Ben-Gurion University of the Negev The Jacob Blaustein Institutes for Desert Research The Albert Katz International School for Desert Studies

Extinction and re-colonization processes in the otter (Lutra lutra) population in Israel - The importance of connectivity and habitat quality

Thesis submitted in partial fulfillment of the requirements for the degree of "Master of Science"

By: Roni Shachal

November 2013

I

Extinction and re-colonization processes in the otter (Lutra lutra) population in Israel - The importance of connectivity and habitat quality

By Roni Shachal

Thesis submitted in partial fulfillment of the requirements for the degree of "Master of Science", Ben-Gurion University of the Negev, Jacob Blastein Institute for Desert Research, Albert Katz International School for Desert Studies, 2013.

The Eurasian otter (Lutra lutra) is a solitary mammal, inhabiting a variety of aquatic habitats, and feeding mostly on aquatic prey. Being a top predator, it is considered as a good bio-indicator species for aquatic habitats; thus its continued existence is a conservation priority. The species has been listed in the IUCN Red List as

“Near Threatened” and in Israel as "Critically Endangered" since 2004, due to its ongoing decline, resulting from water source drainage, water pollution, habitat fragmentation and degradation, and road effects. Roads, in particular, are one of the major factors threatening otter persistence and hampering movement between habitats.

The Israeli otter population forms the southeastern border of the Mediterranean-Arab range of the species. As such, the habitats available to otters are limited to sparsely distributed natural streams, and to distinct artificial water reservoirs and fish ponds.

Consequently, the population forms a metapopulation structure, of small local subpopulations, partially genetically differentiated, inhabiting spatially discrete patches linked by limited dispersal, which are subject to re-occurring local extinction and re- colonization processes.

This study aimed to investigate the extinction and re-colonization dynamics of the population, by evaluating their rates and their changes through time, and determining the relationship between them and the environmental characteristics of the habitats. Additionally, as part of the connectivity issue, I evaluated the effectiveness of II new ledges that were constructed under four "hot spots" bridges to prevent otter road kills. To answer these questions, I used a multi-model inference approach based on occupancy modeling (program MARK), which analyzed multiple-visits presence- absence data (based on spraint detection) from 39 sites in in northern Israel from 2000-

2013. The model derived, for every year, the overall occupancy, extinction and re- colonization rates. The various models included indices of habitat quality and connectivity for each site, comprised of factors known from the literature. To investigate the ledges’ effectiveness, I monitored their use by otters and other mammals, using foot tracks and I.R cameras.

I found a continuous decrease in re-colonization rates and a moderate decrease in the proportion of occupied sites, but only a small increase in extinction rates through the years. Habitat quality, connectivity and the interaction between them revealed extensive effects on the parameters: overall occupancy and extinction are mostly affected by the above interaction, and re-colonization is mostly affected by the connectivity. Road ledges were used regularly by otters and other mammals.

Long-term monitoring and evaluating population processes (occupancy, local extinctions and re-colonizations) are important for detecting negative changes in distribution in "real time," especially when the species is structured as a metapopulation in a highly fragmented and disturbed region. Identifying the environmental factors affecting these processes is crucial for proper management. As part of these management actions, road ledges may be used to mitigate road casualties and to enhance movement between different habitats. Additionally, conservation efforts for the isolated Israeli otter population should focus on the maintenance and restoration of habitats and their connecting ecological corridors.

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Acknowledgments

I want to thank my supervisors Dr. Amit Dolev and Prof. David Saltz, for giving me a great opportunity to study this wonderful animal, and to be a part of this interesting and important project.

I would also like to thank the ecologists of the NPA and to the managers and rangers of the reserves, Talia Oron, Ifat Artzi, Hava Goldstein, Rabia Dabus, Nahel

Dabus, Yoram Malka and many more, for the help in the field work and in everything else that was needed.

Many thanks to Tomer Gueta, for all the great and friendly help in the GIS analysis.

To Amos Bouskila, who was the first to introduce me to the world of Ecology and the study of nature.

To Assaf Ben-David, who introduced me to the world of foot-prints.

To all the people, friends and family who came to search for otter feces with me…

To Shay, for all the support, ideas and motivation through the way.

And to Aya & Amir, who gave me a warm and most friendly home while doing the field work in the north for the last two years.

This study was supported and was done in collaboration with the Mammals Research

Center of the Society for the Protection of Nature in Israel, and with the Nature and Park

Authority. IV

Table of Contents

1 INTRODUCTION ...... 5 1.1 Research objectives ...... 14 1.2 Hypotheses...... 15

2 METHODS ...... 16 2.1 Study area……………… ...... 16 2.2 Field work and data collection ...... 19 2.3 Data analysis...... 21 2.3.1 Occupancy models ...... 21 2.3.2 Indices of environmental covariates ...... 22 2.3.3 Statistical Analysis ...... 33 2.3.4 Model construction process ...... 36

2.4 Monitoring the effectiveness of the ledges ...... 39 2.4.1 Track pads:...... 40 2.4.2 I.R Cameras: ...... 41

3 RESULTS...... 43 3.1 Covariate values ...... 43 3.1.1 Habitat quality...... 43 3.1.2 Connectivity ...... 43

3.2 Model selection results ...... 45 3.3 Trends in time ...... 48 3.3.1 Changes in proportion of occupied sites through time ...... 48 3.3.2 Changes in re-colonization probabilities through time...... 48 3.3.3 Changes in extinction probabilities through time...... 49

3.4 Effect of environmental factors ...... 50 3.4.1 The effect of interaction between habitat quality and connectivity ...... 50 3.4.2 Site connectivity effect on extinction, re-colonization and overall occupancy...... 51 3.4.3 Habitat quality effect on extinction and overall occupancy ...... 52 3.4.4 Sensitivity Analyses Results ...... 53 3.5 Monitoring the effectiveness of the ledges ...... 55 3.5.1 Track pads...... 55 3.5.2 I.R Cameras:...... 55

4 DISCUSSION ...... 56

5 REFERENCES ...... 70

6 APPENDICES ...... 79

4

1 Introduction

The Eurasian otter (Lutra lutra) is a nocturnal, solitary mammalian top predator

(Carnivora: Mustelidae), inhabiting a variety of aquatic habitats, such as rivers with clean running water, as well as water reservoirs and fishponds adjacent to these streams, and feeding mostly on aquatic prey (Kruuk 2006).

As predators at the top of many freshwater food chains (Ruiz-Olmo 2007) that are sensitive to water pollution and to changes in their natural habitats, otters are considered to be a good indicator species for aquatic prey biodiversity, water quality and, in general, for the quality of the natural riparian habitat (Conroy and Chanin 2000,

Ruiz-olmo et al. 2001); thus its conservation is of particular interest. However, the species has been listed in the International Union for Conservation of Nature (IUCN)

Red List of threatened species as “Near threatened” since 2004 due to an ongoing population decline (Baillie et al. 2004).

In Israel, until the middle of the 20th century, otters were abundant in all coastal rivers extending from the Lebanese border in the north to the Soreq River in the south, as well as along the Jordan River basin from its sources to the Dead Sea, including the

Hula Lake and the Sea of Galilee, with the Harod and Yizrael Valleys serving as a corridor between the Jordan River catchments and the coastal plain populations

(Macdonald et al. 1986, Mendelson and Yom-tov 1999, Dolev et al. 2011; Figure 1).

However, during the 1960s, the otter population underwent a dramatic decline, evidently due to illegal hunting, water pollution, the drainage of aboveground water sources, habitat fragmentation and road-related mortalities (Macdonald et al. 1986,

Foster-Turley et al. 1990, Mendelson and Yom-tov 1999, Guter et al. 2005). Since

1964, otters are fully protected in Israel by law. Despite this legislation, the population continues to decline (Dolev et al. 2011). Ongoing loss, degradation and fragmentation 5 of otter habitats increase the risk of isolating small sub-populations (Bender et al. 1998), further increasing extinction probabilities, decreasing the genetic diversity of the subpopulations and decreasing the viability of the Israeli population overall (Templeton et al. 1990, Crooks 2002, Frankham 2008). Consequently, the regional status of the otter has recently been categorized as “Critically Endangered” (Dolev and Perevolotsky

2004).

Figure 1: Past and present (according to presence-absence surveys) distribution, and genetic division of the otter population in Israel. Past distribution is shaded in grey; present distribution in seven areas is represented in circles. Genetic division to three subpopulations is represented by the letter G: Hula (G1), Sea of Galilee and Golan (G2) and Harod (G3).

The Eurasian otter distribution range is mostly Palearctic, distributed over

Europe, Asia and Africa (Mason and Macdonald 1986). The Israeli otter population 6 forms the southeastern border of the Mediterranean-Arab range of the Eurasian otter

(Mason and Macdonald 1986, Ruiz-Olmo et al. 2008). The Mediterranean semi-arid climate in Israel forms the driest edge still tolerable by otters, where wetlands and perennial streams are scarcely found, mostly along the drainage basin of streams in the

Northern Jordan Rift Valley. Moreover, since the Mediterranean area climate is characterized by a long dry summer and a high inter-annual variation in precipitation, it is exposed to many fluctuations in water availability, both within and between years

(Prenda et al. 2001). These conditions results in periodic changes in the size and even the existence of the aquatic patches potentially inhabited by otters, and of the streams connecting them, which occasionally dry up during the summer. Therefore, the habitats available to the otters are limited to these natural streams, and to distinct artificial water systems, such as water reservoirs and fish ponds. Moreover, many anthropogenic activities since the 1960s, such as the drainage of water bodies (e.g., Hula Lake drainage in 1954), the intensive development of settlements and agricultural lands, and massive roads construction, have resulted in the great loss of natural wetland habitats and increased fragmentation. Consequently, the otter population in Israel forms a structure of scattered local subpopulations inhabiting spatially discrete patches, which are restrictively linked by dispersal. This kind of spatial structure is usually referred to as a

"metapopulation" (Levins 1969, Hanski and Gilpin 1991) and has been studied extensively, especially because metapopulations are sparsely distributed over highly fragmented areas and, thus, are often easily threatened by habitat loss and movement barriers (Hanski and Gilpin 1991, Hanski and Simberloff 1997). A number of additional key criteria are commonly used in order to define a metapopulation (Hanski 1994,

Fleishman and Ray 2002): first, limited migration between patches so each patch supports a distinct breeding populations; second, low abundances in the patches that are not sufficiently large to ensure the long-term persistence of the metapopulation; third, 7 not all suitable patches are occupied continually; and fourth, the dynamics of local populations are not strongly coupled so colonizations and local extinctions occur at different times in different patches (Hanski 1999, Fleishman and Ray 2002).

Israel‟s otter population seems to fit well with all these criteria, both genetically and ecologically. Genetically, the population was found to have a clear structure of three distinct groups that differ in their genetic profile (Figure 1): Hula Valley, Sea of

Galilee and Golan Heights, and the Harod Valley (Magory-Cohen et al. 2013). Two individuals that were found in Yizrael Valley were clustered each one to a different source population -- one to the Harod Valley population and the other to the Sea of

Galilee and Golan population (Magory-Cohen et al. 2013) -- indicating that migration from one sub-population to another occurs, but at a limited rate.

Ecologically, annual presence-absence surveys that have been conducted since the year 2000 by the Society for Protection of Nature in Israel (SPNI) suggest that there appear to be seven areas partially separated by landscape elements where evidence of otter activity has been recorded in the past (Guter 2004, Dolev et al. 2006). These areas are: the Hula Valley and the northern Jordan River, the Golan Heights, the Sea of

Galilee and the southern Jordan Valley, the Harod and Bet-Shean Valleys, the Yizrael

Valley, and the northern and central streams along the coastal plain (Figure 1). Some of these areas along the northeastern part of northern Israel seem to support a stable population or subpopulations (Hula Valley, Sea of Galilee and the Jordan Rift Valley), while in other areas, such as the Golan Heights, the Yizrael Valley and the coastal plains, the occurrence is temporary; hence they appear to be under a constant process of extinction and re-colonization (Dolev et al. 2011; figure 2). Their movement between these areas is possible, although limited to some degree, and depends on the spatial structure and conditions of the different areas (Dolev et al. 2011, (Cohen et al. 2013).

