazarica,

Arabidopsis taraxacifolia, Bupleurum nigrescence, Microsisymbrium falccidum and Neottia inayattii were assigned in Extinct (EX) category, Artemisia amydalina as regionally extinct

(RE), Thalictrum secundum ssp. hazaricum and Jasminum leptophyllum as Critically

Endangered (CR) at global level and Meconopsis aculeata as Critically Endangered at regional level. Forty taxa got the status of Endangered and 15 were Vulnerable at regional level. Five of the locations were identified as endemic rich areas. It was concluded that endemics were habitat specific and were exposed to a number of threats.

Protection of selected sites, restoration of species specific area, recovery of the threatened gene pools and rehabilitation of associated areas needs to be worked out for effective conservation of the endemic plants of the region.

Chapter 1 1 INTRODUCTION

1.1 Endemism The term endemic was first introduced by de Candolle who adapted it from its medical meaning describing a disease present continuously in a specific area, defining genres end´emiques as analogous concept of a taxon restricted to a particular geographic region.

The taxa with wide distribution were called as genres sporadiques as antonym to endemic taxa (de Candolle, 1820). Although the term was introduced in bio geographical context, the regular use of the term was started in early twentieth century from where the word is in regular use among the scientific community in books and journals particularly in relation to description of new species having restricted distribution or referring threat classes. However, from the late 1980s and 1990s, the term is in much more use among checklists, red data books, scientific papers and even in media news focusing on ecology, biogeography, biodiversity and conservation (Hobohm and Tucker, 2014b). In short, the endemics are defined as the taxa whose distribution is restricted to certain geographic region. This is however, a relative term as the taxa confined to a region or country will be considered endemic to that particular region (Isik, 2011; Rabinowitz,

1981).

1.1.1 The biology of endemism

There are two main factors responsible for the distribution of a taxon over restricted range. First one includes different biological pressures like mutation, hybridization or polyploidization where the taxon appears in two or more descendant taxa occupying the same geographical region. The second reason is unsuitability of the habitat because of natural or anthropogenic disturbances leading to smaller range of the former wide

1 spread taxon. All such factors make the biology of endemic taxa more altered as compared to ancestors, the face effects being heterozygosity, genetic drift, inbreeding depression and lower adaptability. The former are called as Neoendemics while later are termed as Paleoendemics (Engler, 1882). Examples in the later case are Welwitschia mirabilis and Ginkgo biloba, as they are very old in terms of evolution (Khoshoo and

Ahuja, 1963). The Former case represents the natural process of speciation correlated with life form and life cycle, genetics and genome, pollination, dispersal mode etc. and environmental conditions like warmth, growing period, topography etc. All such processes lower the extinction rate of species by increasing the rate of evolution

(Graham et al., 2006; Hobohm, 2003; Royer et al., 2003; Cowling and Lombard, 2002;

Givnish, 2000; Huston and Huston, 1994; Rosenzweig, 1992; Hendrych, 1980). Engler

(1882) denoted them as evolutionary leftovers where their congeners became extinct.

The neoendemics on the other hand are newly evolved taxa at lower taxonomic level, with closely related congers.

The endemics have also been divided according to ploidy level where apoendemics are the species with high ploidy level than their closest relatives, patroendemics have low ploidy level and Schizoendemics are those with ploidy level equal to relatives. The classification is based on the concept that endemics with lower ploidy level originated earlier than those with higher ploidy level (Trigas and Iatrou, 2006).

Cowling and Lombard (2002) associated the phenomenon of endemism with life cycle of a plant, where soon after speciation (post speciation) every taxon remains confined to small geographic range and so is the case before extinction. The condition cannot be wholly applied on all the species as some of the wide spread taxa may divide into two

2 species by long term diverging genomes into two new taxa each occupying half of the former range (Hobohm, 2014; Cowling and Lombard, 2002).

1.1.1 Endemism and rarity Rabinowitz et al (1981) while describing different forms of rarity distinguished three traits i.e. geographic range, habitat specificity and population size. They described that a rare taxon must has one of the three traits. All the endemics have small geographic range as is essential to be an endemic taxon, risk of extinction increases as the distribution range decreases. In many cases endemics were also found with small population size and habitat specificity as well. According to IUCN Red Lists, most of the Critically Endangered species are restricted to small distribution ranges and are endemic to small geographic regions (Fontaine et al., 2007).

Emergence of new species while the extinction of others is the natural process essential for speciation and evolution, however current extinction rates are much higher than normal and major force behind such losses are anthropogenic activities (Ehrlich, 1995).

The critically endangered endemic species are facing various threats, most important being biological resource use, invasive species, agriculture and aquaculture development, urbanization and industrialization, energy production and mining, geological events and pollution (Abeli et al., 2011; Fontaine et al., 2007). Habitat loss and climate change are other most frequent threats (Keith et al., 2008; Ohlemüller et al., 2008;

Broennimann et al., 2006; Thuiller et al., 2006). The revenue generated through illegal trafficking of endangered species including many endemics have been estimated as 18-

26 billion dollars per year (Alacs and Georges, 2008; Flores-Palacios and Valencia-Diaz,

2007; Maggs et al., 1998), reflecting the use of such species at alarming rate.

1.2 Distribution patterns of endemic plants The conservation of the biodiversity is a core issue in the face of rapid destruction of habitats. The distribution patterns of threatened species has gained keen importance for restoration and management of declining diversity (Balmford et al., 2003; Jenkins, 2003;

Pimm and Raven, 2000; Reid, 1998; Pimm et al., 1995). Identification of the hotspots of biodiversity is a popular approach where endemics are mainly focused, as protection of all the earth habitats is beyond the human capacity (Huang et al., 2011; Myers et al.,

2000). One of the famous studies regarding pattern analysis is the production of distribution maps indicating phytogeographical limitations (Takhtajan, 1986) ecological niches (Peterson, 2011; Mota et al., 2002; Currie, 1991) areas deserving conservation

(Araújo et al., 2004; Myers et al., 2000) and climate change effects (Hannah et al., 2005).

The distribution maps based on voucher specimens from museums and herbaria have been used in past but approaches like computer based statistical models supported by climatic envelop, site topography and related environmental factors are gaining more importance as can also predict the suitable habitats, ecological niche, potential sites, area for reintroduction and future scenarios. (Guisan and Thuiller, 2005).

1.3 Modelling distribution patterns of endemic taxa The endemic rich habitats deserve high attention for protection, where distribution patterns can be helpful (Myers et al., 2000; Reid, 1998). Basic concept of the modelling is the production of prediction maps based on environmental variables describing the probability of distribution. The maps generated in this way were called as ecological response surfaces (Lenihan, 1993) biogeographical models of species distributions

(Guisan et al., 2006) spatial predictions of species distribution (Austin, 2002), predictive

4 maps (Franklin, 1995) predictions of occurrence (Rushton et al., 2004) and predictive distribution maps in conservation actions (Rodríguez et al., 2007).

Conceptual models had not only been utilized for predicting the individual species distribution, but also for biodiversity, species richness, life form and turnover as well

(Carlson et al., 2007; Ferrier et al., 2007; Waser et al., 2004; Smith et al., 1997; Pausas et al.,

1995). In some studies, the distribution of a species was correlated with energy flow.

Austin (1999) proposed the energy diversity hypothesis where more species were found at lower latitudes as because of greater availability of the energy. Currie correlated the species richness with evapotranspiration and topographic heterogeneity (Currie, 1991).

1.3.1 Working with distribution models For each model, there are some of the basic requirements like species occurrence record in geographic space which can be achieved through herbarium records but more precisely by the use of Global Positioning System (GPS), digital maps of the environmental variables indicating habitat variations, available in the form of spatial models processed through GIS models linking the environmental variables to the habitat requirements on statistical, descriptive, logical or rule based tools for applying the model and lastly the criterion for validation so that the uncertainty and error may be interpreted (Burgman et al., 2005; Smith et al., 1997). Such models are now a day utilized for ecological restoration, wildlife management, impact assessment, species reintroduction, identification of suitable habitats and for future predictions (Franklin,

2010). The resulted maps can be used for management and conservation programs like reserve design, ecological restoration, invasive species risk assessments and climate change impacts on species distribution (Elith and Leathwick, 2009b).

Distribution of species not only based on local factors like soil etc. several studies had shown that even these patterns are also related with climate and terrain. (Austin and

Meyers, 1996). Global plant distribution and life form has been associated with bioclimatic variables and soil properties and this framework form the basis for modelling the current plant distribution and future scenarios, commonly used factors were minimum and maximum temperature, growing degree days and evapotranspiration. Potential evapotranspiration has been used as surrogate for soil depth and moisture (Sitch et al., 2003; Cramer et al., 2001; Neilson and Running, 1996;

Neilson et al., 1992).

1.3.2 Observations required for a good model In general more number of observations are positively correlated with performance of the model and accuracy of the results (Reese et al., 2005; Hirzel and Guisan

2002Cumming, 2000). However some of the studies have shown acceptable performance even at 50-100 observations only (Loiselle et al., 2008; Stockwell and

Kadmon et al., 2003; Peterson, 2002) even some of the methods produced optimum results even at 30 observations (Kremen et al., 2008; Anderson et al., 2006).

These particular comparative studies examining the minimum sample size required for

SDM focused on the types of models that require presence-only data. Other studies that examined both sample size and prevalence got decent predictions with sample sizes of

100–500. Gegout (2007) concluded after an experiment using artificial data that there should be at least, 50 observation of species occurrence for estimating the accurate response using logistic regression. The absolute number of observations may be less important than having observations of a species that are well-distributed throughout the environmental space that it occupies (Kadmon et al., 2003).

1.3.3 Historical species data Historical data from museums and herbaria can be used along the recent records of a species. However data from the remote past often lack geographic coordinates and accurate position. Moreover, the historical position where a plant no longer remain distributed can affect the prediction. In this case such data should be removed

(Franklin, 2010). Historic data may also be helpful for comparative studies matching species occurrence over the years in face of climate change (Kremen et al., 2008).

1.3.4 Bioclimatic data for modelling Keeping in view the importance of climatic data as a significant factor responsible for species distribution, a group of scientists in Australia defined a comprehensive set of climatic parameters relevant to species distribution. The modelling procedure was called BIOCLIM provided both parameters and methods (Kremen, et al., 2008; Busby,

1991; Wiegand et al., 1986). Using long term weather data over 30 years, they derived 16 bioclimatic variables (now 19) which can be spatially interpolated using GIS programs.

Today, all SDM studies that encompass regional to global scales use climate parameters.

Species modelling at smaller spatial extents often uses topographic variables as surrogates for radiation, temperature and moisture gradients. (Mackey, 1994; Mackey and Sims, 1993; Walker and Moore, 1988; Walker and Cocks, 1991).

1.3.5 Maximum entropy model A general purpose machine learning method 'Maxent' based on statistical mechanics, machine learning and information theory stating that probability distribution with maximum entropy is the best approximation of an unknown distribution as it agrees with everything known but avoid assuming anything that is unknown (Phillips et al.,

2006). Based on the phenomenon, a software has been developed and shows more

7 accuracy as compared to others while working with present only data (Elith et al., 2009;

Dudik et al., 2007). Maxent requires presence data along environmental information for the whole analysis (Phillips and Dudík., 2008; Phillips et al., 2006).

The model is famous today and has been used in studies of species richness (Pineda and

Lobo, 2009; Graham and Hijmans, 2006), invasive species (Ficetola et al., 2009; Ward,

2007), climate change effects on species distributions (Fitzpatrick et al., 2008; Hijmans and Graham, 2006) endemism hotspots (Murray‐Smith et al., 2009) and to estimate the extent of occurrence (Sergio et al., 2007; Pearson et al., 2006) and quality of protection

(Thorn et al., 2009; DeMatteo and Loiselle, 2008) of rare species. Maxent has also been used to investigate the degree to which climate constrains distributions of species

(Cordellier and Pfenninger, 2009; Echarri et al., 2009) including pathogens and their hosts. Maxent has also been employed as a tool to address ecological questions concerning seasonal changes in habitat use (Suárez-Seoane et al., 2008) and the relative importance of abiotic conditions and biotic interactions in determining species distributions (Cunningham et al., 2009).

While comparing Maxent with such other models like BIOCLIM, ENFA, DOMAIN

AND GARP, the MAXENT out performed in terms of prediction accuracy despite of small sample size (Hernandez et al., 2006; Pearson et al., 2006). Maxent also performed better than GLMs and GAMs (applied to presence, background data) and substantially better than envelope methods (BIOCLIM) and GARP (Elith and Graham, 2009; Phillips and Dudík, 2008; Hernandez et al., 2006). In one comparison with BIOCLIM and

DOMAIN, as well as mechanistic models, both Maxent and GAMs were better able to model species range shifts under future climate change (Hijmans and Graham, 2006).

1.3.6 Ordination models Ordination methods have been proven helpful for presentation of data in ecological space describing the ecological distances in two dimensional graphs. The closer sites lie nearer to each other while sites far apart from each other reflects that the species composition is dissimilar. Ordination techniques can be classified into unconstrained and constrained types. The former are based on species matrix while later use environmental matrices as well along species composition. In the later case, relationship between environmental variables and species composition can be examined (Kenkel and and Orlóci, 1986).

1.3.7 Nonmetric Multidimensional Scaling (NMDS) Non metric multidimensional scaling is a robust ordination technique with unique applications. Unlike many other ordination methods, few axes are chosen for fitting the data to those dimensions. Another advantage is the flexibility of data where NMDS iteratively seeks a solution with acceptable result. Another advantage is flexible use of ordination imagery which can be inverted, rotated and centred to desired configuration.

As no assumptions are made the technique can be applied to a wide variety of data.

NMDS is best suited for simplification of data presentation as it allows any kind of use measure without any specification restrictions (Kenkel and Orlóci, 1986).

Enright et al (2005) analysed the vegetation of Kirthar National park using NMDS for identifying the priority conservation areas. They found this technique superior and suitable for analysing ecologically interpretable ordination, robustness and resistance to quantitative noise (Enright et al., 2005).

1.4 Biodiversity loss and endemism The 21st century is considered to be representing the sixth mass extinction (Barnosky et al., 2011; Estes et al., 2011) . Rate of plant extinction has reached up to one species per day. The important factors toward the such losses are anthropogenic activities where habitat is continuously being altered resulting in fragmentation and destruction.

Climate change is another indirect factor (Thomas et al., 2004). Identification of highly threatened taxa will be the first step towards conservation. Endemics need much attention as they are highly threatened (Coates and Atkins, 2001).

Taking biodiversity loss with great concern, the initial steps towards the conservation is the assessment of conservation status and identification of the level of threat for which an important tool is IUCN red list category and Criteria (Vié et al., 2009;

Callmander et al., 2005). According to IUCN red of 1997 , 33,798 species (12.5% of the world vascular flora) were considered threatened with extinction. Excluding 19 monotypic families, which were consequently 100% threatened, another 20 plant families have at least 50% of their species endangered. About 380 species had become extinct in the wild, whereas 371 additional species were listed as Extinct. Nevertheless, it is likely that many other species have become extinct and have not been detected.

Another 6522 have been listed as under some degree of threat (Walter and Gillet, 1998).

From conservation point of view, endemics merit supreme importance as having low population and limited geographic distribution and single disturbance may trigger their extinction (Bevill and Louda 1999; Rabinowitz, 1981). The small restricted habitat is unable to support them to tolerate change (Eriksson et al., 1993). In recent time endemism has become evolved to achieve the geopolitical significance in a region. The endemism not only provide the basis for country biological heritage but also indicates

10 the over-localized biodiversity at continental level (Vischi et al., 2004; Ribera et al., 1996).

Locally rare taxa are those that are rare or uncommon within a local geographical boundary while more common outside of that boundary (Murray and Lepschi, 2004).

1.5 Determining conservation status Knowledge of species conservation status is critical in immense climate change scenario

(Rivers et al., 2011). Contrary to the past efforts for megafauna like vertebrates and mammals, the current goal emphasis to non-vertebrate species like invertebrates, fungi and plants. Global strategy for plant conservation (2011-2020) calls for assessment of plant species as soon as possible in order to guide conservation actions, for which the realistic approach meeting international standards is IUCN guidelines. Although it is very hard to assess all the plants according to this criteria in near future, the species deserving high conservation priorities can be assessed. Another question is the availability of quantitative data for conservation status, the IUCN red list states that

‘‘the absence of high quality data should not deter attempts at applying the criteria"

(IUCN, 2001). In some cases the range estimate under criterion B can be helpful for assigning a threat category. About 80% of the plants listed in IUCN were assessed on the basis of range estimates (Gaston and Fuller, 2009).

Some of the most richest countries with respect to endemic plant diversity are China

(15,000–18,000 endemics), Indonesia (17,500), Colombia (15,000), Australia (14,000),

Mexico (12,500), Venezuela (8,000), Madagascar (6,500), India (5,000), Peru (5,400),

Ecuador (4,000), Bolivia (4,000) and the USA (4,000), (Huang et al., 2011; Groombridge and Jenkins, 2002). Representative example from each continent are quoted in the following lines.

Cercocarpus traskiae is a small tree in an island at California. The plant contains only seven individuals with a geographic range of 250 Km2 (Oldfield et al., 1998). Diospyros johnstoniana, is a species endemic to Mexico covering only 25 hectare area with 34 remaining individuals. The population is exposed to anthropogenic activities like fire, grazing, alien species and solid wastes. Despite of species included in Mexico's red list, there are no species specific conservation programs (Hobohm and Tucker, 2014a). The cacti are highly threatened group among the plants. An example is Mammillaria sanchez-mejoradae facing continuous decline with 75% loss over past 25 years. The wild population contains 500 individuals only, The species is enlisted as Critically

Endangered by IUCN (Maurice and Maurice, 2013). Acharagma aguirreanum another cactus distributed over a small range of 1 km2, with population numbers less than 100 individuals. The plant is facing illegal collection as the main threat (Anderson et al.,

2002). Cryosophila cookii is a palm distributed in Costa Rica rain forests with less than 100 individuals. Logging, medicinal collection, low pollination and dispersal abilities are major threats. (Evans, 1998; Oldfield et al., 1998). Centropogon pilalensis is another threatened endemic to Ecuador with less than 50 individuals (Moreno and Pitman,

2003). There are many other critically endangered species known from type locality where little is known about recent status. Dimorphandra wilsonii a critically endangered species from South East Brazil having only 10 mature individuals, at last assessment, nine year ago, the species was facing major threat in the form of deforestation, invasive species competition and uprooting (Fernandes, 2006). Acacia anegadensis is endemic to

Anegada, with approximate extent of 25 km2. The major threat is exploitation for resin

(Clubbe et al., 2003). The other threats include tourism, road construction, grazing and

12 natural disasters like hurricanes and coastal inundation (Clubbe et al., 2003). Oenanthe conioides is a biannual herb found at Elbe river nearby Hamburg. The plant is composed of few hundred individuals. The main threats are habitat destruction and invasive species (Hobohlm, 2014).

The Mediterranean region is included among the most endemic rich areas of the world. de Montmollin and Strahm (2005) reviewed the top 50 threatening plants of the

Mediterranean region in a booklet entitled "Wild plants at the brink of extinction and what is needed to save them”. The book includes the taxa with a population of few hundred individuals e.g. Arenaria bolosii (<200 individuals), Bupleurum elatum (c. 400–

600), Bupleurum kakiskalae (c. 100), Convolvulus argyrothamnos (30–35), Silene hecisiae;

430), Apium bermejoi (<100 ), Euphorbia margalidiana ( <200 individuals) and Ligusticum huteri (<100 individuals), Minuartia dirphya (<250), Ribes sardoum ( 100) and Zelkovia sicula (c. 200–250). Situation reflects the overall condition of the endemic populations among some of the highly developed areas of the world having high conservation priorities.

African continent is also witnessing many examples of threatened species. Toussaintia patriciae is an endangered species from Tanzania with about 30 trees (Deroin and Luke,

2005) confined to a national park and a nature reserve. The only hope for conservation of the plant is its occurrence inside the park area. However the plant even then needs protection from the firewood collectors. Another example is Aloe pembana having a population number between 50 and 250 individuals with a restricted geographic extent.

The plant grows along the beach and is exposed to collection from trampling and collection for medicinal purposes along with tourism. Afrothismia baerae is a parasitic

13 plant in Kenya growing on the roots of Zanha golungensis (Sapindaceae), a single population of seven plants could be recorded with occupancy of about 1 m2 (Cheek,

2003).

In Asia Western Ghats, Himalaya, Srilanka, Malayan Island and China have some of the richest biodiversity hot spots in the world. Cryptocoryne bogneri is an aquatic, possibly extinct species in Sri Lanka which has been rediscovered in 1999 after about 99 years.

About 250 individual plants were recorded in a very small area of 75 ha. The agricultural expansions and human settlements are possible reasons for the extinction of the species from the other sites (Jacobsen, 1976). More than half of the vascular plants in China are endemic with some oldest representatives fossils like Ginkgo. Two mountainous species Abies beshanzuensis and Abies yuanbaoshanensis were classified as critically endangered. A. beshanzuensis has only five living specimens while A. yuanbaoshanensis has near one hundred. Major threats are agriculture extensions and fire wood collection. The plant has poor regeneration capability with seed predation by squirrels and competition (Yang, 2013; Fu and Jin, 1992).

Rafflesia magnifica, a charismatic plant belonging to the plant group producing single largest flower in the world. In Philippine, the species is classified as Critically

Endangered because of low population and exposure to threats like highway construction and tourism activities in the range (Madulid et al., 2008).

The most alarming cases of extinction are at generic or family level. Genus

Hibiscadelphus is endemic to the Hawaiian Islands. Among seven reported species, three have become extinct, while the other four are critically endangered. A species

Hibiscadelphus distans is a small tree, the species is known from a single population with

20 individuals only (Wagner et al., 1999; WCMC, 1998). On the whole, there is a growing trend towards the analysis of conservation status of plant species all over the world, the scarcity of resources has however directed the bodies towards the rare and endemic species as assumed to undergo the highest risk.

Along the identification of threatened taxa at global or national level, locally rare and threatened taxa are also important for conservation and sustainable ecological process and so require evaluation and recognition. This can provide powerful tool in conservation planning as the area of a species distribution is among the basic characteristics needed for conservation (Pärtel et al., 2005).

1.5.1 SDMs for assigning IUCN threat categories The species distribution models are successfully in use for determining IUCN risk assessments. The main use is for determining the geographic range and predicting species range in changing climate scenarios. The model can help the evaluation process as they are easy to handle, suitable while using geographic scale and different levels of knowledge can be used during evaluation. As SDMs are based on the theory of ecology, the analysis of species distribution being correlative has more predictive power

(Cassini, 2011). Among IUCN threat categories, area of occupancy (AOO) can significantly reduce the experimental error for estimation of range size if an optimal methodology on logical basis is adopted (Crain and White, 2011). Use of SDMs in determining conservation status is relatively modern trend and most of the studies still adopt subjective approach. However the use of SDMs has been found rigorous as can be efficiently implemented, can be used at different levels of knowledge and can be used at geographical scale (Cassini, 2011).

1.5.2 Pakistan's perspectives Keeping in view the importance of biodiversity, countries worldwide increasingly interested in conserving biodiversity. Red list of threatened species are expanding rapidly and are more influential in determining conservation priorities (Miller et al.,

2007).

Pakistan is blessed with vast diversity with respect to endemism when compared with the countries of equal area (Ali, 2008; Roberts and Bernhard, 1977). About 400 endemic plants have been reported from among 6000 vascular plant species, where majority of the endemics are distributed in northern mountain ranges including study area (Ali and

Qaiser, 1986).