The coastal plain population is considered as extinct, except for some rare evidence of 8 sporadic and temporary re-colonizations, in 2000 and in 2008, presumably from the

Harod Valley subpopulation or alternatively via the Lebanese coast through the

Mediterranean Sea (Guter et al. 2006). Temporal persistence also occurs in the southern

Golan Heights subpopulation. Activity signs that were found in the Golan Heights periodically over the years (2000-2009, and again in 2013) suggest that otters inhabiting this area are exposed to random immigrations and local extinctions repeatedly. This is in contrast to northern Golan ("Bental" and "Orvim" reservoirs) where activity has been found constantly in all surveys from 2000-2013. Genetically, the Golan Heights samples were clustered with the samples from the Sea of Galilee, indicating a close connection between the regions. Thus, the prevalence of otters in the Golan Heights may represent an isolated subpopulation of the Sea of Galilee or the Hula Valley with occasional connections along "winter streams" from the Golan Heights to the Jordan Valley

(Magory-Cohen et al. 2013).

Figure 2: Findings of otter occurrence surveys in different areas, as determined by presence-absence surveys in the years 2000-2013. 01

For species that are distributed across the landscape in a metapopulation-like structure, a spatially based management approach is crucial (Driscoll 2007, MacPherson and Bright 2011), because the habitat structure has a major effect on their dynamics

(Hanski 1999). For instance, unoccupied habitat patches can be potentially colonized in the future. Thus, although un-occupied, they are essential. Evaluating the extent of a suitable habitat, both occupied and potentially occupied in the future, is therefore an important tool in a metapopulation conservation management.

Patch occupancy and persistence are functions of local extinction and re- colonization rates, which are natural, re-occurring processes rather than rare events in a metapopulation (Hanski and Gilpin 1991, Hanski and Simberloff 1997, Barbraud et al.

2003). While range contraction is a result of the effect of continuous local extinctions, range expansion and gene flow maintenance among populations depend on the cumulative re-colonization events, and keeping these processes balanced is a major condition for the metapopulation persistence in the long run (Hanski and Gilpin 1991,

Hanski and Simberloff 1997). Therefore, estimating the rate of these processes and detecting both negative and positive changes in these patterns are crucial steps in the conservation of the focal species (Rodrıguez and Delibes 2003, Marcelli and Fusillo

2009)

The probability of local extinction decreases when patch area and quality increase, and the probability of colonization increases when patch connectivity and dispersal potential increase (Brown and Kodric-Brown 1977, Kindvall and Ahlén 1992,

Thomas and Harrison 1992, Thomas et al. 1992, Bascompte and Solé 1996, Fleishman and Ray 2002). However, re-colonization also depends on the availability of adequate habitats to settle, and not only on the ability to get to them (Carranza et al. 2012).

Similarly, even if suitable habitats are available, but the movements to and from them is extensively limited, the probability for a "rescue effect" of small threatened populations 00 would decrease dramatically, and so would the probability of recovery following local extinction (Sjocren 1991, Gonzalez 1998, Cushman 2006). These effects will result in higher net local extinction rates.

Connectivity is defined as the degree to which the landscape facilitates or impedes the movement of organisms among habitat patches (Taylor et al. 1993).

Animal dispersal and gene flow and, consequently, the metapopulation‟s persistence are highly dependent on the degree of landscape connectivity (Johnson et al. 1992,

Schumaker 1996, Pascual-Hortal and Saura 2006). Thus, connectivity loss, followed by patch isolation, poses a major threat for the conservation of biodiversity (Pascual-Hortal and Saura 2006).

Within the connectivity framework, two disciplines are commonly considered: structural connectivity and functional connectivity. While structural connectivity describes only the physical relationships among habitat patches, such as the distances between the patches, functional connectivity also considers the dispersal potential of the organism, and the interaction between the characteristics of the landscape matrix and the movement behavior of the organisms (Tischendorf and Fahrig 2000, Kadoya 2009).

Since otter movement is limited mainly to the river network (Philcox et al. 1999, Kruuk

2006) , and since otters are highly sensitive to anthropogenic changes in the matrix

(Chanin 2003, Kruuk 2006), both the physical and the functional patterns should be taken into consideration (Loy et al. 2009, Carranza et al. 2012). Furthermore, in addition to the longitudinal linear movement along the hydrological dendritic network, otters can also move between basins in the surrounding dry matrix (lateral movement), in order to colonize neighboring river catchments (Grant et al. 2007, Beger et al. 2010,

Carranza et al. 2012), or whenever water is not available between water sources, e.g., along seasonal streams or between fish ponds (Ruiz-Olmo et al. 2007). Lateral connectivity is very important since it contributes to range expansion and to the 01 maintenance of gene flow among populations living in different river basins (Loy et al.

2009). But this kind of movement is less probable and must also face the resistance

(permeability) of the land matrix to dispersal by otters between catchments (Wiens

2002, Carranza et al. 2012). Therefore, both longitudinal and lateral movements should be evaluated when considering connectivity for otters (Wiens 2002, Beger et al. 2010,

Carranza et al. 2012).

Habitat quality for otters is also derived from a combination of physical, environmental and anthropogenic factors (Barbosa et al. 2001, Chanin 2003, Kruuk

2006, Clavero et al. 2010). Being wide ranging territorial mammals, the quality is determined by physical factors, such as patch size (Kruuk 2006) and topography (as they rarely inhabit altitudes above 2000m a.s.l; Kruuk 2006, Ruiz-Olmo 2007), but it also depends extensively on resource availability (Prenda and Granado-Lorencio 1996), water availability (Prenda et al. 2001) and the level of anthropogenic disturbances, such as land use, water pollution, human density and road networks (Philcox et al. 1999,

Barbosa et al. 2001, Chanin 2003, Clavero and Hermoso 2010).

Roads, in particular, are considered a major factor threatening otter persistence both in Israel (Guter et al. 2005) and elsewhere (Körbel 1994, Philcox et al. 1999,

Grogan et al. 2001, Jancke and Giere 2011). Carnivores are usually even more susceptible to road impacts due to their low reproductive rates, low population densities, and large home ranges (Cain et al. 2003). Road mortality is considered as a leading factor reducing population viability, as it hampers population recovery and may even lead to local extinction (Philcox et al. 1999, Trombulak and Frissell 2000, Glista et al.

2009). Furthermore, roads create barriers to dispersal and movement as a result of crossing inhibitions and collisions, thus reducing the permeability of the landscape and the connectivity between the patches (Rondinini and Doncaster 2002, Shepard et al.

2008). In Israel, road kills occur mostly during winter and spring, and are associated 02 with dispersal and mate searching (Guter et al. 2005). Otters tend to move in and along waterways, but appear to avoid swimming below bridges during floods (Philcox et al.

1999). Instead, they climb onto the bridge where there is a high probability of being hit by a vehicle. Consequently, nowadays, there is a growing interest in mitigating these impacts by means of a wide variety of measures (Glista et al. 2009, Villalva et al. 2013).

One of these measures is the construction of ledges that allow dry passage below bridges (Grogan et al. 2001, Villalva et al. 2013). Past work suggests that otters will prefer to use these dry ledges when crossing a tunnel under a bridge (Grogan et al.

2001), resulting in a reduction of the risk for road kills. However, the information available globally on the effectiveness of dry ledges, combined with fences that prevent mammals from climbing onto the road, is still scarce for otters, as well as for other mammals (Villalva et al. 2013). As a part of the effort for road mitigation in Israel, four ledges were built during May-June 2012 under bridges that are considered "hot spots" where road kills occur at a high rate (Oron 2013). This ledge construction was combined with fencing a short section of about10 meters along the road above the bridges (Oron 2013).

From all the above, it is evident that estimating the overall occupancy of a metapopulation, as well as the extinction and re-colonization rates and changes through time, are key elements in metapopulation conservation and effective management.

Nevertheless, it is equally important to identify, for each species individually, the habitat variables and the spatial factors that are associated with patch occupancy, local extinctions and re-colonizations, as well as determining which are the key factors in each one of these processes and evaluating the relationships between them (Fleishman and Ray 2002, Clavero et al. 2010). This is especially essential when habitat and matrix qualities are affected and can be managed by human activities, such as in the case of the otters (Barbosa et al. 2001, Ruiz‐Olmo et al. 2001, Clavero et al. 2010). It may also 03 clarify whether habitat restoration would be an effective management tool (Fleishman et al. 2002).

The Israeli otter population marks the southern boundary of the species' distribution in the Levant and is genetically unique (Mucci 2008), suggesting that it has been isolated for some time from the European populations (Magory-Cohen et al.

2013). Considering this, and the fact that otters, which are the top predators of the wetland habitats in Israel (Guter 2004), are “Critically Endangered,” it is highly important to invest extra effort in understanding the processes and the causes for the population decline, so proper actions can be taken to protect the isolated and unique otter population in Israel.

1.1 Research objectives

This research had two goals:

1. The main goal was to assess the factors that affect the viability of the endangered otter metapopulation in Israel, by identifying the factors that are crucial for conservation.

This goal specifically focused on three questions:

a. What is the current occupancy throughout the range of the otter

population in Israel and what have been the changes in occupancy and distribution

over time during the years 2000-2013?

b. What are the extinction and re-colonization rates and have they changed

over the years 2000-2013?

c. What are the key spatial factors that affect the extinction and re-

colonization rates, and are different factors dominant in each of the processes?

Specifically, in this context, I aimed to evaluate the relationships between the

habitat quality and connectivity and the population processes of local extinctions

and re-colonizations. 04

2. My second goal was to assess the use, by otters and other mammals, of dry ledges recently constructed under road bridges together with fencing, and to evaluate the effectiveness of such ledges in preventing road casualties.

1.2 Hypotheses

1. Metapopulation processes:

 The otter population in Israel functions as a metapopulation. Hence, the overall

occupancy state changes through time, according to ongoing extinction and re-

colonization processes.

 Spatial features and structure are very crucial for metapopulation processes;

therefore, indices for the quality of a site, as well as its connectivity potential,

will be strong predictors of local extinction and re-colonization rates.

2. Effectiveness of the ledges:

The new ledges that were constructed under bridges can be used as an alternative dry passage for otters and other animals when the streams flow rapidly.

Therefore, I expected otters and other wildlife to use the ledges mostly during the winter, when the water level under the bridges is high. In addition, I expected to see a reduction in the mean number of road kills per year in the area surrounding the bridges where ledges were installed, after the ledges‟ construction, compared with the data prior to their construction.

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2 Methods

2.1 Study area

The study area was defined as that covered by the national survey of otter distribution carried out by the SPNI since the year 2000 (Dolev et al. 2011). The area includes all the hydrographic basins where recent and past information on otter occupation was reported, as well as fishponds, water reservoirs and the Sea of Galilee and its tributaries. Overall, the study area covered about 2000 km2 of wetland areas

(Figure 3).

Survey sites were the same each year to maintain a constant system, thus allowing the use of data that was collected from the surveys since the year 2000, in order to detect the long-term spatial patterns of the population (see also appendix A).

The locations of the surveys were divided into six regions (Guter 2004):

1. The Hula Valley and the northern Jordan river: from the Jordan River tributaries

in the north, the Golan base springs in the east, road 90 in the west (including

the Hula Lake and Hula Reserve) and along the Jordan River tunnels and the

mountainous part of the river, until the river delta in the south (42 locations).

2. The Sea of Galilee basin and the lower Jordan River: from the Golan stream

estuaries in the north, around the Sea of Galilee‟s coasts (west and east), and

along the Southern Jordan Valley to the Harod River in the south (15 locations).

3. The Harod and Bet-shean Valleys: from the Harod Valley in the north to the

Bezek Valley in the south, from the Jordan River in the east to the Sargel Road

in the west (15 locations).

4. The Golan Heights: from the Orvim Stream and Bental Water Reservoir in the

north to the Hital Reservoir in the south, and the Yarmuch River (Botamia

Reservoir) in the southeast, including the water reservoirs and perennial streams

flowing west and south (21 locations). 06

5. The Yizrael and Zvulun Valleys: from the origins of the Kishon Stream in the

northeast, to its estuary on the Mediterranean Sea coast in the west, including the

streams that flow into it, and the Namman Stream and its estuary (18 locations).

6. The coastal streams: from the Taninim Stream in the north to the Alexander

Stream in the south (6 locations).

The climate in all regions is Mediterranean. The average annual rainfall in northern

Israel ranges from 500-600 mm/year at the coast, in the Yizrael, Harod and Bet-shean

Valleys, and at the Sea of Galilee, to 800-900 mm/year in the Golan Heights and the northern Galilee. The rainy season lasts from September to April, with about 70% of the annual total falling during December, January and February (Goldreich 1994, Israel

Water Authority website, Israel Meteorological Service website). 07

(a)

(b)

Figure 3: (a) The study area. Encompasses locations in northern Israel where recent and past data on otter occupation were recorded including: hydrographic basins, fish ponds, water reservoirs and the Sea of Galilee. Survey locations are represented in yellow dots (a total of 117 locations). (b) Otter spraints, deposited on a conspicuous stone. 08

2.2 Field work and data collection

Since otters are nocturnal and highly elusive, the distribution and abundance of their population are difficult to determine through direct observations (Mason and

Macdonald 1986, Balestrieri et al. 2011). Thus, distribution assessment of the population must rely on indirect observations. As in many carnivores, scent marking plays an important role in the social organization of otters. Urine, feces (spraints) and scent from specialized glands are used to delineate territories (Mason and Macdonald

1986), or alternatively, for signaling the use of resources within the territory (Gosling and McKay 1990, Kruuk 1992). The information contained within these signs may enable individuals to distinguish one another, and the signaler may also indicate its sexual condition or status within a dominance hierarchy (Mason and Macdonald 1986).