Unfortunately, little has been done in Pakistan regarding conservation assessments and

IUCN Red List contains few species from the country. Nasir (1991) 12% of the vascular plant species to be threatened. Based on herbarium specimens, Qureshi and Chaudhri,

(1991) prepared a checklist of 707 threatened plant taxa. However threatened categories were less meaning full and no recent field studies or monitoring processes were adopted. For this reason the list was hardly reflecting the actual situation especially in case of country like Pakistan where majority of the areas were little explored (Baker and

Fish, 2007).

Only few studies have been conducted in recent past following IUCN Categories and

Criteria. Conservation status assessments of Astragalus gilgitensis (Alam and Ali, 2009)

Cadaba heterotricha (Abbas et al., 2010), Astragalus gahiratensis, Delphinium chitralensis are

(Ali and Qaiser, 2010) some examples. Astragalus gilgitensis was placed under the

Critically Endangered category, while Cadaba heterotricha (Abbas et al., 2010) was kept under Endangered category due to their narrow geographic distribution, single location

16 and habitat degradation. Alam and Ali (2010) evaluated 19 flowering plants according to IUCN category and criteria. Of these, 16 taxa were exclusively endemic to Gilgit and

Baltistan, one taxon was endemic in Sind while the remaining 2 taxa were reported only along coastal area of Karachi in Pakistan. During field studies, only 8 taxa could be recollected, while remaining 11 taxa could not be found at reported localities or anywhere else, hence declared as possibly extinct (Alam and Ali, 2010). Keeping in view the current biodiversity losses all over the world, it is necessary to accelerate the conservation efforts at local level for which identification and monitoring of rare and endangered species should be at top priority. Endemic taxa deserve keen attention in this regard as they often have restricted distribution with small population size.

1.6 Study area 1.6.1 Location Hazara Division is located between the 33º-44' and 35º-35' north latitude and 72º 45' and 73º-75' east longitude. Administratively, the area is divided into six districts i.e.

Abbottabad, Batagram, Haripur, Kohistan, Mansehra and newly established Tor Ghar districts. Hazara therefore covers a huge array of altitude, soil, climate and associated vegetation along with fauna and flora. From south towards north it has the plain of

Haripur district, subtropical chirpine zone at the lower reaches of Abbottabad,

Mansehra and Batagram, broad leaves forests of Dry Oak and Moist temperate forests of Galiat, Kaghan and Batagram and alpine pastures of upper Kaghan, Allai and

Kohistan. The area represents the western most limits of Himalaya, located near the junction of world famous mountain ranges the Himalaya, Hindukush and Karakoram

(Fuchs, 1970; Tahirkheli, 1979).

1.6.2 Physical features 1.6.2.1 Mountains:

Figure 1.1: Map describing the geographical location of the study area

Special feature of the area are mountain ranges spreading north-east to south-west creating natural barriers and microclimates responsible for speciation and endemism.

Toward east at the left bank of Kunhar river, main chain moves north to south separating Hazara from Kashmir ultimately terminating at Khanpur area of Haripur district. The range may attain a height of 4546 meters with an average height of 2133 to

3048 at Galiat while declining toward the south reaching at 1712m at Siribang of

Khanpur range. At southern edge, different ribs arise making a network of valleys in in lower part of Hazara.

Toward North, Kaghan mountains with an average height of 4546 make the watershed between Kunhar and Neelam river. Towards extreme north these maintain few glaciers while at south the height decreases gradually with Thandiayni having 2425m and

Nathiagali 2122m and Mirajani as high peaks. Towards south east there occur fairly low ranges of Sherwan joining Tanawal hills. The average height of these mountains ranges between 1000-1200m. Mansehra have some of the highest mountain ranges toward the north east in Kaghan valley. The spur moving towards the east of Kunhar river from

Ghari Habibullah to Gittidas separates the area from adjoining Kashmir area. Towrads the north of Manehra low lying Tanawal hills are situated. Allai tehsil of Batagram has high ranges mountains separting it form Kaghan and Kohistan valleys. The highest peak of Kohistan is Lashglash (4500m) followed by mountain ranges along Kohistan

(Rafiq, 1996a; Shams, 1961).

Toward the left bank of Kunhar, several long spurs extend toward west which makes the watershed between Siran and Kunhar rivers, famous one is Musa Ka Mussala

(4200m). Towards the north west of Musa Ka Mussala several large spur arise spreading towards Batagram and Kohistan districts. The Indus separates Himalayas from Hindu Kush. The highest peak of Kohistan is known as Lash Galash (5200m).

Toward the right bank of Indus, there are several valleys making the water shed of

Indus. Famous among those are Dubair, Kandia, Tangir and Darial all joining Swat district of adjoining Malakand Division. Towards the left bank, Palas and Jalkot valleys are located which open in Kaghan valley towards east (Hussain and Ilahi, 1991).

Separating from the above range, another chain flanks on the extreme northern side on the left bank of Kunhar and form a part of the boundary between Hazara and Kashmir.

It contains the highest peak in the area namely Malka Parbat which towers above 5152 meters. Shortly before the junction of Kunhar and Jehlam it passes completely into

Kashmir.

Towards the western side, a chain leads to Agror valley while another forms the Black

Mountain with a maximum height of 2420m. This arc divides into numerous spurs and offshoots making the maze of Tanawal hills. Bhingra (2576m) and Billiana (1876m) are high peaks of this range. Siran river forces through these ranges and joins Indus. The range ends at Gandegar hills of Haripur district lying along the Indus with an elevation of 1200m. The Tanwal Hills covers the western parts of the area between Indus river and Himalayan mountains. Several inter mounatain basins are present in the Tanawal hills and Himalayan mountain belt. There are peaks of relatively plain land encircled by high mountains. Some of the prominent basins are Pakhli, Mangal, Rash, Haripur and

Khanpur. The Pakhli plain is located over 910m and is more wider than the Mangal tract which lies to the southern edge of Pakhli. Mangal and Rash have an elevation of

1212m.

1.6.2.2 Geology

Located at the junction of world famous mountain ranges; Karakoram, Himalaya and

Hindukush, Hazara is also diverse with respect to geology. Northern boundary of

Himalaya 'Main Mantle Thrust' is geographically represented by Kohistan district of

Hazara. Following Tahirkeli (1982), area can broadly be classified into following series. i. Salkhala series: It is represented by the eastern flank of Hazara syntaxis emerging from the type which is located in Upper reaches of Nilam valley. Intruded by the basic and acidic igneous rocks Phyllite, Schists and paragneisses, sand stone, qartizitic sand stone and semi to medium crystalline lime stone and marble and minor conglomerates are common. The rocks can be classified into basic and acidic domains. Gabbro, dolorite and diorite represent the basic while granite of more than one phase along with pegmatite, atite and vein quartz represents acidic domain. The age of the series can be traced back to Precambrian era. ii. Hazara Slates: Represented by phyllites and phyllitic schists with calcarious and siliiceous partings, para-gneisses, amphibolites and megmatite this series is distributed in midranges of Abbottabad, Mansehra and Batagram districts. Hazara slates represent precambrian age with chlorite grade present between Abbottabad and Qalandarabad, granet grade occur near Mansehra and Khaki, biotile grade near Shinkiari, staurolite grade near Batal, Kyanite and sillimanite grade near Batagram and Thakot. iii. The Tanawal formation: Overlying, Hazara slate with erratic distribution outside the Tanawal area extending between east and west as islolated patches, Tanawal formation is composed of quartzites, quartzitic schist, quartzitic sandstone with interbedded arenaceous rocks, arkosic, arksoic wacke, subarkose, arcnite and quartize

21 arcnite. Epidioritic, paraphyritic, micronotanalite, dacite, rhyodacite, pegmatite and quartz veins are minor intrusions. These are the best representitives of igneous rocks. iv. Tanaki Conglomerates: Composed of sand stone, silt stone, quartz quartzite, argillic lime stone and dolomite; Tanaki Conglomerates intervene Hazara slate where Tanawal formation is missing. 40-50 meters in thickness, they are represented at Tanaki, Barwal and Mamda areas of Abbottabad. v. Abbottabad formation: Lithologically classified as basal conglomerates, lower slate and sandstone, lower dolomite, upper shales and sand stone and upper dolomite composed of carbonate sequence they overly Tanaki conglomerate. The type is distributed in Sarban, Kakul and Sobra and extends forwards Gari Habibullah and westwards towards Sherwan, Kacchi and Gandigar area. The thickness may vary from

150-700m vi. The Galdanian formation: Volcanic rocks composed of haematitic claystone, sand stone, oolitic haematite and laterite with silty shales and varicoloured quartz breccia with a thickness of about 120 meters they are mainly distributed in upper Tanawal near

Darband. vii. The Hazara formation: Representing cambrian age, Hazara formation consists of grey and yellowish brown calcareous and shaly siltstone with subordinate dolomite limestone. viii. Mansehra granites: They are augen geneisses, foliated geneisses and normal homogenous granite. The first category consists of course to very course, dull grey to yellowish white, sub equigranular with abundant mica including vermiculite. They are found aroud Thakot Oghi and Batagram. The foliated gneisses constitute fine to

22 medium texture and their contact with the country rock is not sharp. The homogenous granites are white to light gray, medium to coarse and subequiangular to prophyritic.

They are rich in feldspars and quartz. Black tourmaline and ores are also common. They are mostly located in Kohistan (Tahirkheli, 1979).

1.6.2.3 Hydrology

Hazara is rich with respect to water bodies as two of the big rivers Indus and Jehlam passing through the area. Other important rivers are Dor, Kunhar, Siran, Mangal,

Mushagah, Sapat Nala and Nandiar Khawar with many streams emerging from lakes or glaciers.

The Indus flows on the western boundary through Kohistan, Batagram, Torghar and haripur districts. Jehlam river passes on the east for about 40 km along the boundary of

Abbottabad district. The Siran river rises in the north of Pakhli plain then dips into

Tanawal hills flowing through the Phulora and emerges in the Haripur area. It then turn north westly to join Indus at Tarbella. Siran river irrigates parts of Mansehra,

Abbottabad and Haripur with a course of about 113-119 km. Dor has much shorter path and contains much little water as compared to Siran. It originates at the northern end of of Dungagali and flow through the Haripur plain and ultimately joins Siran near the north east end of Gandgar range, slightly before Trabela with a total length of about

65km where it irrigates parts of Abbottabad and Haripur. Haro originates from Galiat joins Khanpur dam irrigating the parts of Haripur. The river Kunhar rises from lake lulusar at the end of Kaghan valley. After a very troublesome and complicated course of

177 km it joins the Jehlam. It traverses deep mountain gorges from its source upto

Balakot where it enters the plain and flows down to Ghari Habibullah. Numerous

23 tributaries joins these rivers with perennial and semi dry streams abundantly found in the mountainous tract with stony, gravelly beds.

A B

C D

E F

Figure 1.2: Major water bodies in the study area: A; Indus in Upper Kohistan, B; Mushagah in Palas valley, C; Sapat Nala in Jalkot, D; Kunhar at Balakot, E; Lake Saiful Muluk, F; Lake Lulusar Upper Kaghan

Nandiar Khawar rises from the eastern mountain ranges of Batagram district with a course of 30 km before joining Indus at Thakot. Its water is mainly used for irrigation purpose in rice paddies. While Mushagah is flowing along 70km long Palas valley emerging from eastern mountains of Koshistan and Batagram districts. The river joins

Indus above Pattan.

Lakes are confined to the upper mountainous regions in the Kaghan and Kohistan. The three famous lakes are Lulusar, Dudipat Sar and Saifulmuluk. Lulusar is an irregular crescent shaped lake about 2.5km long and 274 meters wide, located at west of Babusar pass at an elevation of 3384m. Dudipat sar is circular, about half a kilometer in distance na 3636 m high. Saifulmuluk located 10 km east of Naran is about half km long and 457 m broad at an altitude of 3248 meters. Sapatsar is located at high pasture of Kohistan district called as Sapat (Figure 1.2).

1.6.3 Climate Variations in topography, elevation, aspect, slope, vegetation cover and climate of the area shows tremendous variations. Major effect is made by elevational gradient beginning from less than 500m reaching upto 5000m. The other influential factors includes time of rainfall, snowfall and temperature. Amount of rainfall increases with increase in elevation upto 3000m within the moist part of the study area. Starting from lower foothills where rainfall remains 500mm, it gradually increases to 750mm at

Haripur, 900 mm at Khan pur and 1200-1400mm in the subtropical zones of Mansehra and Abbottabad districts. In Kaghan valley the upper parts receive less rain as the monsoon loses all its moisture before it rises in the valley. However the areas receive more snowfall in winter. All parts located in the shadow are distinctly drier than outer ranges because the air sheds its moisture before rising to higher elevation. At the peak

25 monsoon fades and winter precipitation in the form of snow is more prominent as seen in the Malika Parbat, Musa ka Mussalla and other high peaks with in the area.

The mean annual temperature is around 70ºF with summer temperature rising over

100ºF for few days during June. The temperature is high during June and July with monthly mean of 70ºF to 105ºF. There is a cool winter with a monthly mean of about

50ºF. Frost is rare in the lower plains but occur occasionally at high altitude.

The average relative humidity varies from 50-60%. There is a long dry season tempered in the more northerly parts by more or less winter and spring precipitation and to the south by the summer monsoon. The mean annual rainfall varies from about 80-100mm which is highly variable in its occurrence from place to place and year to year.

Maximum amount of rain is received during the monsoon. Relative humidity during the year fluctuates from 47% to 71%. Total number of rainy days in a year lies in between 60-67. The annual average runs in between 60-67ºF with the highest being in

June and July which may reach to 100ºF. There is a definite cool winter with frost and occasional snowfall. The climate as a whole has Mediterranean touch.

In Chir Pine zone, a true subtropical climate prevails. The rain fall and temperature conditions are tempered by altitude and aspect. The average annual rainfall lies in between 100-130mm which is higher than the lower zones. The bulk of the rain is received during July and August due to southwest monsoon effect. Springs also has appreciable rainfall which fades in March. There are 60-70 rainy days in a year. The number of months with usually less than 50- mm rainfall is 3-4 October and December is appeared to be the primary dry period , which is followed by the secondary dry spell in May to June. The mean annual temperature fluctuates in between 60 to 70ºF. It

26 generally runs high during June and July with a mean monthly mean of 80-85ºF. The maximum sometimes shoots to 100ºF in the lower limits while it remains below 100ºF at the top. A clear cool winter prevails with December and January as the coldest months.

The mean monthly temperature remains around 50ºF. Frost and snowfall is a common phenomenon. The northern parts of Batagram, Balakot, Allai and Galiat ranges the entire Kaghan valley and Siran valleys are characterized by cool temperate climate. The climatic conditions prevailing at Nathiagali, Dunga Gali, Ayubia, Baragali, Kaghan and places lying within the altitudinal range of 1500 to 3000 meters fall within this category.

The summer are cool and pleasant. The mean annual rainfall varies in between 100-

130mm from year to year. It is mainly received during July, August and September owing to South west monsoon effect. A handsome sum of rainfall is also received during winter and spring owing to the westerly disturbances. Precipitation from this source is mainly in the form of snowfall. There are 79-101 rainy days in a year. Number of months with less than 50mm are 3 and 4. In general there is a high monsoon rain in the outer hill which progressively decreases in the interior. Beyond Batakundi Upper

Kaghan, the monsoon rains are rare. The mean annual temperature ranges from 40-55ºF.

In the wet temperate forests winter accumulation of snow may be up to 7 meters or more (Hussain and Ilahi, 1991).

1.6.4 Vegetation types Based on climate, physiognomy and altitude, following vegetation zones can be recognized in study area (Hussain and Ilahi, 1991; Naqvi, 1976).

Submontane zone including northern sub humid and submontane strip extending attitudinally foot hill to a height of 1000m with two sub zones, along with four subhabitats i.e Acacia-Zizyphus subzone, dominated by Acacia modesta and Ziziphus

27 mauritiana extending upto 700 meters. It is typical of Northern part of Indus plains,

Potohar plateau, Acacia-Olea subzone lying above the foot hills with Acacia modesta and

Olea ferruginea sub zone later having dominance at the upper limits of this range and stream bed habitats characterized by the occurrence of Nerium indicum all along the water courses and degraded habitats composed of Maytenus royleana, Periploca aphylla and Rhamnus pentapomica resulting due to degradation of the land.

Montane Zone extending between 1000-2000 meters but may extend on warmer aspect upto 2200 m. Pinus roxburghii is the dominating characteristic species.

Sub alpine Zone extending between 2000-3000m in the outer parts but may ascends slightly higher in the inner dry valleys. They are predominantly coniferous in nature.

The associations recognised can Pinus-Quercus Association consisting of Pinus wallichiana, Quercus incana and Q. dilatata found between 1800-2200m, Pinus-Abies association found in the middle and sub alpine zone usually above the Pinus-Quercus association upto 2600m with Blue Pine and Abies pindrow as characteristics species.

Mixed Coniferous-Hardwood association representing a mixture of hard wood and conifer species within the same zone, especially confined to the gentle slopes and more or less plain areas., Pinus-Abies-Picea association occurring in the upper portion of the sub alpine regions in the outer parts.

Sub alpine zone up to the timber line which lies between 3200-4000m in the with Betula and Salix as indicator species and alpine zone around 4000m with two broad habitat alpine scrub composed entirely of deciduous shrubs reaching upto 2 meter height with some evergreen Juniper, Rhododendron and Ephedra and alpine meadow and pastures

A B

C D

E F

Figure 1.3: Vegetation types: A; Degraded scrub at Khanpur, B; Tropical subhumid zone at Judba, C; Dry inner zone at Kohistan, D; Chirpine zone at Amazai, E; Moist temperate forest, F; alpine pasture

with grasses and forbs and they may be natural or a result of overgrazing. All the plants

are herbaceous (Figure 1.3).

1.6.5 Population Majority of the population is rural. Mansehra having largest, population, followed by

Abbottabad and Haripur district. Kohistan and Batagram had smallest population size.

The rural population is mostly dependent on natural resources especially forest

products including timber wood, fuel wood, food, fodder and medicinal plant species

(Khan, 2012).

Table 1.1: Population, annual growth rate and ratio

District Area (Km2) Population Annual Urban (%) Rural (%) (persons) Growth rate Abbottabad 1967 880666 1.82 17.93 82.07 Batagram 1301 307278 0.58 0 100.00 Haripur 1725 692228 2.19 11.95 88.05 Kohistan 7492 472570 0.09 0 100.00 Mansehra 4579 1152839 2.40 5.32 94.68 Source: Federal Bauru of Statistics, Govt of Pakistan

1.6.6 Flora and Fauna The areas has been recognized as one among the richest area with respect to

biodiversity. Ali and Qaiser (1986) recognize the area and adjacent Kashmir valley as

centre of radiation. Stewart, (1982) described that more than 2000 vascular plant species

are native to the area with at least eighty species endemic to North West Himalayas. He

also reported some 200 species of Pteridophytes and mentioned that a large number of

Fungi, algae and Lichen species were expected from the area (Stewart, 1982). According

to publish record of flora of Pakistan some 1800 native species have been documented

Seven narrow endemics and about seventy plant endemic to the country has also been

reported from the area (Nasir et al., 1972; Nasir and Ali, 1971).

The area has also keen importance due to its faunal diversity. Lion, wild cats, Marmot,

Black and Brown Bear and Markhor species are among important mammals. A large number of resident and migratory birds including Western Tragopon represent the bird diversity (Saqib et al., 2013; Roberts and Bernhard, 1977).

1.6.7 Protected areas Three National parks, 8 wildlife sanctuaries and 10 game reserves are distributed in the study area. In each district wildlife department is responsible for management of protected areas. However only Ayubia National Park can be considered as well managed up to some extent. The other two national parks are poor representative of a protected area. All the protected areas are aiming at the conservation of large mammals and birds and there is no area specified for protection of a plant species. (GOP, 2015).

1.7 Justification of the study Little is known about the status of biodiversity of the area. Threatened flora especially endemic species could not be studied with respect to conservation planning. Keeping in view the keen importance of endemic plants of the country, current study has been done focusing on the distribution patterns, factors responsible for endemic distribution, population status and threats with following specific objectives.

1.8 Aims and objectives i. To analyse the distribution patterns of plants endemic to Pakistan distributed in Hazara region

ii. To determine the conservation status of endemic taxa under focus

iii. To design the strategies for conservation of the threatened endemic taxa

2 Chapter 2 MATERIALS AND METHODS

2.1 Secondary information At the initial step, review of all the related information was made in order to scrutinize the endemic taxa. The taxa were selected on the basis of geographical rarity focusing on the taxa reported from Pakistan and Kashmir only. In order to prepare the list of taxa endemic to Pakistan and Kashmir, the secondary information was extracted from all the relevant literature including Flora of Pakistan, Annotated Catalague of Vascular Plants of Paksitan and Kashmir and Flora Iranica.

2.2 Herbarium record analysis The plant specimens available at National Herbarium Islamabad (RAW), Karachi

University Herbarium (KUH), Hazara University Herbarium (HUP) and Quaid-i-Azam

University Herbarium (ISL) were consulted in order to obtain the information regarding distribution of taxa. Along that, information available through online sources such as Jstor Plants was also taken into account (www.jstorplants.org). In this way a comprehensive checklist was prepared including botanical name, family, altitudinal range, flowering period and reported localities (Table 2.1).

2.3 Field observations Based on locality and habitat information, field visits were arranged in suitable seasons during 2011-2014 in order to examine the occurrence of a taxon. Whenever a taxon was found, its location was georeferenced using hand-held multi-channel GPS device

(Garmin Etrex Vista) at accuracy level ± 5. The data about habit, lifeform, habitat, population size, threats etc. were recorded in field data book. Certain localities were marked for revisit at specific time for analysing the fluctuations in population.

2.4 Preservation and identification Specimens were collected for confirmation of identification and herbarium record. Each specimen was properly tagged at field, pressed using plant press, dried using blotting papers and news papers and mounted on standard herbarium sheet (17.5"x11.5"). At initial surveys, the collected material was properly identified through taxonomic literature mainly through Flora of Pakistan.

2.5 Data organization for SDM The points marked through field visits were transferred to Map SourceTM and were also organised in Microsoft Excel worksheet. The locations were reconfirmed using Google

Earth TM . All the ambiguous points were removed from the dataset before further use.

2.6 Data analysis through SDM A total of 1900 records were used for 63 species. All the used records were based on 4 year continuous field survey at previously reported and potential sites. Nineteen bioclimatic variables (Hijmans et al., 2005) with 30 seconds (ca. 1 km) spatial resolution, obtained from Worldclim dataset (www.worldclim.org), were used along topographic factors i.e. slope, aspect and elevation data layers generated through Shuttle Radar

Topographic Mission (SRTM) Digital Terrain Model (DTM) with 90 m resolution,

(www.srtm.usgs.gov) (Figure 2.1). Land cover data was obtained from Ecoinformatics labs of International Islamic University Islamabad, while Geology data was prepared using cartographic tool by utilizing Geological survey map of the study area (Khan,

2005). The multi-co linearity test was conducted by using Pearson Correlation

Coefficient (r) to examine the cross-correlation and the variables with cross-correlation coefficient value of >±0.75 were excluded.