Otters often deposit their spraints on conspicuous, predictable sites (like stones, rocks, trunks, etc.- Figure 3), and they can be deposited very frequently, as each spraint usually contains only a small quantity of fecal matter. Otter spraints are very easy to identify, since they are composed of fish bones and vertebras fragments, attached to the surface where they were dropped, and have a typical fish smell. In addition, there is a black anal jelly-secretion, probably for protecting the digestive system from sharp bones.

Therefore, otter surveys that follow a standardized methodology, based on the detection of spraints, are recommended by the IUCN Otter Specialist Group and have been widely used since the late 70s (Mason and Macdonald 1987, Foster-Turley et al. 1990,

Reuther et al. 2000).

In Israel, otter presence-absence surveys, based on spraint detection, were conducted during the years 2000-2013. Each location was classified as positive or negative, based on the presence or absence of otter spraints. Surveys were conducted in the winter or beginning of spring (mostly in February), as otters show the highest 11 activity and spraint-marking intensity in this season (Mason and Macdonald 1986,

Guter 2004).

Every survey included a total of 117 locations, distributed within the areas mentioned above, and located mostly near or under bridges, or other narrow water passages. These places constitute typically favorite otter sprainting sites, probably making a good advertisement of the territorial area (Guter 2004). In my two-year study

(2012-2013), the survey process had three repetitions at one-week intervals within the same season (temporal repetitions), following the suggestions of MacKenzie et al.

(2002) and Balestrieri et al. (2011). This system is based on the assumption that the occupancy state in the patches stays constant during a season (in this case, during three weeks of surveying), i.e., there are no local extinction or colonization events occurring within a season (MacKenzie et al. 2002, 2003).

Two clarifications are important to mention in this context. First, spraints degrade and are visible for approximately a one-month period when exposed to sun and rain (Mason and Macdonald 1986, Guter 2004), thus not creating a serious bias in the survey period. Second, within the three temporal visits conducted in my study, over one-week intervals, I used an odorless marking material that was applied on the spraints, so I would be able to distinguish between fresh (i.e., that had been secreted during the week interval since the previous visit) and older spraints. If a sprainting site was “occupied” only with spraints that were already marked in a previous visit, the site was designated as “absent” and was given a score of 0. In this way, I minimized the bias of the detection rate.

In addition to the temporal repetitions, the 117 locations were divided into 39 clusters; each of them consisted of three adjacent locations, within a 5 km stretch of a river, or within one water reservoir, which is isolated from other water sources. The Sea of Galilee was treated as a stream in this manner, i.e., clusters of 5 km along the western 10 and the eastern shoreline of the lake were pooled together, because otters presumably use only the edge of the lake, and not the inner part, which is more than 15 meters deep, too deep for them (Chanin 2003). The rationale for this division was that three adjacent sprainting sites were included within the same home range of an individual otter; thus, the three locations should be considered as repetitions of the same site, rather than independent samples. However, it is difficult to estimate home range size of an individual otter is, and there is considerable variance in reported home range sizes. For example, home ranges in England and Scotland were found to vary between 17 to 36

Km along a river stretch (Kruuk et al. 1993, Coxon et al. 1999). In Mediterranean areas, however, the individual home range is much smaller than in other parts of Europe. For example, it is estimated as a 1-15 km stretch of a stream in northeastern Spain (Ruiz-

Olmo et al. 2001) or larger in other parts of Spain, but with a core area of only 5.1km

(Saavedra 2002). Therefore, I used a 5 km length along a stream as the minimum range in which locations were considered as one cluster, and considered each cluster as a set of three spatial repetitions.

The approach of spatial repetitions by clusters enabled the inclusion of the presence-absence data that had been collected by the SPNI between the years 2000-

2011 in the analysis, as the SPNI surveys used only spatial repetitions (i.e., clusters of adjacent sites), without temporal repetitions in every season. The use of data collected from surveys since the year 2000 enabled me to analyze temporal processes and changes that occurred in the population during the 14 years from 2000 to 2013.

All survey data was georeferenced using a GPS and ArcGIS v10.0 (ESRI 2011).

2.3 Data analysis 2.3.1 Occupancy models

Occupancy models estimate spatial parameters, such as the proportion of occupied sites, as well as colonization and extinction rates of sites (Hanski 1994, 11

MacKenzie et al. 2003, Royle and Nichols 2003). The growing interest in and use of occupancy models in the study of metapopulations is enhanced by some important factors. First, as occupancy models reflect a shift of interest from the number of animals to the number of sample units occupied by animals (i.e., range), they yield inferences about the current status of populations in large-scale monitoring programs, based only on the presence or absence (or more properly, detection-nondetection) of individuals in suitable habitats, using a sample of locations. Second, this data can be relatively easily collected, requiring less effort and cost than abundance estimation (MacKenzie et al.

2002, 2003, Royle and Nichols 2003). This is especially true when the studied species is nocturnal and elusive, which is the case with many mammalian carnivores (Balestrieri et al. 2011), such as the otters. Third, in metapopulation studies, patch occupancy (i.e., the proportion of occupied patches) often is used as a state variable, in which habitat patches are classified as occupied or empty without regard to the dynamics of the population present. In this approach, sites represent discrete habitat patches in a metapopulation dynamics context, similar to individuals in a mark-recapture study. The occupancy data can be incorporated into "incidence functions," which describe the probability of the occurrence of a species in a patch, expressed as a function of patch characteristics, such as habitat quality and connectivity (MacKenzie et al. 2003, Royle and Nichols 2003). If a long-term study is being carried out, it may be used to estimate the rate of change in site occupancies, and local extinction and colonization probabilities through time (e.g., Hanski 1994, Moilanen 1999). Estimating these process rates enables a focus on the long-term mechanisms underlying site occupancy dynamics

(Mackenzie et al. 2003).

2.3.2 Indices of environmental covariates

Following the suggestions of MacKenzie et al. (2002, 2003), covariates that take into consideration the environmental attributes of the sites were incorporated into the 12 model, to assess their effect on the model parameters. For each of the sites, two covariates were quantified: habitat quality and connectivity.

In Eurasian otters, several factors are known to determine habitat suitability and connectivity levels between patches. I used the most relevant parameters that are documented in the literature as important and created two indices for habitat quality and connectivity, as described in detail in the next section.

2.3.2.1 Habitat Quality Analysis

Habitat quality of otters depends on a wide set of factors. However, many studies that have strived to discover the most important factors that contribute most heavily to the quality and suitability of the habitat for otter occurrence have typically found the same attributes. In this study, I mainly chose factors that were found important in studies concerning the Mediterranean habitat of the species, since this habitat has the most similarity to Israel in climatic and environmental conditions (Cortés et al. 1998, Barbosa et al. 2001, Janssens 2006, Loy et al. 2009, Clavero et al. 2010,

Ruiz-Olmo et al. 2011). Specifically, I used the following well-known, most important factors in order to determine a reliable index for habitat quality:

1. Food availability: Food is the main limiting factor for otter abundance in

Mediterranean areas (Prenda et al. 2001). For instance, otter declines in Spain

have been attributed directly or indirectly to prey availability (Delibes 1990). In

Israel, fish constitute the main item in the otters' diet on average; 85.1% of the

spraints contain fish (Guter 2004). Similar findings are reported in other

Mediterranean areas (Ruiz-Olmo and Palazón 1997). Therefore, I considered

fish abundance as the main factor determining habitat quality. Information about

fish abundance in all the streams of Israel was collected from surveys that were

done by the NPA, or independently by other researchers (Goren and Ortal 1999,

Krotman 2003, 2004, 2009, 2012a,b, Goren 2011). Because survey methods 13

varied and otters tend to be generalists in their fish choice, consuming fish of

different sizes and species, according to their abundance in the habitat (Ruiz-

olmo et al. 2001), I considered both the species diversity and the densities of the

fish as equally affecting the index. Each of the streams in my study received a

score from 1 to 4, reflecting the average of the density and diversity. Fish ponds,

the Sea of Galilee, and the reservoirs were scored as 4, because they all have a

high abundance of fish.

2. Water availability: The availability of water represents a main ecological factor

affecting otter occurrence (Prenda and Granado-Lorencio 1996, Barbosa et al.

2001, Prenda et al. 2001, Kruuk 2006). Water flow information was taken from

the Israel Hydrological Service, which measures the stream flow on a monthly

basis. The measure that was taken into account was an average of the measures

since the year 2000, in the driest month (September), reflecting the minimal

monthly water flow in a given year. Each of the streams was scored from 1-4: 1

designates a seasonal stream (no flow in the summer), 2 for 100 to 1000m3, 3 for

1100 to 3000m3, and 4 for above 3000m3 which represents the strongest stream

flow. In addition, all water reservoirs, fish ponds and the Sea of Galilee were

scored as 4, since they are permanent water bodies. Streams adjacent to fish

ponds within their area were considered as 4 as well, regardless of their

particular flow rate.

3. Patch dimensions: Patch dimension is a crucial factor determining habitat

quality, in general (Kindvall and Ahlén 1992), and in otters, in particular (Kruuk

2006). In this study, I focused of the area of water bodies available to the otters.

Therefore, this parameter was calculated by the ArcGIS 10.0 software, and

included the aquatic area available to otters within the habitat site, including the

stretch of the stream and the fish ponds that are adjacent to the stream. I 14

considered 700 meters as the maximum distance at which fish ponds are still

considered within the same site, as otters can easily move this distance. Otters

are capable of diving to depths of up to 15 m (Chanin 2003). Since the water

depth of fish ponds and reservoirs does not exceed 15 meters, their entire area

may be used by otters; thus, I calculated this parameter as the area of the entire

water body. However, since the Sea of Galilee becomes deep very fast, reaching

a 15 m depth within ca. 500 m and reaching a maximum depth of 47 meters

(calculated by ArcGIS using a contour layer of the Sea of Galilee; appendix B), I

considered only the lake margins with a buffer of 500 meters from the shoreline.

4. Vegetation cover: Riparian vegetation is used as a shelter and for breeding dens,

enhances the filtering of pollutants, and promotes fish productivity (Jenkins and

Burrows 1980, Macdonald and Mason 1982, Loy et al. 2009). Overall riparian

vegetation cover, rather than species composition, is usually referred to as a key

factor in otter habitat preference (Prenda and Granado-Lorencio 1996, Prenda et

al. 2001). Therefore, I used data that has been collected in the surveys every year

by visual observation. Each of the sites received a score between 1 (no

vegetation) to 4 (very dense cover). Sites that received different scores in

different years were averaged between the years.

5. Road density: Roads are a key factor driving population declines in Israel

(Foster-Turley et al. 1990, Dolev and Perevolotsky 2004, Guter et al. 2005) and

elsewhere in Europe (Green 1991, Philcox et al. 1999, Grogan et al. 2001).

Moreover, high road density usually correlates with higher human density and

the presence of urban areas, which are also widely recognized as having major

negative influences on otter habitat quality (Barbosa et al. 2001, Prenda et al.

2001, Loy et al. 2009). Therefore, I used the road density measure as a leading

negative factor in habitat quality, reflecting human disturbance in general. Using 15

ArcGIS 10.0, I counted all the roads that cross each of the sites, and then scored

the sites in the following way: zero roads were scored as 4 (considered as the

"best" condition), 1 road was scored as 3, etc. The maximum number of roads

crossing a site in the study area was 4 (scored as 1).

All the above five factors were incorporated into the following equation, giving a different weight to each one of them according to their importance and contribution to the habitat quality. Thus, the habitat quality of a site is given by:

( )

( ) ( ) ( ) ( ) ( )

I then divided each value by the largest value of all sites, in order to calibrate the index to a 0 to 1 index.

It may be noted that some additional factors that are known from the literature are missing from the index, mainly because of irrelevance: First, altitude is known to have a negative effect on otter occurrence, as otters are rarely found above 2000 m a.s.l., probably due to the scarcity of food available at high altitudes (Kruuk 2006, Ruiz-Olmo

2007). Altitude was not included in my habitat quality index because the highest site in the study area was in the northern Golan Heights (Bental Reservoir- 940 m a.s.l).