Maximum Entropy Model (Maxent version 3.3.4) Phillips et al., 2006; http://www.cs.princeton.edu/wschapire/maxent/) The model uses presence-only data (Phillips et al., 2006) to predict the distribution of a species based on the theory of maximum entropy. Other advantages of Maxent include automatic inclusion of variables, facility in handling categorical and continuous data, regularization parameter for controlling model over fitting, generation of response curves for every predictor and jackknife option for estimating relative influence of each predictor. Model performance was measured through AUC (Fielding and Bell, 1997). Arc GIS 9.3 was used to create spatial data layers, maximum number of background point were selected as 10,000 and linear quadratic and hinge features.

Output maps generated through Maxent were further analysed using R after installing

Raster library package. From each of the raster layer data was extracted at three probability levels i.e. 0.5, 0.7 and 0.9. Data was classified according to elevation, land use, geology and administration units. Presence of a species in each class or group was statistically summarized using Microsoft Excel. Probability maps were generated using

R raster library.

2.1 Associated species analysis Field data collected during 2012-2014. The site where an endemic taxon was found was selected for associated species analysis. Uniform quadrates of 5x5m were laid randomly within the occupancy of an endemic taxon. Over all, the data was composed of 198 samples representing 345 species including 50 endemics. The unidentified plants were properly tagged and identified using the flora of Pakistan. Data classification and ordination analyses were made using different packages in R. Two way indicator species analysis (TWINSPAN), was used in order to classify different associates.

Annual Mean Temperature Mean Diurnal Range Isothermality

Temperature Seasonality Max Temperature Warmest Month Minmum Temp coldest Month

Temperature Annual Range Mean Temp of Wettest Quarter Mean Temp of Driest Quarter

Mean Temp of Warmest Quarter Mean Temp of Coldest Quarter Annual Precipitation

Precipitation of Wettest Month Precipitation of Driest Month Precipitation of Wettest Quarter

Precipitation of Driest Quarter Precipitation of Warmest Quarter Precipitation of Wettest Quarter

Precipitation of Driest Quarter Precipitation of Warmest Quarter Precipitation of Coldest Quarter

Elevation Slope Aspect

Figure 2.1: Topoclimatic variables used in Distribution modeling

Figure 2.2: A; Geological layer used in analysis, A; Archean-Proterozoic granitic biotite orthogneis, Cb; Proterozoic-Camrian quartizite and schist, CC; Chilas complex mafic-ultramafic stratiformplutonic complex, Glac; Permanent ice and snow coverage, Gm; Gilgit complex metasedimentary rocks, Ism; JCg; Gijal complex garnet granulite Chromitite, Ka; Kamila amphibolite complex, KB; Calc-alkaline gabbro- diorite, Kv; Kalam Groupfore arc andesitic volcanic rocks, Mg; Mango gusar meca granite, Ms; Mesosoic meta sedimentry rocks with protolith, Mss; Mesozoic sedimentry rocks, Triassic marl-dolomite and limestone, Pal; Paleocene, Pr; Proterozoic meta-clastic and meta carbonate sedimentry rocks, Mesozoic sedimentry rocks: Triassic marl, dolomite and limestone, Pz; Proterozoic meta-clastic and meta carbonate sedimentry rocks, Su; Sapat ultramafic-mafic complex, SWg; Cambrian Ordovician: Megacrystic granite, Wtrbd; Water bodies, B; 0; Dense conifer, 1; Sparse Conifer, 3; Broad leaf Conifer, 4; Peatland, 5; Grasses 6; Agriculture; 7; Bare soil, rock, 8; Snow, 9; Water bodies, 10; Cloud & Shadow, 11; Mix broadleaf conifer 12; Scrub forest.

Shanon-Weaver Index (H') (Shannon & Weaver, 1948) was used as a measure of diversity for the respective communities. It can be calculated as

Diversity H' = Σp ln pi ……………………… (Eq. 1) pi = proportion of ith individual in a community; ln is the natural logarithm.

Evenness or equitability: Shannon’s-evenness index (E1) was used to quantify the evenness component of species.

E1= H' / ln s ……………………………..…….... (Eq. 2) where H' = Shannon’s diversity; s = the total number of species in a community and ln

= natural logarithm

2.2 Conservation status analysis Extent of Occurrence determined using Global MapperTM . A convex hull was drawn by joining the outer most boundaries generated through Maxent at p = 0.5 or above. Area of occupancy (AOO) was measured by resampling the raster layer at 4 Km2 as recommended by IUCN at p=0.5 using Arcview version 3.1.

Subpopulations were identified using the prediction maps generated through Maxent demarking each subpopulation from the others through natural barriers like mountains, large unsuitable area etc. A distance of at least 10 Km2 was used as standard in isolating a subpopulation from the other. Fluctuations in number of individuals were measured by counting number of individuals at fixed localities each year at the same time.

Threats were identified using IUCN threat classes through personal observations and by conducting local community interviews. The threat categories were assigned using

IUCN Categories and Criteria and guideline for using the criteria at regional level.

Graphs were prepared using Mircosoft Excel program.

2.3 Habitat loss analysis Habitat loss was estimated by analysing sixteen days MODIS time series images of thirteen years (2001-2013) using GIS program IDRISI Selva. The data were obtained from Land Processes Distributed Active Archive Center

(ftp://e4ftl01.cr.usgs.gov/MOLT/MOD13Q1.005/) and consisted of a total of 276-16- day composite NDVI images per tile (23 images/year; except 2000 and 2011). The low quality NDVI data (‘VI Usefulness’ index 7-15) was removed from the series and replaced by linear interpolation of neighbouring values each of the land cover category included MOD14Q1time-series MOD13Q1; tiles h23v05 and h24v05) at 250 m resolution and twelve-year period (Jan. 2001–Jan. 2013) i.e., inter-annual change in NDVI as proxy for habitat loss.

2.4 Determination of endemic rich area Predicted maps were summed up using ArcGIS raster calculator. Output map was classified at p=0.5 and p=0.7 cutoff levels. Final maps were produced using Global

Mapper TM .

Table 2.1 Detailed list of endemic plants showing family, habitat, altitudinal range, flowering period and localities as obtained through secondary information

S. Altitudinal Flowering No Botanical Name Family Habit range period Localities 1 Aegopodium burttii E. Nasir Apiaceae Annual herb 2500m Chail sar, Galiat On way to 2 Alchemilla cashmeriana Rothm. Rosaceae Perennial herb 3100m August Makra 2400- Dunga Gali, 3 Androsace hazarica R. R. Stewart ex Y. Nash Primulaceae Perenial herb 2900m July-Aug Changla Gali 2100- Nathia, Dunga 4 Anemone falconeri Thoms. Ranunculaceae Perenial herb 3100m May-June gali, Kund Anemone obtusiloba D. Don var. potentilloides (Camb. 2500- Thandiani, 5 Ex Berant) Lauener Ranunculaceae Perenial herb 3000m July-Aug Saifulmuluk 2500- Mirajani, 6 Anemone tetrasepala Royle Ranunculaceae Perenial herb 3700m July-Aug Bhunja 7 Aquilegia fragrans Benth. var. fragrans Ranunculaceae Perenial herb 3000m July-Aug Saifulmuluk 3000- Palas, 8 Aquilegia nivalis Falc. ex Baker Ranunculaceae Perenial herb 3500m June-July Saifulmuluk 9 Arabidopsis taraxacifolia (T. Anders.) Jafri Brassicaceae Perenial herb 2500m April-June Upper Kaghan 3600- 10 Arenaria festucoides Benth. Caryophyllaceae Perenial herb 4300m Aug-Sept Chapran 2500- 11 Artemisia amygdalina Decne. Asteraceae Perennial herb 3700m July-Sept Naran Saifulmuluk, 12 Berberis orthobotrys Bien. ex Aitch. ssp. capitata Jafri Berberidaceae Shrub 2600m May-June Gali 13 Berberis parkeriana Schneid. Berberidaceae Shrub 900-2600m April-June Oghi, Bhunja Bistorta amplexicalis (Don) Green var. speciosa (Meisn) 2500- 14 Muni et Jave Polygonaceae Perennial herb 4000m June-Sept Gali, Thandiani 15 Bupleurum nigrescense Nasir Apiaceae Perennial herb 1500m August Sathan gali 16 Calamintha hydaspidis (Falconer ex Beath.) Hedge Lamiaceae Perenial herb 2300m June-Sept Dunga Gali 40 2700- Chail Sar, 17 Caltha alba Camb. var. alba Ranunculaceae Perenial herb 4300m June-July Kaghan 2700- 18 Cortusa matthioli L. ssp. hazarica Y. Nasir Primulaceae Perenial herb 3200m May Mirajani 19 Corydalis govaniana Wall. var. malukiana Jafri Fumariaceae Perenial herb 3000m July Saifulmuluk 20 Corydalis govaniana Wall. var. swatensis (Kitam.) Jafri Fumariaceae Perenial herb 2500m July Siran valley 21 Corydalis pakistanica Jafri Fumariaceae Perennial herb 12000m Gitidas 22 Crotalaria sessiliflora L. subsp. hazarensis Ali Papilionaceae Herb 600m-900m September Batal 1300- 23 Cynanchum jacquemontianum Decne Asclepiadaceae Herb (suberect) 2500m July Manur 2400- 24 Delphinium palasianum R. Rafique Ranunculaceae Perennial herb 2700m Aug Ilobek Palas Dicliptera bupleuroides Nees var. nazimii Malik & A. 25 Ghafoor Acanthaceae Perenial herb 900m June-Dec Balakot Dicliptera bupleuroides Nees var. ciliata Malik & A. Whole 26 Ghafoor Acanthaceae Perenial herb 1-1200m year Abbottabad Dicliptera bupleuroides Nees var. qaiseri Malik & A. Ghari 27 Ghafoor Acanthaceae Perenial herb 1000m Habibullah 1500- August- 28 Epipogium tuberosum Duthie Orchidaceae Herb 2500m sept Thandiani 3000- 29 Festuca hartmannii (Markgr.-Dannenb.) Alexeev Poaceae Perenial herb 5000m July-Aug Saifulmuluk 30 Gagea rawalpindica Levichev et Ali Liliaceae Bulbous herb 500-2000m March Tasala Haryan 31 Galium asperifolium Wall. var. obovatum Nazim. Rubiaceae Herb 2200m July-Sept Nathia Gali 32 Galium subfalcatum Nazim. & Ehrend Rubiaceae Herb 2200m July-Aug Dunga Gali July- 33 Galium tetraphyllum Nazim. & Ehrend Rubiaceae Perenial herb 1200m August ? Gentianodes eumarginata Omer var. harrissii (Omer et 2500- May- 34 al.) Omer Gentianaceae Biennial 3700m August Makra, Sari hut, 35 Gentianodes nasirii Omer, Ali & Qaiser Gentianaceae Annual/Biennial 3300m June Below Makra 2500- 36 Gentianodes cachemirica (Decne.) Omer, Ali & Qaiser Gentianaceae Perenial herb 4200m Aug Musalla, Kagan 1900- 37 Hackelia macrophylla (Brand) I.M. Johnston Boraginaceae Perenial herb 2800m June-July Ayubia, Naran 41

Impatiens bicolor Royle subsp. pseudobicolor (Grey- 1700- Shumlai, 38 Wilson) Y. Nasir Balsaminaceae Annual herb 2500m Murree 2000- July- 39 Inula royleana DC. Asteraceae Perennial herb 3500m September Sari hut, Galiat 1500- 40 Jasminum leptophyllum R. Rafique Oleaceae Shrub 2200m Bangah, Palas 2200- Mahandri, 41 Lavatera cachemiriana Camb. var. haroonii S. Abedin Malvaceae Herb 2700m Dung gli 42 Meconopsis aculeata Royle Papaverceae Herb 3600m August Shinger, Bhunja 43 Microsisymbrium flaccidum O.E. Schulz Brassicaceae Herb 1500m April-June Kaghan 44 Neottia inayatii (Duthie) Beauverd Orchidaceae Herb 1700m July Bhurj Abbottabad- 45 Otostegia limbata (Benth.) Boiss Lamiaceae Shrub 500-2000m April-May Nathia August- 46 Pimpinella stewartii (Dunn) E. Nasir Apiaceae Herb 700-2500m sept Balakot 2000- Naran- 47 Potentilla bannehalensis Cambess. Rosaceae Herb 2900m July Saifulmuluk 3000- 48 Potentilla curviseta Hook f. Rosaceae Herb 4000m June-Aug Chitta Katha 4000- 49 Potentilla pteropoda Royle Rosaceae Perenial Herb 5000m June-Aug Nila, kagan 3300- 50 Primula hazarica Duthie Primulaceae Perenial herb 4300m July-Aug Bhunja, Saral 51 Pseudomertensia elongata (Decne.) Riedl Boraginaceae Perenial herb 3900m June-Aug Makra 2500- 52 Pseudomertensia flavescens R. Rafique Boraginaceae Perennial herb 2900m July Tikotop, Palas Pseudomertensia moltkioides (Royle ex Benth.) Kazmi Naran- 53 var. moltikoides Boraginaceae Perenial herb 2600m July-Aug Saifulmuluk 2500- 54 Pseudomertensia moltkioides var. leichtlinii Kazmi Boraginaceae Perenial herb 3000m June-July Naran-Besal Pseudomertensia moltkioides var. primuloides 2100- 55 (Decne.) Kazmi Boraginaceae Perenial herb 2900m June-July Saifulmuluk 42

1500- Changla, 56 Pseudomertensia parvifolia (Decne.) Riedl Boraginaceae Perenial herb 2700m April-June Charwai 57 Pseudomertensia sericophylla (Riedl) Y. Nasir Boraginaceae Perenial herb 3000m August Makhair, Naran Pseudomertensia trollii (Melchior) Stewart & Kazmi Makra, 58 var. trollii Boraginaceae Perenial herb 3900m may-June kalabagh Pyrola rotundifolia Linn. subsp. karakoramica (Krisa) 3500- 59 Y. Nasir Pyrolaceae Perenial herb 4200m June-July Upper kaghan 1400- Siran, 60 Ranunculus munroanus Drum ex Dunn Ranunculaceae Perennial herb 4250m April-June Saifulmuluk 61 Rhamnella gilgitica Mansf. & Melch Rhamnaceae Tree (small) May-July Upper Palas Rosularia adenotricha (Wall. Ex Edgew. Jansson & Rh 1900 - Bkt, Purana 62 ssp. chtralica GRS Crassulaceae Perennial herb 4500m May-July gora 63 Rumex crispellus Rech.f. Polygonaceae Perennial herb 2700m ? Naran-Kaghan 2300- March- Lachi mali, 64 Salix denticulata subsp. hazarica (R. Parker) Ali Salicaceae Shrub 3500m April Mokshp Ghari 65 Scaligeria indica Wolff Apiaceae herb 700-1000m April Habibullah 1500- Manda Gucha, 66 Scutellaria chamaedrifolia Hedge & Paton Lamiaceae Perenial herb 1800m April-June Chatar Malkandi, 67 Sophora mollis (Royle) subsp. mollis Papilionaceae Shrub 900m April-Sept Kaiwai Makra way, 68 Spiraea hazarica R. N. Parker Rosaceae Shrub 3000m July Siran 2500- Naran, 69 Swertia thomsonii Clarke Gentianaceae Perenial herb 3000m Jul-Nov Saifulmlk Galiat, 70 Thalictrum secundum Edgew var. hazaricum H. Riedl Ranunculaceae Perenial herb 2400m July-Aug Thandiani 1700- 71 Vincetoxicum arnottianum (Wight) Wight Asclepiadaceae Perenial herb 2500m April-June Bhunja

3 Chapter 3 RESULTS 3.1 General distribution 3.1.1 Taxonomic distribution Seventy one (71) endemic taxa were reported from the study area distributed among 28 families and 51 genera (Figure 3.1). Boraginaceae and Ranunculaceae were among the largest families comprising 8 taxa each followed by Rosaceae (5), Apiaceae (4) and

Gentianaceae (4). Pseudomertensia had highest number of taxa (8), followed by Anemone,

Corydalis and Dicliptera having three taxa each (Figure 3.1, Table 3.1).

30 Numberofendemics 20

0 Species Genera Families

Figure 3.1. Distribution of endemics among families, genera and species

Seven families had two endemic taxa each, while eleven others were having one taxon each. No species could be recorded as endemic among algae and archigoniates (Table

3.1).

Table 3.1: Families and Genera comprising highest number of endemic taxa

Family Endemic taxa Genus Endemic taxa Boraginaceae 9 Pseudomertensia 8 Ranunculaceae 9 Anemone 3 Rosaceae 5 Corydalis 3 Apiaceae 4 Dicliptera 3 Gentianaceae 4 Potentilla 3

3.1.2 Distribution within plant habit and life form Highest number of endemic taxa were herbs (63) followed by shrubs (7) and trees (1).

The dominant class among the different life forms were hemicryptophyte (60), followed by phenarophyte (8) and threophyte (2). Only a single geophyte Gagea rawalpindica was found, (Figure 3.2).

70 60 50 40 30 20 10 0

Figure 3.2. Life form and plant habit representation among endemics

3.2 Species distribution modelling (SDM) 3.2.1 Model performance Based on Receiving Operator Curve (ROC) commonly described as Area Under Curve

(AUC), model performed better than random for 62 analysed taxa. Remaining taxa could not be analysed because of deficiency and inaccuracy of georeferenced points.

AUC was ranging from 0.92 to 0.99. All the analysed taxa had AUC value lying under

45 the excellent range. Lowest AUC value was recorded for Pimpinella stewartii while highest was noted for Thalictrum secundum ssp. hazaricum.

3.2.2 Contribution of predictor variables Jackknife analysis revealed that among different predictors, geology was highest contributing factor affecting 39 taxa followed by elevation (28 taxa) and land cover (27 taxa). Slope and aspect were least contributing factors. overall the contribution of topoclimatic was higher than climatic factors.

Among climatic variables Precipitation of the Coldest quarter (Bio 19) was highly contributing followed by Mean Diurnal range (Bio 2), Minimum Temperature of the coldest Month (Bio 6) while Precipitation of Wettest Month (Bio 13) and Precipitation of

Warmest Quarter (Bio 18) were lowest contributing factors. Predictors contribution for major taxa is described below. a) Topographic factors i) Geology

Geology was the highest contributing factor among all the selected topoclimatic factors.

A total of 29 taxa appeared to be affected by geology. Meconopsis aculeata (96.9%), P. moltkoides var. litchnilii (81.7), Rhamnella gilgitica (75.9), Swertia thomsonii (71.2), Corydalis govaniana var. malukiana (64.9), Scutellaria chaemedrifolia (64.7), Jasminum leptophyllum

(64.2) and Rosularia adenotricha ssp. chitralica (61.3) were among the top ten taxa. All the narrow endemics highly responded to geology. Least significant affects were noted for

Pimpinella stewartii (5.7), Berberis orthobotyrus ssp. capitata (4.5), Potentilla bannehalensis

(3.7), Aegopodium burttii (3.5) and Scaligeria indica (3.4). ii) Elevation

Elevation remained prominent factor for nine of the evaluated taxa. The highest percent contribution was shown for Otostegia limbata (68.7) followed by Caltha abla var. alba

(55.8), Aquilegia fragrans (55.3), Alchemilla chasmeriana (48.5), Bistorta amplexicalue var. speciosa (47.4). Gentianodes cashmerica (40.4) and Festuca hartmanii (38.5). Elevation was least significant in case of Rhamnella gilgitica, Gagea rawalpindica, Pseudomertensia moltikoides ssp. moltikoides, Inula royaleana and Gentianodes eumarginata var. harrissii.

(Figure 3.3). iii) Land cover

The highest contribution of the landcover was for Thalictrum secundum ssp. hazaricum

(60) followed by Rosularia adenotricha (61.3), Inula royleana (46), Pseudomertensia trollii ssp. trollii (36.7) and Pyrola rotundifolia ssp. karakoramica. Land cover was least significant contributing factor for Anemone falconeri, Meconopsis aculeata and Pseudomertensia elongata. iv) Slope

Top five taxa having significant slope factor were Corydalis govaniana var. malukiana

(22.9), Sophora mollis ssp. mollis (18.2), Crotallaria sessiliflora ssp hazarica (13.8), Rosularia adenotricha ssp. chitralica (10.4), Salix denticulata ssp. hazarica (8.6), Pimpinella stewartii (4),

Anemone falconeri (2.8) and Gentianodes nasirii (2.4). In general, the slope remained a minor factor in distribution of endemic taxa. v) Aspect

Aspect contributed toward five species only. Percent contribution for Rumex crispilles was 7.5, followed by Pseudomertensia parviflora (6.3), Gentianodes nasirii (6), Festuca

47 hartmanii (5.9), Thalictrum secundum var. hazaricum (5.6), Ranunculus munroanus (4.1) and

Scaligeria indica (4.1).

45 40 35 30 25 20

15 NumberofEndemics 10 5 0 Geology Elevation Landcover Slope Aspect Topographic factors

Figure 3.3: Contribution of topographic factors in endemic taxa distribution b) Climatic factors

Mean diurnal range (Bio 2) significantly contributed towards Pseudomertensia elongata

(70.4%), Arenaria festucoides (64.4), Cortusa matheolii ssp hazarica (37.2), Corydalis govaniana var. swatensis (36.6), Berberis orthobotrys var. capitata (34) and Salix denticulata ssp hazarica (33.3). Isothermality (Bio 3) was contributing significantly for Berberis parkeriana (61.6) and Scalegeria indica (13.2). Precipitation of the Driest Month was affecting the distribution of Pimpinella stewartii (69), Scutellaria chaemdrifolia (29.4) and

Crotallria sessiliflora ssp hazarica (20.5). While Calamentha hydispidis (52.2) and Cortusa matheolli (52.2) highly responded to Precipitation Seasonality (Bio 7) followed by

Pseudomertensia parviflora (47.5) and Impatiens bicolor ssp pseudobicolor (26.4).

Precipitation of Warmest Quarter (Bio 8) was affecting Berberis orthobotyrs ssp capitata

(39.8), Meconopsis aculeata (33.1) and Aegopodium burttii (24.2) while Precipitation of

Coldest Quarter (Bio 19) was noted to be effective in case of Galium asperifolium ssp

48 obovatum (42.4), Vinecetoxicum arnotianum (35.7), Aquilegia fragrans (32.4) and Anemone falconeri (25.3), (Figure 3.4).

10 Numberofendemics 5

0 Bio19 Bio2 Bio11 Bio4 Bio7 Bio15 Bio3 Bio8 Bio13 Bio18 Bioclimatic variables

Figure 3.4: Biclimatic factors affecting endemic distribution

3.3 Predicted distribution patterns Model predicted the distribution along elavation gradient, land cover types, geological classes and administrative units. Distribution was analysed at two different probability level; i. e P=0.5 and P=0.7.

3.3.1 Distribution along elevational gradient Endemic taxa distribution along elevational gradients made a bell shape curve with highest number of endemics confined to middle ranges while extreme lower and upper ranges with least endemics. At p=0.5, only six endemics were distributed upto 500m, between 501 and 1000m another 11 endemics were located. Highest number of endemics were found between 2501 and 3000m having 43 endemics followed by 41 taxa between 3001 and 3500m and 40 taxa between 2001 and 2500m. Only 3 endemics were distributed between 4001-4500 while no taxon was found above 5000m. At p=0.7; only three (3) endemics were distributed below 500m. Highest number was recorded as 37

49 taxa at 2501 and 3000m. Only two endemics were reported between 4500 and 5000m

(Figure 3.5).

Dicliptera bupleuroides var. qaiseri, D. bupleuroides var. nazimii, Gagea rawalpindica,

Otostegia limbata, Pimpinella stewartii and Rhamnella gilgitica were distributed at lowest altitude while Festuca hartmanni, Potentilla pteropoda and Pseudomertensia elongata were confined to highest altitudinal ranges (Figure 3.5).

50 45 40 35 p=0.5 30 p=0.7 25 20

15 Numberofendemics 10 5 0

Elevation

Figure 3.5: Distribution of endemic taxa along elevational gradients

3.3.2 Distribution among land cover types At p=0.5; temperate broad leaf and coniferous mix forest had 61 endemic taxa followed by temperate broad leaf scrub having 60 taxa, subalpine grass and Bush and Dense

Coniferous Forest (59 each), dry Soil and rocks (55) and agricultural land (54). Sparse conifer and Scrub forest had 2 and 7 endemics respectively. Similar trend was observed at p=0.7, (Figure 3.6).

70 60 50 40 p=0.5 30 p=0.7 20

Number of Endemics of Number 10 0

Landcover Classes

Figure 3.6: Distribution of endemics among land cover types

3.3.3 Distribution among administrative units Highest number of endemics were found in Mansehra (58), followed by Batagram (46)

Kohistan (38) and Haripur districts (12) at p=0.5. At p=0.7 model predicted Haripur having lowest number of endemics (8), followed by Kohistan (22), Abbottabad (34),

Batagram (31) and Mansehra (56), (Figure 3.7)

40 Series1 30 Series2

20 Numberof endemics 10

0 Haripur Kohistan Abbottabad Batagram Mansehra Administrative Unit

Figure 3.7: Endemic taxa distribution among political divisions

3.3.4 Distribution with in geological classes At p=0.5; Proterozoic meta-clastic and meta carbonate sedimentry rocks had highest number of endemics (58) followed by Proterozoic-Cambrian quartizite and Schist (55),

`Mesosoic meta sedimentry rocks with protolith (51), Cambrian Ordovician Megacrystic granite (51) and Palaezoic metasedimentry rock (47). While Calc-alkaline gabbro- diorite, Permanent ice and snow coverage and Palaeozoic greenschist, had less than ten endemics. Mesozoic sedimentary rocks, Triassic Marl, dolomite and limestone had no endemic species.

At p=0.7, similar pattern was observed with Proterozoic meta-clastic and meta carbonate sedimentary rocks (52), Proterozoic-Camrian quartizite and Schist (51) and

Mesosoic meta sedimentary rocks (48). Mesozoic sedimentary rocks Triassic marl and

Kalam group Volcanic rocks had no taxa distributed among each.

Meconopsis aculeata had strong association with Mesozoic meta sedimentry rocks with protolith. Thalictrum secundum subsp. hazaricum with Paleocene, Rhamnella gilgitica with

Gijal complex garnet granulite Chromitite, Schutellaria chaemidrifolia with Cambrian

Ordovician Megacrystic granite, Otostegia limbata Quaternary alluvium, Corydalis govaniana var. malukiana with Sapat ultramafic-mafic complex. and Cortusa mathiollii ssp hazarica with Mesosoic meta sedimentry rocks (Figure 3.8).

Pzg Wtrbd p=0.7 SWg p=0.5 CC ISm Mg Kv Ka Q JCg Pal A Su Mss Pz Swg Ms Cb

0 10 20 30 40 50 60

Figure 3.8: Endemics distribution among geological classes, see figure 2.2 for abriviations

3.4 Endemics with highest and lowest occupancy At p=0.5, ten of the endemic taxa occupying highest area were Scutellaria chaemidrifolia,

Pimpinella stewartii, Otostegia limbata, Caltha alba, Alchemilla cashmeriana, Calamentha

hydispidis, Bistorta amplexicalus, Scaligeria indica, Berberis parkeriana and Aegopodium

burttii. Taxa confined to a smallest area were Thalictrum secundum ssp. hazaricum,

Meconopsis aculeata, Jasminum leptophyllum, Gagea rawalpindica and Rhamnella gilgitica.

3.5 Analysis of endemic associates Detailed analysis of the taxa making associations with endemics were classified in five

major communities. Seven environmental factors were responsible for limiting the

communities distribution.

3.5.1 Classification Cluster analysis using Twinspan, divided the whole data into two main branches

( Figure 3.13). First branch toward the left side was composed of 54 sample plots. The group was characterized by the endemic species like Berberis parkeriana, Pimpinella stewartii, Imptiens bicolor ssp. pseudobicolor, Scutellaria chamaedrifolia, Otostegia limbata,

Dicliptera bupleuroides var. qaiseri and Dicliptera bupleuroides var. nazimii., at 20% cutoff another subgroup with Jasminum leptophyllum, Potentilla bannehalensis and Rhamnella gilgitica can be differentiated representing an isolated identity but of little interest.

Second branch further divided at 58% cutoff level where left branch again subdivided into two sub branches at 33% cutoff level. The group at left side consisting of 49 sample plots was further subdivided into two subgroups. Group at left side composed of 15 sampled plots while the other group had 34 sample plots.

Endemic species in the community were Otostegia limbata associated with Artemisia scoparia, Lantana camara, Dicanthium annulatum, Desmodium bipinnatus, Chenopodium album and Vitex negundu. Dicliptera bupleuroides var. nazmii was associated with

Melilotus indicum, Ehertia aspera, Alternentha pungens and Solanum nigrum.

Group four consists of 56 sample plots. Endemic plants distributed were Arenaria festcoides, Meconopsis aculeata, Alchemella cashmeriana, Aquilegia fragrans, Corydalis pakistanica, Festuca hartmanii, Gentianodes nasrii, Potentilla curveseta, Potentilla pteropoda,

Salix denticulata subsp. hazarica, Swertia thomsonii, Pseudomertensia sericophylla,

Pseudomertensia elongata.

35.5

35.0

34.5 34.5

N N

34.0 34.0 0 10 20 0 10 20 Aegbur km km Anemone falconeri

72.0 72.5 73.0 73.5 74.0 74.5 72.0 72.5 73.0 73.5 74.0

35.5

35.0

34.5 34.5

34.0 34.0 0 10 20 0 10 20 Anemone tetrasepala km km Berberis orthobotrys ssp capitata

72.0 72.5 73.0 73.5 74.0 72.0 72.5 73.0 73.5 74.0

N N

35.5

35.0

34.5

34.0 34.0 0 10 20 0 10 20 Calamintha hydaspidis km km Corydalis govaniana var swatensis

72.0Figure72.5 3.9: Prediction73.0 73.5 maps74.0 describing the 72.0endemics72.5 distribution73.0 73.5 in middle74.0 ranges mainly in moist temperate zone. White colour indicating the natural barriers delimiting populations

35.5 35.5

35.0 35.0

34.5 34.5

34.0 34.0 0 10 20 0 10 20 km Epipogium tuberosum km Hackelia macrophylla

72.0 72.5 73.0 73.5 74.0 72.0 72.5 73.0 73.5 74.0

N N

35.5

35.0

34.5

34.0 34.0 0 10 20 0 10 20 Inula royaleana km Impatiens bicolor subsp pseudobicolor km

72.0 72.5 73.0 73.5 74.0 72.0 72.5 73.0 73.5 74.0

35.5 35.5

35.0 35.0

34.5 34.5

34.0 34.0 0 10 20 0 10 20 km Lavatera cachemiriana var haroonii km Potentilla bennehalensis

72.0Figure72.5 3.10: 73.0Endemic73.5 taxa74.0 distributed in middle72.0 ranges72.5 73.0with highly73.5 restricted74.0 distribution

35.5

35.0

34.5 34.5

34.0 34.0 0 10 20 0 10 20 Alchemilla cashmeriana km km Arenaria festucoides

72.0 72.5 73.0 73.5 74.0 72.0 72.5 73.0 73.5 74.0

35.5

35.0

34.5 34.5

34.0 34.0 0 10 20 0 10 20 Corydalis govaniana var malukiana km Aquilegia fragrans km

72.0 72.5 73.0 73.5 74.0 72.0 72.5 73.0 73.5 74.0

N N

35.5

35.0

34.5

34.0 34.0 0 10 20 0 10 20 Corthusa matthioli ssp hazarica km Caltha alba var alba km

72.0Figure72.5 3.11: Endemics73.0 73.5 significantly74.0 affecteted72.0 by 72.5geology73.0 and elevation73.5 74.0

N N

35.5 35.5

35.0 35.0

34.5 34.5

34.0 34.0 0 10 20 0 10 20 km Festuca hartmannii km Corydalis pakistanica

72.0 72.5 73.0 73.5 74.0 72.0 72.5 73.0 73.5 74.0

N N

35.5 35.5

35.0 35.0

34.5 34.5

34.0 34.0 0 10 20 0 10 20 km Gentianodes cachemirica km Gentianodes nasirii

72.0 72.5 73.0 73.5 74.0 72.0 72.5 73.0 73.5 74.0

N N

35.5

35.0

34.5

34.0 34.0 0 10 20 0 10 20 Potentilla curviseta km Gentianodes eumarginata var harrissii km

72.0Figure72.5 3.12: 73.0Distribution73.5 map74.0 showing endemics72.0 72.5confined73.0 to alpine73.5 areas74.0

N N

35.5

35.0

34.5

34.0 34.0 0 10 20 0 10 20 km Potentilla pteropoda km Pseudomertensia elongata

72.0 72.5 73.0 73.5 74.0 72.0 72.5 73.0 73.5 74.0

N N

35.5

35.0

34.5

34.0 34.0 0 10 20 0 10 20 km Primula hazarica km Pseudomertensia trollii var trollii

72.0 72.5 73.0 73.5 74.0 72.0 72.5 73.0 73.5 74.0

N N

35.5

35.0

34.5

34.0 34.0 0 10 20 0 10 20 km Pseudomertensia moltkioides var moltkiodes km Spiraea hazarica

72.0Figure72.5 3.12: 73.0Endemics73.5 with74.0 high habitat specificity72.0 72.5 73.0 73.5 74.0

Endemics among the community were found to be Berberis parkeriana, Impatiens bicolor subsp pseudobicolor, Pseudomertensia sericophylla, Anemone falconeri, Anemone obtusiloba,

Berberis orthobotyrs ssp. capitata, Aegopodium burttii, Bistorta amplexicalis, Caltha alba ssp. alba, Corydalis govaniana var. malukiana, Corydalis govaniana var. malukiana Galium asperfolium var. obovatum, Hackelia macrophylla, Lavatera cashmerianaa and Vincetoxicum arnotianum. Details of each community are given below.

Community I: Oxalis-Adiantum-Cymbopogon

The community represented the endemics association at a range of 1000-1800m. The dominant species were Oxalis corniculata, Adiantum capillus-veneris and Cymbopogon.

The group contains highest number of plants (164) with highest diversity value H'=

4.65, with evenness = 0.91. The most frequent species were Viola odorata, Heracleum candicans, Setaria verticillata, Hedera nepalensis, Dicanthium annulatum, Poa annua,

Indegofera heterantha, Zanthoxylum armatum, Berberis lycium, Impatiens balsamina,

Origanum vulgare, Themeda anathera and Bothreoclova pretens.

Endemics species found in this group were Berberis parkeriana, Pimpinella stewartii,

Imptiens bicolor, Scutellraia chamaedrifolia, Otostegia limbata, Dicliptera bupleuroides var. qaiseri and Dicliptera bupleuroides var. nazimii. Towards the extreme left of this group

Jasminum leptophyllum , Potentilla bannehalensis and Rhamnella gilgitica were present.

Berberis parkeriana and Pimpinella stewartii were highly associated with Poa annua,

Indegofera heterantha, Zanthoxylum armatum, Imaptiens balsamina, Pteris crestata, Medicago polymorpha, Pyrus pashya, Cheileanthus persica, Sarcococa saligna, Impatiens edgwartii,

Impatiens glandulifera, Myrsine africana, Bromus pectinatus and Isodon rugosus. Impatiens bicolor ssp. pseudobicolor was associated with Viburnum grandiflorum, Geranium lucidum,

Galium aprine, Jasminum humile, Rubia cordifolia and Cornus macrophylla. Scutellaria chaemedrifolia was making an association with Apluda mitigans, Cuscuta campestris,

Hypericum perforatum and Imptiens cylindrica. Otostegia limbata was found in association with Punica granatum, Verbascum thapsus, Chenopodium album and Vitex negundu.

Dicliptera bupleuroides var. qaiseri was associated with Justicia adhatoda, Daphne mucronata, Malvastrum coromandilianum Andrachni cordifolia, Quercus baloot, Campanula palllida, Celtis caucasica, Dicliptera bupleuroides var. nazimii, Daphne papyracea, Achyranthus aspera, Cissampelos pareira, Dalbergia sissoo and Clematis graveolens. Jasminum leptophyllum was associated with, Setaria sp Apluda mutica, Cuscuta campestris, Hypericum perforatum,

Imptiens cylindricum, Rubus fruticosus and Rumex hastatus, Potentilla bannehalensis and

Rhamnella gilgitica were present (Table 3.2, Figure 3.13).

Community II: Justicia-Acacia-Cymbopogon

Dominant plants among this community were Justicia adhatoda, Acacia modesta and

Cymbopogon sp. Other major species were Cyanoglosum sp, Olea ferrugenia, Ziziphus mauritiana, Achyranthus aspera, Amaranthus viridis, Averrhoa carmbola, Maytinus royalena,

Stipa jacquemontii and Setaria viridis. Endemic species distributed in the community were

Otostegia limbata associated with Artemisa scoparia, Lantana camara, Dicanthium annulatum, Desmostachia bipinnata, Chenopodium album and Vitex negudo. Dicliptera bupleuroides var. nazmii is associated with Melilotus indicum, Ehertia aspera, Alternentha pungens and Solanum nigrum. Overall, the community represented the vegetation found in lower foothills scrub forest.

III: Trifolium-Pinus -Viburnum

This community was represented by 121 species having Simpson diversity value, H'=

3.98 with evenness=0.83. Dominant species of the community were Trifolium repens,

Pinus wallichiana, Viburnum grandiflorum and Androsace rotundifolia. Endemics among the community were found to be Berberis parkeriana, Impatiens bicolor ssp. pseudobicolor,

Pseudomertensia sericophylla, Anemone falconeri, Berberis orthobotyrus ssp. capitata,

Aegopodium burttii, Bistorta amplexicalus, Caltha alba ssp alba, Corydalis govaniana var. malukiana,, Galium asperfolium var. obovatum, Hackelia macrophylla, Lavatera cashmeriana var. harooni and Vincetoxicum arnotianum. Prominant associated species were, Anagalis arvensis, Ajuga parviflora, Skimmia lauriola, Taxus wallichiana, Verbena sp. Dioscorea diltoidea, Draba glomerata, and Viburnum cotonifolium.

Impatiens bicolor ssp pseudobicolor, Pseudomertensia sericophylla, Anemone falconeri,

Aegopodium burttii, Bistorta amplexicalus, Caltha alba ssp. alba, Corydalis govaniana var. malukiana, Galium asperfolium var. obovatum, Hackelia macrophylla, Lavatera cashmeriana var. harooni and Vincetoxicum arnotianum had common association with Ajuga parviflora,

Skimmia laureola, Taxus wallichiana, Dioscorea deltoidea, Draba glomerata, Picea smitheana and Viburnum cotonifolium.

Community IV: Poa-Kobresia-Pseudonaphalium

The community was represented by 67 species with diversity value H'=2.95 and evenness=0.70. The representative species of the community were Poa alpina, Kobresia sp., Pseudonaphalium sp, Thymus linearis, Loentopodium jacquemontianum, Saxifraga moorcroftiana, Aconogonon rumosus, Bistorta affins, Chaerophyllum acuminatum, Carex

62 divisa, Jurnea himalaica, Poa versicolor, Juniperus communis, Gaultheria trichophylla and

Sibbaldia cuneata.

Endemic plants distributed were Arenaria festcoides, Meconopsis aculeata, Alchemilla cashmeriana, Aquilegia fragrans, Corydalis pakistanica, Festuca hartmanii, Gentianodes nasrii,

Potentilla curveseta , Potentilla pteropoda, Salix denticulata ssp. hazarica, Swertia thomsonii,

Pseudomertensia sericophylla and Pseudomertensia elongata.

Arenaria festcoides was associated with Juniperus communis, Gaultheria trichophylla,

Sibbaldia cuneata and Carex stenophylla. The other endemics like Meconopsis aculeata,

Alchemella cashmeriana, Aquilegia fragrans, Corydalis pakistanica, Festuca hartmanii,

Gentianodes nasrii, Potentilla curveseta, Potentilla pteropoda, Salix denticulata ssp hazarica,

Swertia thomsonii, Pseudomertensia sericophylla, Pseudomertensia elongata, were showing associations with Rhododendrron lepidotum, Iris hookeriana, Aconitum heterophyllum,

Viburnum grandiflorum, Senecio chrysanthmoides, Geranium lucidum, Rhododendron lepidotum and Scrophularia calycina (Table 3.2, Figure 3.13).

Community represented the alpine vegetation with harsh climatic conditions.

Community V: Valeriana-Salix-Abies

The community was represented by 67 species having H'=2.81 with evenness=0.67 The

Dominant species of the group were Valeriana jatamansii, Salix denticulata and Abies pindrow. The other dominant plants were Androsace rotundifolia, Fragaria vesica,

Geranium walichianum, Ranunculus sp and Viola odorata. Endemic plants found among the community were Pseudomertensia sericophylla, Anemone obtusiloba, Anemone falconeri,

Gentianodes cashmeriaca, Pseudomertensia moltkoides var. moltcoides, Pseudomertensia parviflora and Ranunculus munroanus.

Pseudomertensia sericophylla was identified to be associated with Prunella vulgaris,

Gentianodes sp, Senecio chrysanthmides, Hackelia uncinata and Rhus punjabensis. Anemone

falconeri was found in association with Picea smithiana , Leycesteria formosa, Acer caesium,

Actaea spicata and Euonymus hamiltonicus. Anemone tetrasepala, Gentianodes cashmeriaca,

Pseudomertensia moltkoides ssp. moltkoides, Pseudomertensia parviflora, Ranunculus

munroanus were combinable with Dryopteris odontolema and Primula denticulata. The

community represents upper ranges of moist temperate forests (Table 3.2, Figure 3.13).

Table 3.2: Communities with number of species, diversity index and evenness value

Community I II III IV V Species 164 72 121 67 67 Simpson diversity (H') 4.65 3.80 3.98 2.95 2.81 Evenness 0.91 0.89 0.83 0.70 0.67

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Height 6

0 140141151 188 162 177 66 130 13474 155 1211 24 25 27 43 138 168119 172 81 52 71 110 13532 54 109 10048 80 58 117 12599 131 97106 127118 124 159 174 76 42 57 128 171167 185 166 133 139 14 2 4 5 8 11167102 75101 152182 156 89 120 146122 44 50 56 163 2145 17 40 84 49 59 30 112 6472 36 22 53 13 28 29 1 1885 55 90 46 47 142 143121 169 145 96 129 95149 7719 23 6 269 1520 3 7 1063 33 37 60 91 31 51 69 136 92173 3462 115 126165 65 107 8886 104 132164 103 123 78 87 35 39 68 82 94 105 4170 73 93 16 38 61 178 183186 160 179 147 184 161 114 148 153 170 79 108 187180 137 175 176 154 158 157 113 144 150 181 116 83 98

C1 C2 C3 C4 C5

Figure 3.13 : Dendrogram describing all five communities with major and minor groups

3.5.2 Ordination: Nonmetric Detrended Scaling (NMDS) revealed that Oxalis-Adiantum-Cymbopogon

(OAC) and Justicia-Acacia-Cymbopogon (JAC) communities were positively correlated with axis 1, while negatively correlated with axis 2. Trifolium-Pinus-Viburnum (TPV) and Valeriana-Salix-Abies (VSA) were positively correlated with both axis, while Poa-

Kobersia-Pseudnaphlium (PKP) was negatively correlated with axis 1 and positively correlated with axis 2. (Figure 3.17, 3.18)

Among environmental variables, elevation and slope were negatively correlated with

Mean diurnal range (B.2), Mean temperature of Wettest Quarter (B 8), Mean temperature of Warmest Quarter (B10), Mean temperature of Coldest Quarter (B 11) and Precipitation of Wettest Quarter (B 16), while Precipitation of Driest Quarter (B 17) and Precipitation of the Wettest Month (B 13) were negatively correlated with

Temperature Seasonality (B4). Aspect was negatively correlated with Temperature

Annual range (B 7) however the contribution of aspect was non significant (Figure 3.19)

While analysing the relationship among climatic variables and communities, elevation was positively correlated with VSA and PKP while negatively correlated with AOC and

JAC communities. B 2, B 8, B 10, B 11 and B 16 were positively correlated AOC and JAC while negatively correlated with PKP and VSA communities. B 4 had positive correlation with PKP while negative correlation with TPV and VSA communities. B 13

Caltha alba var. alba Potentilla bannehalensis

Aquilegia fragrans var. fragrans Corydalis pakistanica

Cortusa matthioli ssp. hazarica Ranunculus munroanus

Figure 3.13: Plant distributed in Poa-Kobresia-Pseudonaphalium (PKP) community

Gentianodes eumarginata var. harrissii Anemone obtusiloba var. potentilloides

Pseudomertensia trollii var. trolli Pseudomertensia elongata

Primula hazarica Potentilla curviseta

Figure 3.14: Plant distributed in Poa-Kobresia-Pseudonaphalium (PKP) community

Anemone teterasepala Salix denticulata ssp. hazarica

Vincetoxicum arnotianum Lavatera cashmeriana var. harooni

Epipogium tuberosum Pseudomertensia sericophylla Figure 3.15: Plant distributed in Valeriana-Salix-Abies VSA community

Corydalis govaniana var. malukiana Corydalis govaniana var. swatensis

Swertia thomsonii Potentilla pteropoda

Gentianodes cachemirica Gentianodes nasirii Figure 3.15: Plant distributed in Poa-Kobresia-Pseudonaphalium (PKP) community

Pseudomertensia parviflora Scutellaria chaemidrifolia

Anemone falconeri Bistorta amplexicalis var. speciosa

Pseudomertensia moltkioides var. moltikoides Inula royleana Figure 3.17: Plant distributed in Trifolium-Pinus-Viburnum

Meconopsis aculeata Thalictrum secundum ssp. hazaricum

Jasminum leptophyllum Rhamnella gilgitica

Figure 3.16: Endemic taxa with small population size

and B 17 were positively correlated with TPV and VSA while negatively correlated with

PKP.

Endemics Non-Endemics

ele 1

slope B.17

aspv B.13

0 NMDS2 B.4 B.15 B.8 -1 B.7 B.11 OAC Community JAC Community B.10 TPV Community B.2 PKP Community

VSA Community -2

-2 -1 0 1 2

NMDS1

Figure 3.17: Ordination Matrix of the communities describing correlation within communities and environmental variables; OAC; Oxalis-Adiantum-Cymbopogon, Justicia-Acacia-cymbopogon, Trifolium-Pinus-Viburnum, Poa-Kobresia- Pseudonaphalium and Valeriana-Salix-Abies

Figure 3.18: Ordination matrix describing species ecological distances. Annexure 1 mentioning the detailed names of species.