Second, industrial and organic pollutants such as PCBs and other chemical contaminants, which are known to be harmful to otters and to have a negative influence on their occurrence (Chanin 2003), were not included because streams in Israel are no longer heavily polluted by industrial activity (information from the Hydrological

Service). Other types of pollution are assumed to be considered within the fish availability category, because they are directly linked to the fish community survival. 16

2.3.2.2 Connectivity Analysis

For the connectivity analysis, I used the network and graph theory approach.

Specifically, for calculating each patch's connectivity, I used the "closeness centrality" measure (Freeman 1978). The application of network and centrality disciplines to the field of landscape ecology is relatively recent and has been suggested as an effective and useful tool in analyzing connectivity in landscapes, particularly with regard to metapopulation dynamics in a patchy landscape (Urban and Keitt 2001, Bodin and

Norberg 2006, Borgatti and Everett 2006, Pascual-Hortal and Saura 2006, Estrada and

Bodin 2008, Erős et al. 2011, Galpern et al. 2011).

According to this approach, a landscape of scattered habitat patches is presented graphically as a network consisting of nodes and links (Bodin and Norberg 2006). Each habitat patch is represented by a node, and a link between any two nodes represents the connectivity between the two corresponding patches. If two patches are connected with a link, there is possible movement between these patches, which implies a potential flow of organisms (i.e., dispersal) between the two (Urban and Keitt 2001, Pascual-

Hortal and Saura 2006). This dispersal potential depends on the link's suitability to the biology and capabilities of the target species, in terms of distance and permeability. It is important to mention in this context, as will be explained later in detail, that a distance of a "link," in our case, is not merely the Euclidean distance, but is a weighted value of the distance and additional costs that hinder the movement along it, and is named the effective distance.

In my analysis, all sites within a 5 km radius were clustered together, each cluster creating a subcatchment, here referred to as a “patch,” with one connectivity value. This is based on the assumption that sites within a radius of 5 km are located within the same hydrological network and the same ecological conditions, and that otters are able to move up to 7 km in one night within their home range (Chanin 2003), 17 so the connectivity level is the same. Thus, an individual‟s dispersal from any site in this cluster to another cluster is the same in terms of effort and cost. Since a number of sites were clustered together into one patch receiving a single connectivity value, all the sites within the same patch were considered to be of the same value in the further analysis (i.e., in the MARK module).

Within the broad interdisciplinary field of network analysis, the concept of closeness centrality is used to assess the individual node's level of influence on all the other nodes in the same network, based on their structural position relative to others in the network (Wasserman 1994, Estrada and Bodin 2008). In this case, I had a simple graph, in which all the nodes are connected (i.e., there are no isolated components in the graph) with only one link between every two nodes. Therefore, I used the most basic measure (Freeman 1978, Borgatti and Everett 2006). According to this calculation, the centrality of a node is measured by summing all the effective distances (links) from it to all other nodes in the graph. Since larger values indicate less centrality, this actually measures the “farness” rather than the “closeness” of a point. Consequently, the value is inversed to create the closeness centrality measure for each one of the nodes. This value implies the connectivity level of each one of the patches.

Therefore, the connectivity value ( ) of node will be:

[ ]

where d is the sum of the effective distances between node and all other nodes.

For the analysis, I created a graph of 22 nodes and 21 links (Figure 4). A node is represented by the centroid of a patch (a cluster of all the sites within a 5 km buffer).

Each one of the nodes is linked to at least one other node, but there is no more than one shortest "ideal" link (here referred to as a path) between any two nodes. The shortest path was determined as follows: otters move preferentially along the hydrological 18 network (Kruuk 2006) which forms a longitudinal movement along a dendritic network

( Grant et al. 2007, Cote et al. 2008, Erős et al. 2011). However, otters can also move between basins in the surrounding dry matrix (lateral movement), in order to colonize neighboring river catchments (Grant et al. 2007, Beger et al. 2010) or whenever water is not available between water sources, for example, along seasonal streams or between fish ponds (Ruiz-Olmo et al. 2007). Consequently, the shortest path was primarily determined as the natural path along the flowing river system. Wherever water was not available between two nodes, e.g., between separate basins or along dry streams, I used the shortest route, preferentially inside a ravine. Between the coastal streams, where the shortest route was along the shoreline, I used the distance along the shore, as Eurasian otters often swim in the sea (Chanin 2003, Kruuk 2006) and, in Israel, were twice seen swimming along the Mediterranean coast (Guter 2004). All the Euclidean distances from each node to all the other nodes were calculated and summed up separately for every link, using ArcGIS 10.0 (ESRI 2011).

22

Figure 4: The network graph for connectivity analysis. Habitat patches (nodes) are represented as circles and numbered by 1-22; shortest connectivity pathways (links) are represented as lines and ordered by letters A-U. 21

The effective distances of the links were calculated using the Euclidean distances in Km, incorporated into an "effective distance equation" accounting also for the cost of movement. The cost term in this equation was composed of four factors known from the literature as the most limiting factors for otter movement. Each of these factors was weighted in the equation according to its contribution to the cost magnitude, as follows:

1. Movement between basins: Lateral movement between basins is much more

difficult and less probable than movement within the same hydrological

network, as it usually involves crossing dry lands, often highly populated by

humans or disturbed with human land uses (Carranza et al. 2012). Therefore,

movement between basins was considered to produce the highest cost. Each link

was scored with a cost score of 1 or 3: 1 for movement within the same basin

and 3 for movement between basins through a completely dry land. This cost

factor was weighted as 0.45 in the equation.

2. Movement in partially wet lands/sea: For movement through a seasonal stream

(i.e., one that flows during winter only), the path was scored as 3. Similarly,

movement in the ocean is possible but less common; therefore, if the shortest

path was through the ocean (along the coastal streams), the path was also scored

as 3. Movement within a flowing stream was scored as 1. This cost factor was

weighted as 0.25 in the equation.

3. Slope: Steep slopes (above 450) are also known as a movement limiting factor in

otters (Janssens 2006, Carranza et al. 2012). I identified the maximal slope in

each path with ArcGIS (using a DEM raster layer of resolution 25*25; see

appendix C). Steep slopes were found only along routes linking the Golan

Heights (maximal slopes ranged from 44-49), due to big differences in altitude

between the upper and the lower reaches of the streams. Therefore, I gave a 20

score of 3 only to the paths connecting the Hula valley with the northern Golan

site and the paths connecting the Sea of Galilee with the southern Golan sites

(reservoirs and streams), whereas all other paths were scored as 1. This factor

was weighted as 0.15 in the equation.

4. Water pollution and human disturbance: The quality of the path decreases when

the water is polluted (Chanin et al. 1978, Chanin 2003), having a negative effect

on the fish production of the river, and on the bank-side vegetation. Similarly, a

very disturbed path (i.e., goes through roads, urban areas and massive

agricultural areas) adds a risk factor for otter movement and dispersal (Philcox et

al. 1999, Loy et al. 2009; Carranza et al. 2012). Since both of these factors

(pollution and human disturbance) are strongly related (streams in the study area

are mostly polluted from agricultural waste), I scored them within the same

category, where a clean, undisturbed path was scored as 1, a disturbed or

polluted path was scored as 2, and a path that is both polluted and massively

disturbed was scored as 3. This cost factor was weighted as 0.15 in the equation.

All these four factors were incorporated into an "effective distance" equation, multiplying the Euclidean distance with the cost term (comprised by the weighted cost factors). Thus, the effective distance of a link , is given by:

( ) * ( ) ( ) ( ) ( )+

These effective distances of all the links were summed up for each site

separately, according to its position in the network. Then, this was used as the

term in the closeness centrality equation (see above), to produce the final 21

connectivity value for each patch. Finally, I divided each value with the largest

value of all sites, in order to calibrate the index from 0 to 1.

2.3.2.3 Sensitivity Analyses for the environmental indices

To evaluate the robustness of the environmental indices I carried out two different sensitivity analyses as follows:

1. The first analysis was created for both habitat quality and connectivity indices,

by giving more weight to the anthropogenic effects, rather than the spatial and

geometrical effects. Therefore, the equation for the habitat quality (for a site s)

index was computed by:

( ) ( ) ( ) ( ) ( ) ( )

and the effective distance equation for the connectivity index was computed by:

( ) * ( ) ( ) ( ) ( )+

2. The second analysis was created by giving more weight to the spatial and

geometrical effects, rather than the anthropogenic effects. Therefore, the

equation for the habitat quality (for a site s) index was computed by:

( ) ( ) ( ) ( ) ( ) ( )

And the effective distance equation for the connectivity index was computed by:

( ) * ( ) ( ) ( ) ( )+ 22

I used the sensitivity analyses in the later model analysis, computing the same model set with both of these modofied sets. Since the sensitivity analysis was done for the environmental indices (habitat quality and connectivity), I was interested to examine if there was a strong change in the results following the change in the indices, only for the relationships of the environmental variables with the parameters of interest

(occupancy, extinction and re-colonization). Therefore, I extracted those relationships from the results of both of the sensitivity analyses, and compared them with the results from the basic analysis. Specifically, I checked to see if the trendlines of the sensitivity analyses fell within the confidence intervals of the basic analysis.

2.3.3 Statistical Analysis

In order to estimate the occupancy and temporal processes of local extinctions and re-colonizations of sites, I used a multi-model inference (Burnham and Anderson

2002a), based on the Kullback- Leibler information-theoretic approach (K-L). Analyses were carried out with the occupancy module in program MARK (White and Burnham

1999). The estimation in program MARK uses the maximum likelihood for fitting potential predictor models to the data, and provides estimates for the model's parameters. Models are ranked based on the “information” lost when a certain model is used to approximate the full truth (i.e., the "distance" between the model and reality), using Akaike's Information Criteria (AIC; Burnham and Anderson, 1998). A lower AIC value for a given model indicates less information is lost relative to full reality by the fitted model (Anderson 2008). The model with lowest AIC value is considered to be the most parsimonious model approximating the unknown reality that generated the observed data.

AIC is computed as:

( ( ̂)| ) 23

where K is the number of parameters in a given model ̂, given the data, and L is the „fit‟ of the model. The program produces a second order correction (AICc) to adjust for the small sample size relative to the estimated number of parameters, given by:

( ( ̂)| ) ( ( )

is calculated for all models as the difference between the current model and the best fitting model. Models with are commonly considered as weakly supported by the data (Burnham and Anderson, 1998). In addition, evidence ratios are also used to compare between the models. This quantifies the power of the evidence supporting a model by computing the relative likelihood of one model versus another

(Cooch and White 2011, Anderson 2008). Evidence ratios are calculated from the

Akaike weight, which is calculated for each approximating model in the candidate

( ) model set as: ( ) ∑* +

For most animal sampling situations, detection of a species is indeed indicative of "presence", but non-detection of the species is not equivalent to absence, because a species may go undetected at these sites even when present (“false negative detections”). Thus, estimates of the proportion of patches occupied and the colonization and extinction rates may be negatively biased to some unknown degree when the detection probability is <1, and may vary as a function of site characteristics, time, or environmental variables. Therefore, the model derives the required estimated parameters that maximize the likelihood of the observed reality, also considering and estimating the detection probability of the species at each site (Mackenzie et al. 2003).

The occupancy module in program MARK (Cooch and White 2011) was employed using the “robust-design” structure (Pollock 1982, MacKenzie et al. 2003).

According to this design, each season is considered as a closed period, in which the occupancy state of sites remains constant during the sampling period, but between the 24 sampling periods (in a time interval of a year), a site may go extinct, be re-colonized, or stay the same; therefore, the totaled occupancy of all sites may change (Figure 5). Using this module, I analyzed the dataset of multiple visits to the sites during the two seasons of the study period, combined with the data of past surveys. I used three spatial repetitions for each site, and added the site temporal repetitions from my two-year study

(2012-2013) as a single combined repetition. This means that if one of the locations was positive in any of the three temporal repetitions, it was assigned as positive in this particular sampling event. So, in fact, the years 2012 and 2013 actually got nine repetitions, instead of three, for each site in each season. Notably, this may change the detection probability in these two years, as the number of visits has been changed. To examine this possibility, one of the models was constructed with a detectability (p) estimated separately for 2012-13 and for the period up to 2012. Models in which the presence-absence determination is assessed via spatial subsampling within a site, rather than via a true temporal replication (as I did for the 2000-2011 data), have been proposed and have been examined successfully in the recent years (Kendall and White n.d., Guillera‐Arroita 2011).

Each site received a score of “1” for detection and “0” for non-detection in each sampling event. Missing observations (i.e., if a site was not visited) were designated as

"." and were removed from the calculation. Explanatory variables ('covariates') used in the analysis included the indices of habitat quality and the connectivity potential of each site (as mentioned in the previous section), as well as q time effect.