1.0

4000

0.8

3000

0.6

Slope

Elevation

0.4

2000

Cos Transformed Aspect CosTransformed

0.2

1000

0.0 0

AOC JAC TPV PKP VSA AOC JAC TPV PKP VSA AOC JAC TPV PKP VSA

Communities Communities Communities

700

120

100

600

100

500

400

Bio17

Bio11

Bio16

300

-50

200

100

-100

0 0

AOC JAC TPV PKP VSA AOC JAC TPV PKP VSA AOC JAC TPV PKP VSA

Communities Communities Communities

140

300

8000

120

250

100

6000

200

Bio2

150

Bio4

Bio10

4000

100

2000

0 0

AOC JAC TPV PKP VSA AOC JAC TPV PKP VSA AOC JAC TPV PKP VSA

Communities Communities Communities

Figure 3.19: Boxplots describing significant and non significant predictors influencing communities distribution

3.6 Conservation status:

3.6.1 Geographic Range Geographic range can be determined in the form of Extent of Occurrence (EOO) and

Area Of Occupancy (AOO). a) Extent of Occurrence

Applying the Convex Hull joining the outermost points of the range boundary, the

Extent of Occurrence (EOO) was determined. The least extent was observed in

Meconopsis aculeata with 92.3 Km2 only, followed by Jasminum leptophyllum (10 Km2),

Thalictrum secundum ssp. hazaricum (4 Km2), Gagea rawalpindica (374.58), Rhamnella gilgitica (439.92) and Pyrola rotundifolia (506.94). Another six taxa had extent between

500-2000 Km2. Corydalis pakistanica (636.8) had least extent with in this class followed by

Inula royaleana (900.8), Crotallaria sessiliflora (1127), Gentianodes cashmeriaca (1348) and

Gentianodes eumarginata (2068.5). Between the range 2000 and 5000 Km2 thirty one species were present while between 5000 and 14000 another seventeen species were distributed. The taxa with highest extent were Sophora mollis (13775), followed by

Otostegia limbata (10212), Festuca hartmanii (10077), Pimpinella stewartii (9761.6) and Salix denticulata ssp. hazarica (9306.9), (Figure 3.20)

35 30 25 20 15 10

Number of endemics of Number 5 0 <100 100-500 >500-2000 >2000-5000 >5000 Extent of Occurrence

Figure 3.20: Extent of Occurrence (EOO)

76 b) Area of Occupancy

The area of occupancy was determined at At p = 0.5; ten of the species having occupancy less than 200 km2. Only ten other species were lying between 200 and 500 km2, 24 species were occurring between 501 and 1000 km2 and sixteen species from

1000-3000. The taxa with least occupancy were Thalictrum secundum ssp hazaricum

(4km2) followed Jasminum leptophyllum (8 Km2) and Meconopsis aculeata (24 Km2 ),

Gagea rawalpindica (105), Epipogeum tuberosum (133) and Pyrola rotundifolia (135). The species with highest occupancy were Pimpinella stewartii, (2948), followed by Otostegia limbata (2875), Scutellaria chaemadrifolia (2646) and Caltha alba var. alba (1735), (Figure

3.21)

Numberofendemic taxa 5

0 <100 >100-500 >500-1000 >1000-3000 Area of Occupancy

Figure 3.21: Area of Occupancy; one third of the taxa falling below 500 Km2

3.6.2 Sub populations Six (6) of the taxa were distributed in one population only. Seven (7) taxa were distributed in two populations and seven others had three subpopulations, eleven having four subpopulations each and six other taxa with five subpopulations each. 19 of the taxa had subpopulations numbers between 6 and 14. The taxa with a single

77 population only were Thalictrum secundum ssp. hazaricum, Gagea rawalpindica, Jasminum leptophyllum, Pyrola rotundifolia and Rhamnella gilgitica. Taxa having only two sub populations were Cortusa mathiolli, Inula royleana, Pseudomertensia trollii, Spiraea hazarica and Swertia thomsonii. Taxa with highest number of subpopulations were Pimpinella stewartii (14), Otostegia limbata (13), Calamintha hispidis (11) and Berberis parkeriana (10).

(Figure 3.22)

16 14 12 10 8

6 Numberofendemics 4 2 0 One Two Three Four Five Six Seven Eight Nine Ten >Ten Number of locations/subpopulations

Figure 3.22: Subpopulations distributed within the study area

3.6.3 Population trends Based on continuous observations each year at fix sites, decline and fluctuations were noted among populations. Among 65 monitored endemics, 4 had only a single population with declining trend, while 26 taxa shown high fluctuations in their populations. Twenty eight other taxa were facing continuous decline.

3.6.4 Habitat loss Twelve years analysis of vegetation layers revealed a declining trend in dense coniferous forests (Protected forest) at a rate of 2.85 percent. Rate of agricultural expansion is also very high with 18.37 percent increase during twelve years study

78 period. Another huge degradation was noted in forests distributed along rivers and streams where 14.74 % of the area had been degraded.

Table3.3: Regressive and progressive trends in vegetation cover (2001-2012)

Reduction Increase Stable Reduction Increase Landcover types (Km2) (Km2) (Km2) (Percent) (Percent) Agriculture 2 108 478 0.34 18.37 Dense conifer 94 47 3156 2.85 1.43 Broad leaf coniferous forest 42 27 216 14.74 9.47 Grasses/Bush 47 640 5468 0.76 10.40 Mix Broadleaf Scrub/shrub 20 533 1777 0.86 22.88 Mixed (conifer, broad leaf) 71 272 3042 2.10 8.04 Bare soil and rocks 56 554 5606 0.90 8.91 Sparse Conifer 21 69 853 2.23 7.32 Tropical Scrub 2 623 6 1.72 93.10

Progressive trends were found in Tropical scrub forest, Sparse Coniferous forests and

Grassland, Bare soil and Coniferous forests at lower elevation (Figure 3.23).

Figure 3.23: Thirteen years (2001-2013) trends in vegetation cover change

3.6.5 Threats The most common threat to maximum species was the collection of plant material for fodder, medicina and such other purposes. Thirty Nine taxa (39) were facing overexploitation followed by land sliding (37), overgrazing (34), soil erosion (31), agriculture extension (29) , tourism (28) and road construction (27). The least number of taxa were suffered by dam construction (5) energy production (7), fire (7), waste water

(8) and mining (13), (Figure 3.24).

Figure 3.24: Various threats responsible for declining endemic populations

3.6.6 IUCN threat categories Based on IUCN Categories and Criteria the taxa were assigned the threat categories following alphanumeric classification system. a) Extinct Taxa

Despite through surveys four endemic taxa could not be found in the study area nor were reported from any other areas of the country. Androsace hazarica, Arabidopsis taraxacifolia, Bupleurum nigrescence, Microsisymbrium falccidum and Neottia inayattii were assigned Extinct (EX) category. b) Regionally extinct taxa

Artemisia amydalina was reported from Naran in 1879. However after that no individual could be reported. The plant has been reported from a single locality in adjacent Indian

Occupied Kashmir valley where it is reported to be threatened. The species was assigned Regionally Extinct (RE) category. c) Critically endangered taxa

Jasminum leptophyllum was found at single location in Palas valley. Extensive surveys of other potential habitats could not discover any other location. Extent of Occurrence

(EOO) for the species had been measured as 10Km2 and Area of Occupancy (AOO) was measured as 8Km2. The plant is facing multiple threats and current population is continuously decline at the hand of habitat degradation. The species is assigned

Critically endangered (CR) at global level.

Meconopsis aculeata was earlier repoted from Kashmir valley. In 1993 it was also reported from a location in Palas valley of Kohistan district. In our study, species was discovered at two new location in Kaghan valley however it could not be rediscovered from Palas. EOO has been measured to be less than 100Km2 and Area of Occupancy

(AOO) as 24 Km2. The species is assigned Critically Endangered (CR) at regional level.

Thalictrum secundum ssp hazaricum was earlier reported from two locations Galiat and

Thandiani. However it could not be found in Thandiani despite several visits. The

Extent of Occurrence (EOO) and Area of Occupancy (AOO) is calculated as 4Km2

Approximately 200 individual were present. Continuous decline was reported in AOO and quality of habitat. The species is assigned Critically endangered (CR) category

(Table 3.4)

81 d) Endangered taxa

Based on EOO, AOO, quality of habitat and continuous decline forty taxa were assigned Endangered Category (EN) at local level. Anemone falconeri, Anemone tetrasepala, Aquilegia fragrans var. fragrans, Aquilegia nivalis, Arenaria festucoides, Berberis orthobotrys ssp. capitata, Cortusa matthioli ssp. hazarica, Corydalis govaniana var. malukiana,

Corydalis govaniana var. swatensis, Corydalis pakistanica, Crotalaria sessiliflora ssp. hazarensis, Cynanchum jacquemontianum, Dicliptera bupleuroides var. ciliata, Dicliptera bupleuroides var. nazimii, Dicliptera bupleuroides var. qaiseri, Epipogium tuberosum, Gagea rawalpindica, Galium asperifolium var. obovatum, Gentianodes cachemirica, Gentianodes eumarginata var. harrissii, Gentianodes nasiri , Hackelia macrophylla , Impatiens bicolor subsp. pseudo-bicolor, Inula royleana, Lavatera cachemiriana var. haroonii, Potentilla curviseta,

Potentilla pteropoda, Primula hazarica, Pseudomertensia elongata, Pseudomertensia moltkioides var. moltikoides, Pseudomertensia moltkioides var. primuloides, Pseudomertensia sericophylla,

Pseudomertensia trollii var. trollii, Pyrola rotundifolia subsp. karakoramica, Rhamnella gilgitica, Rumex crispellus , Scaligeria indica , Scutellaria chamaedrifolia, Spiraea hazarica,

Swertia thomsonii (Table 3.4). The species are however assigned the threat categories at regional level.

e) Vulnerable species

Aegopodium burttii, Alchemilla cashmeriana, Anemone obtusiloba var. potentilloides, Berberis parkeriana, Bistorta amplexicalulis var. speciosa, Calamintha hydaspidis, Caltha alba var. alba,

Festuca hartmannii, Otostegia limbata, Pimpinella stewartii, Potentilla bennehalensis,

Pseudomertensia moltkioides var. leichtlinii, Pseudomertensia parvifolia, Ranunculus munroanus, Rosularia adenotricha ssp. chitralica, Salix denticulata ssp. hazarica, Sophora

82 mollis ssp. mollis and Vincetoxicum arnottianum were assigned vulernable category at local level. g) Data Deficient (DD)

Delphinium palasianum, Galium subfalcatum, Galium tetraphyllum and Pseudomertensia flavens could not completely studies. Delphinium palasianum was earlier reported from a single locality in Kohistan. The other species had also certain imbiguities in identification. The criteria could not be applied and Data Deficient category.

Table 3.4 Consevation status of endemic plants following IUCN alphanumeric scheme

S. EOO AOO Threat No Species (Km2) (Km2) Population Trend Locations Category IUCN alpha numeric scheme 1 Aegopodium burttii 8787 1068 Decline and fluctuations 8 VU B1 ab(i,ii,iii) c(iv)+B1 ab(i,ii,iii) c(iv) 2 Alchemilla cashmeriana 4034 1604 Decline and fluctuations 5 VU B1 ab(i,ii,iii) c(iv) 3 Androsace hazarica ------EN ------4 Anemone falconeri 4328.2 756 Decline and fluctuations 4 EN B1 ab(i,ii,iii) c(iv) 5 Anemone obtusiloba var. potentilloides 4788.5 1020 Decline and fluctuations 5 VU B1 ab(i,ii,iii) c(iv) 6 Anemone tetrasepala 4163.2 824 Decline and fluctuations 5 EN B1 ab(i,ii,iii) c(iv) 7 Aquilegia fragrans var. fragrans 2684.3 1224 Decline and fluctuations 3 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 8 Aquilegia nivalis 2312 982 Decline and fluctuations EN B1 ab(i,ii,iii) c(iv) 9 Arabidopsis taraxacifolia ------Ex ------10 Arenaria festucoides 4440.8 1040 Decline and fluctuations 5 EN B1 ab(i,ii,iii) c(iv) 11 Artemisia amygdalina RE 12 Berberis orthobotrys ssp. capitata 3206.1 476 Continuous decline 3 EN B1 ab(i,ii,iii) +B2 ab(i,ii,iii) 13 Berberis parkeriana 6284 1580 Continuous decline 10 VU B1 ab(i,ii,iii) +B2 ab(i,ii,iii) 14 Bistorta amplexicalis var. speciosa 7340.7 1244 Continuous decline 9 VU B1 ab(i,ii,iii) +B2 ab(i,ii,iii) 15 Bupleurum nigrescence ------EX ------16 Calamintha hydaspidis 6673 1736 Decline and fluctuations 7 VU B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 17 Caltha alba var. alba 7794.3 1448 Continuous decline 11 VU B1 ab(i,ii,iii) +B2 ab(i,ii,iii) 18 Cortusa matthioli ssp. hazarica 3844 1020 Decline and fluctuations 4 EN B1 ab(i,ii,iii) c(iv) 19 Corydalis govaniana var. malukiana 4443 1080 Decline and fluctuations 2 EN B1 ab(i,ii,iii) c(iv) 20 Corydalis govaniana var. swatensis 4932.9 756 Decline and fluctuations 5 EN B1 ab(i,ii,iii) c(iv) 21 Corydalis pakistanica 764.6 668 Decline and fluctuations 3 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 22 Crotalaria sessiliflora ssp. hazarensis 1127.8 140 Decline and fluctuations 1 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 23 Cynanchum jacquemontianum 2477.4 552 Decline and fluctuations 3 EN B1 ab(i,ii,iii) c(iv) 24 Delphinium palasianum ------DD ------25 Dicliptera bupleuroides var. ciliata 3127.1 880 Continuous decline 5 EN B1 ab(i,ii,iii)

26 Dicliptera bupleuroides var. nazimii 3500.58 872 Continuous decline 4 EN B1 ab(i,ii,iii) 27 Dicliptera bupleuroides var. qaiseri 3072.4 972 Continuous decline 5 EN B1 ab(i,ii,iii) 28 Epipogium tuberosum 2906.7 132 Decline and fluctuations 3 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 29 Festuca hartmannii 1007.7 740 Continuous decline 8 VU B1 ab(i,ii,iii) +B2 ab(i,ii,iii) 30 Gagea rawalpindica 374.58 104 Decline and fluctuations 1 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 31 Galium asperifolium var. obovatum 4016.8 568 Decline and fluctuations 5 EN B1 ab(i,ii,iii) c(iv) 32 Galium subfalcatum ------DD ------33 Galium tetraphyllum ------DD ------34 Gentianodes cachemirica 1348.1 324 Decline and fluctuations 4 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 35 Gentianodes eumarginata var. harrissii 2068.5 204 Decline and fluctuations 3 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 36 Gentianodes nasirii 2312 552 Decline and fluctuations 4 EN B1 ab(i,ii,iii) c(iv) 37 Hackelia macrophylla 5057.7 304 Decline and fluctuations 4 EN B2 ab(i,ii,iii) c(iv) 38 Impatiens bicolor ssp. pseudo-bicolor 3354.5 376 Decline and fluctuations 5 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 39 Inula royleana 900.85 152 Decline and fluctuations 2 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 40 Jasminum leptophyllum 10 8 Continuous decline 1 CR B1 ab(i,ii,iii) +B2 ab(i,ii,iii) 41 Lavatera cachemiriana var. haroonii 3893.5 388 Decline and fluctuations 4 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 42 Meconopsis aculeata 100 100 Continuous decline 1 CR B1 ab(i,ii,iii) +B2 ab(i,ii,iii) 43 Microsisymbrium flaccidum ------EX ------44 Neottia inayattii ------EX ------45 Otostegia limbata 10212 2876 Continuous decline 13 NT B1 ab(i,ii,iii) +B2 ab(i,ii,iii) 46 Pimpinella stewartii 9761.6 2948 Continuous decline 14 NT B1 ab(i,ii,iii) +B2 ab(i,ii,iii) 47 Potentilla bannehalensis 5139.3 1264 Decline and fluctuations 9 VU B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 48 Potentilla curviseta 2241.6 464 Decline and fluctuations 4 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 49 Potentilla pteropoda 4858.2 724 Decline and fluctuations 5 EN B1 ab(i,ii,iii) c(iv) 50 Primula hazarica 2136.9 408 Decline and fluctuations 4 EN B2 ab(i,ii,iii) c(iv) 51 Pseudomertensia elongata 4873.4 620 Decline and fluctuations 5 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 52 Pseudomertensia flavens ------DD ------53 Pseudomertensia moltkioides var. leichtlinii 5091.9 932 Decline and fluctuations 4 VU B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 54 Pseudomertensia moltkioides var. moltikoides 2837.1 440 Decline and fluctuations 5 EN B2 ab(i,ii,iii) c(iv)

55 Pseudomertensia moltkioides var. primuloides 3 EN 56 Pseudomertensia parvifolia 5671.1 524 Decline and fluctuations 5 VU B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 57 Pseudomertensia sericophylla 5983.2 316 Decline and fluctuations 4 EN B2 ab(i,ii,iii) c(iv) 58 Pseudomertensia trollii var. trollii 4228.7 540 Decline and fluctuations 2 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 59 Pyrola rotundifolia ssp. karakoramica 506.94 136 Decline and fluctuations 1 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 60 Ranunculus munroanus 5498.6 712 Decline and fluctuations 4 VU B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 61 Rhamnella gilgitica 439.92 200 Continuous decline 1 EN B1 ab(i,ii,iii) +B2 ab(i,ii,iii) 62 Rosularia adenotricha ssp. chtralica 8619.6 508 Decline and fluctuations 6 VU B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 63 Rumex crispellus 4990.2 952 Decline and fluctuations 3 EN B1 ab(i,ii,iii) c(iv) 64 Salix denticulata ssp. hazarica 9306.9 1172 Continuous decline 6 VU B1 ab(i,ii,iii) +B2 ab(i,ii,iii) 65 Scaligeria indica 4399.1 764 Decline and fluctuations 4 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 66 Scutellaria chamaedrifolia 4811.9 2648 Decline and fluctuations 5 EN B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv) 67 Sophora mollis ssp. mollis 13795 1120 Continuous decline 6 VU B1 ab(i,ii,iii) +B2 ab(i,ii,iii) 68 Spiraea hazarica 2238.3 568 Continuous decline 2 EN B1 ab(i,ii,iii) 69 Swertia thomsonii 2360 160 Decline and fluctuations 2 EN B2 ab(i,ii,iii) c(iv) 70 Thalictrum secundum ssp. hazaricum 4 4 Continuous decline 2 CR B1 ab(i,ii,iii) +B2 ab(i,ii,iii) 71 Vincetoxicum arnottianum 5652.8 512 Decline and fluctuations 4 VU B1 ab(i,ii,iii) c(iv)+B2 ab(i,ii,iii) c(iv)

3.7 Endemic rich areas At p=0.5 eastern range of Kaghan valley and adjoining Batagram and Kohistan area,

Machai Sar of Torghar, Galiat area of Abbotabad and Khanpur range of Haripur district were identified as endemic rich areas. At p=0.7 the model predicted eastern boundry of

Mansehra District joining Neelam valley, Batagram and Palas valley boundaries and

Galiat area of Abbottabad district were identified as Endemic Rich Areas.

A B

Figure 3.25: Endemic rich area A, at p=0.5, B;, at p=0.7

3.8 Species specific conservation strategies Keeping in view the current status of each analysed endemic species, conservation strategies were proposed for individual species. Table 3.5 contains species specific conservation strategies.

Table 3.5: Species specific strategies for endemic plant taxa

S. No Species Species specific conservation strategy 1 Plant is found as under growth under thick forest cover. The common threats include deforestation and harvesting of plant along other forbs for fodder purposes. Declaration of plant habitats as protected areas and awareness generation among communities can be effective tools. Regular preservation of seed, reintroduction and propagation through seed and Aegopodium burttii ex situ conservation may also be effective. 2 The plant is found in alpine pastures at open sunny places. Grazing is major threat. The plant Alchemilla cashmeriana can be protected through establishment of protected area in the core habitat of the species. 4 The plant prefers thick undisturbed vegetation areas with high humus contents. The other Anemone species often found outcompeting with plant. Germplasm collection and Anemone falconeri reintroduction at undisturbed sites can be an effective strategy for conservation. 5 The plant is found at gentle slope at moist shady places. Landslides, wood transportation and harvesting for fodder are major threats. The ban on such activities can be helpful in population Anemone tetrasepala restoration. 6 The plant is found in alpine areas. Habitat loss because of agricultural extinctions and digging for medicinal plant collection are among major threats. Declaration of suitable sites as Aquilegia nivalis protected area can be effective along ex situ conservation. 7 The plant is exposed to snow avalanches and soil erosion at the hand of overgrazing in alpine Arenaria festucoides areas. Overcoming such problems can be suitable solution. 8 The plant is confined to forest margins and open sunny areas along agricultural land. Main threats include agricultural intensification, use of plant root for medicinal purposes, use as fuel and fodder and for construction and fencing. Considerable area should be declared as Berberis orthobotrys ssp. capitata protected land for conservation. 9 The species is also exposed to various threats as above mentioned species. Uprooting is one among major threats. However current population will be sustainable if wise utilization Berberis parkeriana practice is adopted. 10 The plant has considerable population, however collection for fodder and vegetable and Bistorta amplexicalulis var. speciosa medicinal purposes is among major threats. Plant root is used in making tea for which large 88

amount of plants are uprooted every year. The population lying inside Ayubia National park is also facing high competition with an invasive species Leucanthemum vulgare. The plant can be protected by imposing ban on uprooting and coping with invasive species. 11 Plant is found at open sunny area often exposed to grazing. Protection of plant population Calamintha hydaspidis through protected area can be an effective strategy. 12 The plant is found along slow moving steams, lake and other water bodies. Glaciers and spring water naturally provide water source for this species. Less amount of water availability is alarming threat for plant populations. Deforestation and soil erosion are other indirect factors as they are responsible for water storage. Soil and water conservation is the only Caltha alba var. alba effective strategy for conservation of this species. 13 The plant is a handsome perennial herb, confined to rocks beds in alpine areas. The populations remain exposed to grazing by cattle and goat hence very few plant can be able to produce mature seeds. The record also reveals the upward movement of plant at the face of global warming. The declaration of protected areas in the habitat of plant, germplasm Cortusa matthioli ssp. hazarica collection, ex situ conservation can be effective. 14 The species is distributed in temperate and subalpine areas at steep slopes. The plant is exposed to habitat loss and degradation due deforestation and grazing activities. Declaration Corydalis govaniana var. malukiana of protected areas is suggested of plant conservation. 15 The species is distributed subalpine and alpineareas at steep slopes at open sunny areas. The plant is exposed to habitat loss and degradation because of and grazing activities. Declaration Corydalis govaniana var. swatensis of protected areas is suggested of plant conservation. 16 The species is confined to Upper Kaghan and Kohistan areas. Soil erosion, mining and habitat degradations are major threats. Effective management of existing National Park will be Corydalis pakistanica effective in species conservation along ex situ conservation. 17 The distribution of plant in agricultural land has made it exposed to threat. Reintroduction at Crotalaria sessiliflora subsp. hazarensis protected sites, germplasm collection and other ex situ conservation is recommended. 18 The species is confined to subalpine area in Manur valley of Mansehra and Moru area of Cynanchum jacquemontianum Kohistan. Habitat should be declared as protected area. 19 The species is exposed to agricultural extension, natural habitat degradation, modification and Dicliptera bupleuroides var. ciliata urbanization. habitat protection is highly required. 89

20 The habitat of the plant has continuously degraded by mining, deforestation and habitat degradation. Conservation of the plant require habitat restoration along protection of the Dicliptera bupleuroides var. nazimii current sites. 21 The species share the same threats like the previous one and can be conserved through in situ Dicliptera bupleuroides var. qaiseri and ex situ conservation programs. 22 The plant has delicate succulent habit. The species remains confined to damp soils with thick humus layer at shady places. The bare and thin soil layers are insuitable habitats for the plant. Carefull reintrodution and ex situ conservaion are possible solutions for conservation of this Epipogium tuberosum species. 23 The species is confined to alpine areas at gentle slopes. The plant is exploited for fodder and in Festuca hartmannii floor covering. Control over the overexploitation is possible solution. 24 The plant is found in early spring. Current habitat is highly exposed to industrial develpoment and solid wastes. Protection of some pristine populations, reintroduction along in situ Gagea rawalpindica conservation can be helpful. 29 The plant is confined to moist temperate forests under thick forests. Forest fragmentation and exposure to light make the habitat unsuitable. Conservation of remaining populations at Hackelia macrophylla undisturbed habitats along ex situ conservation can be helpful in conservation. 30 The species is confined to moist shady places along streams. The plant harvesting for fodder and occupancy of other aggressive Impatiens species of are among major threats. The populations along streams are also exposed to flood effects. Control over competing invasive Impatiens bicolor subsp. pseudo-bicolor speccies is recomended along ex situ conservation. 31 The plant is confined to moist temperate forest as under growth. The plant produce less number of seeds and the population in Ayubia National park is competing with Leucanthemum vulgare. The other populations are exposed to deforestation and other kinds of habitat loss. Spread of invasive species should be controlled along reintroduction at suitable Inula royleana sites can be effective. 32 The plant is narrow endemic and is only found in upper Palas valley at a single location. The habitat is exposed to soil erosion, land sliding and deforestation. The marginal area is located along Palas river Mushgah and flood in 2010 has extremely damaged the habitat. Another Jasminum leptophyllum important threat is construction of a dam which will lead to complete destruction of the 90

habitat. The declaration of site as protected area, management of landslides and allocation of some other site for dam construction are urgently required. The ex situ conservation and introduction at other suitable sites is also beneficial. 33 The use of root for medicinal purposes has made the plant threatened. The other major threat Lavatera cachemiriana var. haroonii is agricultural extension. In situ and ex situ conservation are required. 34 The plant is found at a single locality in the study area and is found in gravely soil in alpine areas. The population is exposed to snow avalanches and habitat degradation. Ex situ conservation through tissue culture techniques and protection of current sites along with Meconopsis aculeata reintroduction is urgently required. 35 The species is found at calcareous strata along road side and ridges in subtropical zone. Otostegia limbata Although current populations are out of threats, the plant is facing habitat competition 36 Species mostly grows in subtropical area along road sides. The root and fruits of plants are utilized for medicinal purposes. The plant cannot compete with grasses and thus restricted to marginal areas. Ban on over exploitation and germplasm collection can be effective in Pimpinella stewartii conservation. The current population size is however able to make the species non threatened. 37 Species is found at dry slopes where it is exposed to land sliding, grazing and soil erosion. Establishment of protected area in core habitat of plant is better approach to conserve the Potentilla bannehalensis species. 38 The species is found in bare rocks in alpine areas. Rocky habitat and unpalatibility makes the plant protected. Mining is great threat to species in Kohistan district where rocks are exploded Potentilla curviseta for precious stones. Awareness 39 The plant prefers relatively moist habitats and found at ridges and cliffs. The main threats are snow avalanches and land erosion. Protection of plant habitat and ex situ conservation can be Potentilla pteropoda effective for conservation. 40 The plant is confined to alpine areas growing among herbs and grasses. Grazing and uprooting for medicinal uses are the major threats. Declaration of protected areas in the core Primula hazarica habitats is suggested for conservation of this species. 45 The species is distributed in subalpine and alpine areas at open sunny areas. The plant emerges soon after melting snow and remains highly exposed to grazing as sheep flocks move Pseudomertensia parvifolia upward areas. No protected areas contains any subpopulation. Declaration of some suitable 91

site as protected area can be useful for plant protection. 46 The plant is confined to sandy gravel soil in moist temperate areas. Distribution of one of the populations in National Park is encouraging. The other populations can also be protected by Pseudomertensia sericophylla protecting such sites. 47 The plant appears soon after snow melting like other species of Pseudomertensia species. The grazing soon after plant emergence is the main threat as few species are available to sheep and Pseudomertensia trollii var. trollii goats. Declaration of some protected areas will be helpful along germplasm storage. 48 The species is confined to specific habitats at marshy places in Upper Kaghan and Kohistan areas. Road construction, eutrophication caused by application of fertilizers in Pea and Potato crops and other kinds of water pollutants are among major threats. Effective management of areas along water bodies will be helpful in species conservation. Ex situ conservation is also Pyrola rotundifolia ssp. karakoramica suggested. 49 The species is confined to moist temperate and subalpine areas at most shady places. The plant produce less number of seeds. The artificial dispersal at suitable sites will be helpful in Ranunculus munroanus plant conservation. 50 The species is confined to upper dry regions of Hazara showing strong association with Gijal complex garnet granulite Chromitite. The plant is under extreme pressure of overutilization for fodder, as vegetable, fuel wood, grazing and browsing . The habitat specificity is another disadvantage for the plant. The protection of considerable population and reintroduction at Rhamnella gilgitica suitable sites is suggested as use strategy. 51 The plant is associated with old stony walls and rock beds having clayey soil. Air pollution and less moisture are main threats. The plant can be conserved through in situ and ex situ Rosularia adenotricha ssp. chitralica conservation practices. 52 The plant grows along streams and near marshy area. The extensive use of fertilizers and pesticides is a potential threat. protection of such habitats is urgently required for plant Rumex crispellus conservation. 53 The species is found in moist temperate and subalpine areas. It often grows at steep slopes exposed to snow avalanches. The other main threats include utilization for fuel wood, hedge Salix denticulata ssp. hazarica and fencing and for construction purposes where insitu conservation can only be effective. 54 Scaligeria indica The species prefers moist shady habitats and flowers in early summer. Utilization of habitat in 92

urbanization and colonization and fires in early spring are harmful for this species. Effective protected area can be beneficial for plant. 55 The plant is found in moist temperate forests at transitional zone between Chirpine and Bluepine forests. Deforestation and fodder collection are important threats. The protection of Scutellaria chamaedrifolia the habitat and awareness generation among locals can be effective. 56 The plant is exposed to road construction and afterward sliding. Germplasm collection and Sophora mollis ssp. mollis reintroduction can be helpful. 57 The alpine shrubby plant is exposed to habitat degradation, chopping and browsing. Spiraea hazarica Establishment of protected area can be effective in plant conservation. 58 The plant could only be found in Upper Kaghan showing high association with Ultramafic and Mafic rocks. The extensive agriculture and livestock grazing are among severe threats followed by tourism. The effective conservation at Saiful Muluk and Dodipatsar-Lulusar Swertia thomsonii national parks can save the plant population. 59 The plant could only be found at single location inside National Park. The population is exposed to other competing species and pollution. The plant can only propagate through asexual reproduction. Ex situ conservation, restoration of current habitat quality and Thalictrum secundum var. hazaricum reintroduction can be effective tools. 60 The plant is found in undisturbed moist temperate forests. The population in Ayubia National Park is exposed to invasive Leucanthemum vulgare. The plant is also sensitive to humidity and Vincetoxicum arnottianum open sunny areas are unsuitable. Reintrdution of the plant at suitable sites will be effective.

4 Chapter 4 DISCUSSION

4.1 General distribution Distribution is a key factor in decision making for conservation planning. Lack of proper knowledge regarding distribution patterns creates confusion in developing conservation strategies. In many cases distribution patterns focusing on few envirmental variables like ecological zones, altitude and latitude. Such studies have been conducted some exclusively focusing on endemic flora. Huang et al (2011) described the distribution patterns of Chinese endemic plants, Georghiou et al (2010) analysed such pattern for endemic plant species of Greece. Türe and Böcük (2010) analysed the quantitative approach for analysing the distribution patterns of threatened endemic plants of Turkey and used such approaches for conservation measures.

4.1.1 Taxonomic distribution

Seventy one endemic taxa were reported from the study area with Boraginaceae and

Ranunculaceae as largest families and Pseudomertensia as largest genus. Distribution of about seventeen (17) percent of the taxa with in the study area reflects the richness and biodiversity importance of the area. One of the key aspects in selection of hotspot for conservation is the number of endemic taxa reported from a locality. Ali and Qaiser

(1986) described that 80% of the endemics are distributed in northern mountainous regions of the country. Boraginaceae and Ranunculaceae were richest families with respect to endemism denoting the importance of specific environment for diversity and speciation. However at country level, the richest families reported were Fabaceae and

Asterceae where most of the species were reported from Blauchistan and other dry zones (Ali, 2008). One of the reasons for high endemism of the aforementioned families

94 in study area may be due to their unpalatability as Stewart mentioned that because of grazing impact, families like Ranunculaceae and Labiatae have more chances to flourish as compared to Caryophyllaceae, Poaceae etc. (Stewart, 1982). Genus Pseudomertensia is a diverse genus having about 2000 species worldwide (Dar et al., 2012). The process of speciation and variability is more high in certain genera as compared to the others. The poor representation of the otherwise richer families with in the area may also be attributable to Phytogeographical distribution of certain taxa within different zones

(Takhtajan, 1986). Three species from genus Potentilla were reported to be endemic. Our results reflects the previous studies where study area and adjacent Kashmir valley were identified as centre of radiation for this genus (Shah and Wilcock, 1997).

4.1.2 Life form and endemic distribution

Among endemic taxa, herbs were significantly dominating over shrubs and trees.

Herbaceous flora richness over woody species shows that the process of speciation is more high in herbaceous flora than woody species (Levin and Wilson, 1976). Our results are in line with many other findings (Huang et al., 2011; Nowak et al., 2011; Georghiou and Delipetrou, 2010). Comparing with the status of endemism at country level, 5 trees are reported to be endemic and Rhamnella gilgitica is one among these, distributed in study area. (Ali and Qaiser, 1986; Ali, 2008). Dominant life form was hemicryptophyte, while only a single geophyte could be reported. As many of the herbaceous species are confined to upper mountains, cryptic nature is helpful in survival under harsh conditions (Khan et al., 2013).

4.2 Distribution modelling Application of Species Distribution Models (SDMs) for distribution patterns and conservation status analyses is emerging trend. These models provide logical basis for

95 threat analysis and are helpful in strategy design (Cassini, 2011). Maxent is especially helpful in cases where no systematic surveys had been done in past. As the model performs better than other such models even with few occurrence records, model has been found useful for rare and endemic species often reported from few localities

(Phillips and Dudík, 2008). John et al. (2009) used such models in order to discover new populations for six rare and endemic plants in Northern California. They called the these models as an effective tool especially for discovery of new populations in case of rare plants (Peters, 2009; Gogol-Prokurat, 2011). Some other studies have reported that integration of distribution using statistical models is much better than simple maps

(Bustamante and Seoane, 2004).

4.2.1 Model performance

AUC was ranging between 0.92 and 0.98 reflecting the high performance of the model.

Recieving operator Curve (ROC) is considered as the measure of model performance.

The model predictions are considered better than random if AUC lies above 0.5 below

(Wang et al., 2007). Here the model performed much better as the minimum AUC value was recorded as 0.92.

Similar studies were performed for analysing the distribution of Justicia adhatoda, a medicinal plant, over an area of 1877Km2. The bioclimatic variables along slope, elevation, aspect and land cover were used as predictor variable. AUC value was reported as 0.92 and authors found this approach promising for restoration and conservation planning of threatened species (Yang et al., 2013). Many other studies using Maxent for identification of suitable areas for reintroduction of recommended the

AUC range above 0.80 as reliable (Booth et al., 2014; Phillips and Dudík, 2008; Merow et al., 2013; Adhikari et al., 2012).

4.2.2 Contribution of predictor variables

Jackknife analysis was performed in order to evaluate the contribution of each predicted variable. Topographic variables contributed more than climatic variables which describes the importance of local factors in the species distribution (Virkkala et al., 2005). Khanum et al., (2013) used 19 bioclimatic factors for analysing the future trends under climate change for medicinal Asclipiads of Pakistan. They realized the importance of land cover and soil characteristics for gaining accuracy in analysis. a) Topographic Factors i) Elevation

In our study elevation appeared as a significant factor among biophysical factors. The elevation was used as a surrogate for Mean Annual Temperature (Bio 1). Elevation contributed toward limiting the range of species like Otostegia limbata, Caltha alba var. alba, Aquilegia fragrans, Bistorta amplexicalus var. speciosa, Anemone tetrasepala and

Aegopodium burttii. One of the basic needs required in modelling is the selection of appropriate variables which may play direct or indirect role in distribution. Most important limiting factor in species distribution is temperature. Altitude is considered as surrogate for temperature (Franklin, 1995). Elevation have been used in many cases for modelling of a certain species as an important predictor variable. The distribution modelling studies based on bioclimatic data are considering distribution as important predictor (Kearney and Porter, 2009).

97 ii) Slope

Among the topological factors, slope is attributable for the reflection of microhabitat where it can be helpful for niche requirements like storage and long term availability of water or vice versa. In our case Corydalis govaniana var. malukiana and Sophora mollis ssp. mollis were distributed along the steep slopes where sharp sunlight and little water is required as both the species had characteristic deep roots. Likewise Salix denticulata ssp hazarica, Pimpinella stewartii, Anemone falconeri and Gentianodes nasirii were also found on steep slopes.. (Elith and Leathwick, 2009a). Slope remained a minor factor among selected topographic factors. iii) Aspect

Aspect can be correlated with the availability of sunlight and moisture retention as northern aspects are more moist than southern. A species requires specific amount of moisture and sunlight both associated with aspect. Festuca hartmanii and

Pseudomertensia parviflora always found growing in open sunny areas as opposed to

Scaligeria indica reported from moist shady places. Thalictrum secundum ssp hazaricum and Ranunculus munroanus were also found growing along southern aspects (Jones,

2013). iv) Geology

Phenomenon of endemism mainly focuses on certain distinguished environmental features where a species adopt some new characteristics in response to biotic and abiotic stresses. Microclimatic conditions are more important in this regard. Endemism has a strong link with rock, soil and geology of the area. As for as geology is concerned, key factor is the evolutionary history where some rocks are more older than the other

98 keeping the older vegetation with them. Discussing geology in terms of rock and soil types, many studies have associated endemics distribution with rock types (Austin and

Niel, 2011; Just, 1947; Cottle and Nature, 2004). Essl et al. (2009) analysed the distribution pattern of Austrian endemic plants and found 59% taxa associated with calcareous bedrock, 28% on siliceous bedrocks while 5.8% of the taxa were distributed on intermediate substrate (Essl et al., 2009).

In current scenario Meconopsis aculeata was found associated with Mesozoic meta sedimentary rocks which is spreading toward Kashmir. The species can only be found from those areas in past. Another example is Swertia thomsonii which could only be found at Upper Kaghan and Koshistan is associated with Sapat ultramafic-mafic complex. Similarly Jasminum leptophyllum was restricted to a single locality at Palas. The plant shows strong association with andesitic volcanic rocks (Kalam Groupfore arc) of late cretaceous era. Despite producing large number of seeds, plant could not spread out of the original range. This is a reflection of strong association of the species with geological strata. (Cottle and Nature, 2004; Essl et al., 2009).

Trigas and Latrou (2006) analysed similar patterns. While studying the local endemic flora of Greece, 43.6% were exclusively distributed on limestone, 23% were growing on ultramafic rock while some others were occurring exclusively on Schist. v) Land cover

The highest contribution of the land cover types was made for Thalictrum secundum ssp. hazaricum, Rosularia adenotricha, Inula royleana, Pseudomertensia trollii ssp. trollii and

Pyrola rotundifolia ssp. karakomica. Although, topoclimatic factors like elevation can be used to predict the potential suitable space for a species but in some cases, certain

99 factors like land utilization for agriculture, industry, urbanization etc. may be helpful.

Likewise influence of many different kinds of disturbances affecting the distribution of species can be studied in this way (Luoto et al., 2007; Pearson et al., 2004). Land cover use in prediction models is emerging trend. Luoto et al (2007), while studying the distribution of 88 bird species, found that the use of land cover at fine resolution can enhance the model accuracy. b) Climatic factors

Climatic factors like Mean diurnal range, Isothermality, Precipitation of the Driest

Month, Precipitation Seasonality, Precipitation of Warmest Quarter (Bio 8) and

Precipitation of Coldest Quarter were significantly contributing towards endemics distribution. Use of bioclimatic factors available through worlclim is a common practice in many SDM studies. Such predictors have been used in many studies like identification of high conservation value, from field and museum data (Loiselle et al.,

2008; Wilson et al., 2011), potential range of invasive species (Lozier and Mills, 2011), in assigning threat categories (Cassini, 2011), reintroduction of threatened species

(Adhikari et al., 2012) and future predictions under climate change (Khanum et al.,

2013). Modern studies regarding the impact of climate on plant physiology and distribution, take into account a complex set of environmental factors like temperature and precipitation with extreme weather events and seasonal records are more helpful than annual data (Beaumont et al., 2005; Schenk and Jackson, 2002).

4.2.3 Endemic with high distribution area

Pimpinella stewatii, Otostegia limbata, Caltha alba var. alba, Alchemilla cashmeriana,

Calamentha hydispidis, Bistorta amplexicalus var. speciosa, Scaligeria indica, Berberis

100 parkeriana and Aegopodium burttii were occupying largest area. As described by

Rabinowitz et al (1981) many endemics can have large population at their distributional zone but they remain unable to spread outside of that zone. Hedge (1990) reported that

Otostegia limbata can frequently found within Pakistan and even along borders of India and Afghanistan but could never be reported from two neighbouring countries (Hedge,

1990). One possible reason may be the suitability of environment and rock strata which is unavailable outside this country.

4.2.4 Altitudinal gradients

Middle ranges showed highest endemism as compared to lower and upper zones. The trends has also been observed in many other studies in other Himalayan areas (Vetaas and Grytnes, 2002). Similar trend has also been reported among Chinese endemic plants species and in Greece. (Huang et al., 2011; Georghiou and Delipetrou, 2010). The trend has been reported as a general pattern in many other ecological studies (Becker et al.,

2007).

4.3.2 Endemic distribution among land cover types

Land cover pattern has been found helpful in order to evaluate the anthropogenic influences on vegetation diversity. In our case Broad leaved and dense coniferous forests were found to be having highest endemic diversity which reflects the high endemic diversity within natural forests. Another endemic rich isolated environment was identified as bare rocks which provide the suitable conditions for speciation and evolution. The area modified for agriculture were also having considerable number of endemics mostly shrubs and perennial herbs. It has been noted that disturbed area are occupied by shrubs. Rhamnella gilgitica and Otostegia limbata, Pimpinella stewartii, Berberis

101 parkeriana and B. orthobotys ssp. capitata were mostly found alongside the cultivated areas. Agriculture extinction is the major threat for such species. Importance of land cover in conservation planning has been recognized in many other studies(Kier et al.,

2009; Rouget et al., 2003; Virkkala et al., 2005).

4.2.5 Distribution with in administrative divisions

Highest number of endemics were found in Mansehra followed by Batagram, Kohistan and Haripur districts. Analysing distribution pattern among political divisions may be helpful in biodiversity management programs at small scale. Government has established wildlife divisions at district level responsible for implementation of rules and regulations regarding conservation planning. Huang et al. (2011) found that among

Chinese endemic plants, Yunnan and Sichuan provinces in southwest China were richest with respect to endemism.

4.2.6 Geological classes and endemics distribution

Highest number of endemics were found in Proterozoic meta-clastic and meta carbonate sedimentary rocks followed by Proterozoic-Cambrian quartizite and Schist,

Mesozoic meta sedimentary rocks, Cambrian Ordovician Megacrystic granite and

Palaezoic meta sedimentary rock. While Calc-alkaline gabbro-diorite, Palaeozoic green schist had less than ten endemics. As a general rule weathered product of rock is responsible for influencing the distribution of plants where soil can affect physical and as well as chemical nature of the a species. In some studies focusing on geology and biodiversity relations, some of the rarest trees species were confined to carboniferous limestone. Plants like Primula, Polygala and Epipactis were found to be having strong association with carboniferous. The species richness in sedimentary rocks is also

102 attributed to the diverse nature and source of sediments responsible for harbouring many different kinds of species as compared to calcareous rocks having few species

(Cottle and Nature, 2004). In our case Thalictrum secundum ssp. hazaricum was only found one calcarious rocks where as Potentilla curviseta, P. pteropoda, Meconopsis aculeata,

Alchemilla cashmeriana and many other endemics were found on Palaeozoic metasedimentary rocks. The specificity of Swertia thomsonii and Jasminum leptophyllum can be attributed to Mafic and Ulramafic rocks of igneous nature. Similar effects has also been reported by Cottle and Nature (2004) during a study on vascular plant in UK.

Appearance of Geology as dominating factor reflects the process of endemism under special circumstances. The influence of substrate rocks in plant biology is an understood phenomena. High contribution of geology in distribution of narrow range species support this phenomenon.

4.3 Analysis of endemic associates 4.3.1 Classification

Analysing endemics associates is helpful for drawing the true picture of microhabitat, which is essential for endemics species reintroduction and habitat restoration (Ren et al,

2012). Application of modern techniques has made such analyses easy and comprehension. Classification and ordination methods based on statistical tool available through computer based programs are in common use now a days. In our case the species were divided into different communities applying Twinspan classification.

All the sample plots were divided into five communities. The first community had

Oxalis-Adiantum- Cymbopogon as dominant species having high diversity value

H'=4.65 indicating the distribution of endemic species in relatively more protected areas having more species diversity. This community represented endemic species like

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Berberis parkeriana, Pimpinella stewartii, Imptiens bicolor, Scutellaria chamaedrifolia, Otostegia limbata, Dicliptera bupleuroides var. qaiseri and Dicliptera bupleuroides var. nazimii.

Jasminum leptophyllum, Potentilla bannehalensis and Rhamnella gilgitica.

Justicia-Acacia-Cymbopogon represented the second community with diversity value less than all the other communities. The community represented the endemic associates at lower foot hills. Endemics reported from this community were Otostegia limbata and

Dicliptera bupleuroides var. nazimii. This association is the reflection of little endemic diversity at lower altitudes.

Trifolium-Pinus-Viburnum is the third representative community having Trifolium repens, Pinus wallichiana, Viburnum grandiflorum as dominant plants making association with endemic species Berberis parkeriana, Impatiens bicolor ssp. pseudobicolor,

Pseudomertensia sericophylla, Anemone falconeri, Anemone obtusiloba, Berberis orthobotyrus ssp. capitata, Aegopodium burttii, Bistorta amplexicalus, Caltha alba var. alba, Corydalis govaniana var. malukiana, Corydalis govaniana var. swatensis, Galium asperfolium var. obovatum, Hackelia macrophylla, Lavatera cashmeriana, and Vincetoxicum arnotianum. The community represented the moist temperate forests with highest number of endemic species. Poa-Kobresia-Pseudonaphalium was the forth community representing the endemics species like Arenaria festcoides, Meconopsis aculeata, Alcamella cashmeriana,

Aquilegia fragrans, Corydalis pakistanica, Festuca hartmanii, Gentianodes nasrii, Potentilla curviseta, Potentilla pteropoda, Salix denticulata ssp. hazarica, Swertia thomsonii,

Pseudomertensia sericophylla and Pseudomertensia elongata. Valeriana-Salix-Abies was the fifth community having Valeriana jatamansii, Salix denticulata and Abies pindrow and dominant species making association with endemic plants like Endemic plants

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Pseudomertensia sericophylla, Anemone obtusiloba, Anemone falconeri, Gentianodes cashmeriaca, Pseudomertensia moltkoides var. moltkoides, Pseudomertensia parviflora and

Ranunculus munroanus. The association represented the upper areas of temperate forests.