The parameters that were estimated in the model are: occupancy (Ψ) –the proportion of occupied sites within the total number of sites at time 1 (year 2000), extinction ( )-- the probability of an occupied site becoming unoccupied within a season, and colonization ( )-- the probability of an un-occupied site becoming occupied within a season. In addition, the model produces the derived estimates of the occupancy 25

- Ψ in the consecutive years 2001-2013. The program also calculates the detectability

(p) – which is the probability that a site is classified as occupied (positive), conditional on it being occupied (White and Burnham 1999, Mackenzie et al. 2003).

(a)

(b)

Figure 5: Shows: (a) The general approach of the occupancy analysis; a site may be occupied or un-occupied in time , and may go extinct or be re-colonized respectively, or stay the same at time . (b) The Robust Design; closure is assumed in the three repetitions within the same season, while between seasons, the occupancy state might change. (Figures were taken from Cooch and White, 2011).

2.3.4 Model construction process

Since there was no good biological justification to assume that the detection probability is affected by environmental factors, such as habitat quality and connectivity, nor by time, the detection parameter (p) was considered constant. One exception to this is the model that takes into consideration a different detection probability in the two-year study (2012-2013), where both temporal and spatial repetitions were performed, as opposed to all other years, where only spatial repetitions were done. 26

In this study, I considered four possible covariates: habitat quality, connectivity, the interaction of quality*connectivity and a time effect (2000-2013), plus a “no effect”

(null) model. Since there are three parameters to be estimated--occupancy in time one

(year 2000), extinction and colonization--there is a large number of possible a priori models, all of which are biologically feasible. The strategy of constructing „„all possible models‟‟ is not recommended in the model selection approach (Burnham and Anderson

2002), due to the high possibility of biased results. Therefore, in order to limit the number of models in the model set, I constructed a model set based on the "ad hoc" strategy, suggested by Doherty et al. (2012), when there are too many reasonable models to construct. In general, the method applies the "step-down" approach (Lebreton et al. 1992), in which one of the parameters is fixed at a high dimensionality (i.e., full covariate effect), while the other parameter is investigated with one by one covariates.

Once the best model structure for the investigated parameter is settled upon, the other is then focused upon, based on the resulting best structure (Doherty et al. 2012).

In my case, since I have three parameters, I adjusted this approach to my model construction analysis, and fixed two parameters at a time. In addition, since I was trying to find the one most leading influential factor for each of the parameters, I examined only one of the three covariates at a time (habitat quality, connectivity or the interaction term) while investigating each parameter separately. Specifically, the following steps were carried out:

1. First, I kept two of the parameters, occupancy (Ψ) and colonization ( ), at a full additive covariate structure (i.e., Habitat Quality+ Connectivity+ Habitat Quality

*Connectivity+ time effect), while constructing every possible covariate model on the extinction ( ) parameter. Since occupancy is the derived parameter, and thus estimated in the model selection only for the first year (2000), I used the fixed time effect only for the colonization parameter. Models examining the time effect on the other two 27 parameters were added later based on the selected model (step 4). Thus, this step resulted in three models in the model set.

2. After choosing the top model for , I fixed the structure on according to results of step 1, while still holding the full covariate dimensionality for Ψ, and constructed all the model possibilities for a single covariate on colonization- This step resulted in three additional models to the model set.

3. After choosing the best model for and for , I did the same procedure for examining the leading covariate factor for Ψ, resulting in three additional models.

4. In order to examine the effect of time, I selected the best of the above models and added to it all the variations of time effect (three models for time effect on and on both).

5. In order to test the hypothesis that detection probability was different in my two-year study, I constructed another model, in which I took the leading model, giving a constant parameter to the detectability in the years 2000-2011, and another parameter to the detectability in 2012-2013.

6. Finally, I added three more models, each one testing a hypothesis in which one of the parameters was missing its most influential covariate (as was determined in the best model). This allowed me to compare the model likelihood between models with and without covariate effects on each parameter. In addition, I added a "null" model with no covariate effects.

Overall, 17 models were constructed in the program "MARK." Each of the models was evaluated according to its AIC value, for which the most parsimonious model is considered the one with the lowest AIC value. Then, I used model averaging

(Stanley and Burnham 1998) for final estimations of the derived parameters, which is calculated by weighted model averaging across all models, using the Akaike weights as the model probability functions. In addition, for each of the parameters, I calculated the 28 likelihood ratios between the model that considers this parameter's main factor/s and the reduced one, which assumes no effect of any of the spatial factors or time on this specific parameter. This was done in order to assess the leading spatial factor's contribution to each one of the parameters in the model.

For the sensitivity analysis, I constructed the same model set following the same process twice, each time for one of the aforementioned sensitivity sets, considering different habitat quality and connectivity indices (as described before).

2.4 Monitoring the effectiveness of the ledges

During May-June 2012, four ledges were constructed with the cooperation of the

Public Work Authority (MAATZ) and the Stream Drainage Authority (RASHUT

NIKUZ NECHALIM). These ledges were located under four bridges (Figure 6), which were chosen based on two criteria: a high incidence of otter road kills ("hot spots" that were extracted from the NPA database, Figure 7) and the feasibility of construction. The ledges are located at the crossings over four streams: the Dafna Stream, which is a small tributary of the Dan Stream (road 918; N33°13'09.450" E035°38'15.781"), the Klil

South Stream (road 918; N33°10'49.954" E035°38'45.155"), the Banias Stream (road

918; N33°12'39.615" E035°38'38.767") and the Shlomo Bridge on the eastern canal of the Jordan River (road 977; N33°09'47.542" E035°35'50.469").

Another passage that already existed at the start of the study (located at the Klil

North Stream- road 918; N33°09'59.227" E035°38'21.997") was excluded from the analysis to prevent a bias in the results, as wildlife might have used the underpass regularly before the other ledges were built and there was no way to control for this.

The ledges were designed based on similar projects in Britain (Grogan et al.

2001) and according to the conditions and needs of the particular locations. Three of the ledges (located at Dafna, the Shlomo Bridge and Klil south) were made of coarse galvanized tin attached to the bridge‟s concrete wall, and were 40 cm wide and about 20 31 m long (this varied according to the bridge length). These three ledges were designed exclusively as wildlife passages. The fourth ledge (Banias) was also built for touristic proposes, and thus was wider (1.5 m), and was far more disturbed by humans. Still, I chose to include the monitoring of this passage in the analysis, as track pads were still possible to keep on the passage without human disturbance for an interval of one night.

Figure 6: Locations of the four ledges constructed and monitored under four bridges, during May-June 2012.

I examined the effectiveness of the dry ledges by using three methods:

2.4.1 Track pads:

I used track pads in order to monitor the use of the ledges by otters and other

mammals. The track pads are made of a very soft material used by orthopedists

to measure footprints (bio-foam, manufactured by Smithers Bio-Medical

Systems). This material is also used by the police and is considered to capture a

very precise and gentle footprint. Every time an animal crosses the pad, an 30

imprint of its tracks is left. Two pads were placed at two opposite sides of the

ledge (a total of four pads for each ledge), so footprints would be recorded even

if an animal started to cross from one side but then moved, retreated, or jumped

into the water (possible in the case of otters when the ledge is close to the

surface water). Additionally, on each side, two pads were placed in an

approximate one-animal-step distance from each other, so if an animal made a

hop and missed the first pad, it would certainly step on the next one. The size of

each pad was 30 15 cm, which is almost the width of the ledge, so every animal

that walked across the ledge must have stepped on it. One exception was the

passage in the Banias Stream, which is much wider and is also often used by

hikers. Therefore, in this particular passage, I placed two pads, within an area of

natural mud that covered most of the passage surface, in which clear footprints

were imprinted as well.

The pads were checked daily, for five nights during February and March 2013

(four consecutive nights in February and one night in March). At each visit, any

evidence of animal tracks was recorded and the pad was photographed and

replaced.

Tracks left on the pad were identified using an animal track key. Since it was

difficult to determine whether more than one footprint belonged to the same

individual (Herzog et al. 2007), I recorded only the species crossing in one night,

regardless of the number of footprint crossings.

2.4.2 I.R Cameras:

Infra-red motion activated cameras (RECONYX PC900) were placed above

each one of the ledges attached to the bridge wall, in order to catch every animal

that crossed the ledge. Cameras were located inside aluminum locked-boxes that

were fixed to the concrete wall of the bridge, directed to the ledge. They were 31

programmed to take five photos each time they were activated, with high

sensitivity (for high-quality night photos). Every animal that was detected

walking on the ledge by the camera was recorded.

(b)

(a)

Figure 7: a) Otter road-kills in 2000 to 2013 in all the study area, and b) road kills only around the monitored ledges, zoom in marked black frame. 32

3 Results

3.1 Covariate values

3.1.1 Habitat quality

The quality index of the sites in the study area ranged from 0.43-1.00 (Figure 8).

Most of the sites with the highest quality valued were concentrated in the Hula Valley

(9/19) and the reservoirs in the Golan Heights (5/19). The Alexander Stream received the lowest quality value (0.43).

3.1.2 Connectivity

The connectivity index of the patches in the study area ranged from 0.36 -1.00

(Figure 9). The most connected patches (i.e., with the highest connectivity score of 1) are the ones located in the western part of the Sea of Galilee and the Jordan River connection area with the south of the Sea of Galilee. Both the northern and southern parts along the Jordan River were scored as the next highest values (0.93-0.97). The lowest connectivity values were received for the coastal streams: Alexander, Taninim and Na'aman (0.36, 0.43 and 0.44, respectively, Figure 9).

33

Figure 8: a) The study area with all the sites characterized by their quality values. Values range from 0.43 to a maximum value of 1, and are categorized as low quality (yellow 0.43- 0.62), medium quality (orange 0.62-0.81) and highest quality (red 0.81-1). A closer look as an example is given by: b) the Hula Valley area, marked in black frame, and c) the Harod Valley area, marked in black frame.

(b)

(a)

(c)

34

Figure 9: The study area with all the patches characterized by their connectivity values. Each patch represents a node in the network graph, given by a single connectivity value, ranging from 0.4 to a maximum value of 1, and divided into seven categories according to their level of connectivity (blue shading).

3.2 Model selection results

A total of 17 models were evaluated, of which five (with ) carried 73% of the weight of all the models in the model set (Table 1). 35

Table 1: Program MARK output table with all 17 models, evaluated and listed by the AICc values and the . Akaike weights, likelihood ratios and the number of parameters of each model are presented as well. Model parameters are: 'Ψ'-Occupancy, 'ε'-extinction, 'γ'- colonization and 'p'-detection probability. In the brackets are the spatial covariate effects: 'HabQual' -Habitat quality, 'Connect'-Connectivity, 'Inter'- interaction of Habitat quality* Connectivity, 't' accounts for time effect and (.) for no effect (constant).

The two most parsimonious models (i.e., with the lowest AICc values) carried almost the same weight ( ). They both consisted of the same following spatial factors: the occupancy and extinction rates are affected mostly by the interaction of habitat quality* connectivity; the re-colonization of sites is most affected by connectivity. The slightly better model was the one assuming a time effect on the colonization rate. Similarly, the next two models (third and fourth) had the same evidence weight ( ), with the same factors influencing the extinction rates

(interaction habitat quality*connectivity) and colonization rates (connectivity), but 36 differing in the time effect on the extinction rate, and the influencing factor on overall occupancy (habitat quality vs. interaction).

The top five models (with ) included connectivity and the interaction between habitat quality and connectivity as explanatory factors, suggesting that these are very influential spatial factors determining otter distribution. Habitat quality alone

(not in an interaction) was included in one of the top five models (third model). Time effect was included in the first top model, which considers a time trend in the colonization process, and in the fourth model, which considers a time trend in the extinction process.

Detection probability (the probability of a site to be detected as positive, conditional on being occupied), was estimated as 0.78 after the model averaging process. The model that assumed a different detection probability in the years of my study (2012-2013) was ranked 6th with a , which means that the data is explained better when considering equal detectability across all survey years whether or not both spatial and temporal or only spatial repetitions were conducted.