Arshad et al (2013) assessed the vegetation of Kashmir Markor in Chital Gol National

Park using Twinspan. The natural grouping resulted in four communities. They found such methods useful in identification of different habitats for the species. Khan et al

(2011) analysed the species and community diversity through multivariate approach.

They recognised five communities and found the use of modern approaches useful.

4.3.2 Ordination

Nonmetric Detrended Scaling (NMDS) was used to determine the ecological distances among communities and correlation of topoclimatic factors with endecmics associates.

Analysis revealed that ordination method performed best in distributing the communities along both axis where clear demarcations were notable among different niches. Similar patterns were observed for environmental variables. The communities found at lower elevation showed strong negative correlation with altitude. The closely related species were distributed near each other while negatively correlated one were opposite to each other. Overall ordination technique remained suitable in summarizing the large data. Khan et al (2013) evaluated the distribution of different life forms along environmental variables in a Western Himalayan valley. They found the tree species distributed along northern aspect while shrubs towards southern sides. They found the ordination techniques like CCA and DCA helpful in analysing the phytoclimatic gradients. Enright et al (2005) surveyed the vegetation of Kirthar National park using

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NMDS ordination technique. The demonstrated that major differences were existed between the riparian vegetation, mountains, plains and wetlands. Physical factors affecting distribution were slope, angle, aspect as compared to soil, chemicals and human impacts. They found this approach superior than other available methods as characterized by robustness and resistance to quantitative noise.

4.4 Conservation status Conservation status assessments are of prime importance in threatened species management programs. Assessments based on IUCN categories and Criteria are considered most reliable source which can be used as powerful tool in conservation planning and decision making (Callmander et al., 2005, Rodrigues et al, 2006).

Application of Species Distribution Models (SDMs) in determining the threat status according to IUCN is relatively new trend is gaining popularity because of its comprehension and logical bases necessary of such analyses. In a study at New

Caledonia, narrow endemic species were assigned threat categories and hotspots were identified using Maxent, one among the famous SDMs (Wulff et al, 2013). Such models have also successfully been used in determining sub populations, locations, extent of occurrence and area of occupancy. The results of such studies were consistent other red list assessments (Fivaz et al, 2014).

We adopted multiple approach based on herbarium record, detailed field studies and statistical model approach for assigning the threat categories to endemic plants under focus.

4.4.1 Geographic range

Among IUCN five criteria, geographic range in the form of Extent of Occurrence (EOO) and Area of Occupancy (AOO) is most utilized criterion for assigning the threat classes.

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(IUCN, 2001).The criterion even sometimes can be applied to herbarium records well. In our case however, the combination of herbarium record and field study remain a better approach. We mainly relied on field surveys and model predictions were verified through revisits. The poptential sites where no individual could be found were removed while determining the geographic range. Thalictrum secundum ssp. hazaricum previously reported from Thandiani (Riedl and Nasir, 1991) but could not be found in recent surveys that identification of suitable site through logistic models along with confirmation at ground can produce the better picture (Pearson et al., 2007; Loiselle et al., 2008). Likewise Meconopsis aculeata could not be found from one of the previously reported site located at Palas valley of Kohistan district (Rafiq, 1996a). Another example is Cortusa matthiolii ssp. hazarica found absent at type locality Mirajani. in Galiat area of

Abbottabad. A detailed survey of the vegetation by (Chagtaii et al.,1974) confirmed our finding. The plant was found higher altitudes than the reported locality. Phenomenon behind that may be the upward shift of the species because of the climate change as reported in some other studies conducted in Himalaya (Forrest et al., 2012). Monitoring subpopulations is an important part of the criteria. As suggested by the IUCN, subpopulations can be used as locations where it is difficult to define precisely a location (IUCN, 2014). Spatial maps are highly helpful as natural barriers, unsuitable areas and distances can easily be identified which can help to monitor the fragmentation in population (IUCN, 2001; IUCN, 2014).

4.4.2 Sub populations

Determination of sub populations is one of the basic requirements for applying IUCN

Criteria. As defined by IUCN "Subpopulations are defined as geographically or

107 otherwise distinct groups in the population between which there is little demographic or genetic exchange" which can only be identified through maps describing natural barriers. In our case use of Maxent generated maps excellently differentiated the subpopulations when a grid of 10Km2 was applied in order to delimit a subpopulation form the others. Use of GIS based map had remained effective in many other studies.

Rivers et al., (2010) assessed the population structure by applying four different kinds of grids. They recommended circular buffer method better than other.

In current study, majority of the endemics species populations were highly fluctuating or were continuously decline at the face many threats including habitat loss. The fluctuation trend was prominent in herbs where tenfold increase or decrease were observed. The higher continuous declining was noted in case of Thalictrum secundum ssp. hazaricum, Jasminum leptophyllum and Meconopsis aculeata. Similar trend were reported for many of the threatened species worldwide and at country level (Alam et al., 2010; Nowak et al., 2011; Ali and Qaiser, 2011).

4.4.3 Habitat loss

We adopted NDVI analysis for measuring the continuous loss or degradation of habitat in past. As majority of the endemics were distributed in forests or in alpine zone, loss of vegetation cover can be assumed as the loss of habitat. Prediction maps described progressive, regressive and stable trends during from 2001-2013. Regressive trends were shown in remote areas of Mansehra, Batagram and Kohistan where forests were protected because of unavailability of wood transportation facilities like road and vehicles (Knudsen, 2011). In recent year because of road construction, mass deforestation has been done in such areas. The area nearby water channels has been

108 cleared for road construction followed by huge flood in 2010. The progressive trends were mainly shown in the subtropical areas of Abbottabad, Batagram, Haripur and

Mansehra districts where at large scale Chirpine (Pinus roxburghii) has been cultivated under Tarbela Watershed Management Project (TWMP) for coping with soil erosion problems. The project is successive phase from 1964 where aforestation campaigns are regularly carried out on communal lands (Swati, 2003). Monoculture forestry however have negative effectives on biodiversity and ecosystem. Other progressive trend were shown in agricultural land where bare soil are continuously brought under cultivation both in low land and in alpine areas. Stable trend have been noted in the areas where timber wood has been used in remote past. These area were in easy access during

Colonial and post Colonial periods and no effective programs could be launched for habitat restoration (Knudsen and Madsen, 1999).

Forest cover loss using satellite imagery has also been used in many other studies.

Hansen et al., (2010) quantified the global gross forest cover loss from 2000 to 2005.

Comparing the forest cover loss, they noted the highest loss ratio for America while smallest for Congo. Similar studies were also conducted for identifying deforestation and disturbances in Masola National Park in Madagascar. Monitoring seven years changes concluded that most of the disturbances were anthropogenic. They highlighted the importance of high resolution imagery for statistical accuracy during such studies.

Use of NDVI for analysing the forest cover or vegetation loss has been proved fascinating practice (Allnut et al., 2013; Saqib et al., 2013; Lunetta et al., 2006; Li et al.,

2005).

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4.4.4 Threats

Multiple threats were faced by endemic species in our case most prominent being over exploitation, land sliding, overgrazing, invasive species, agricultural extinction and tourism activities. As the area lacks effective protected area, species remain always exposed to such threat. The only area namely Ayubia National Park has however been managed in a better way, however one of the most serious threat to biodiversity of the park is an invasive Leucanthmum vulgare. The plant was early introduced as ornamental species like other hill stations during English period (Qaiser and Abid, 2003). The species have become naturalized and is vigorously competing with native species. The spread of this species as noxious weed has also been reported from other parts of the world. recently no management programs could be launched for controlling further spread. Other threats have been reported in many other studies worldwide.

Conservation status of Ononis tridentata L. ssp. crassifolia a narrow endemic species of

South east Spain was determined as per IUCN criteria. Major threats reported were ploughing, overgrazing and afforestation (Ballesteros et al., 2013). Abbas et al., (2010) assessed conservation status of Cadaba heterotricha a rare species of Pakistan. The major threat was invasive species influence followed by overgrazing and overutilization.

Collection of plant material for various purposes is the most common threat. As majority of endemics are distributed in rural areas where fodder and medicinal plant collection is a common practice (Abbasi et al., 2013a; Abbasi et al., 2013b; Khan et al.,

2011; Khan et al., 2013). Endemic species face more threat where underground parts are collected. Bistorta ampexicalus ssp. speciosa, Berberis orthobotyrus ssp. capitata, Berberis parkeriana and Lavatera cashmeriana var. harooni are under high pressure as their root

110 part is used. This pattern has also been noted in many parts of Himalaya (Beigh et al.,

2006; Rana and Samant, 2011). Another factor is indirect effect of medicinal plant collection where associated endemics are also uprooted. Landslide is another big factor where whole habitat become damaged. In past the forest along slide streams were logged as wood transportation was made through water channels. Many microhabitats alongside rivers and stream have been gone under water because of this factor.

(Knudsen and Madsen, 1999).

Possible reasons for soil erosion are over grazing, agriculture intensification and deforestation. Lands covered by Juniperus forest in Upper Kaghan and Quercus baloot in

Kohistan districts are best examples where soil remains exposed to wind, rain and snow avalanches (Schickhoff, 1995; De Scally and Gardner, 1994). Snow avalanches are big threat for alpine and temperate forests communities, large fragmentation, steep slopes and loose soil make the areas more vulnerable to avalanches and these features are evident in most parts of the study area (De Scally and Gardner, 1994; Bebi, 2009)

Introduction and commercial varieties of pea and potato has changed the trend from livestock rearing to agriculture in temperate forests and alpine pastures. Land is continuously acquired for agriculture. Temperate and alpine pasture species are exposed to agricultural extensions. Similar fashion have been observed in many other parts of the country (Ali and Qaiser, 2011). Tourism is among highly growing threat bringing unhealthy environment. Biodiversity resources are heavily exposed to tourism in many parts of the study area especially in Kaghan valley and Galiat. Harmful effects of tourism activities on narrow endemic species has been reported many times.

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Lamyropsis microcephala a member of Asteraceae in Sardinia has been assigned Critically

Endangered category where tourism is the main threat (Fenu et al., 2011).

4.4.5 IUCN threat categories

IUCN Categories and Criteria are considered standard in conservation assessments (Vié et al., 2009). It is desirable to a assign some threat category to a plant under focus even in presence of some uncertainties (IUCN, 2014). Apart from direct observation IUCN accept logical approaches to draw for estimation and inference.

Androsace hazarica, Arabidopsis taraxacifolia, Bupleurum nigrescence, Microsisymbrium falccidum and Neottia inayattii were reported from type localities only. Analysis of herbarium specimens representing some recent record did not confirm correct identification (Khan et al., 2013). The important causes for extinction of these species may include mass deforestation followed by habitat loss soon after their discoveries

(Knudsen, 2011).

Artemisia amydalina from a single locality in 1879 (Qaiser and Abid, 2003). Few recent records from Kashmir valley which reports the plant severely threatened (Dar et al.,

2006). Jasminum leptophyllum was discovered and described from Palas valley in 1993

(Rafiq, 1996b). New other population could be found in any other areas. A big potential threat for the plant is deforestation and potential threat of a proposed hydropower dam in the area.

Meconopsis aculeata represents its western most population in the study area. The distribution of plants in other parts of Kashmir is also reports the plant as critically endangered (Dar et al., 2006). Thalictrum secundum ssp. hazaricum was earlier reported

112 from two locations Galiat and Thandiani. However it could not be found in Thandiani despite several visits.

Forty endemic species were reported to be endangered at regional level which reflects high threats to biodiversity. Anemone falconeri, Anemone tetrasepala, Aquilegia fragrans var. fragrans, Aquilegia nivalis, Arenaria festucoides, Berberis orthobotrys ssp. capitata,

Cortusa matthioli ssp. hazarica, Corydalis govaniana var. malukiana, Corydalis govaniana var. swatensis, Corydalis pakistanica, Crotalaria sessiliflora ssp. hazarensis, Cynanchum jacquemontianum, Dicliptera bupleuroides var. ciliata, Dicliptera bupleuroides var. nazimii,

Dicliptera bupleuroides var. qaiseri, Epipogium tuberosum, Gagea rawalpindica, Galium asperifolium var. obovatum, Gentianodes cachemirica, Gentianodes eumarginata var. harrissii,

Gentianodes nasiri , Hackelia macrophylla, Impatiens bicolor ssp. pseudobicolor, Inula royleana,

Lavatera cachemiriana var. haroonii, Potentilla curviseta, Potentilla pteropoda, Primula hazarica, Pseudomertensia elongata, Pseudomertensia moltkioides var. moltikoides,

Pseudomertensia moltkioides var. primuloides, Pseudomertensia sericophylla, Pseudomertensia trollii var. trollii, Pyrola rotundifolia ssp. karakoramica, Rhamnella gilgitica, Rumex crispellus,

Scaligeria indica, Scutellaria chamaedrifolia, Spiraea hazarica, Swertia thomsonii. The species are however assigned the threat categories at regional level.

e) Vulnerable species

Fifteen species were assigned vulnerable category at regional level. These plants were relatively found with wide distribution as compared to previous category. f) Near Threatened species (NT)

Caltha alba var. alba, and Otostegia limbata were containing more than ten subpopulations. However, all the species were exposed to multiple threats. Keeping in view continuous decline in quality of habitat and future estimates for decline in 113 populations, the species have been assigned Near Threatened (NT) category. It has been recommended that precautionary approach may be adopted (Gärdenfors et al., 2001,

IUCN, 2014). g) Data Deficient (DD)

In case of certain uncertainties Data Deficient category is assigned, however it does not however shows that a species is out of danger. The taxa were assigned Data deficient category owing to certain ambiguities in their identification, unavailability of type specimens for comparisons and confusions in marking exact localities. IUCN recommended the assignment of this category in case a species could not be monitored and there are high uncertainties (IUCN, 2001; IUCN, 2014).

4.5 Endemic rich areas SDMs are most suitable for comprehensive analysis of the areas rich in endemics. At country level, no protected areas were established with the aim of conserving endemic plants resources. One of the suggested localities is partly represented by Ayubia

National Park, however extension in current area including Mirajani hills and Beran

Gali areas is required for effective conservation. The other proposed area towards the eastern mountains representing high peaks of Lower Kaghan valley is another hotspot with respect to endemism. Between the borders of Batagram and Kohistan including

Palas valley may also be a great conservation area.

Keeping in view budget allocations to biodiversity activities, a tiny proportion of the earth can be conserved. In such scenarios, identification of biodiversity hotspot is a reliable approach (Araújo and New, 2007). Establishment of reserves in such hotspots will maximize the uniqueness and number of protected species (Myers et al., 2000).

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4.6 Species specific conservation strategies Designing species specific conservation strategies is an important part of conservation planning. Most of the species are facing threats at the hand of anthropogenic activities .

Identification of such threats along their impact on endemic species is essential for conservation actions.

4.7 CONCLUSION Distribution pattern analyses has been proven a good tool in conservation planning.

Application of statistical based species distribution models (SDMs) is helpful in identifying the factors responsible for endemic plants distribution. Climatic variables have been found suitable in identification of suitable areas where a plant is distributed.

However the use of topographic factors like geology, land cover etc. is more helpful in drawing the true picture at ground level. Precise georeferenced points based on careful observations are more meaningful as compared to herbarium data. Application of

SDMs for determining conservation status has been found as comprehensive approach in drawing logical conclusions as compared to subjective approaches. Multivariable analyses and ordination methods are suitable for analysing the ecological distances among endemic associates as compared to simple checklists, while vegetation cover analyses are better approaches in habitat loss analyses. Most of the endemic species have restricted distribution having small ecological niche. Narrow endemics are highly associated with geological factors while relatively large range taxa are dependent on climatic factors. Most of the endemic species are confined to small geographic ranges having little suitable natural habitat. Use of IUCN Categories and Criteria is standard approach in determining the conservation status. At regional level, most of the studied plants were found threatened which reflect overall situation. In short, the endemic taxa

115 under focus are exposed to multiple threats and effective measures must be taken in order of conserve them.

4.8 RECOMMENDATIONS Based on the current findings regarding the endemic plants of Pakistan and Kashmir distributed in Hazara regions, following are important recommendations.

 Regionally extinct species Artemisia amydalina may be reintroduced in its natural

habitat.

 Critically endangered species Jasminum leptophyllum, Thalictrum secundum ssp.

hazaricum and Meconopsis aculeata may be protected at earliest. Ex situ

conservation of these species is urgently needed.

 Molecular studies may be conducted for analyzing genetic variability in order to

design plans for genetic diversity conservation and management.

 Species specific biological studies focusing on whole life cycle of threatened

plants.

 Modern techniques likes application of Statistical Distribution Models may be

used in other conservation studies.

 There should be established at least one or two national parks based on

identified endemic rich area

 The management of existing national parks may be improved and a severe

threat i.e. spread of invasive species Leucanthemum vulgare may be checked

urgently. Tourism activities in National Parks and protected areas may be

adopted on environment friendly basis.

 A comprehensive program may be designed involving conservation experts

government and nongovernmental organizations in order to take useful 116 measures for threatened plants conservation with special focus on endemic species.

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Annexure 1 Importance Index Value (IVI) of all five communities

1 Abelia triflora Abetri 0.00 0.00 0.04 0.00 0.00 2 Abies pindrow Abipin 0.00 0.00 0.38 0.04 0.28 3 Abutilon indicum Abuind 0.00 0.13 0.00 0.00 0.00 4 Acacia modesta Acamod 0.00 0.80 0.00 0.00 0.00 5 Acer caesium Acecae 0.00 0.00 0.00 0.00 0.07 6 Acer pentopomica Acepen 0.00 0.00 0.00 0.02 0.00 7 Achyranthus aspera Achasp 0.03 0.60 0.00 0.00 0.00 8 Achillea millefolium Achmil 0.00 0.00 0.04 0.00 0.00 9 Aconitum chasmanthum Acocha 0.00 0.00 0.00 0.04 0.10 10 Aconitum heterophyllum Acohet 0.00 0.00 0.00 0.09 0.00 11 Aconogonon rumicifolium Acorum 0.00 0.00 0.00 0.41 0.00 12 Actaea spicata Actspi 0.00 0.00 0.00 0.00 0.07 13 Adiantum capillus-veneris Adicap 0.44 0.00 0.32 0.00 0.03 14 Adiantum venustum Adiven 0.15 0.00 0.00 0.00 0.14 15 Aegopodium burttii Aegbur 0.00 0.00 0.09 0.00 0.00 16 Aesculus indica Aesind 0.00 0.00 0.02 0.00 0.00 17 Agrostis stolonifera Agrsto 0.00 0.00 0.00 0.17 0.00 18 Ainsliaca aptera Ainapt 0.00 0.00 0.11 0.00 0.14 19 Ajuga bracteosa Ajubra 0.09 0.00 0.02 0.00 0.00 20 Ajuga parviflora Benth Ajupar 0.00 0.00 0.09 0.00 0.10 21 Alchemilla cashmeriana Alccas 0.00 0.00 0.00 0.09 0.00 22 Alternanthera pungens Altpun 0.00 0.20 0.00 0.00 0.00 23 Alternanthera pungens Altpun.1 0.00 0.13 0.00 0.00 0.00 24 Amaranthus spinosus Amaspi 0.00 0.13 0.00 0.00 0.00 25 Amaranthus viridis Amavir 0.00 0.60 0.00 0.00 0.00 26 Anaphalis adnata Anaadn 0.00 0.00 0.05 0.00 0.00 27 Anagalis arvensis Anaarv 0.00 0.00 0.09 0.00 0.14 28 Anaphalis busua Anabus 0.12 0.00 0.00 0.00 0.00 29 Andrachne cordifolia Andcor 0.06 0.00 0.00 0.00 0.00 30 Androsace foliosa Andfol 0.00 0.00 0.05 0.00 0.00 31 Androsace rotudifolia Androt 0.00 0.00 0.48 0.00 0.28 32 Anemone falconeri Anefal 0.00 0.00 0.05 0.00 0.07 33 Anemone obtusiloba Aneobt 0.00 0.00 0.43 0.00 0.03 34 Anemone tetrasepala Anetet 0.00 0.00 0.00 0.00 0.17 35 Angelica glauca Anggla 0.00 0.00 0.00 0.06 0.00 36 Apluda mittica Aplmit 0.15 0.00 0.00 0.00 0.00 37 Aquilegia fragrans Aqufra 0.00 0.00 0.00 0.09 0.00 38 Aquilegia pubiflora Aqupub 0.00 0.00 0.38 0.00 0.10 39 Arabidopsis sp. Arasp. 0.06 0.00 0.00 0.00 0.00 40 Arenaria festucoides Arefes 0.00 0.00 0.00 0.22 0.00 41 Arisaema flavum Arifla 0.00 0.00 0.00 0.02 0.00

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42 Arisaema jacquemontii Arijac 0.00 0.00 0.16 0.00 0.00 43 Arisaema utli Ariutl 0.00 0.00 0.27 0.00 0.10 44 Artemisia scoparia Artsco 0.00 0.40 0.00 0.00 0.00 45 Arundo donax Arudon 0.00 0.20 0.00 0.00 0.00 46 Asparagus gracilis Aspgra 0.00 0.27 0.00 0.00 0.00 47 Asplenium rutamuraria Asprut 0.06 0.00 0.00 0.19 0.00 48 Aster falconeri Astfal 0.09 0.00 0.11 0.00 0.00 49 Averrhoa carambola Avecar 0.00 0.60 0.00 0.00 0.00 50 Bergenia ciliata Bercil 0.00 0.00 0.23 0.00 0.03 51 Berberis lycium Berlyc 0.29 0.00 0.00 0.00 0.00 52 Berberis orthobotyrus ssp. capitata Berort 0.00 0.00 0.13 0.00 0.00 53 Berberis pachyacantha Berpac 0.00 0.00 0.00 0.11 0.00 54 Berberis parkeriana Berpar 0.24 0.00 0.07 0.00 0.00 55 Bistorta affinis Bisaff 0.03 0.00 0.00 0.39 0.00 56 Bistorta amplexicaulis ssp. speciosa Bisamp 0.00 0.00 0.09 0.00 0.00 57 Boenninghausenia albiflora Boealb 0.00 0.00 0.11 0.00 0.00 58 Bothriochloa pertus Botper 0.24 0.00 0.09 0.00 0.00 59 Broussonetia papyrifera Bropap 0.00 0.13 0.00 0.00 0.00 60 Bromus pectinatus Bropec 0.21 0.00 0.00 0.00 0.00 61 Bupleurum lanceolatum Buplan 0.00 0.00 0.18 0.00 0.00 62 Caltha alba var. alba Calalb 0.00 0.00 0.09 0.00 0.00 63 Calotropis procera Calpro 0.00 0.20 0.00 0.00 0.00 64 Campanula pallida Campal 0.06 0.00 0.00 0.00 0.00 65 Capparis decidua Capdec 0.00 0.13 0.00 0.00 0.00 66 Carex divisa Cardiv 0.00 0.00 0.00 0.37 0.00 67 Cardamine impatiens Carimp 0.00 0.00 0.30 0.00 0.03 68 Carissa opaca Caropa 0.00 0.27 0.00 0.00 0.00 69 Carex stenocarpa Carste 0.03 0.00 0.00 0.17 0.00 70 Caralluma tuberculata Cartub 0.00 0.33 0.00 0.00 0.00 71 Cassiope fastigiata Casfas 0.00 0.00 0.00 0.20 0.00 72 Celtis caucasica Celcau 0.06 0.00 0.00 0.00 0.00 73 Cenchrus biflorus Cenbif 0.15 0.00 0.04 0.00 0.00 74 Cerastium cerastioides Cercer 0.00 0.00 0.00 0.06 0.00 75 Chaerophyllum acuminatum Chaacu 0.03 0.00 0.00 0.37 0.00 76 Chenopodium album Chealb 0.09 0.33 0.00 0.00 0.00 77 Cheilanthus pteriodes Chepte 0.21 0.00 0.46 0.00 0.14 78 Chrysopogon fulvus Chrful 0.24 0.20 0.04 0.00 0.00 79 Cissampelos pareira Cispar 0.03 0.13 0.00 0.00 0.00 80 Clematis grata Clegra 0.03 0.00 0.00 0.00 0.00 81 Clematis montana Clemon 0.00 0.00 0.13 0.00 0.00 82 Colchicum luteum Collut 0.09 0.00 0.00 0.00 0.00 83 Conyza canadensis Concan 0.00 0.27 0.00 0.00 0.00 84 Corydalis govaniana var. swatensis Corgov 0.00 0.00 0.09 0.00 0.00 85 Corydalis govaniana var. malukiana Corgov.1 0.00 0.00 0.09 0.00 0.00

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86 Cornus macrophylla Cormac 0.15 0.00 0.11 0.00 0.00 87 Corydalis pakistanica Corpak 0.00 0.00 0.00 0.09 0.00 88 Cotoneaster affinis Cotcog 0.12 0.20 0.00 0.00 0.00 89 Cotoneaster rosea Cotros 0.03 0.00 0.14 0.00 0.03 90 Cuscuta campestris Cuscam 0.15 0.00 0.00 0.00 0.00 91 Cyanodon dactylon Cyadac 0.09 0.20 0.00 0.00 0.00 92 Cymbopogon distans Cymdis 0.06 0.00 0.00 0.00 0.00 93 Cymbopogon sp. Cymsp. 0.44 0.73 0.13 0.00 0.00 94 Cynanchum auriculatum Cynaur 0.00 0.00 0.04 0.00 0.00 95 Cynoglossum glochidiatum Cynglo 0.12 0.67 0.00 0.00 0.00 96 Dalbergia sissoo Dalsis 0.03 0.13 0.00 0.00 0.00 97 Daphne mucronata Dapmuc 0.06 0.20 0.00 0.00 0.00 98 Daphne papyracea Dappap 0.03 0.00 0.14 0.00 0.00 99 Desmostachya bipinnata Desbip 0.12 0.33 0.00 0.00 0.00 100 Desmodium elegans Desele 0.06 0.00 0.00 0.00 0.00 101 Dichanthium annulatum Dicann 0.32 0.33 0.02 0.00 0.00 Dicliptera bupleuroides var. 102 bupleuroides Dicbup 0.03 0.13 0.00 0.00 0.00 103 Dicliptera bupleuroides var. nazmi Dicbup.1 0.00 0.20 0.00 0.00 0.00 104 Dicliptera bupleuroides var. qaiseri Dicbup.2 0.03 0.07 0.00 0.00 0.00 105 Dicliptera bupleuroides var. ciliata Dicbup.3 0.06 0.00 0.00 0.00 0.00 106 Digitaria ciliaris Digcil 0.00 0.13 0.00 0.00 0.00 107 Dioscorea diltoidea Diodil 0.00 0.00 0.09 0.00 0.03 108 Dodonaea viscosa Dodvis 0.12 0.47 0.00 0.00 0.00 109 Draba glomerata Draglo 0.00 0.00 0.09 0.00 0.00 110 Dryopteris odontoma Dryodo 0.00 0.00 0.30 0.00 0.17 111 Dryopteris odontolema Dryodo.1 0.06 0.00 0.05 0.00 0.10 112 Duchesnea indica Ducind 0.09 0.00 0.00 0.00 0.00 113 Ehertia aspera Eheasp 0.00 0.07 0.00 0.00 0.00 114 Eragrostis pilosa Erapil 0.06 0.00 0.00 0.00 0.00 115 Eragrostis sp. Erasp. 0.06 0.00 0.00 0.00 0.00 116 Eremurus himalaicus Erehim 0.00 0.00 0.00 0.06 0.00 117 Erigeron pseudoeriocephalus Eripse 0.00 0.00 0.00 0.13 0.00 118 Erigeron unflorus Eriunf 0.09 0.00 0.05 0.15 0.00 119 Euonymus hamiltonicus Euoham 0.00 0.00 0.00 0.00 0.07 120 Euonymus japonicus Euojap 0.00 0.00 0.04 0.00 0.00 121 Euphrasia aristlata Eupari 0.03 0.00 0.00 0.09 0.10 122 Festuca hartmani Feshar 0.00 0.00 0.00 0.09 0.00 123 Ficus carica ssp. carica Ficcar 0.00 0.07 0.00 0.00 0.00 124 Ficus palmata Ficpal 0.00 0.07 0.00 0.00 0.00 125 Fraxinus hookeri Frahoo 0.06 0.00 0.00 0.00 0.00 126 Fragaria vesca Fraves 0.12 0.00 0.39 0.00 0.28 127 Fritillaria cirrhosa Fricir 0.00 0.00 0.00 0.04 0.00 128 Gagea sp Gagsp. 0.00 0.00 0.00 0.00 0.14 129 Galium aprine Galapr 0.15 0.00 0.30 0.00 0.00

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130 Galium asperifolium Galasp 0.06 0.00 0.25 0.00 0.00 131 Galium asperifolium var. opositifolium Galasp.1 0.00 0.00 0.09 0.00 0.00 132 Galium elegans Galele 0.03 0.00 0.00 0.00 0.00 133 Gaultheria trichophylla Gautri 0.03 0.00 0.00 0.22 0.00 134 Gentianodes cashmiriaca Gencas 0.00 0.00 0.00 0.00 0.17 135 Gentianodes kurroo Genkur 0.03 0.00 0.00 0.00 0.00 136 Gentianodes nasirii Gennas 0.00 0.00 0.00 0.09 0.00 137 Gentianodes sp. Gensp. 0.09 0.00 0.00 0.00 0.10 138 Gerbera gossypina Gergos 0.06 0.00 0.00 0.00 0.00 139 Geranium lucidum Gerluc 0.15 0.00 0.05 0.06 0.00 140 Geranium ocellatum Geroce 0.03 0.00 0.00 0.00 0.00 141 Geranium wallichianum Gerwal 0.12 0.00 0.14 0.00 0.28 142 Geum elatum Geuela 0.00 0.00 0.30 0.00 0.00 143 Grewia tenax Greten 0.09 0.13 0.00 0.00 0.00 144 Gymnopteris vestita Gypcer 0.00 0.00 0.00 0.11 0.00 145 Hackelia macrophylla Hacmac 0.00 0.00 0.09 0.00 0.00 146 Hackelia uncinata Hacunc 0.00 0.00 0.00 0.04 0.07 147 Hedera nepalensis Hednep 0.35 0.00 0.41 0.00 0.14 148 Heracleum cachemiricum Hercac 0.00 0.00 0.30 0.00 0.00 149 Herteropogan contortus Hercon 0.41 0.20 0.04 0.00 0.00 150 Hypericum perforatum Hypper 0.15 0.00 0.00 0.00 0.00 151 Impatiens balsamina Impbal 0.26 0.00 0.20 0.00 0.00 152 Impatiens bicolor ssp. pseudo-bicolor Impbic 0.15 0.00 0.05 0.00 0.00 153 Imperata cylindrica Impcyl 0.15 0.00 0.00 0.00 0.00 154 Impatiens edgeworthii Impedg 0.21 0.00 0.11 0.00 0.00 155 Impatiens glanduliflora Impgla 0.21 0.00 0.11 0.00 0.00 156 Indigofera heterantha var. gerardiana Indhet 0.29 0.13 0.05 0.00 0.00 157 Innula cappa Inncap 0.03 0.00 0.04 0.00 0.00 158 Iris hookeriana Irihoo 0.00 0.00 0.00 0.09 0.17 159 Isodon rugosus Isorug 0.21 0.00 0.00 0.00 0.00 160 Jasminum humile Jashum 0.15 0.00 0.18 0.00 0.00 161 Jasminum leptophyllum Jaslep 0.15 0.00 0.00 0.00 0.00 162 Jasminum officinale Jasoff 0.09 0.13 0.00 0.00 0.00 163 Juncus articulatus Junart 0.06 0.00 0.00 0.00 0.00 164 Juniperus communis Juncom 0.00 0.00 0.00 0.24 0.00 165 Jurinea himalaica Jurhim 0.03 0.00 0.00 0.28 0.00 166 Justicia adhatoda Jusadh 0.06 0.87 0.00 0.00 0.00 167 Kobresia schoenoides Kobsch 0.03 0.00 0.00 0.54 0.00 168 Lantana camara Lancam 0.00 0.40 0.00 0.00 0.00 169 Launaea secunda Lausec 0.03 0.00 0.00 0.00 0.00 170 Lavatera cashmeriana var. harooni Lavcas 0.00 0.00 0.09 0.00 0.00 171 Leycesteria formosa Leyfor 0.00 0.00 0.02 0.00 0.07 172 Ligularia fischeri Ligfis 0.00 0.00 0.00 0.13 0.00 173 Lindelofia longiflora var. longiflora Linlon 0.00 0.00 0.00 0.06 0.00

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174 Loentopodium jacotianum Loejac 0.09 0.00 0.05 0.46 0.00 175 Loentopodium loentopodinum Loeloe 0.00 0.00 0.00 0.13 0.00 176 Lonicera caucasica Loncau 0.00 0.00 0.04 0.00 0.00 177 Lonicera quinciquelecularis Lonqui 0.06 0.00 0.25 0.00 0.00 178 Lotus corniculatus var. tenuifolius Lotcor 0.06 0.00 0.00 0.00 0.00 179 Lysimachia chenopodioides Lysche 0.12 0.00 0.04 0.00 0.00 180 Malvastrum coromendelianum Malcor 0.06 0.13 0.00 0.00 0.00 181 Mallotus filipense Malfil 0.03 0.00 0.00 0.00 0.00 182 Maytenus royleanus Mayroy 0.00 0.60 0.00 0.00 0.00 183 Meconopsis aculeata Meclat 0.03 0.00 0.00 0.09 0.00 184 Medicago denticualata Medden 0.18 0.20 0.00 0.00 0.00 185 Medicago polymorpha Medpol 0.24 0.27 0.00 0.00 0.00 186 Melilotus indica Melind 0.09 0.27 0.00 0.00 0.00 187 Micromeria biflora Micbif 0.12 0.00 0.00 0.00 0.00 188 Myrisine africana Myrafr 0.21 0.40 0.00 0.00 0.00 189 Nepeta erecta Nepere 0.00 0.00 0.14 0.00 0.03 190 Oenothera rosea Oenros 0.06 0.00 0.00 0.00 0.00 191 Olea ferruginea Olefer 0.12 0.67 0.00 0.00 0.00 192 Onosma thomsonii Onotho 0.00 0.13 0.00 0.00 0.00 193 Onychium contiggum Onycon 0.03 0.00 0.41 0.00 0.34 194 Origanum vulgare Orivul 0.26 0.00 0.05 0.00 0.00 195 Oryzopsis munroi Orymun 0.09 0.00 0.00 0.00 0.00 196 Otostegia limbata Otolim 0.09 0.33 0.00 0.00 0.00 197 Oxalis corniculata Oxacor 0.53 0.40 0.02 0.00 0.00 198 Paeonia emodi Paeemo 0.00 0.00 0.00 0.00 0.14 199 Parrotiopsis jacquemontiana Parjac 0.09 0.00 0.13 0.00 0.00 200 Pedicularis kashmiriana Pedkas 0.00 0.00 0.00 0.06 0.00 201 Periploca aphylla Peraph 0.00 0.47 0.00 0.00 0.00 202 Phytolacca latbenia Phylat 0.00 0.00 0.02 0.00 0.03 203 Picrorhiza kurrooa Pickur 0.00 0.00 0.00 0.04 0.00 204 Picea smithiana Picsmi 0.00 0.00 0.07 0.00 0.07 205 Pimpinella stewartii Pimste 0.24 0.00 0.00 0.00 0.00 206 Pinus roxburghii Pinrox 0.09 0.13 0.00 0.00 0.00 207 Pinus wallichiana Pinwal 0.15 0.00 0.61 0.00 0.10 208 Piptatherum hilariae Piphil 0.12 0.00 0.00 0.04 0.00 209 Piptatherum munroi Pipmun 0.06 0.00 0.00 0.00 0.00 210 Pistacia integerrima Pischi 0.00 0.47 0.00 0.00 0.00 211 Plantago lanceolata Plalan 0.06 0.00 0.00 0.00 0.00 212 Poa alpina Poaalp 0.12 0.00 0.00 0.67 0.00 213 Poa annua Poaann 0.29 0.00 0.41 0.00 0.10 214 Poa versicolor Poaver 0.09 0.00 0.00 0.24 0.00 215 Podophyllum emodi Podemo 0.00 0.00 0.38 0.00 0.03 216 Polygala abyssinica Polaby 0.06 0.00 0.00 0.00 0.00 217 Polygonatum multiflorum Polmul 0.00 0.00 0.16 0.00 0.14

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218 Potentilla bannehalensis Potben 0.15 0.00 0.00 0.00 0.00 219 Potentilla curviseta Potcur 0.00 0.00 0.00 0.09 0.00 220 Potentilla pteropoda Potpte 0.00 0.00 0.00 0.09 0.00 221 Primula denticulata Priden 0.00 0.00 0.27 0.00 0.17 222 Prinsepia utilis Priuti 0.09 0.00 0.00 0.00 0.00 223 Prunus cornuta Prucor 0.03 0.00 0.16 0.00 0.00 224 Prunella vulgaris Pruvul 0.00 0.00 0.05 0.00 0.10 225 Pseudomertensia elongata Pseelo 0.00 0.00 0.00 0.04 0.00 226 Pseudonaphalium hypolecum Psehyp 0.00 0.00 0.00 0.50 0.00 Pseudomertensia moltkoides ssp 227 moltkoides Psemol 0.00 0.00 0.00 0.00 0.17 228 Pseudomertensia parvifolia Psepar 0.00 0.00 0.00 0.00 0.17 229 Pseudomertensia sericophylla Pseser 0.00 0.00 0.05 0.04 0.07 230 Pteridium aquilinum Pteaqu 0.00 0.00 0.43 0.00 0.14 231 Pteris cretica Ptecre 0.24 0.00 0.36 0.00 0.38 232 Punica granatum Pungra 0.09 0.47 0.00 0.00 0.00 233 Pyrus pashia Pyrpas 0.24 0.00 0.00 0.00 0.00 234 Quercus baloot Quebal 0.06 0.27 0.00 0.00 0.00 235 Quercus dilatata Quedil 0.00 0.00 0.20 0.00 0.00 236 Quercus incana Queinc 0.09 0.00 0.00 0.00 0.00 237 Ranunculus laetus Ranlae 0.06 0.00 0.05 0.00 0.00 238 Ranunculus munroanus Ranmun 0.00 0.00 0.00 0.00 0.17 239 Ranunculus sp Ransp. 0.00 0.00 0.13 0.00 0.21 240 Reinwardita trigyna Reitri 0.18 0.00 0.00 0.00 0.00 241 Rhamnella gilgitica Rhagil 0.15 0.00 0.00 0.00 0.00 242 Rhododendron arboreum Rhoarb 0.00 0.00 0.04 0.00 0.00 243 Rhododendron campanulatum Rhocam 0.00 0.00 0.00 0.04 0.00 244 Rhododendrron lepidotum Rholep 0.03 0.00 0.00 0.09 0.00 245 Rhus punjabensis Rhupun 0.12 0.00 0.21 0.00 0.07 246 Rosularia adenotricha ssp. chitralense Rosade 0.12 0.00 0.00 0.00 0.00 247 Rubia cordifolia Rubcor 0.15 0.00 0.13 0.00 0.00 248 Rubus fruitcosus Rubfru 0.15 0.00 0.00 0.00 0.00 249 Rubus lanata Rublan 0.12 0.00 0.18 0.00 0.10 250 Rumex hastatus Rumhas 0.15 0.00 0.00 0.00 0.00 251 Rumex nepalensis Rumnep 0.06 0.00 0.00 0.00 0.00 252 Ruscus hypophyllum Rushyp 0.06 0.00 0.00 0.00 0.00 253 Salix alba Salalb 0.00 0.00 0.05 0.00 0.00 254 Salix denticulata Salden 0.00 0.00 0.13 0.00 0.31 255 Salix denticulata ssp. hazarica Salden.1 0.00 0.00 0.00 0.09 0.00 256 Salvia lanata Sallan 0.03 0.00 0.00 0.00 0.00 257 Salvia moorortiana Salmoo 0.09 0.00 0.00 0.00 0.00 258 Sarcococa saligna Sarsal 0.21 0.00 0.20 0.00 0.00 259 Saxifraga moorcroftiana Saxmoo 0.00 0.00 0.05 0.41 0.14 260 Scandix pecten-veneris Scapec 0.06 0.00 0.00 0.00 0.00 261 Schutelaria chamaedrifolia Schcha 0.15 0.00 0.00 0.00 0.00

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262 Scrophularia calycina Scrcal 0.06 0.00 0.00 0.06 0.00 263 Scutellaria linearis Sculin 0.09 0.00 0.00 0.00 0.00 264 Sedum ewersii Sedewe 0.09 0.00 0.05 0.00 0.00 265 Sedum multicaule Sedmul 0.09 0.00 0.00 0.00 0.00 266 Senecio chrysanthimoides Senchr 0.00 0.00 0.18 0.06 0.07 267 Setaria verticilata Setver 0.38 0.53 0.02 0.00 0.00 268 Sibbaldia cuneata Sibcun 0.00 0.00 0.00 0.22 0.10 269 Silene viscosa Silvis 0.00 0.00 0.00 0.04 0.03 270 Silene vulgaris Silvul 0.00 0.00 0.00 0.04 0.00 271 Skimmia laureola Skilau 0.00 0.00 0.09 0.00 0.07 272 Smilax aspera Smiasp 0.03 0.00 0.00 0.00 0.00 273 Smilax aspera Smiasp.1 0.00 0.00 0.20 0.00 0.00 274 Solena amplexicaulis Solamp 0.00 0.07 0.00 0.00 0.00 275 Solanum nigrum Solnig 0.00 0.13 0.00 0.00 0.00 276 Sonchus arvensis f. brachyotus Sonarv 0.03 0.00 0.00 0.00 0.00 277 Sorbaria tomentosa Sortom 0.06 0.00 0.20 0.00 0.03 278 Spiraea affense Spiaff 0.18 0.00 0.11 0.00 0.00 279 Stachys emodi Staemo 0.00 0.13 0.00 0.00 0.00 280 Stachys floccosa Staflo 0.00 0.07 0.00 0.00 0.00 281 Stelaria media Stemed 0.12 0.00 0.11 0.00 0.00 282 Stellaria monosperma Stemon 0.06 0.00 0.00 0.00 0.00 283 Stipa jacquemontii Stijac 0.00 0.60 0.00 0.00 0.00 284 Stipa sibirica Stisib 0.00 0.00 0.00 0.04 0.00 285 Strobilanthus gautinosus Strgau 0.18 0.00 0.13 0.00 0.00 286 Swertia ciliata Swecil 0.00 0.00 0.05 0.00 0.00 287 Swertia thomsonii Swetho 0.00 0.00 0.00 0.09 0.00 288 Syringa emodi Syremo 0.00 0.00 0.00 0.02 0.00 289 Taxus wallichiana Taxwal 0.00 0.00 0.09 0.00 0.07 290 Thalictrum pedunculatum Thaped 0.00 0.00 0.09 0.00 0.00 291 Themeda anathera Theana 0.26 0.00 0.00 0.00 0.00 292 Thevetia peruviana Theper 0.00 0.13 0.00 0.00 0.00 293 Thevetia peruviana Theper.1 0.00 0.13 0.00 0.00 0.00 294 Thlaspi andersonii Thland 0.00 0.00 0.05 0.00 0.00 295 Thlaspi cochleariforme Thlcoc 0.00 0.00 0.00 0.00 0.17 296 Thymus linearis Thylin 0.00 0.00 0.00 0.48 0.00 297 Tragopogon stewartii Traste 0.00 0.00 0.00 0.04 0.00 298 Trigonella fimbriata Trifim 0.00 0.00 0.00 0.04 0.00 299 Trifolium repens Trirep 0.00 0.00 0.71 0.00 0.00 300 Tribulus terrestris Triter 0.00 0.13 0.00 0.00 0.00 301 Trollius acaulis Troaca 0.06 0.00 0.00 0.00 0.00 302 Tulipa clusiana Tulclu 0.09 0.00 0.00 0.00 0.00 303 Urtica dioica Urtdio 0.00 0.00 0.43 0.00 0.00 304 Valeriana himalayana Valhim 0.03 0.00 0.00 0.00 0.00 305 Valeriana jatamansii Valjat 0.00 0.00 0.39 0.00 0.45

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306 Veronica biloba Verbil 0.12 0.00 0.00 0.06 0.00 307 Veronica lasiocarpa Verlas 0.06 0.00 0.00 0.00 0.00 308 Verbena officinalis Veroff 0.06 0.00 0.00 0.00 0.00 309 Veronica sp. Versp. 0.00 0.00 0.09 0.00 0.07 310 Verbescum thapsus Vertha 0.09 0.47 0.00 0.00 0.00 311 Viburnum cotinifolium Vibcot 0.00 0.00 0.07 0.00 0.00 312 Viburnum grandifolium Vibgra 0.15 0.00 0.50 0.07 0.03 313 Vicia sp. Vicsp. 0.00 0.20 0.00 0.00 0.00 314 Vintoxicum arnotianum Vinarn 0.00 0.00 0.09 0.00 0.00 315 Viola odorata Vioodo 0.41 0.00 0.04 0.00 0.21 316 Vitis jacquemontii Vitjac 0.00 0.00 0.11 0.00 0.00 317 Vitex negudo Vitneg 0.09 0.33 0.00 0.00 0.00 318 Woodfodia fruticosa Woofru 0.00 0.33 0.00 0.00 0.00 319 Wulfenia amherstiana Wulamh 0.00 0.00 0.11 0.00 0.10 320 Zanthoxylum armatum Zanarm 0.29 0.27 0.00 0.00 0.00 321 Ziziphus jujuba Zizjuj 0.12 0.00 0.00 0.00 0.00 322 Ziziphus mauritiana Zizmau 0.00 0.67 0.00 0.00 0.00 323 Ziziphus oxyphylla Zizoxy 0.03 0.00 0.00 0.00 0.00

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Annexure 2

Aegopodium burttii Alchemilla cashmeriana

Anemone falconeri Aquilegia fragrans

Berberis parkeriana Otostegia limbata Representative maps presenting Extent of Occurrence as suggested by IUCN

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