Calculating the likelihood ratios between the best model and the reduced model for each one of the parameters (i.e., the models that differ in the inclusion or exclusion of the leading explanatory covariate for each of the parameters) produced the following: the model that assumed no effect of the leading covariate (interaction habitat quality*connectivity) on overall occupancy (model num. 9) was 4.84 times less likely than the best model; the model that assumed no effect of the leading covariates (time and connectivity) on colonization (model num. 13) was 298 times less likely than the best model; the model that assumed no spatial effect on extinction (num. 16) had a weight of 0, as did the null model (num. 17) that assumed no effect of any spatial factors on any of the model parameters. These results suggest that models that assume no effect of the explanatory factors are poorly supported by the data. 37

3.3 Trends in time

3.3.1 Changes in proportion of occupied sites through time

Results of the averaged estimates of the proportion of occupied sites over time

(derived estimates) show that besides the small increase in the first two years, overall there was a continuous moderate decline with a large variance in overall occupancy throughout the years (Figure 10), declining from 0.7 (SE 0.09, CI 95% for Wgt. Ave.

Est. is 0.5 to 0.85) in year 2000, to 0.67 (SE 0.07, CI 95% for Wgt. Ave. Est. is 0.53 to

0.78) in 2013.

1 2 0.95 y = -0.0005x + 2.2015x - 2203.7 0.9 R² = 0.9349

0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 Proportion of occupied sitesoccupied of Proportion 0.15 0.1 0.05 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year

Figure 10: Program MARK output for occupancy-derived estimates averaged across all models (± SE), over the 14 years of surveys.

3.3.2 Changes in re-colonization probabilities through time

Results from the model-averaged estimates show a continuous decline in the colonization probability estimates of un-occupied sites along the years, decreasing from

0.39 (SE 0.12, CI 95% for Wgt. Ave. Est. is 0.2 to 0.63) in year 2000 to 0.28 (SE 0.08, 38

CI 95% for Wgt. Ave. Est. is 0.15 to 0.46) in 2013 (Figure 11). Therefore, the probability of a site being re-colonized during the year 2013 is about one-third lower than it was in the year 2000. The model that assumes a trend in time in re-colonization rates was ranked as the best model within the model set (Table 1).

1 0.95 y = -0.0095x + 19.328 0.9 R² = 0.9979 0.85

0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15

0.1 Colonization rate estimates rate Colonization 0.05 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year

Figure 11: Program MARK output for colonization rate estimates averaged across all models (± SE), over the 14 intervals between years of surveys 2000-2013.

3.3.3 Changes in extinction probabilities through time

Extinction probability of occupied sites increased slightly along the years from

0.13 (SE 0.03, CI 95% for Wgt. Ave. Est. is 0.085 to 0.21) in the year 2000, to 0.14 (SE

0.03, CI 95% for Wgt. Ave. Est. is 0.09 to 0.22) in the year 2013 (Figure 12). The model that assumes a trend in time in extinction rates was ranked as the 4th model, with

(Table 1). 41

1 y = 0.0007x - 1.178 0.95 0.9 R² = 0.9984

0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 Extinction rate estimates rate Extinction 0.15 0.1 0.05 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure 12: Program MARK output for extinction rate estimates averaged across all models (± SE), over the 14 intervals between years of surveys 2000-2013.

3.4 Effect of environmental factors

To examine the effect of environmental factors on the population parameters, I calculated a weighted average for each one of the values from the two top-ranking models, which considered the same environmental factors affecting the parameters.

Both of the top models together carried a weight of 0.44. All graphs of relationships with environmental variables were extracted from the real parameter estimates at time 1

(i.e., the year 2000).

3.4.1 The effect of interaction between habitat quality and connectivity

The interaction of habitat quality*connectivity was found to be a very important factor in all of the five top-ranked models, affecting both extinction probabilities and 40 overall occupancy. Direct interaction values and their effect on the model parameters could not be computed. Thus, each of the spatial factors was computed separately for their effect with the extinction and occupancy. Increases in both connectivity and habitat quality were found to have a positive and negative effect on re-colonization and extinction rates (respectively); therefore, I conclude that the general effect of the interaction is synergistic (i.e., a stronger combined effect than for each factor separately).

3.4.2 Site connectivity effect on extinction, re-colonization and overall occupancy

Colonization rates increased with connectivity, ranging from 0.09 (SE 0.05, CI

95% 0.03 to 0.25) in the lowest connectivity of 0.36, to 0.75 (SE 0.13, CI 95% 0.44 to

0.92), in the highest connectivity of 1 (Figure 13). Extinction descreased with connectivity and ranged from 0.56 (SE 0.1 CI 95% 0.36 to 0.74) when connectivity was lowest, to 0.05 (SE 0.01 CI 95% 0.02 to 0.09) when connectivity was highest. Overall occupancy increased with better connectivity, ranging from 0.33 (SE 0.17 CI 95% 0.1 to

0.69) in the lowest connectivity, to 0.85 (SE 0.08 CI 95% 0.6 to 0.96) in the highest connectivity.

41

1 Colonization 0.9 Extinction Occupancy

0.8

0.7

0.6

0.5

0.4

0.3

Parameter estimates Parameter 0.2

0.1

0 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 Connectivity Figure 13: Program MARK output for connectivity effect on colonization, extinction and overall occupancy changes. Both actual estimates (i.e., a parameter estimate for every actual connectivity value of a specific site) and trendlines are presented (± SE). Values were averaged from the two top-ranking models.

3.4.3 Habitat quality effect on extinction and overall occupancy

Extinction probability decreased with habitat quality, ranging from 0.49 (SE 0.09

CI 95% 0.32 to 0.67) in the lowest habitat quality of 0.43, to 0.06 (SE 0.02 CI 95% 0.03 to

0.1) in the highest quality of 1 (Figure 14). Overall occupancy increased with better habitat quality, ranging from 0.38 (SE 0.16 CI 95% 0.14 to 0.7) in the lowest connectivity, to 0.83 (SE 0.09 CI 95% 0.59 to 0.94) in the highest quality. Habitat quality was a poor predictor of colonization (Table 1); hence, the habitat quality effect was not derived for this parameter. 42

1 Occupancy 0.9 Extinction

0.8

0.7

0.6

0.5

0.4

0.3

Parameter estimates Parameter 0.2

0.1

0 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 Habitat Quality

Figure 14: Program MARK output for habitat quality effect on extinction and overall occupancy changes. Both actual estimates (i.e., a parameter estimate for every actual connectivity value of a specific site) and trendlines are presented (± SE). Values were derived from the two top-ranking models.

3.4.4 Sensitivity Analyses Results

When comparing the relationships of population parameters with the environmental variables, both of the sensitivity analyses lines (sensitivity 1- anthropogenic effects and sensitivity 2- spatial effects) fell within the confidence intervals of the basic analysis (Figure 15). The biggest different was in sensitivity analysis 1, in which habitat quality was a poor predictor for extinction rates (model ranked as 12th, with ; see also appendix D). Therefore, the relationship of extinction and habitat quality for sensitivity 1 was not included in the graphs.

43

Extinction 1.2 Occupancy Colonization sensitivity 1

1 sensitivity 2

0.8

0.6

0.4

Parameter estimates Parameter 0.2

0 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 Connectivity

Occupancy 1 Extinction 0.9 sensitivity 1 sensitivity 2 0.8 0.7 0.6 0.5 0.4 0.3

0.2 Parameter estimates Parameter 0.1 0 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 Habitat Quality

Figure 15: Sensitivity analyses output (MARK) for (a) connectivity and (b) habitat quality effects on population parameters. The basic analysis is presented by thick colored lines (confidence intervals are shaded in the matched colors). Both of the sensitivity analyses are presented, referred as "sensitivity 1" (marked with black) and "sensitivity 2" (grey).Values were derived from the top-ranking model. 44

3.5 Monitoring the effectiveness of the ledges 3.5.1 Track pads The footprints recorded on the track pads during the five nights included the Eurasian otter Lutra lutra (15%), the Egyptian mongoose Herpestes ichneumon (30%), the stone marten Martes foina (20%), the porcupine Hystrix indica (15%) and the rat Rattus rattus (15%). The tracks were well preserved and easily recognizable (Figure 16).

10 Otter 9 Mongoose 8 Marten 7 Porcupine 6 Rat 5 4 3

Number of nights of Number 2 1 0

Dafna Banias Shlomo Klil south

Figure 16: Results of footprints recorded on track-pads from five nights, in the ledges that were studied. Otter footprints were found on one ledge (Dafna) for three nights.

3.5.2 I.R Cameras:

The camera information was considered unreliable, because one camera (from the

Banias Bridge) was stolen, and the other cameras didn't work consistently; thus the data was not consistent. Therefore, I excluded the camera results from the analysis.

However, their results are worth mentioning, as they provide valuable information about the wildlife using the ledge. The cameras recorded extensive activity on the ledges

(Figure 17), similar to the activity that was recorded by the track pads. Several photos of one and two otters (possibly a mother and a cub) crossing the ledge were received from the Dafna ledge, as well as many photos of mongooses and martens. In addition, a photo of a porcupine was received from the camera at the Shlomo Bridge (where porcupine footprints were also recorded). 45

4 Discussion

Detecting changes in the distribution of a species and the spatio-temporal processes of its populations over time can be a critical step in conservation management, especially when the focal species is rare and elusive (Barbraud et al. 2003,

Perinchery et al. 2011). Of particular interest is the case of metapopulations in highly fragmented and disturbed areas, where local extinctions and re-colonizations are natural processes (Hanski and Gilpin 1991, Hanski and Simberloff 1997). Changes in the recolonization rates may result in a difficult-to-detect decline in patch occupancy and the associated changes in the distribution and range of the species. A failure to detect these changes in time may make the recovery of the population difficult, if not impossible (Moilanen 2000).

The approach of occupancy modeling, which was used in this study, allowed me to detect the spatial structure of the otter population in Israel, and its long-term processes through the 14 years of surveying. Indeed, the results present some worrying findings: the otter population has been experiencing a trend of a continuous but moderate decrease in the proportion of occupied sites during the period 2000 to 2013

(Figure 10). This pattern was mainly due to a decrease in re-colonization rates (Figure

11). After model averaging, a very shallow positive time trend in extinction rates was found as well (Figure 12). Nevertheless, the re-colonization of empty sites is the main contributor to reduced occupancy and is insufficient to compensate for site extinction, thus resulting in a net loss of overall occupancy. Notably, the models that assume a time trend in re-colonization and extinction rates were ranked at the first and fourth positions, respectively (Table 1). Part of the very moderate decrease in occupancy and extinction rates is probably due to scarcity of data and missing data from many sites during the years (for example, only one location in each of the reservoirs in the southern Golan 46

Heights was visited during the years up to 2012). More data and more years of this study will probably reveal a stronger time trend.

In addition to detecting changes in time, the occupancy modeling framework allows one to address ecological questions on the relationships between these parameters and spatial and environmental factors. This study provides evidence that both habitat quality and connectivity, as well as the interaction between the two, are crucial factors affecting the population occupancy, extinction and re-colonization.

Similar findings were found in European populations (Prenda and Granado-Lorencio

1996, Barbosa et al. 2001, Janssens 2006, Loy et al. 2009, Clavero et al. 2010). Here, connectivity was evaluated based on the Euclidean distance between sites, and on the costs for movement, including: movement between basins and between partially wet lands, steep slopes and disturbance (water pollution and human disturbances).

According to the model, connectivity contributes to all three population parameters

(occupancy, local extinctions and re-colonizations of unoccupied sites). This can be emphasized by looking at the maximum value of the connectivity (1) in Figure 13.

Theoretically, we can determine that improving the connectivity between sites to a maximum value (1) would result in a drop of extinction rates to only 0.05, would increase the re-colonization rate to 0.7, and would cause the overall proportion of occupied sites to be estimated as 0.85, a very dramatic change in the occupancy state of the otter population.

Habitat quality was also found to be a major factor, contributing to overall occupancy and preventing local extinctions. Theoretically, an improvement of all the sites to a maximum quality (1), similar to that existing in the Hula Reserve, would yield a drop in local extinction rates to 0.06, and a jump of re-colonization rates to 0.5. This will lead to an overall occupancy of 0.83 (Figure 14). 47

The assessment of the relationships between population processes and the spatial factors also enables a comparison between different sites, characterized by different spatial factors, and the probabilities of extinction and re-colonization linked to them. For example, the site "south Jordan" received a score of 0.61 in habitat quality, and the highest score (1) in connectivity, while "Kishon Delta" received the same habitat quality value (0.61), but a much lower connectivity value (0.51), as this patch is more isolated. Therefore, since both have the same habitat quality, it is possible to extract the probabilities of extinction and re-colonization for only the connectivity values and compare between the two sites. For the "south Jordan," the probability to be re-colonized is 0.75/year, and 0.06 to go extinct. However, for the "Kishon Delta," the probability of extinction is 0.4/year, which is almost seven times higher than the former site, with the same habitat quality but a maximum connectivity. Furthermore, the probability of the "Kishon Delta" to be re-colonized in a year is only 0.18, which is more than three times lower than that of a same quality site that is better connected, such as the "Southern Jordan." The same approach can be implemented in sites with the same connectivity but with different quality. For example, the habitat quality of the middle reaches of the Kishon Stream is scored as 0.89 and the connectivity as 0.66, while the upper reaches have the same connectivity value (0.66) but a much lower habitat quality value (0.64). The extinction and re-colonization rates for the first site are

0.09 and 0.49, respectively, and 0.26 and 0.45 for the second site. Thus, although the re- colonization rates are similar (0.49 vs. 0.45), the extinction rate for the lower quality site is almost three times higher than the higher quality site (0.26 vs. 0.09).

The use of a network analysis and "closeness centrality" as a measurement for spatial connectivity seems to be a useful and simple method, which allows for assessing the relative contribution of each one of the patches in the network to the overall connectivity. The technique may be used to identify areas where conservation efforts to 48 preserve both valuable habitats and movement pathways should be focused. Future analyses on connectivity should include more complicated aspects of the network, e.g., taking into account lateral connectivity (between basins) as a separate term in the overall analysis (Carranza et al. 2011).

In this study, both habitat quality and connectivity were measured as a weighted index, which allowed me to give a single value, for quality and for connectivity, to each one of the sites, thus simplifying the statistical analysis and increasing power. Similar indices have been developed and used successfully in other studies in Mediterranean areas (e.g., Ottino et al. 1995, Loy et al. 2009, Ottaviani et al. 2009, Carranza et al.

2012). The factors that I used to create the indices were those that repeatedly show up in the scientific literature as those important for otters. However, this method has some limitations, as it assumes a pre-defined relative weight for each one of the factors that contributes to the overall quality or connectivity. Nevertheless, the inclusion of each of the spatial factors alone, as explanatory variables in the models, would have been impossible due to the limited dataset I had; there was not enough power to include so many predictors. Moreover, sensitivity analyses 1 and 2 showed similar results to the basic analysis: the relationship lines between the environmental variables (habitat quality and connectivity) and the population parameters (occupancy, extinction and re- colonization) fell within the confidence interval of the basic analysis (Figure 15). This suggests that the basic indices that I used in my analysis are robust enough, and thus reliable. It can be noted that one exception in this context was the relationship between habitat quality and extinction in sensitivity analysis 1, where habitat quality was a poor predictor for extinction rates.

In Israel, many of the environmental features determining the habitat quality for the otters were damaged during the last decades, mainly due to water pollution, the drainage of natural pools and streams as a result of poor policy (e.g., Hula Lake), over- 51 pumping and aggressive riverbank vegetation cutting (Guter 2004). Stream restoration projects that are being carried out should strive to re-establish the natural conditions of the streams as much as possible. A good example of stream restoration and its possible effects on otter occurrence is a natural pond in the industrial area of Kiryat-Shmona, where the western canal of the Jordan River flows through the city. The canal used to be very narrow, with concreted steep side walls. During the project, the canal was widened, the concrete was removed and the bank slopes were moderated. In addition, water from the En-Zahav spring has been diverted to the canal, as it historically used to be, and riparian vegetation was planted on the banks outside the canal (information from the

Drainage Authority). Consequently, the wetland ecological system has been repaired, fish have naturally colonized the area, and otter signs have been continuously found since the restoration (Shachal et al. 2012). These findings suggest that wetland restoration and quality improvement are possible, and that the natural re-colonization of otters, as a result, can be achieved. Other restoration projects in Israel might include areas that were occupied by fish ponds in the past, e.g., adjacent to the coastal streams, and could be restored as wetland habitats, providing ecological conditions for otters and other aquatic and semi-aquatic species. In this case, natural re-colonization by otters might be difficult and could take an extended period of time, but could be promoted by artificial re-introductions (Guter 2004, Dolev et al. 2011, Magory-Cohen et al. 2013).

My study points to the importance of ecological corridors. Corridors are defined as linear landscape elements that connect two or more larger patches of wildlife habitat that were connected in historical time (Soule and Gilpin 1991, Beier and Noss 2008)

Corridors are known, to some degree, to improve movement between habitat patches, thus facilitating re-colonization potential and reducing some of the negative effects of habitat fragmentation (Beier and Noss 2008, Kadoya 2009). If corridors facilitate movement, then the dispersal capabilities of otters (up to 40 km- White et al. 2003, 50

Carranza et al. 2012) make corridors a vital component of otter dynamics for movements, compared to species with limited dispersal capabilities. This was demonstrated in other wide-ranging large carnivores inhabiting highly fragmented areas

(e.g., Beier 1993, Dixon et al. 2006). Moreover, otter corridors (i.e., waterways) are especially crucial for otter dispersal in the dry climate of the Mediterranean region, as they can only cross a limited distance of dry terrestrial terrain (Kruuk, 2006).

Results of the connectivity analysis indicate that the Jordan River serves as an essential ecological corridor, connecting and enhancing movement between the northern part of the otter population (the Hula Valley) and the southern part (the Harod Valley and further west towards the Yisrael Valley and the coastal streams). In the past, otters inhabited the lower Jordan River as far south as the Dead Sea (Dolev et al. 2011), as well as using it as the main movement pathway for spreading west. However, since the construction of dams at the southern outlet of the Sea of Galilee, the flow of fresh water south from the Sea of Galilee has become intermittent, restricted mostly to the late winter of wet years. Furthermore, this part of the river has been intensively changed due to rapid anthropogenic development (Holtzman et al. 2005, Farber et al. 2005), so in addition to the dramatic decline in water flow, the water quality has deteriorated because water flows to the river today contain poorly treated sewage, agricultural waste, and water from a saline water carrier (Holtzman et al. 2005, Farber et al. 2005). As a result, the Jordan River south of the Sea of Galilee no longer provides a natural corridor as it used to (Perelberg 2013).

Since 2009, the Jordan Rehabilitation Administration has been developing a master plan for the conservation and restoration of the Lower Jordan River. The plan includes the introduction of fresh water into the river, restoration of the natural vegetation, and a proper management of invasive species and their agricultural effects on the natural system. These actions will hopefully lead to a rehabilitation of this part of 51 the Jordan River and its tributaries, as an important wetland habitat, and a central ecological corridor for wildlife (Perelberg 2013). Considering my results regarding the importance of connectivity, in general, and the Jordan River as a critical connector, in particular, this plan might be a very critical step in the conservation of the otter population, maintaining its persistence and movement to all the wetlands south of the

Sea of Galilee. These findings are especially critical in the light of the disappearance of otters from the Harod and Bet-Shean Valleys since 2011 (Shachal 2013), after showing a stable, and even genetically unique, subpopulation in all the years before that

(Magory-Cohen et al. 2013). The Harod subpopulation was connected directly through the lower Jordan River to the source populations in the Sea of Galilee, the Hula Valley and the Upper Jordan River, and thus is dependent on the rehabilitation of this part of the river in order to re-colonize this area again, following a possible further expansion toward the western parts (the Yizrael Valley and the coastal streams).

One of the major issues regarding the degradation of habitat quality and connectivity for otters is the road effect (Philcox et al. 1999; Guter et al. 2005).

Therefore, road planning efforts towards reducing barrier effects and wildlife-vehicle collisions are critical for a sustainable road network management (Forman 2003). The fact the the ledges that were constructed under high mortality-related bridges were used by otters and other mammals regularly during the nights they were monitored (Figure

16) highlights the importance of dry ledges, under bridges with flowing streams, to maintain habitat connectivity and prevent road mortality for these animals. The ledges were also used by other mammals, demonstrating that these structures provide an opportunity for passage by mammals that could otherwise be restricted, even if the river flow was minor.

Of the methods used to monitor the wildlife use of the ledges, the track pads were the most effective, in addition to being simple and low-cost. The I.R camera 52 results were inconsistent, but still recorded valuable information about the wildlife using the ledges, which showed extensive activity on the ledges (Figure 17), similar to the activity that was recorded by the track pads. It is worth mentioning an additional finding that there were no otter road kills detected in the road sections around those bridges since the ledges‟ construction in May 2012 (within a buffer of 3 km on the same road). This is in contrast with the years 2000 to May 2012, where 44 road kills were documented in the Hula Valley region, in which 19 of them occurred around the bridges within the buffer zone (information taken from the NPA data base). This apparent reduction in road kills in the vicinity of the ledges may suggest that the ledges are effective in preventing road kills, when combined with fencing to prevent road crossing.

However, road kills in general have been lower since May 2012, and only one road kill was recorded in the Hula Valley during the period of monitoring until the time of writing (October 2013), compared with an average of 3.4/year during 2000-2012.

Therefore, this evidence of reduction in road kills due to the ledge construction is not yet conclusive, which is not surprising considering the very short period of monitoring

(only 17 months). Thus, it is necessary to continue an extensive monitoring program in order to evaluate reliably the long-term effect of the ledges. This should be done both by monitoring their use (with track pads, etc.) and by a long-term comparison of the ledges‟ effect on road kills occurring around them. In addition, more work is needed in identifying the bridges that act as "hot spots" for road kills of otters and other mammals, especially where streams are more likely to be flooded and can be mitigated by ledges and fences or by other means (e.g., bridges, wide underpasses, reflectors and warning signs for motorists; Grogan et al. 2001), as well as exploring the role of ledge design and construction materials to increase the likelihood of being used by animals. Future planned constructions should also be followed by a monitoring program before and after the construction. 53

A "classic" metapopulation (as described by (Levins in 1969) 1970) is composed of a fixed number of unstable local populations that are always in a balance between

“deaths” (local extinctions) and “births” (re-colonizations). Such classic metapopulations do exist (e.g., Hanski 1994, Hanski and Simberloff 1997). It can be argued that the otter population is not a classic metapopulation, and thus the sites might not represent distinct habitats with limited movement between them and the inhabitants cannot be defined as subpopulations. The combined evidence of both genetic and ecological studies suggests that, at a large scale, the population in Israel is divided into subpopulations, differentiated by their geographic characteristics, their genetics and their permanency. The genetic study on the Israeli otters shows a clear division of

Israel's otter population into three distinct groups: the Hula Valley, the Sea of Galilee and Golan, and the Harod Valley subpopulations (Magory-Cohen et al. 2013). The genetic aspect of this study suggests that the population is divided into at least three subpopulations, and moreover, because the study relied on carcasses, not all sites from the present study were represented by genetic samples. Thus, it is possible that with a larger sample size covering more sites, more subpopulations would be recognized. The presence-absence surveys conducted in Israel during 2000-2013, including those in my study, also suggest a population structure that is patchily distributed. Moreover, these surveys show that there is a stable population in the northeastern areas (the Hula Valley, the Sea of Galilee and the Jordan Rift Valley), while in other areas, such as the Golan

Heights, the Yisrael Valley and the coastal plains, the activity is temporary and even sporadic, and it is not clear to what extent these areas can be sustainable for source populations. Similar source-sink dynamics in otter populations were described in

Finland, where a few high quality river systems were very reproductive, creating several small source populations, whereas the sink populations in the secondary habitats had very low reproduction rates (Sulkava et al. 2007). 54

A site in this study usually represents a 5 km segment of a stream considered as a home range (with the exception of the water reservoirs which stand alone as separate sites). These sites represent spatial units that might go extinct or recolonized in a time step of one year. This definition is somewhat problematic, because there is a lot of uncertainty about the otter home range, in general (Chanin 2003), and particularly in

Israel where no specific home-range study has been done (e.g., with radio-collared individuals). Thus, it is unclear whether a site in this study can be defined as an independent unit that is closed to changes in occupancy within a sampling season. If the assumption of “closure,” which is one of the basic assumptions in this estimation method, is violated, then sites may be considered as being "extinct" or "re-colonized," when, in fact, it might reflect only a temporal movement within a home range, or because the "season" has changed (MacKenzie et al. 2002, 2003, MacKenzie and Royle

2005, MacKenzie 2006). Consequently, there might be a negative or, even worse, a positive bias of the overall occupancy and re-colonization rates. Because the annual surveys were usually carried out within one week and because the three spatial repetitions that were used (instead of temporal repetitions) were usually surveyed during the same day, the probability that the state of the occupancy changed is low. In my two- year study, in which three temporal repetitions were also added, I used a one-week interval between each session, totaling a three-week period of time between the first and the last repetition. However, the "closed site" assumption might have been partially violated, because of an erroneous assumption regarding what a closed unit is for the otters. I assumed that a cluster of three locations within a 5 km stream range is an average home range, thus creating an independent site that is closed to changes within the same season in the same survey. This assumption is supported by the literature, which indicates that the size of a territory is smaller in Mediterranean areas than in other northern European countries (Ruiz-Olmo et al. 2001, Saavedra 2002, Kruuk 2006). 55

Nevertheless, it is possible that otter home ranges are generally larger than the size of my sampling units (sites). Additionally, there might be a problem with the "edges" of the sites, since it is unknown where a site "ends" and where the next one "begins" (when they are on a continuous hydrographical line). Moreover, overlap in territories has been documented in this species, especially between males and females or when resource density is very high (Chanin 2003, Kruuk 2006). In such cases, MacKenzie and Royle

(2005) and MacKenzie (2006) suggest that relaxing the “closure” assumption would mean that changes in occupancy within a season within a site are random (i.e., the probability of a species being present within a site at one time interval does not depend upon whether it was physically present at the previous point in time). In this case, occupancy estimates should be interpreted in terms of sites that are being used, rather than occupied. This is an important distinction, as it changes the meaning and interpretation of the occupancy parameter. A site that is being "used" is a site in which the species is sometimes physically present during the season, while a site that is being

"occupied" is a site in which the species is always physically present during the season.

The proportion of area “used” by a species will often be larger than the proportion of area where the species physically exists (MacKenzie 2006).

Even if occupancy in my study should be referred to as "use," as a result of the partial violation of the “closure” assumption, the results still suggest a clear downward trend in the distribution of otters in Israel in the past decade, resulting from a decrease in overall habitat occupancy or use. Furthermore, if indeed the occupancy is overestimated, creating an optimistic view, then extra-caution should be taken, all the more so when there is a decrease trend appearing even in the optimistic view. Therefore, my results should be considered as even more extreme and should serve as an alarming sign. 56

This study demonstrates that occupancy modeling, where detectability is modeled simultaneously, provides a powerful tool in population dynamics studies, enabling us to address important ecological questions for conservation assessments

(MacKenzie et al. 2002, MacKenzie 2006, Perinchery et al. 2011). This is particularly important for poorly studied, elusive species of conservation concern, such as otters.

Because in most of species, rare species in particular, detection probabilities are lower than 1, a failure to account for imperfect detection will bias occupancy and the relationships of estimated parameters with site-covariates (MacKenzie et al. 2002, Gu and Swihart 2004, Perinchery et al. 2011). Poor detection, when not accounted for, can lead to erroneous conclusions--for example, assessing important habitat variables as unimportant due to non-detection errors (Gu and Swihart 2004). In my study, the estimate for detection probability was relatively high: 0.78. This suggests that the standardized method of presence-absence surveys of Eurasian otters, through sprainting- site detection (Reuther et al. 2000), is an efficient method, when site-visits are repeated three times, providing a high probability of detection when the species does appear at the site. The use of the robust design structure (Pollock 1982) of multiple visits between periods of change allows the assessment of changes in occupancy rates over the years, modeled as functions of site colonization and extinction rates. Moreover, occupancy modeling allows us to test for the existence of relationships between site-specific extinction and re-colonization probabilities and the spatial characteristics of the sites, and to give an objective evaluation of the strength of these relationships without restrictive assumptions regarding the constancy of these processes' rates or constant occupancy, and regarding perfect detection (Mackenzie et al. 2002). This is in contrast to previous incidence function models that pre-assume these kinds of relationships (e.g.,

Hanski 1994, Moilanen 2002, MacKenzie et al. 2003). Finally, the multi-model inference approach strives to get as close as possible to the complex reality, by drawing 57 estimates from a series of potential models, based on their relative likelihoods that are averaged across all models (Burnham and Anderson 2002), rather than hypothesis testing that focuses on rejecting a given null hypothesis (Neyman and Pearson, 1933).

Model averaging, although not extensively studied, has been shown to reduce bias

(Stanley and Burnham 1998). Therefore, this approach is more robust and accurate and a preferable tool in complicated field studies where conditions are dictated and inference is weak, but are intended to drive conservation-based decision making (Saltz

2011).

To summarize, this study demonstrates the importance of regular long-term monitoring of population processes (local extinctions, re-colonizations and overall occupancy), in order to detect negative changes in distribution of this critically endangered and elusive species. Identification of the spatial and environmental factors affecting these processes is crucial for proper management. Overall occupancy and extinction rates were found to be mostly affected from the interaction of habitat quality and connectivity, while re-colonization of new sites was found to be mostly affected from the connectivity potential between the sites. Therefore, the maintenance and restoration of both good habitat quality and movement possibilities within and between river catchments is a priority to guarantee the continued survival and gene flow of the isolated and small otter subpopulations in Israel. Natural connections and the sites they connect should be integrated and managed as one system, in a single planning framework (Carranza et al. 2012). In this context, at least one road ledge was evident to be used by otters regularly and thus can most likely reduce damage to the entire population, both by improving of the quality within the habitat, and by enhancing the movement and dispersal between different habitats. Management actions should therefore include additional road-passages construction under the bridges that constitute 58 the "hot spots" of road kills, along with restoration programs, such as the rehabilitation of the Lower Jordan River, which is a main dispersal route for the otters.

\

Figure 17: A sample of photos from the ledge constructed under the Dafna Bridge. A number of species were detected by the I.R camera: stone marten (upper left), Egyptian mongoose (bottom left) and Eurasian otter (right). During some of the nights, two otters were seen walking together on the ledge.

61

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6 Appendices

Appendix A. Sites and locations of the otter spraint-survey.

Region Site Location x coor. y coor. Hula agamon agamon east 257994.00 779053.10 Hula agamon mizpor agurim 256206.44 779306.13 Hula agamon agamon west 257548.33 778138.05 Hula ayun bethilel 256973.93 791921.31 Hula ayun kfar yuval 255463.45 794354.03 Hula ayun road 99 255932.91 791933.82 Hula banias gesher havalim 263975.59 793643.13 Hula banias maianot 264972.53 794852.45 Hula banias Banias tank 263694.72 793243.52 Hula dan betkvarot dafna 260005.60 792017.60 Hula dan peleg tal 260306.13 792665.07 Hula dan Dan 260091.01 791519.60 Hula dofen golan en a tina 260213.87 776741.47 Hula dofen golan en dibsha 260354.24 777149.30 Hula dofen golan en gonen 260662.22 779268.34 Hula kalil Kalil south 260116.73 785144.98 Hula kalil Kalil north 260605.41 786828.15 Hula kalil Kalil middle 260125.38 785796.15 Hula lehavot lehavot ponds 260461.50 782700.80 Hula lehavot gesher lehavot 259050.40 783654.80 Hula lehavot lehavot ponds 260072.87 783524.89 Hula shmurat hula maaravitknisa 255621.20 775843.40 Hula shmurat hula machsanim 256552.79 774936.66 Hula shmurat hula miglash saduk 256444.80 776174.40 Hula snir gesher nucheile 258587.52 792984.47 Hula snir miiglash maagar 258651.60 793517.80 Hula snir nachal,mapal tr 258411.46 793113.16 Hula tel dan hoze plagim 260775.23 794516.71 Hula tel dan maayan rashi 260941.80 794941.85 Hula tel dan morad furelim 260804.08 794453.98 Golan golan north maagar bental N 272990.60 783208.00 Golan golan north maagar bental s 273645.10 782492.90 Golan golan north maagar orvim 268267.40 783141.10 Golan golan south chital chital reservoir 273633.94 746614.25 Golan golan south Hamat-gader hamat gader 263237.00 732594.00 Golan golan south east Botamia botamia 281560.00 760350.00 Golan golan south reservoirs maagar semech N 273467.60 754624.80 Golan golan south reservoirs maagar semech S 272456.00 752616.90 Golan golan south reservoirs magar bneisrael 274992.00 751650.40 Golan golan south streams yahudia 270523.40 764643.00 Golan golan south streams zavitan 267239.00 765504.10 71

Golan golan south streams Yahudia 270452.84 765636.67 Harod harod betshean gesher kantara 244925.20 712871.10 Harod harod betshean western bridge 246821.30 712639.10 Harod harod betshean eastern bridge 249013.41 712542.85 Harod harod kibuzim amal 243748.30 711682.90 Harod harod kibuzim mesilot 244542.20 710828.70 Harod harod kibuzim reshafim 245393.30 710073.00 Harod harod middle brechot bethash 240720.80 715137.70 Harod harod middle kele shata 239235.40 715787.40 Harod harod middle Harod middle 240220.29 715327.16 Harod harod south brechot sdeheli 249496.30 704846.40 Harod harod south en kaftor 248964.30 707434.10 Harod harod south Harod south 248971.07 706534.15 Harod harod upper brechot enharod 236119.00 717420.50 Harod harod upper gidona 234590.70 717949.40 Harod harod upper navot 233111.20 718467.00 Kineret kineret btecha gesher daliot 262074.30 755078.00 Kineret kineret btecha meshoshim 260421.93 756658.24 Kineret kineret btecha Btecha Yahudia 261255.30 756203.44 Kineret kineret east en gev namal 259866.30 743497.00 Kineret kineret east gesher semech 261189.00 748446.60 Kineret kineret east gesher susita 260362.80 743459.40 Kineret kineret west hamei tveria 251930.00 741410.70 Kineret kineret west maagan hadaig 250898.00 744833.40 Kineret kineret west tayelet 251239.40 743464.00 Yisrael kishon lower Yagur 207963.00 739333.00 Yisrael kishon lower zipori w 208674.20 740775.40 Yisrael kishon lower Zipori 208693.68 741015.99 Yisrael kishon lower junctions bet zid 209881.20 733584.10 Yisrael kishon lower junctions hahmakim 209415.90 736747.10 Yisrael kishon lower junctions hatishbi 211519.00 732065.00 Yisrael kishon middle kfarbaruch pool 219411.10 726835.30 Yisrael kishon middle kfar baruch 220839.04 727527.68 Yisrael kishon middle kfar baruch 218642.56 727334.40 Yisrael kishon delta shmura nitur 204870.10 744837.50 Yisrael kishon delta Kishon delta 204574.08 745306.88 Yisrael kishon delta kishon delta 203585.63 745218.30 Yisrael kishon upper gilboa sargel j 224437.80 722713.60 Yisrael kishon upper minhat megido 223027.20 723038.40 Yisrael kishon upper kishon road 65 223797.30 722339.00 Coastal plain Coastal streams North karei naaman 210339.80 753845.90 Coastal plain Coastal streams North kfar masarek 210265.70 754835.60 Coastal plain Coastal streams North naaman shefech 208332.70 757182.60 Coastal plain Coastal streams Taninim Ada 192575.00 717178.00 Coastal plain Coastal streams Taninim Taninim 192573.00 717261.00 Coastal plain Coastal streams Taninim Taninim bridge 192102.08 717224.48 70

Coastal plain Coastal streams Alexander Alexander 187977.00 700052.00 Jorden river yarden border einot hasida 251766.80 713490.80 Jorden river yarden border doshen ponds 252691.12 715291.88 Jorden river yarden border Tel ishmael 253611.87 717371.51 Jorden river yarden border north neve ur 253438.30 722463.50 Jorden river yarden border north maavar ketef 253694.60 723534.20 Jorden river yarden border north Neve ur ponds 254380.35 722077.27 Jorden river yarden mountain gesher kfarhanasi 258852.01 764345.32 Jorden river yarden mountain hidro station 258844.93 765997.82 Jorden river yarden mountain meizad ateret 258935.63 767540.56 Jorden river yarden delta gesher arik 257860.78 756500.07 Jorden river yarden delta gesher hadodot 258588.41 759787.62 Jorden river yarden delta park hayarden 258626.03 757184.23 Jorden river yarden south gesher betzera 253557.30 732943.70 Jorden river yarden south seher alumut 253076.90 734248.00 Jorden river yarden south Yardenit 253879.92 735211.24 Jorden river yardencanal e gesher sdenehem 257966.73 787633.44 Jorden river yardencanal e gesher shlomo 256193.00 785409.68 Jorden river yardencanal e Kfar blum 257416.17 786107.85 Jorden river yarden canal s gesher hahamisha 258462.40 774605.80 Jorden river yarden canal s gesher hapkak 259084.16 771801.54 Jorden river yarden canal s gesher junction 258319.30 775249.25 Jorden river yarden canal w gesher blum 254137.30 789027.30 Jorden river yarden canal w en hashmona 254413.10 789238.30 Jorden river yarden canal w gome junction 253741.30 786089.71

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Appendix B. Contour layer of the Sea of Galilee.

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Appendix C. Graphs of slopes along the pathways towards the Golan Heights.

(1) Bental reservoir (Orvim stream) (2) Golan streams (Yahudia stream); (3) Golan reservoirs (Daliot stream) and (4) Botamia reservoir (Daliot stream).

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Appendix D. Tables of models of Sensitivity analyses.

Table of models- Sensitivity Analysis 1:

Table of models- Sensitivity Analysis 2: