Oryx Leucoryx) Reintroduced in Different Areas

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

Using fecal analysis to assess nutritional differences between three herds of Arabian oryx (Oryx leucoryx) reintroduced in different areas

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

By Ido Isler

Under the Supervision of David Saltz and S. Yan Landau

Mitrani Department of Desert Ecology

Author's Signature …………….……………………… Date …………….

Approved by the Supervisor…………….…………… Date …………….

Approved by the Director of the School …………… Date ………

Abstract

The Arabian oryx (Oryx leucoryx), a specialized desert ungulate of the Bovidae family found in arid and hyper-arid zones, is among the first rare animals to be reintroduced to the wild. In 1997 a reintroduction program was initiated in the central

Negev desert of Israel. Releases were carried out in three regions: Ein Shachak, Paran, and Nahal Kezev. Thirty-one individuals were released in 1997-1998 in the Ein

Shachak (Arava) population, and today there are over 50 individuals. In the Paran area, where a total of 40 individuals were released in 2000 and 2002, only approximately 14 survive today, while the Kezev population has declined from the 30 released to 21. Observations indicate that the main cause of poor performance in the

Paran and Kezev populations is low recruitment and not adult mortality. Vegetation differs in these regions, as Paran belongs to the Saharo-Arabian biogeographic zone in the Negev plateau, while Ein-Shachak is in the Sudanese biogeographic zone in the

Arava valley, and Kezev is a transition zone between the two zones. This thesis uses fecal NIRS to test the hypothesis that poor nutritional quality is the cause of low recruitment in the Paran population compared to the Arava population. A NIRS calibration was performed with several captive oryx at the Hai-Bar Yotvata nature reserve. Calibration results provided spectra needed for Fecal NIRS analysis, giving diet predictions for different nutritional components (crude protein, ADF, NDF, condensed tannins, polyphenols, and acacia fruits). NIRS for fecal chemistry was also performed. Results showed high fecal protein levels, especially in the Arava, as well as more than adequate protein in Fecal NIRS predictions. In addition, Fecal NIRS indicated much higher percentages of Acacia tortillis fruits in the Arava diet predictions, as well as high condensed tannins and polyphenols compared with the iii other regions. High concentrations of tannins in Arava diets may be responsible for higher reproductive success in the Arava population, related to control of parasites. iv

Acknowledgements

I would like to thank all the people who joined me in the field and supported me in my search for fresh oryx feces, who helped collect feces (with their bare hands). I’d like to thank Maarten Hofman, who helped me with fieldwork many times and also did chemical analysis of feces samples which were crucial for validation of the Fecal

NIRS calibration. Thanks to Doron Nakar for provided a graphing calculator which was immensely helpful in telemetry triangulation calculations. A special thanks to

“The Patio Ranch” in Texas, who provided funding for radio tracking equipment for locating oryx in the field. A very special thanks to the staff of the Hai-Bar and the

Nature Reserves and Parks Authority who made this research possible and were always ready to lend a hand and give me the opportunity to fly around. Of course, I couldn’t have done this without the guidance and assistance of my supervisors, David

Saltz and Yan Landau. Also, Levana Dvash who helped in the lab at Volcani. The most special thanks of all I give to my wifey, Phoenix who was there for me and helped me in so many ways I can’t even describe. Thank you thank you thank you

Phoenix.

Table of Contents

1. Introduction...... 1 2. Background ...... 3 2.1 Reintroduction...... 3 2.2 Arabian oryx – a reintroduction pioneer...... 6 2.3 Oryx Reintroduction in Israel...... 9 2.4 Ruminant nutrition...... 13 2.5 Methods for monitoring nutritional quality...... 15 2.6 Near Infrared Reflectance Spectroscopy – NIRS: ...... 16 2.7 Research Subject and Hypothesis...... 20 3. Materials and methods ...... 22 3.1 Calibration experiment ...... 22 3.2 Fieldwork...... 31 4. Results...... 39 4.1 Hai-bar fecal NIRS calibrations ...... 39 4.2 Fecal NIRS (indirect) analysis of diet quality for wild oryx populations ...40 4.3 Regional and Seasonal difference in fecal chemistry...... 44 5. Discussion...... 50 5.1 Seasonal variations in nutritional constituents ...... 50 5.2 Nutritional differences among regions...... 51 5.3 Relationship between nutrition and recruitment in different regions ...... 53 6. Conclusions...... 56 7. References...... 58

List of Tables and Figures

Figure 2.1 Three release regions in Israel ………………………………………… 10 Figure 3.1 The oryx enclosure, during cleaning………………………………….. 23 Table 3.1 The feed type and chemical components …………………………...... 26 Table 3.2 Experimental Cycle ……………………………………………………. 27 Table 3.4 Feeding cycle for three oryx………………………………………...... 28 Table 3.3 Feeding cycle for two oryx…………………………………………….. 29 Table 3.5 Amount of feces samples collected in the different regions by date …... 32 Figure 3.2 NIRS Spectra with no mathematical treatment………………………... 37 Figure 3.3 1st derivative spectra after De-trend………………………………...... 37 Figure 3.4 2nd derivative spectra after De-trend…………………………………... 38 Table 4.1 Calibration performance for 19 nutritional constituents for Fecal NIRS for Arabian oryx……………………………………………………………. 39 Figure 4.1 Fecal NIRS prediction of crude protein dietary content …………...... 40 Figure 4.2 Fecal NIRS prediction of ADF dietary content……………………….. 41 Figure 4.3 Fecal NIRS prediction of NDF dietary content……………………….. 41 Figure 4.4 Fecal NIRS prediction of ADL dietary content……………………….. 42 Figure 4.5 Fecal NIRS prediction of polyphenols dietary content……………….. 42 Figure 4.6 Fecal NIRS prediction of condensed tannins dietary content………… 43 Figure 4.7 Fecal NIRS prediction of acacia fruit dietary content…………...... 43 Figure 4.8 Comparison of seasonal variations in fecal crude protein content in different regions……………………………………………………………… 45 Figure 4.9 Comparison of seasonal variations in fecal NDF in different regions… 45 Figure 4.10 Comparison of seasonal variations in fecal ADF in different regions... 46 Figure 4.11 Comparison of seasonal variations in fecal ADL in different regions.. 46 Figure 4.12 Comparison of seasonal variations in fecal polyphenols in different regions …………………………………………………………………………….. 47 Figure 4.13 Comparison of seasonal variations in fecal condensed tannins in different regions…………………………………………………………………………….. 47 Table 4.2: Comparison between fecal constituent percentages in each region … 48 Table 4.3 Effects of region, season and combined region/season on each constituent…………………………………………………………………… 49 Table 5.1 Phenolic and CT content of Acacia tortillis…………………………. 53 vii

List of Abbreviations

ADF – Acid Detergent Fiber

CP – Crude Protein

NDF – Neutral Detergent Fiber

NIRS – Near Infrared Reflectance Spectroscopy

SD – Standard Deviation

SEC – Standard Error of Calibration

SECV – Standard Error of Cross Validation

SEP – Standard Error of Prediction

RSQ – Coefficient of Determination

1-VR – Correlation between laboratory data and cross-validation results

1. Introduction

Understanding ecological requirements of reintroduced species is vital for a successful reintroduction of endangered or extinct species. All biological factors can play a role in population viability by acting on survival and fecundity rates (Sarrazin and Barbault, 1996). Specifically, knowledge about resource requirements, life traits, and demography of the species is highly important for planning reintroduction programs, especially since endangered species (the main target of reintroduction), are among the least understood species. Consequently, many reintroductions have failed

(Sarrazin and Barbault, 1996).

The Arabian oryx (Oryx leucoryx), a specialized desert ungulate of the Bovidae family found in arid and hyper-arid zones, is among those reintroduced rare animals of which little is known. The species was extinct in the wild by 1972 due to uncontrolled hunting. Prior to its extinction in the wild, the Arabian oryx roamed the

Arabian Peninsula, southern Israel, and parts of the Sinai desert. Several reintroduction programs have successfully established wild populations in Oman,

Bahrain, Saudi Arabia and Israel.

In 1997 a reintroduction program for Arabian oryx was initiated in the central Negev desert of Israel. The reintroduction program relies on repeated removals from a permanent local breeding core (Hai-Bar Arava) and releases to the wild (Saltz, 1998).

To date, releases have been carried out in three different locations: Ein Shachak,

Paran, and Nahal Kezev. The Ein Shachak population was the first to be reintroduced, with 31 individuals released in 1997 and 1998. Today there are over 50 individuals.

By contrast, in the Paran area, where a total of 40 individuals were released between 2

2000 and 2002, the population has declined to approximately 14 individuals. In Nahal

Kezev a total of 40 individuals were released between 2003 and 2007; today there are approximately 30 individuals. Growth projections carried out before the reintroductions began (Saltz, 1998) indicated that within 8 years the population should exceed 100, but this has not occurred.

Observations indicate that the main cause for the poor performance of the Paran and

Kezv populations is low recruitment and not adult mortality. There is little evidence of predation in these areas, although some activity was seen in the Ein Shachak area.

Free water is available year round in all areas. In the Paran and Nahal Kezev areas, water is supplied and in the Ein Shachak area there is a local water source. Thus, the most probable cause of the low recruitment is nutritionally based.

The release areas belong to different biogeographical zones. The Paran area belongs to the Saharo-Arabian biogeographic zone in the Negev plateau, while Ein-Shachak is in the Sudanese biogeographic zone in the Arava valley. The Nahal Kezev area is a transition zone. The Sudanese region supports a higher number of plant species. This is due to the fact that plants in the Paran area are more rainfall-dependent, while plants in the Ein Shachak area benefit from runoff and underground water accumulation as well. This may also affect plant nutritional quality.

Assessing the nutritional quality of the forage consumed by the reintroduced oryx poses a serious problem since the animals are behaviorally well-adapted and do not allow humans to approach them to determine what plant species are consumed.

Furthermore, it is very hard to locate them in the wild due to the rough desert terrain, 3 and once found they quickly run away. Consequently, to check the nutritional quality of the oryx diet an indirect method must be applied.

A new emerging indirect method of assessing the quality of ruminant nutrition is fecal analysis using Near Infrared Spectroscopy (NIRS) (Foley et al., 1998). This technique allows one to locate chemical bonds in organic matter through near infrared absorption. The NIRS method is based on interaction between electromagnetic radiation and the analyzed material, which absorbs the radiation. Several stages are required to build calibration equations that will enable the linkage between the spectral data of a specific material and its chemical components.

The aim of this research was to develop the NIRS technique and calibrate it so it could be applied to the reintroduced oryx. Based on these results I assessed the nutritional quality of the diets of the three reintroduced oryx herds.

2. Background

2.1 Reintroduction

Reintroduction is defined by the IUCN as an attempt to establish a species in an area which was once part of its historical range, but from which it has been extirpated or become extinct (IUCN, 1998). In a way, reintroduction is one of the stages of ecosystem rehabilitation, a subject within the realm of ecosystem conservation, but usually performed as a step to save specific species or populations. The

Reintroduction Guidelines published by the IUCN (World Conservation Union)

Reintroduction Specialist group state that “the principle aim of any reintroduction should be to establish a viable, free-ranging population in the wild, of a species, 4 subspecies or race, which has become globally or locally extinct, or extirpated, in the wild. It should be re-introduced within the species' former natural habitat and range and should require minimal long-term management” (IUCN, 1998). The goals of reintroductions are to: (1) enhance the long-term survival of the species, (2) reestablish an ecological and/or cultural keystone species, (3) increase or maintain biodiversity, and/or (4) provide long-term economic benefits to local people (Kleiman et al., 1994). Kleiman et al. (1994) suggest 13 criteria to plan and proceed with species reintroduction, one of which is that the habitat is sufficiently protected and a quality food resource exists.

Many reintroduction programs have taken place in the past 30 years, and there is much to learn from prior experience. Generally, reintroductions should be part of research and conservation programs that provide basic natural history information on the ecological requirements of the species (Sanz and Grajal, 1998). An understanding of ecological requirements of reintroduced species is vital for successful reintroduction and future management. Knowledge about life history traits and demography of the species is highly important for planning reintroduction programs, especially since endangered species (the main target of reintroduction) are among the least understood species. All biological factors can play a role in population viability by acting on survival and fecundity rates (Sarrazin and Barbault, 1996).

Understanding as much as possible about the ecological and habitat requirements of the target species is needed. Fernandez et al. (2006) point out that “species restoration must be founded on prior assessments of population viability and associated risks, which are only possible when there is a clear understanding of the interactions 5 between the demographic traits of the species and the landscapes designated as targets for reintroduction.”

However, evaluating habitat availability and connectivity for reintroductions is often problematic because of the lack of data on habitat associations and demography for the areas in which the species went extinct (Kramer-Schadt et al., 2005). Information on species that have become endangered or extinct in the wild before modern ecology developed is usually scarce or anecdotal, making it difficult to understand the species ecological requirements (Hirzel et al., 2004).

Reintroductions carried out today can provide us with information to fill in these gaps in knowledge. They pose a unique opportunity to clarify specific niche requirements because reintroduced species are likely to colonize the most suitable habitats first.

Information drawn from the first releases could be helpful for optimizing species’ management policy as reintroductions continue (Hirzel et al., 2004).

In their study of wild boar (Sus scrofa) reintroduction in Denmark, Fernandez et al.

(2006) found that incorporation of ecological knowledge from other persisting populations can help in developing predictive habitat and population models to assess the viability of wild boar reintroduction. However, incomplete knowledge of habitat selection and dispersal behavior may present a limitation. The experience of conservation biologists working to reintroduce pygmy lorises (Nycticebus pygmaeus) in Vietnam showed that it is very difficult to plan a reintroduction program for a species when little is known about its natural behavior, ecology, and habitat preference. In this case, lorises were released on a large scale based on incorrect 6 assumptions about loris's ecology, and, as a result, the introduced population did not survive. However the study contributed valuable data for future releases (Streicher and Nadler, 2003).

Having prior knowledge about reintroduced species ecology is important to design the reintroduction plan, but does not guarantee success. As Sarrazin and Barbault (1996) state, “the management of reintroduced populations implies long term monitoring with direct interactions between ecologists and managers.” Monitoring of reintroduced populations is necessary for several purposes: evaluating survival and recruitment rates, evaluating model predictions with regard to both viability and risk and updating models (Bar-David et al., 2005), understanding actual ecological needs of the target species, and discovering previously unknown factors affecting survival.

2.2 Arabian oryx – a reintroduction pioneer

The Arabian oryx is adapted for arid and hyper-arid zones. The species was extinct in the wild by uncontrolled hunting. The Arabian oryx eats mainly grasses, seedpods, fruits, fresh tree growth, tubers, and roots. Oryx can survive without drinking water for long periods of time - up to 20 months (Price, 1989). They live in nomadic herds that follow the rare rains and are able to effectively utilize the fresh plant growth that occurs after a rainfall. Herds exhibit fission-fusion behavior, with group size and composition varying over relatively short time frames (days-weeks) (Gidron, 2005). A herd contains all ages and both sexes.

The life span of the Arabian oryx is up to 20 years and the age of sexual maturity is two years. After a pregnancy period of nine months the female gives birth to a single 7 calf. After giving birth the female ruts, and thus under good conditions reproductive success may exceed 1.0 (Saltz, 1998).

The Arabian oryx was extinct in the wild by 1972. Several reintroduction programs have established wild populations in Oman, Bahrain, Saudi Arabia and Israel, with a total reintroduced population in the wild of approximately 886 in 2003. The first reintroduction took place in Oman in 1982 (Price, 1989). This program was a milestone in what was a relatively new discipline. Although it had a successful start, between 1996 and 1999 poachers for live trade decimated the reintroduced herds of oryx. Thus, the wild herd in Oman declined from approximately 450 in the wild at the beginning of 1996 to an estimate of 96 by 1999 (Spalton et al., 1999). Four years later there were just six female oryx in the wild and an estimated 100 males and poaching had not been stopped (Re-introduction NEWS No. 23: November 2003). The situation in Oman has continued to deteriorate due to ineffective planning and management, poaching, oil and gas exploration and extensive and uncontrolled use of off-road vehicles within the site. Furthermore the Sultanate of Oman issued a Royal Decree reducing the size of the World Heritage site by 90% from 27,500 to 2,854 km2. There is effectively no longer a functioning sanctuary, and in 2007 the sanctuary was removed from the UNESCO list of World Heritage sites, the first site ever to be deleted from the list (ENS, 2007).

In 1978-79 Jordan received oryx from the World Herd and Qatar, which were kept and bred in the Shumari Wildlife Reserve. The reserve is an area of 22 km2 originally fenced in 1958 for an experimental study of desert farming. In total, 11 oryx were received. Oryx numbers at Shumari increased from 11 at the end of 1978 to 70 at the 8 end of 1986, a 28% increase per year, with the population doubling every 2.75 years.

In recent years, several attempts were made to release small groups of oryx to an enclosure in the Wadi Rum Nature Reserve. I visited the site in 2006 and discussions with the local Bedouins revealed that the reintroduction has not been successful. I was told that the oryx did not survive in the enclosure (1 sq. km.) although they were fed and the enclosure included local vegetation. From my observations of the remaining oryx, they appeared to be malnourished. The area itself seems to be affected by severe overgrazing.

Saudi Arabia started a captive breeding program for oryx at the National Wildlife

Research Center (NWRC) in Taif in 1986. In 1989, Mahazat as-Sayd, a 2,244-km2 tract of flat, arid steppe desert in western-central Saudi Arabia (28º15’N, 41º40’E), was surrounded by a fence to exclude domestic livestock. Between 1990-1993, 72 oryx from the NWRC and foreign collections (e.g. San Diego Wild Animal Park,

USA) were moved to the reserve and held within a 2 km2 enclosure, from which they were released into the protected area in which no supplemental food or water were provided. Sporadic rains amounting to 90-100 mm of rain per year typify this area.

After the rains, temporary pools form, but other than these there is no free drinking water. During the first years since reintroduction a team of rangers under the supervision of the reserve manager tracked and located the oryx on a daily basis but due to population growth and the fissioning of animals into many small herds it became difficult to track all the oryx daily. Therefore, since May 1995 regular transect surveys have been carried out in the reserve and population trends documented (Seddon et al., 2003). Between 1990 and 1997 the population increased steadily up to 400 individuals. Due to drought in 1998-1999 the population leveled off 9 at 350-400 individuals but after some rainfalls in 2001 and 2003 and resulting good forage conditions, the population recovered and increased to an estimated 720 individuals.

Abu Dhabi has recently begun an oryx reintroduction program with the release of 98

Arabian oryx into the desert of the United Arab Emirates. The program aims to release 100 captive bred oryx into the wild each year until 2012, i.e., a total of 500 individuals. The 4,000 square-mile habitat is in the process of being declared a protected area and will have rangers patrolling it. There are scattered shelters and feeding centers in the area to help the oryx adjust, but these will be removed once the oryx learn to survive independently (Calderwood, 2007).

2.3 Oryx Reintroduction in Israel

To date, releases have been carried out in three different locations: Ein Shachak,

Paran, and Nahal Kezev (Fig. 2.1). 10

Figure 2.1 Three release regions in Israel

These reintroduction areas are located in different biogeographic zones. The Paran area belongs to the Saharo-Arabian biogeographic zone in the Negev plateau, while the Ein-Shachak and Nahal Kezev areas are in the Sudanese biogeographic zone in the Arava valley, which is part of the Great Rift Valley which begins in Africa and stretches into Lebanon.

The Israeli reintroduction program is based on repeated releases from a permanent breeding core (the Hai-Bar). The maximum number of animals that could be removed from the breeding core without depleting it (maximum sustained yield) is based on a

Leslie matrix projection model (Saltz, 1998). The model was then tested against the

Hai-Bar population. Based on this model, the number of adult females in the Hai-Bar breeding-core population would grow from 12 in 1990 to 36 in 1995. The actual number in 1995 was 36. Thus, the life table for the local breeding-core population of oryx appeared to fit well. 11

Based on observation of the local breeding-core herd, oryx exhibit strong social bonds and hierarchic organization. Consequently, removed groups in the model consisted of females with an age-group structure representative of the age structure in the breeding-core (which included mostly females aged 1–5 years). Based on the Leslie matrix model (Saltz, unpublished data), it was concluded that a released oryx herd should include approximately 14 adult females(aged 1 year or older) in order to bring the probability of extinction due to demographic stochasticity to < 1% over 100 years.

The simulations suggested that annual removals or removals every other year of this magnitude could not be achieved without causing an eventual decline of the breeding- core population.

However, with one removal every three years, it would be possible to remove the required number of adult females and the local breeding-core would return to its original size before the next removal. This translated into a first removal of 14 adult females, followed by removals of 16.3 ± 3.1 adult females every third year thereafter.

Assuming a 1:1 sex ratio in the wild, the simulations suggest that the wild population would reach the target size of 100 adults (50 adult females) within 6–9 years. If the adult sex ratio is female-skewed with twice as many females than males, it would take

7–10 years to reach the target size of 100 adults (66 adult females).

The Ein Shachak population was the first to be reintroduced, with 31 individuals (20 females and 11 males) released on two separate occasions between 1997 and 1998

(Maoz, 2003). In the Paran area 40 individuals (21 females and 19 males) were released on three separate occasions between 2000 and 2002 (Gidron, 2005). In Nahal 12

Kezev there were three releases between 2003 and 2007, with a total of 40 individuals

(22 females and 18 males).

Ten years have passed since the first reintroduction. At present, there are approximately 90 adult Arabian oryx in the wild; this number is well below the projected populations and has yet to reach minimal size. The cause appears to be reduced reproductive success and recruitment. Comparison between the release areas shows differences: the Paran population consists of approximately 14 individuals; Ein

Shachak is estimated at over 50 individuals and Nahal Kezev at 30 individuals. Thus, the poor performance of the oryx in the wild is due mostly to the Paran population that has exhibited a negative growth rate. Observations indicate that low recruitment in the Paran herd, rather than adult mortality, is the main cause of this negative growth. Migration between release areas has been minimal – during the study period, one individual moved from the Kezev herd to the Paran herd.

Low recruitment may be caused by many factors. These commonly include predation, nutrition, and water supply (Owen-Smith, 1990). However, there is very little evidence of oryx predation and predators (wolves) are rarely observed in the release areas (especially in the Paran area). Furthermore, water is present year round in all release areas. Thus, the most probable explanation for the poor recruitment of oryx is malnutrition.

There is little difference between the areas in terms of annual precipitation. However, the Sudanese region supports a higher number of plant species, providing a more diverse source of nutrition. This is due to the fact that plants in the Paran area are 13 more rainfall-dependent, while plants in the Ein Shachak area benefit from runoff and underground water accumulation which may affect plant nutritional quality.

In deserts, precipitation is the dominant controlling factor for biological processes. It is highly variable throughout the year and occurs in infrequent and discrete events; i.e., precipitation has a large random (unpredictable) component (Noy-Meir, 1973).

Since the Arabian oryx can survive without drinking for long periods of time, I assume that the presence of fresh green plant material, and not free water, is the key resource that influences oryx survival.

2.4 Ruminant nutrition

Even though population size is often dictated by such factors as weather and predation, ultimately, the nutritional quality of diets will determine the maximum number of animals that an area can support (Stephenson, 2003). As the animal is a product of its environment, it represents the quality of its environment (Stephenson,

2003). The population dynamics of large ungulates are dictated mostly through reproductive success and recruitment, rather than adult survival (Gaillard et al., 2000).

Poor nutrition is usually expressed in poor reproductive success, while high forage quality and quantity often increase pre-weaning survival of large herbivores.

To meet their nutritional requirements, which vary with age, physiological state, and environmental conditions, ruminants select their diets from a diverse array of plant species that vary in kinds and concentrations of nutrients and toxins. Thus, ruminants possess a degree of nutritional wisdom in the sense that they generally select foods that meet nutritional needs and avoid foods that cause toxicosis (Provenza, 1995). 14

Nutritional well-being, toxicosis, and nutritional deficiencies are directly related to survival and reproduction in ruminants (Provenza, 1995). Although herbivores are often surrounded by many food types that are relatively easy to locate and consume, each food type is likely to have different nutrient properties, making diet choice an important consideration (Yearsley et al., 2001). Food intake is therefore complicated by factors such as gathering sufficient nutrients and avoiding toxins (Yearsley et al.,

2001).

For herbivores, the value of a plant is mostly a function of available protein, and in large desert ungulates protein is a probable limiting factor. The division of feed into cell contents and cell walls is important because it affects the availability of protein, which is a major component of the animal’s body. Also, cell wall limits intake.

Dietary protein consumption fluctuates depending on dietary protein quality.

Availability of protein is affected by several factors, such as secondary compounds that include tannins- the most ancient, widespread and successful generalized defensive plant compound known to bind dietary and animal proteins (Robbins,

1983). Some of the smaller, hydrolysable tannins can be absorbed by vertebrates that ingest them and thereby produce extensive physiological damage (Robbins, 1983).

Substances that physically impede digestive enzymes or microorganism, such as lignin, cutin-suberin, and biogenic silica also affect protein availability. Restricted by these factors, crude protein may become limiting sooner than water (Spalton, 1999).

2.5 Methods for monitoring nutritional quality

Various methods, each having advantages and disadvantages, can be used to determine food composition of animal feed. o Esophagus fistula:

In this method, a silicon tube is inserted into the animal’s esophagus and feed samples that were gathered by the animal are collected, providing the actual food gathered by the animal, sampled directly from the gut. There is no need for simulation or estimation of the food intake. However, this method is invasive and might harm the animal's welfare and grazing activity. It also requires direct contact with the animal, and it is difficult to determine if the collected sample represents all feed intake.

Accordingly, many researchers consider this method unreliable (Coates et al., 1987;

Jones and Lascano, 1992; Clements et al., 1996). o Direct observations:

This method is based on direct observation of animals that graze freely, recording all types and parts of vegetation that is consumed. The advantage is that a direct estimation of the animal's preferences is achieved, as well as a real-time behavioral pattern with no invasive or expensive equipment. However, it is difficult to determine the amount of food eaten from each plant and, in the case of wild animals, the minimal distance from which they can be observed prohibits plant identification

(Nastis and Meuret, 1987). o Micro-histology analysis:

This method is based on the identification of indigestible plant parts in the feces under a microscope and on the fact that the components of the vegetative plant cell, especially the epidermis, are unique to the species level. 16

The structure of the epidermis is compared to plant samples and in this way plant samples can be identified (Rentfleish and Hansen, 2000). It is non-invasive and can be applied to wild animals, but results are qualitative because of differences in digestion of different plants (e.g., some plants are digested in full and leave no mark for analysis). Also, this method is time consuming. o Alkanes method:

Alkanes are chemical compounds that consist only of the elements carbon (C) and hydrogen (H). They are found in the plant cuticle (Chinbal et al., 1934). In most leaves there is a layer of wax of different thickness in different plant parts. Alkanes are easy to analyze, and are unique for each species. The advantage is the alkanes' low digestibility and that they can be extracted from the feces after digestion. However, sample preparation for analysis is costly. Another disadvantage is that secretion of alkanes in feces changes with age and physiological state. This method also requires the use of hazardous materials for extraction.

2.6 Near Infrared Reflectance Spectroscopy (NIRS):

Fecal analysis using the Near Infrared Reflectance Spectroscopy (NIRS) technique allows one to locate chemical bonds in organic matter through near infrared absorption. The NIRS method is based on the interaction between electromagnetic radiation and the analyzed material, which absorbs the radiation. The energy absorption increases molecule vibration. The energy absorption can be measured and specific matter concentration determined by the Beer-Lambert law. This determines that there is a constant ratio between energy absorption to a certain material (Naes, et. al. 2002). 17

Absorption in the near infrared is caused by 3 mechanisms:

• Repeated fluctuations of basic absorption in the (MIR) Mid Infrared

range of 2500-10000nm, known as overtones. These fluctuations are

(approximately) multiplications of basic absorption (a) which create a

series of absorptions known as first overtone (2a), second overtone

(3a), and third overtone (4a).

• Combinations of the basic absorption in the NIR range. These

combinations are mainly a joining of overtones (Murray and Williams,

1987); therefore, there is a very wide range of possible combinations.

This fact has a central impact on the NIR range:

1. Absorption appears in unexpected areas in the NIR

range.

2. The area of absorption appears as wide peaks that are

caused by the overlapping of a large number of different

absorptions (Naes et al., 2002).

• Electronic absorption is caused by the passage of electrons from one

orbital to another at a higher energy level. Normally this absorption

will be noticed in the ultraviolet radiation range, but sometimes it will

also be noticed in the NIR range, mainly in the range of 780-1100nm.

The chemical information is gathered from the rate of change in the curve of the

spectral graph. The progression in wave length forms the spectral graph after the

scan (Murray and Williams, 1987), and therefore differentiation of the spectral 18

graph in the first or second derivative can solve overlaps that will look like

“shoulders” on the original graph. Differentiation can also decrease disturbances

in diagnosing the spectral data caused by differences in particle size between

samples (Shenk and Westerhaus, 1994).

Since this is an indirect method, several stages are needed to build calibration

equations that allow the NIRS machine to connect the spectral data of a specific

material to its chemical components.

The stages are:

• Collecting samples that represent the target population.

• Chemical analysis of the samples for reference.

• Scanning the samples in NIRS in a range of 1100-2498nm.

• Treating the spectral graphs to eliminate effects of wetness, particle

size and light scattering that might distort the desired spectra.

• Building a calibration equation by the calibration model.

• Validation of the calibration equation with samples that were analyzed

chemically.

After this, the desired samples can be scanned to obtain data on the specific

component in the NIR range.

The main advantages of this method are that after building a calibration equation for a certain component, any sample can be tested and results received in a few seconds with no need for chemical compounds or an extremely trained operator, and the 19 sample is not ruined so this analysis can be done later. In addition, the cost of scanning is very low.

The disadvantages are first, that it is indirect and as such, the error range can be large

(therefore, the process of building a calibration equation needs to be precise).

Secondly, the NIRS machine and the building of the first database for the equation is very expensive.

The NIRS method is used in many areas, such as farming, chemistry, the food industry, and the medicine industry. In the area of animal research it is widely used in the study of ruminant feed. Many studies have shown that the NIRS has great potential in predicting animal feed and pasture quality (Landau et al., 2002; Meuret et al., 1993; Coleman et al., 1999). Non-chemical variables, such as digestibility and feed intake, can also be tested in NIRS.

Fecal NIRS

Nunez-Hernandez et al. (1992) demonstrated the importance of the chemical composition of feces in understanding nitrogen and energy status in goats. The “fecal

NIRS” technology, first applied in goats by Leite and Stuth (1995), is aimed at predicting the dietary composition of nutrients or botanical composition (Walker et al., 2002; Landau et al., 2004). This method was developed to predict the concentration of protein and digested organic matter in cattle feed in pasture (Lyons and Stuth, 1992). Fecal NIRS depends on multivariate calibration of the relationship between dietary composition and the spectra of feces scanned with a near infrared spectrometer followed by a validation process. 20

Today this method is used to predict protein feed, dry matter digestibility in a feed,

ADF, NDF, etc. An advantage of this method is that the animal has already eaten the examined sample; therefore, the results express the interaction between the different feed components.

A separate Fecal NIRS calibration needs to be established for each animal species or type (e.g. cattle, goats, antelope, etc.). Lyons and Stuth (1992) observed that the fecal calibration equation developed for cattle could not work properly on goats, since goats apparently have different fecal biochemistry from that of cattle.

A Fecal NIRS database is made up of pairs of fecal spectra and nutrient analysis

(reference values) of the diets consumed by the animals. Thus, the value of NIRS calibrations is highly dependent on the quality of the dietary information entered into the database (Landau et al., 2005). In captive animals, samples of individual animal diets and residues are easily obtained, making it easier to determine which nutrients were ingested (Landau et al., 2004). In contrast, the assessment of diets consumed by animals in the wild is complex because the animals’ diet can be affected by many variables such as season, rain, and geographic location.

2.7 Research Subject and Hypothesis

The purpose of this research was to assess and compare the nutritional status of the three reintroduced Arabian oryx populations using fecal analysis and Near Infrared

(NIR) techniques in order to determine if nutritional constraints can explain differences in population performance between the Ein Shachak and Paran 21 populations. This is the first attempt to use fecal NIRS analysis to evaluate nutritional status of Arabian oryx. Fecal NIRS was chosen as the analytical method for two main reasons:

o It is very difficult to locate the oryx in the wild as each population

ranges over a vast area typified by rough terrain, not always accessible

by vehicle. In addition, only the Paran and Kezev populations had

active radio collars, so location of the Arava population was done by

search.

o To minimize interference with the wild population, a method which

does not require direct handling of the animals was needed. Fecal

NIRS requires only the collection of fresh feces, with no need for

actual contact with the animals.

Hypothesis:

The difference in birth and juvenile survival rate between the Ein Shachak and Paran populations is nutritionally based and this will be reflected in fecal chemistry and fecal NIRS diet predictions.

Predictions:

Nutritional indices in the Paran area will be lower than in Ein Shachak and Nahal

Kezev.

(A) Fecal analysis of the Paran population, relative to Shachak and Kezev, will show:

1. higher concentrations of fiber content

2. higher tannin concentrations, and

3. lower protein concentrations. 22

(B) Plant nutritional value is expected to be lower during summer time, but there will be an interaction with area, with a lesser decline in the Ein Shachak and Kezev populations.

3. Materials and methods

The nutritional status of the oryx in the different areas was compared using fecal analysis. Specifically used Near-Infra-Red Spectroscopy (NIRS) was used to assess the nutritional status of the Paran, Ein Shachak (henceforth Arava) and Nahal Kezev populations. Two approaches, both based on near infrared spectrometry of feces, were used. One was based on direct fecal analysis - specifically, feces samples were collected from the three areas of reintroduction and analyzed for fiber content, tannins concentrations, and nitrogen concentration, based on known NIRS spectra for these constituents. The second approach was based on the elucidation of diets according to fecal spectra analysis (indirect, or fecal NIRS) (Glasser 2004). A calibration experiment using captive oryx was carried out in order to provide data for calibration.

3.1 Calibration experiment

To build a calibration model for Fecal NIRS, fecal samples were collected under controlled conditions. The experiment took place at Hai-Bar Yotvata, with 5 different individual oryx from the captive herd there. The experiment and procedure were based on Glasser (2004).

The experiment was carried out in two sessions. The first session included three oryx and the second, two. The oryx were moved from the free roaming area of the Hi-Bar in Yotvata, to smaller enclosures in the Hi-Bar that were prepared in advance. All 23 experimental animals were males, with the assumption that digestion is similar for males and females. The enclosures were 4x4 m with a packed earth floor, and included a shaded area. Each enclosure had a bucket for water.

Figure 3.1 The oryx enclosure, during cleaning.

In each session of the experiment there were 10 cycles of 14 days each, for a total of

140 days. In each cycle the oryx received feed with a different composition with known chemical structure. Feed samples were taken only in the last three days of each cycle to ensure that the feces came from that specific diet.

In order to establish dietary chemical composition, the following feed samples were taken for analysis:

• Hay sampling (3 types of hay – lucerne, oats, and vetch): 10 samples of hay

were taken from different locations in the haystack, and from different

haystacks. The samples were dried and in order to establish dry matter

percentage the sum of all hay samples was averaged for each type.

• Pellet concentrate sampling: Commercial feed pellets for herbivores were

used. Since the pellet is homogeneous, two samples of pellets were taken. The

samples were dried and dry matter percentage was calculated.

• Fruit sampling: Acacia tortilis fruits were collected in a random way. Ten

random samples of fruit were taken, the samples were dried, and in order to

establish dry matter percentage the sum of all fruit samples was averaged.

Leftovers gathered at the end of each session were subjected to the same

analyses.

The following analyses were done on these samples:

A. Dry matter: each sample was dried in an oven at 60C0 for 72 hours and weighed.

B. Cell fiber content:

Content of acid detergent fiber (ADF) and NDF was measured using an ANCOM fiber analyzer as determined by Goering & Van-soset (1970).

C. Crude protein:

Half-automatic Kjehldal system with Tecator machine model 1030 Kjeltec Analyzer

(Sweden) was used.

D. Feed digestion: 25

To establish what should be the feed digestion the artificial belly system was used according to Tilley and Terry (1963).

E. Tannins:

To establish Peg-binding tannins, NIRS method with an already calibrated equation was used (Landau et al, 2004) 26

Table 3.1 The feed types and chemical components CT %PEG- (condensed binding TP (total Sample Dry mat. % Ash% NDF% ADF% ADL% Protein% Dig. % tannins) -tannins polyphenols Lucerne 89.74 11.07 62.48 37.04 3.84 8.94 57.7 1.31 2.85 1.57 Oats 89.62 8.43 63.04 35.97 3.12 7.75 59.19 2.62 3.06 4.10 Vetch 89.31 10.43 53.82 38.35 7.26 16.1 56.91 3.54 3.23 3.97 Acacia 90.76 20.64 45.92 28.28 6.14 12.34 56.91 9.66 7.42 11.62 Pellet 89.73 6.77 47.32 10.96 2.16 20.04 84.45 0 0 2.14 27

The oryx were separated, and fed during the morning. On day 11 the enclosure was cleaned of any food leftovers and on days 12, 13, and 14, feces (for later analysis – see below) and leftovers were collected and the enclosure cleaned before the feed of that day was served.

At the end of each cycle all leftover feeds were dried at 60C0 for 72 hours, and weighed. Subtracting the amount of leftovers from the amount served, gave the amount of food actually eaten. Collection of feces from the floor was done each morning on days 12, 13, and 14. Since each morning the enclosure was cleaned, only fresh feces were collected. Leftover feed was collected manually from the ground.

The following tables summarize the experimental cycle:

Table 3.2 Experimental Cycle day 1 day 2 day 3 day 4 day 5 day 6 day 7 serving serving serving serving serving serving serving experiment experiment experiment experiment experiment experiment experiment dose dose dose dose dose dose dose day 8 day 9 day 10 day 11 day 12 day13 day 14 serving serving serving serving serving serving serving experiment experiment experiment experiment experiment experiment experiment dose dose dose dose dose dose dose and and and and cleaning the collecting collecting collecting enclosure residue of residue of residue of food food and food and feces feces

Table 3.4 Feeding cycle for three oryx Cycle N=3 .1. 23,24/01/06 .2. 5,6,7/02/06 .3. 20,21,22/02/06 food served pellet fruits* cereal lucerne pellet fruits* cereal lucerne pellet fruits* cereal food composition % 10% 10% 50% 40% 10% 10% 30% 60% 10% 10% 40% food composition kg 0.25 0.25 1.25 1 0.25 0.25 0.75 1.5 0.25 0.25 1 Cycle .4. 7,8/03/06 .5. 22,23,24/03/06 .6. 6,7,8/04/06 food served pellet fruits* cereal lucerne pellet fruits* cereal lucerne pellet fruits* cereal food composition % 0% 0% 20% 80% 0% 0% 100% 0% 0% 0% 0% food composition kg 0 0 0.5 2 0 0 2.5 0 0 0 0 Cycle .7. 22,23/04/06 .8. 6,7/05/06 .9. 24,25/05/06 food served pellet fruits* cereal lucerne pellet fruits* cereal Vetch pellet fruits* cereal food composition % 0% 0% 80% 20% 10% 10% 10% 80% 10% 10% 60% food composition kg 0 0 2 0.5 0.25 0.25 0.25 2 0.25 0.25 1.5 Cycle .10. 5,6/06/06 food served pellet fruits* cereal Vetch food composition % 20% 20% 30% 50% food composition kg 0.5 0.5 0.75 1.25 * one oryx will receive instead of pellet, acacia fruits

Table 3.3 Feeding cycle for two oryx Cycle N=2 .1. 18,19/06/06 .2. 1,2/07/06 .3. 18,19/07/06 food served pellet fruits* cereal pellet fruits* cereal Vetch pellet fruits* oats food composition % 0% 0% 100% 40% 40% 0% 60% 0% 0% 100% food composition kg 0 0 0 0 0 0 0 0 0 2.5 Cycle .4. 7/31/2006 .5. 13,14/08/06 .6. 26,27/08/06 food served pellet fruits* oats pellet fruits* oats pellet fruits* oats food composition % 20% 20% 80% 20% 20% 80% 10% 10% 90% food composition kg 0 0 0 0 0 0 0 0 0 Cycle .7. 8,9/09/06 .8. 21,22/09/06 .9. 4,5/10/06 food served pellet fruits* oats pellet fruits* oats pellet fruits* oats food composition % 10% 10% 90% 0% 0% 100% 10% 10% 90% food composition kg 0 0 0 0 0 0 0 0 0 Cycle .10. 17,18/10/06 food served pellet fruits* oats food composition % 10% 10% 90% food composition kg 0 0 0 * one oryx will receive instead of pellet, acacia fruits 30

Calculation of chemical composition of experimental diets:

In order to get the data necessary for fecal NIRS calibration equations, the chemical composition of the experimental diets needed to be calculated. The equations below describe how chemical component intake was calculated for each diet (one diet per animal per cycle). Since the amount of feed each oryx received and the chemical components of each feed type were known (see Table 3.1 above) it was possible to look at each feed type separately and calculate its dry matter in each cycle by multiplying each feed type in each cycle with its dry matter % for that cycle. Adding these results together gave the total dry matter served, and subtracting the dry matter leftover gave the total intake of dry matter (Tintake) (Eq.1). Then, to calculate each component's intake, each feed type was multiplied by the percentage of each component (determined in the laboratory) (Eq.2). The total intake of each chemical component was calculated by adding the total chemical component amounts served for each feed type and subtracting the total leftover chemical components (Eq.3).

Then, percentage of each chemical component was calculated by dividing total chemical component intake by total intake (Eq.4).

Acronyms and equations used to calculate chemical composition of experimental diets:

FT = Feed type mass in each diet (e.g. pellet, fruit, cereal, lucerne, vetch)

DM = Dry matter %DM = Percentage of dry matter of each feed type, determined in

planning of each diet

CComp = Chemical component (e.g. tannin, ADF, etc.) 31

TS = Total dry matter served TL = Total dry matter leftover (dry matter

and chemical composition calculated in the lab)

%CCompFT = % of each component in each feed type (determined in the laboratory)

Eq. 1: Dry matter intake

FT* %DM = DM (for each feed type)

Total Feed served = ΣDM(FT1 + FT2 + FT3+FT4+FT5) = TS

TS – TL = TIntake

Eq. 2: Chemical component in each feed type

FT * %CCompFT = CComp (for each feed type)

Eq. 3: Total intake of each chemical component

ΣCComp(FT1 + FT2 + FT3+FT4+FT5) = TCComp Served

TCComp Served – TCComp Leftover (determined in laboratory)

= TCCompIntake

Eq. 4: Percentage of each chemical component in each diet

TCCompIntake / TIntake = % CComp in each diet

3.2 Fieldwork

Because the reintroduced populations inhabit military firing zones, feces samples from the field were collected only on weekends (the time the firing zones are open to non-military use). Telemetry was used to locate the oryx, in order to reach them and 32 collect fresh feces. Three weekends per month were devoted to collect field samples: two weekends in the Paran and Nahal Kezev area and one weekend in Ein Shachak area, since it was more difficult to locate the oryx in Paran. Oryx feces was easily recognizable due to its size and shape, and only fresh samples were collected after sighting the oryx. Samples were collected throughout the year, in order test for seasonal patterns. A total of 456 individual fecal samples were collected over the study period (Table 3.5). The samples were dried in an oven at 60Co for 72 hours and ground to pass through a 1mm sieve. Before scanning, the ground samples were dried for one hour at 60C0 to eliminate any remains of humidity. The samples were than left to equilibrate in a dissector at ambient temperature and scanned.

Table 3.5 Amount of feces samples collected in the different regions by date Ein Shachak Nahal (Arava) Kezev Paran samples date samples date samples date 19 2/12/2005 1 11/20/2004 4 12/18/2004 17 2/26/2005 2 12/4/2004 5 1/1/2005 17 3/12/2005 3 12/17/2004 4 1/15/2005 7 4/16/2005 1 12/31/2004 4 2/5/2005 20 6/11/2005 6 2/19/2005 8 3/5/2005 14 7/22/2005 19 3/4/2005 7 4/6/2005 16 8/27/2005 6 3/25/2005 2 5/7/2005 12 10/1/2005 13 4/9/2005 14 6/17/2005 16 10/29/2005 5 4/15/2005 17 6/24/2005 11 12/2/2005 15 5/7/2005 12 7/9/2005 9 1/20/2006 9 6/18/2005 3 8/12/2005 158 TOTAL 16 8/13/2005 5 8/19/2005 6 8/20/2005 7 9/19/2005 6 9/3/2005 4 10/14/2005 11 9/17/2005 5 11/11/2005 8 10/15/2005 2 11/18/2005 7 11/12/2005 6 12/24/2005 9 11/19/2005 1 2/10/2006 9 12/17/2005 110 TOTAL 8 12/23/2005 12 2/4/2006 10 2/11/2006 6 3/25/2006 188 TOTAL 33

3.3 Fecal NIRS work procedure

This procedure had 6 steps:

1. Fecal samples from the Hai-bar were dried in an oven at 60 Co for 72 hours and ground to pass a 1mm sieve. Before scanning, the ground samples were dried for one hour at 60 C0 to eliminate any remains of humidity. The samples were then left to equilibrate in a dissector at ambient temperature and scanned.

2. Feces and feed samples were scanned in a Foss NIRSystem5000 (Tecator,

Hoganas, Sweden) spectrophotometer.

3. A calibration equation was formulated: A calibration equation is an empirically detected statistical connection between absorption values and concentrations of the content substance in the calibration samples. The calibration refers to a regression equation in which the value of reflectance (1/R) in the near infrared is the independent variable used to predict the value of the tested component. To build a calibration model I used the ISI program (Infrasoft International, 1999), with which the connection between the NIR reflectance and the reference data was measured.

NIRS is an indirect method, and therefore needs to be calibrated. This means building a regression equation in which the value of the near infrared spectral absorption is the independent variable used to predict the dependent variable (Walker, 2002).

The ISI program is provided with the spectral and reference data, and then the samples are checked (for spectral differences or irregularities) and the optional statistical adjustment for each equation is tested (derivatives and different spectral treatments). 34

Modified Partial Least Square (MPLS) that give best estimates for this data were used

(Shenk & Westerhaus, 1991). This method is not perfect since the entire spectra are used with their many independent variables (wave length), and there is an abundance of irrelevant data that might even repeat itself. This is an outcome of overtones and combinations of different absorptions (see section 2.6). Also since there are more independent than dependent variables it might cause co-linearity (dependence between the X variables). The use of standard normal variant (SNV) as part of the spectral treatment helps to solve some of this problem (Barnes et al., 1989).

To overcome these difficulties, MPLS was used to reduce the amount of data that enters the model into the most explained variables (based on data from all of the spectral information).

4. Wet chemistry procedures:

Chemical analysis of feces is necessary to provide further validation for the NIRS process. A chemical analysis of feces was done at the Van Hall Institute (Belgium) by

M. Hofman, using 20 samples from each region collected between January and

February 2005. And the samples were chemically analyzed for crude protein, Ash,

ADF, NDF and Ca. The results for crude protein, ADF, and NDF were used in the validation process (see below).

5. Validation:

Validation refers to a process that checks the ability of the calibration equation to predict the real value of a sample that does not belong to the group of calibration samples (Walker, 2002). Validation is done by predicting samples in which the predicted element is known from an analysis done in a parallel standard method. In 35 this case, chemical analysis of oryx feces done by M. Hofman was used, as described above.

The cross validation method was developed to handle cases in which the sample number is small. In this method, samples are divided to groups for prediction, which means scanning the sample through the NIRS machine using the fecal spectra given by the calibration. Each group is predicted once by calibration that is done on the rest of the samples, then the sample is returned to its calibration group and another sample is taken out for prediction.

Indicators of calibration quality:

In order to compare several equations and to determine their ability to predict unknown samples there are several statistical indexes with which we can test the equation:

• Standard error of calibration-SEC: Calculates the variance in differences

between predicted values and the values found, the equation is based on the

samples for calibration.

• Standard error of prediction-SEP: tells the variance in differences between

predicted values and the values found, the equation is applied on a group of

unknown samples (not from the calibration samples). This value expresses an

estimation of error in units that apply to the units in which our tested

component is measured.

• Standard error of validation-SEV: the variance of difference between

predicted values and actual values, when the equation is applied on a group of

samples for validation (samples that are not part of the calibration samples). 36

• Standard error of cross-validation-SECV: the variance of difference between

predicted values and actual values, when the equation is applied on a sub

group of the samples for validation (samples that are not part of the calibration

samples).

• Coefficient of determination (R2): the relative part of the variance between the

reference data that is explained by the calibration equation.

• Bias: average difference between predicted values and actual ones. This is

presented as 1-VR, the correlation between laboratory data and cross-

validation results.

For this validation, SEC, SECV, RSQ and 1-VR were used.

6. Nutritional assessment of Field animals:

After determining the calibration equation for each component, the feces samples from the field were scanned in order to receive data on diet quality. The same procedure used for scanning the feces from the Hai-Bar was used on the feces from the field (steps 1 and 2, described above).

Preliminary treatment of spectral graphs:

In order to diffuse any effects caused by humidity, particle size and light scattering that might disorder the spectral image, Standard Normal Variant (SNV) and De-trend combined with first or second derivative was used.

In SNV method each spectra is centered to the zero on Y by the equation: Xik=(Xik–

Mi)/Si

While Xik represents the spectral measurement in wave length K of sample i, Mi represents the spectral measurement average foe sample i, Si represents the standard deviation for the measured K (Naes et al. 2002). 37

The use of De-trend is common and acceptable among studies in the field (Dryden,

2003). The derivative makes it easy to see absorption peaks that are hard to notice in the untreated spectra and to fix affects of particle size (Deaville & Flinn 2000).

Figures 3.2-3.4 below show feces spectra with no mathematical treatment, 1st derivative after De-trend, and 2nd derivative after De-trend.

Figure 3.2 NIRS Spectra with no mathematical treatment

Figure 3.3 1st derivative spectra after De-trend

Figure 3.4 2nd derivative spectra after De-trend

Statistical analysis of results:

The data for both fecal chemistry (NIRS) and Fecal NIRS for each region were analyzed using the ISI program (Infrasoft International, 1999). SAS was used to analyze variance in the predicted values (PROC-GLM – SAS Inc.). Results were given in Least Squares Means with standard deviation for all constituents (ADF,

NDF, ADL, dry matter, organic matter, condensed tannins, polyphenols, and crude protein). The fecal NIRS diet predictions included acacia fruits in addition to the constituents listed above.

Results were also analyzed for seasonal variation, dividing the data into samples from wet and dry seasons. The dependence between variation of each constituent and season or region as well as the combined effect of season and region on constituent value were analyzed using ANOVA (PROC GLM - SAS Inc.)

4. Results

4.1 Hai-Bar fecal NIRS calibrations

The Hai-Bar experiment successfully provided fecal NIR spectra. Data from the experiment were used to build calibration equations for the NIRS machine.

Calibration equations for 19 nutritional constituents were calculated and validated using the Infrasoft International (ISI) program. Most of the RSQ values approached 1

(Table 4.1), meaning the equations were reliable and could be used for fecal NIRS for oryx. The best-predicted constituents were: ASHi, ADLi, CPi, Ash%, NDF%, ADL%,

Protein%, Fruit%, Pellet%, Hay%, Polyphenols, condensed tannins (CT) and PEG- binding tannins. Some of the constituents were not predicted well: NDFI, ADFI,

IVDIGI (digestion), IVDIG%.

Table 4.1 Calibration performance for 19 nutritional constituents for Fecal NIRS for Arabian oryx. (see above for calibration performance indicators)

Constituent N Mean SD SEC RSQ SECV deriv 1-VR DMI 40 2.15 0.13 0.07 0.69 0.09 2 0.58 ASHI (g/day) 42 0.23 0.03 0.01 0.87 0.02 1 0.75 NDFI (g/day) 41 1.45 0.11 0.06 0.66 0.07 2 0.58 ADFI (g/day) 41 0.83 0.08 0.04 0.73 0.06 1 0.44 ADLI (g/day) 42 0.09 0.02 0.01 0.95 0.01 2 0.85 CPI (g/day) 43 0.22 0.05 0.01 0.93 0.02 2 0.83 IVDIGI (g/day) 42 1.43 0.09 0.06 0.54 0.08 2 0.33 Ash% 43 0.11 0.01 0.00 0.96 0.00 2 0.92 NDF% 44 0.68 0.03 0.01 0.92 0.01 2 0.86 ADF% 43 0.39 0.02 0.01 0.90 0.01 2 0.62 ADL% 42 0.04 0.01 0.00 0.94 0.00 2 0.82 Protein% 43 0.11 0.03 0.01 0.94 0.01 2 0.87 IVDIG% 43 0.68 0.03 0.02 0.51 0.03 1 0.37 fruit% 42 0.03 0.06 0.01 0.96 0.02 2 0.91 pellet% 43 0.04 0.07 0.03 0.84 0.04 1 0.71 hay% 43 0.92 0.09 0.03 0.91 0.04 2 0.77 POLYPH (g/day) 43 4.15 1.23 0.19 0.98 0.28 1 0.95 CT (g/day) 42 2.81 0.88 0.16 0.97 0.22 2 0.94 TANRAD (g/day) 42 3.38 0.43 0.18 0.82 0.22 2 0.73

4.2 Fecal NIRS (indirect) analysis of diet quality for wild oryx populations

Predictions of nutritional components using fecal NIR spectra determined in the calibration experiment. Results were given in Least Squares Means with standard deviation for all constituent, significance was determined by looking at the P-value for each constituent, with a value of P<0.05 (Sample size: Arava N=165, Kezev

N=84, Paran N= 110) indicating significance. Significant differences between the three regions were found in percent dry matter of crude protein, polyphenols, crude proteins and acacia fruits (Figures 4.1- 4.7).

Crude Protein%

16.0 15.0 14.0 13.0 Crude Protein% 12.0 11.0 10.0 9.0 8.0 ARAVA KEZEV PARAN

Figure 4.1 Fecal NIRS prediction of crude protein dietary content

Acid Detergent Fiber%

36.0 34.0 32.0 30.0 ADF% 28.0 26.0 24.0 22.0 20.0 ARAVA KEZEV PARAN

Figure 4.2 Fecal NIRS prediction of ADF dietary content

Neutral Detergent Fiber%

60.0

55.0

50.0

NDF% 45.0

40.0

35.0

30.0 ARAVA KEZEV PARAN

Figure 4.3 Fecal NIRS prediction of NDF dietary content

Acid detergent lignin%

6.5

6.0

5.5

5.0 ADL% 4.5

4.0

3.5 3.0 ARAVA KEZEV PARAN

Figure 4.4 Fecal NIRS prediction of ADL dietary content

Polyphenols%

7.0 6.5 6.0 5.5 Polyphenols %(LS 5.0 Mean) 4.5 4.0 3.5 3.0 ARAVA KEZEV PARAN

Figure 4.5 Fecal NIRS prediction of polyphenols dietary content 43

Condensed Tannins%

6.0 5.5 5.0 4.5 Condensed Tannins % 4.0 3.5 3.0 2.5 2.0 ARAVA KEZEV PARAN

Figure 4.6 Fecal NIRS prediction of condensed tannins dietary content

Acacia Fruit%

45.0

40.0

35.0

30.0 Acacia Fruit % 25.0

20.0

15.0

10.0 ARAVA KEZEV PARAN

Figure 4.7 Fecal NIRS prediction of acacia fruit dietary content

Significant differences were found in Fecal NIRS predictions for several constituents.

Crude protein predictions ranged from 12-15%, which is high for ungulate diets. CP was significantly lower in the Arava than Paran by 1.7% (P<0.05) and Kezev by 2.8% 44

(P<0.05) (Figure 4.1). ADF predictions were above 30% in all regions (Figure 4.2), and ADF was 1.8% higher in Kezev than in Paran (P<0.05) and 1.6% higher in Kezev than in Arava (P<0.05). NDF predictions were slightly above 50%, but differences between the regions were not significant (Figure 4.3). The Arava region was lower than Kezev in ADL by 1.2% (P<0.05). Polyphenols were significantly higher in the

Arava (P<0.05) than Paran and Kezev, while the differences between Paran and

Kezev were not significant. Condensed tannins in the Arava were higher than in Paran by 1.6% (P<0.05) and higher than in Kezev by 2.3% (P<0.05). Acacia fruits in the

Arava were over 40% of the dry matter vs. The Paran (~20%) and the Kezev ~17%

(P<0.05).

4.3 Regional and Seasonal difference in fecal chemistry

Regional and seasonal differences were also determined using NIRS analysis of feces samples (e.g. crude protein, ADF, condensed tannins, etc.). Results were given in

Least Squares Means with standard deviation for all constituent , significance was determined by the P-value for each constituent, with a value of P<0.05 (Sample size:

Arava; wet season N=88, dry season=78, Kezev; wet season N=56, dry season N=28,

Paran; wet season N=45, dry season N=65) indicating significance. For most constituents, seasonal differences were small but consistent in all three regions

(Figures 4.8 - 4.14). Table 4.2 shows an overview of regional differences year-round.

Crude Protein

18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0 dry wet dry wet dry wet

ARAVA KEZEV PARAN

Figure 4.8 Comparison of seasonal variations in fecal crude protein content in different regions

NDF

80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 dry wet dry wet dry wet

ARAVA KEZEV PARAN

Figure 4.9 Comparison of seasonal variations in fecal NDF in different regions

AD F

50.00 45.00 40.00 35.00 30.00 25.00 20.00 dry wet dry wet dry wet

ARAVA KEZEV PARAN

Figure 4.10 Comparison of seasonal variations in fecal ADF in different regions

ADL

19.0 17.0 15.0 13.0 11.0 9.0 dry wet dry wet dry wet

ARAVA KEZEV PARAN

Figure 4.11 Comparison of seasonal variations in fecal ADL in different regions

Polyphenols

10.0

8.0

6.0

4.0

2.0

0.0 dry wet dry wet dry wet -2.0 ARAVA KEZEV PARAN

Figure 4.12 Comparison of seasonal variations in fecal polyphenols in different regions

Condensed Tannins

8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 dry wet dry wet dry wet

ARAVA KEZEV PARAN

Figure 4.13 Comparison of seasonal variations in fecal condensed tannins in different regions

Fecal crude protein was higher in the dry season in all regions and was greater in the

Arava than in Paran by 2.8% (P<0.05) and greater than Kezev by 5.1% (P<0.05)

(Figure 4.8). Likewise, NDF was higher in the dry season (Figure 4.9). Comparison between regions (Table 4.2) shows lower NDF in the Arava than Paran by 9.2%

(P<0.05) and than Kezev by 12.3% (P<0.05). ADF was higher in the wet season 48

(Figure 4.10), and comparing between regions ADF was lower in the Arava than

Paran by 8.5% (P<0.05) and lower than Kezev by 14.3% (P<0.05). ADL was also higher in the wet season (Figure 4.11), and regional comparison shows lower ADL in the Arava than the Paran by 2.5% (P<0.05), and than Kezev by 4.9% (P<0.05). Total polyphenols (Figure 4.12) and condensed tannins (Figure 4.13) were higher in the dry season. Regional comparison of polyphenols and condensed tannins shows they were higher in the Arava (P<0.05), polyphenols were much lower in Kezev (Table 4.2).

Table 4.2: Comparison between fecal constituent percentages in each region

Hai- Constituent ARAVA bar KEZEV PARAN Organic Matter 78.3 85.9 79.7 81.2 Crude Protein 15.2 7.2 10.1 12.4 Neutral Detergent Fiber 56.3 63.4 68.6 65.5 Acid Detergent Fiber 29.7 39.7 43.9 38.1 Acid Detergent Lignin 12.9 12.6 17.7 15.4 Total polyphenols 6.8 1.6 1.8 4.4 Condensed Tannins 5.4 3.8 3.5 4.0

All constituents were affected by region and season (separately), and some constituents (crude protein, dry matter, organic matter, and ADF) experienced the combined effect of region and season, indicating that in some regions, seasonal differences were greater. Seasonal differences seemed to be greater in the Arava, except with regards to crude protein and tannins.

Table 4.3 Effects of region, season and combined region/season on each constituent

Constituent Main effect Pr>F Constituent Main effect Pr>F CP site <.0001 ADL Site <.0001 season <.0001 season <.0001 Site*Season <.0001 Site*Season NS Ash site 0.02 OM site <.0001 season <.0001 season <.0001 Site*Season 0.01 Site*Season 0.04 NDF Site <.0001 PolyPh site <.0001 season <.0001 season <.0001 Site*Season NS Site*Season NS ADF site <.0001 CT site <.0001 season <.0001 season <.0001 Site*Season 0.00 Site*Season NS

5. Discussion

5.1 Seasonal variations in nutritional constituents

The results of the NIRS of fecal chemistry and fecal NIRS analysis of nutritional quality by fecal NIRS did not support the hypothesis of higher seasonal variation for nutritional components in Paran in comparison to the Arava. All constituents were affected by season in all regions (Figures 4.8-4.13). Some constituents (crude protein and ADF) experienced the combined effect of region and season, indicating that in some regions the seasonal differences were greater than others. Seasonal differences were greater in the Arava, which might be due to the different vegetation in this area.

The Arava is in the Sudanese penetration biogeographic zone, while Paran is in the

Saharo-Arabian biogeographic zone, and Kezev transitions between these two biogeographic zones. In the Sudanese Penetration, the Acacia tortliis trees behave in the Israeli dry season as if it were the African wet season, bearing their fruit in the summer (Shmida and Aronson, 1986). This means that the oryx in this region have a supply of acacia fruits year-round, since Acacia radiana bears fruit in the winter. The

Kezev region nutritional constituents have similarities to both Paran and Arava, depending on season. In summer, constituents appear more similar to those in the

Arava, while in winter they are more similar to those of Paran (see Figures 4.8-4.13).

Field observations placed the Kezev herd in the eastern part of the Kezev region in summer, which is the Sudanese Penetration biogeographic zone in the Arava. In winter they move west to the area of Har Znifim, which is in the Saharo-Arabian biogeographic zone on the Negev Plateau. This may indicate that the oryx migrate between regions where more Acacia fruits can be found. 51

In all regions, tannins and polyphenols varied somewhat more between seasons than the other constituents, but the difference was less pronounced in the Arava. This may be due to the year-round presence of more polyphenol-rich forage (such as acacia fruits, which have 11.2% total polyphenols, see Table 3.1) in the Arava. Field observations indicate there are more acacia trees in the Arava, which produce more fruit than those in the other regions. In addition, of the three Acacia species in the

Negev, A. pachyceras (A. negevensis) develops at the highest elevations (Saharo-

Arabian), while A. raddiana and A. tortilis prevail in the Arava (Sudanese

Penetration) (Halevy & Orshan 1972). The differential fruiting patterns of A. raddiana and A. tortilis were discussed above. These trees seem to provide the oryx in the Arava with a more constant supply of polyphenol-rich forage. Indeed, the fecal

NIRS predictions show a much higher percentage of acacia fruits in the Arava diet compared to the other two regions (Figure 4.7).

5.2 Nutritional differences among regions

In contrast to my prediction that poorer nutrition in Paran was leading to low recruitment compared to the Ein Shachak population, it appears that the oryx consume good quality diets in all three regions. The NIRS fecal chemistry analysis (Figure 4.8) showed high crude protein percentage. However, high fecal protein in feces is ambiguous because some crude protein is been bonded to tannins and is not necessarily available to animals. The fecal NIRS predictions for crude protein content also showed high percentages ranging from 12-14.5% (Figure 4.1).

The values using the fecal NIRS were higher in Kezev and Paran than in the Arava, but fecal chemistry showed higher fecal protein in the Arava. The difference in the 52

Arava may be explained by the abundance of tannins in the Arava diet. Tannins are phenols with the ability to bind proteins. Some of the proteins in the Arava diet may be bound in complexes with tannins, leading to higher fecal crude protein in the

Arava.

While the minimum level of dietary crude protein necessary for Arabian oryx to meet maintenance needs is not known, approximately 8 % has been reported for domestic ungulates (Spalton, 1998). In a study of food supply of Arabian oryx in Oman,

Spalton (1998) suggests that in the Arabian Oryx Sanctuary, oryx should be able to obtain a diet of at least 5% crude protein from local grass species, even in severe drought. Thus, crude protein intake among the oryx populations in Israel seems to be several times greater than the minimum necessary for survival for this species and is not a limiting factor on reproductive success. The oryx seem to be very selective in their choice of vegetation and will apparently roam great distances in order to find the more nutritious plants.

Although crude protein was high for all three regions, there were some relevant differences in other nutritional components. NDF, ADF, and ADL were higher in

Paran and Kezev than in the Arava, indicating that the oryx in these regions eat more woody forage. Both the NIRS and Fecal NIRS predictions showed higher percentages of condensed tannins and polyphenols in the Arava, most likely due to a higher proportion of acacia fruits in the diet, as indicated by the Fecal NIRS prediction of

41% acacia fruits in this region. High polyphenols in feces must come from high dietary polyphenols. The high protein diet and relatively low NDF and ADF

(compared with goats see Ramirez et. al. 1991) throughout the year, show that oryx 53 consume young grass rather than dead, dry grass. In order to do this, they must roam long distances.

5.3 Relationship between nutrition and recruitment in different regions

As discussed above, crude protein levels were high in all three areas, discounting the hypothesis that inadequate protein in Paran was causing low recruitment.

Nevertheless, the Paran population does suffer from a very low recruitment rate. If this difference is to be attributed to nutritional differences, one significant difference between the regions stands out: the high proportion of acacia fruits in the diet of the

Ein Shachak population. It may be that this difference is meaningful and gives this population an advantage somehow. Specifically, tannins, which are found in high concentrations in Acacia tortillis, (Table 5.1) and are concentrated in the seed pods

(fruits) may be the important factor.

Table 5.1 Phenolic and CT content of Acacia tortillis (Wrangham and Waterman, 1981) Condensed Plant part Phenolics tannins mature leaf 5.3% 5.4% immature leaf 2.4% 3.5% seeds 1% 1% seed pods (- seed) 3.4% 6% whole flowers 2.3% 0.77% bark 4% 2.8% gum 38-56% 28-71%

Tannins have been shown to have several potentially beneficial effects on nutrition and overall health in ungulates. Indirectly, condensed tannins can improve protein nutrition by binding to plant proteins in the rumen and preventing microbial 54 degradation, thereby increasing amino acid flow to the duodenum (Min and Hart,

2003).

Several studies have also shown that tannins can be used as an inhibitor of parasites.

Paolini et. al. (2003) tested condensed tannins on goats infected with the nematode

Haemonchus contortus, and found that the tannins reduced fecal egg count and reproduction of the nematode. Min and Hart (2003) concluded that condensed tannins in forages could markedly decrease the viability of the larval stages of several nematodes in sheep and goats and have the potential to aid in the control of gastrointestinal parasites. Molan et. al., (2000) tested the effects of CT on larval migration of the nematode Trichostrongylus colubiriformis and found that CT could play a role in ruminant diets to reduce dependence on proprietary antihelminthics.

Parasites have been shown to adversely affect reproductive success among wild ungulates. Intestinal parasites affect the fecundity of reindeer (Rangifer tarandus plathyrynchus) – anthelminthic treatment in breeding season increased the probability of a reindeer having a calf in the next year, compared with untreated controls (Albon et. al., 2002). The effect of parasites on wild ungulate population dynamics was extensively explored by Gunn and Irvine (2003). They argue that subclinical infection with parasites affects individual reproductive success through reductions in forage intake, which, in turn, reduces body condition. The study presents accumulated evidence which indicates that hosts have evolved adaptive strategies, especially in foraging behavior, to minimize their exposure to parasites; for example, reducing the risk of parasitism by avoiding areas of high fecal contamination. Appetite reduction, dung aversion, and parasite intensities reducing fecundity have been demonstrated experimentally in a wild ungulate (Gunn and Irvine, 2003). So, it may be that the high 55 concentration of tannins found in acacia fruits gives the Ein Shachak population a natural defense against certain parasites, which might otherwise harm recruitment. At the same time, Gidron (2005) shows that oryx herds converge into larger groups in the

Paran region during summer, since they are forced into the wadi beds to find food.

This closer contact may increase their risk of contracting parasites. In order to examine this potential relationship, a survey of parasite prevalence among the different oryx herds should be carried out, which could be easily done through fecal analysis.

There may even be a relationship of mutualism between Acacia tortillis and Oryx leucoryx. If it is true that oryx populations in the wild depend upon tannins from acacia fruits then there is a possibility of a relationship between acacia and Arabian oryx in which the acacia protects the oryx from parasites that might otherwise decrease oryx recruitment, and the wide-ranging oryx help disperse acacia seeds over greater distances. One half of this relationship has already been investigated and established in Israel.

Rohner and Ward (1999) concluded that large mammalian herbivores are essential components of arid Acacia savannas and that wild and domestic ungulates must be included in future conservation plans. Direct observations confirmed that ungulates were the main seed dispersers of Acacia species and seed germination was facilitated by gut passage through ungulates (Rohner and Ward, 1999). Field observations in this study seemed to confirm this, as acacia seeds were easily observable in oryx feces, mainly in the Arava population. The interaction between acacia consumption and parasite control in large ungulates has not yet been researched, to my knowledge. In 56 addition the extent of acacia seed dispersal by Arabian oryx has not been specifically researched. Both of these questions could be very interesting and useful directions for future research of Arabian oryx.

6. Conclusions

1. Seasonal comparison of nutritional constituents in the Kezev region show a resemblance to the Arava region in the summer and Paran region in the winter. This complies with field observations of the Kezev herd moving in summer to the eastern part of the Kezev region (in the Sudanese penetration biogeographic zone) and in winter to the western part of the Kezev region (in the Saharo-Arabian biogeographic zone).

2. Polyphenols and condensed tannins varied less seasonally in the Arava than in the other regions, but the interaction was not statistically significant (Table 4.3).

Nevertheless, the difference may still be significant nutritionally, and may indicate more year-round availability of these compounds in the Arava, supported by much higher percentages of Acacia tortillis fruits in the diet predictions of the Ein Shachak population.

3. High crude protein levels in all three regions indicated by both NIRS analysis of fecal chemistry and Fecal NIRS predictions disproved the hypothesis that inadequate nitrogen in Paran is the cause of low recruitment there, since all regions showed high fecal protein indicating that dietary protein or dietary energy were high or that tannins bonded to dietary protein, as seen in the Arava.

4. High concentrations of tannins in the Arava diets may be responsible for higher reproductive success in the Ein Shachak population, related to control of parasites.

Tannins have been proven to be effective in controlling nematode infections in 57 ungulates. Parasites have been shown to adversely affect fecundity in wild ungulates, and this may be the actual cause of low recruitment in the Paran population. A survey of parasite prevalence in the three populations would be a useful next step to check this hypothesis.

7. References

Albon, S. D., Stien, A., Irvine, R. J., Langvatn, R., Ropstad, E. and Halvorsen, O. (2002). The role of parasites in the dynamics of a reindeer population. Proceedings of the Royal Society of London 269: 1625-1632.

Bar-David, S., D. Saltz, and T. Dayan (2005). Predicting the spatial dynamics of reintroduced populations – The Persian fallow deer. Ecological Applications 15: 1833–1846.

Barnes R., M. Dhanoa, S. Lister (1989). Standard normal variate transformation and detrending of near-infrared diffuse reflectance spectra. Appl. Spectroscopy 43: 772- 777.

Calderwood, J. “Arabian oryx released to wild after 40 years of extinction in UAE desert.” Associated Press, 26 March 2007.

Cardenas, M. and R. Valdez (1992). Evaluation of fecal indicators for assessing energy and nitrogen status of cattle and goats. J. Range Management 45: 143–147.

Chinbal, A.CV., Pipere, S.H., Pollard, A. Williams, E.F. and Sahai, P.M., (1934). The constitution of the primary alcohols, fatty acids and paraffins resent in plant and insect waxes. Biochemistry.J., 28: 2189-2108.

Clements, R.J., Jones, R.M., Valdes, L.R. and Bunch, G.A. (1996). Selection of Chamaecrista rotundifolia by cattle. Tropical Grasslands 30: 389–394.

Coates, D. B., P. Schachenmann, and R. J. Jones. 1987. Reliability of extrusa samples collected from steers fistulated at the oesophagus to estimate the diet of resident animals in grazing experiments. Australian J. Experimental Agriculture 27: 739-745.

Coleman, S.W., H. Lippke and M. Gill (1999). Estimating the nutritive potential of forages. In: H.J.G. Jung and G.C. Fahey Jr., Editors, Nutritional Ecology of 59

Herbivores, Proc. 5th Intl. Symp. Nutr. Herb., San Antonio, Texas Am. Soc. Anim. Sci. (Savoy, IL) 647–694.

Colman, S.W. Lippke, H. and Gill, M., (1999). Estimating the Nutritive Potential of Forages. The Fifth International Symposium On The Nutrition Of Herbivores April 11-16, 1999, San Antonio, Texas.

Deaville, E.R. and P.C. Flinn (2000). Near-infrared (NIR) spectroscopy: an alternative approach for the estimation of forage quality and voluntary intake. In: D.I. Givens, E. Owen, R.F.E. Axford and H.M. Omed, Editors, Forage Evaluation in Ruminant Nutrition, CAB International, Wallingford, UK, 301–321.

Dryden, G. McL., (2003). Near Infrared Reflectance Spectroscopy: applications in deer nutrition. A report for the Rural Industries Research and Development Corporation. www.ridc.gov.au/reports/DEE/w03-007.pdf. Environment News Service (2007). Oman’s oryx sanctuary off world heritage list: 22 sites inscribed. July 2, 2007 (http://www.ens-newswire.com/ens/jul2007/2007-07-02- 01.asp)

Fernández, N., S. Kramer-Schadt, and H. Thulke (2006). Viability and risk assessment in species restoration: planning reintroductions for the wild boar, a potential disease reservoir. Ecology and Society 11(1): 6.

Foley, W., A. McIlwee, I. Lawler, L. Aragones, A. P. Woolnough, N. Berding (1998). Ecological applications of near infrared reflectance spectroscopy a tool for rapid, cost- effective prediction of the composition and animal tissues and aspects of animal performance. Oecologia 116: 293-305.

Gaillard, J. M., M. Festa-Bianchet, N. G. Yoccoz, A. Loison, and C. Toõgo. (2000). Temporal variation in fitness components and population dynamics of large herbivores. Annual Review of Ecology and Systematics 31: 367-369.

Gidron, A. (2005). Habitat quality and group size dynamics in a social ungulate exhibiting fission-fusion social behavior: The reintroduced Arabian Oryx (Oryx 60 leucoryx) in Israel. M.Sc Thesis, Ben-Gurion University of the Negev, Jacob Blaustein Institute for Desert Research, Albert Katz International School for Desert Studies.

Glasser, T. (2004). Qualitative and Quantitative Monitoring of Grazing-Goats' Diet by Faecal NIRS Analysis. M.Sc Thesis. Faculty of Agricultural, Food and Environmental Quality Science. Hebrew University of Jerusalem.

Goering, H. K., and P. J. Van Soest (1970). Forage fiber analyses (apparatus, reagents, procedures, and some applications). U.S. Department of Agriculture Agricultural Handbook no. 379, U.S. Department of Agriculture, Washington, D.C.

Gunn, A. and R. J. Irvine (2003). Subclinical parasitism and ruminant foraging strategies—a review. Wildlife Society Bulletin 31(1): 117–126.

Halevy, G. & Orshan, G. 1972. Ecological studies on Acacia species in the Negev and Sinai. I. Distribution of Acacia raddiana, A. tortilis and A. gerrarddii ssp. negevensis as related to environmental factors. Isr. J. Bot. 22: 120-138.

Hirzel, A., B. Posse, P. Oggier, Y. Crettenand, C. Glenz, R. Arlettaz (2004). Ecological requirements of reintroduced species and the implications for release policy: the case of the bearded vulture. Journal of Applied Ecology 41: 1103–1116

IUCN. 1998. IUCN Guidelines for Re-introductions. Prepared by the IUCN/SSC Reintroduction Specialist Group. IUCN, Gland, Switzerland and Cambridge, United Kingdom. Available from IUCN Publications Services Unit or www.iucn.org/themes/ssc/pubs/policy

Jones, R. J. and E. Lascano (1992). Oesophageal fistulated cattle can give unreliable estimates of the proportion of legume in the diets of resident animals grazing tropical pastures. Grass and Forage Science 47 (2): 128–132.

Kleiman, D.G., Stanley Price, M.R. & Beck, B.B. (1994). Criteria for reintroductions. In Creative Conservation: Interactive Management of Wild and Captive Animals (eds P.J.S. Onley, G.M. Mace & A.T.C. Feistner), 287–303.

Chapman and Kramer-Schadt, S., E. Revilla, T. Wiegand (2005). Lynx reintroductions in fragmented landscapes of Germany: Projects with a future or misunderstood wildlife conservation? Biological Conservation 125: 169–182.

Landau, S., Nitzan, R., Barkai, D. and Dvash, L., (2002). Monitoring the intake of pasture and concentrates in young ostriches by using fecal NIRS spectra. Xth European Intake Workshop, Reykjavik, Iceland.

Landau, S. Glasser T. Dvash, L., Perevolotzsky, A. (2004). Fecal NIRS to monitor the diet of Mediterranean goats. S. African. J. Animal Sciences 34 (5): 76-80.

Landau,S., L. Dvash,, M. Decandia, A. Cabiddu, F. Shapiro, G. Molle, N. Silanikove, (2004). Determination of Poly(ethylene glycol)-binding to Browse Foliage, as an Assay of Tannin, by Near-Infrared Reflectance Spectroscopy. J. Agricultural Food Chemistry 52: 638-642.

Landau, S., T. Glasser, H. Muklada, L. Dvash,., A. Perevolotsky, E.D. Ungar, J.W. Walker,. (2005). Fecal NIRS prediction of dietary protein percentage and in vitro dry matter digestibility in diets ingested by goats in Mediterranean scrubland. Small Ruminant Research 59: 251-263.

Landau, S., R. Nitzan, D. Barkai, and L. Dvash (2006). Excretal Near Infrared Reflectance Spectrometry to monitor the nutrient content of diets of grazing young ostriches (Struthio camelus). S. African J. of Animal Science 36 (4): 248-256.

Leite E.R. and J.W. Stuth (1995). Fecal NIRS equations to assess diet quality of free ranging goats. Small Ruminant Research 15, 223–230. 62

Lyons, R.K. and J.W. Stuth (1992). Fecal NIRS equations for predicting diet quality of free-ranging cattle. J.Range Management 45: 238-244.

Maoz, N. (2003). The impact of repeated release on the space use patterns and social organization of Arabian Oryx (Oryx leucoryx) reintroduced in the central Negev, Israel. M.Sc. thesis, Ben-Gurion University of the Negev, Faculty of Natural Sciences, Department of Life Sciences.

Meuret, M., Dardenne, P., Biston, R. and Poty, O., (1993). The use of NIR in predicting nutritive value of Mediterranean tree and shrub foliage. J. Near Infrared Spectroscopy 1: 45–54.

Min, B., and S. Hart (2003). Tannins for suppression of internal parasites. J. of Animal Science, 81 (E. Supplement 2): E102-E109.

Molan, A.L., G.C. Waghorn, B.R. Min and W.C. McNabb (2000). The effect of condensed tannins from seven herbages on Trichostrongylus colubriformis larval migration in vitro. Folia Parasitologica 47: 39–44.

Murray, I. and Williams, P.C., (1987) Chemical principles of near infrared technology. In Near Infrared Technology In Agriculture And Food Industries. Eds: PC Williams and K.H. Norris, 17. American Association of Cereal Chemistry Inc. St. Paul. MN.

Naes, T. T. Isaksson, T. Fearn and T. Davies (2002). A User-friendly Guide to Multivariate Calibration and Classification, NIR Publications, Chichester, UK.

Nastis, A.S and Meuret, M., (1987). Methods for estimating the nutritive value of range forage and intake by goats in the Mediterranean area. In: Agriculture: Programme de recherché Agrimed, 142-158. Noy-Meir, I. (1973). Desert ecosystems: environment and producers. Annual Review of Ecology and Systematics. Hall, London, UK.

Owen-Smith, N. 1990. Demography of a large herbivore, the greater Kudu Tragelaphus strepsiceros, in relation to rainfall. J. of Animal Ecology 59: 893-913.

Paolini, V., J.P. Bergeaud, C. Grisez, F. Prevot, Ph. Dorchies, H. Hoste (2003). Effects of condensed tannins on goats experimentally infected with Haemonchus contortus. Veterinary Parasitology 113: 253–261.

Provenza, F. D. (1995). Postingestive feedback as an elementary determinant of food preference and intake in ruminants. Journal of Management 48(1): 2-17.

Ramirez, R.G., A. Loyo, R. Mora, E. M. Sanchez and A. Chaire (1991). Forage intake and nutrition of range goats in a shrubland in northeastern Mexico. J. of Animal Science 69 (3): 879-885.

Rentfleish,J. & Hansen H,. (2000). Microhistology in the ENRECA project. Department Of Animal Science & Animal Health, The Royal Veterinary & Agricultural University, Denmark.

Robbins, C. T (1983). Wildlife Feeding and Nutrition. Academic Press, INC. Harcourt Brace. Jovanovich, Publishers. Florida. USA.

Rohner, C. and Ward, D. (1999). Large Mammalian Herbivores and the Conservation of Arid Acacia Stands in the Middle East Conservation Biology 13 (5): 1162–1171.

Saltz, D. (1998). A long-term systematic approach to planning reintroductions: the Persian fallow deer and the Arabian Oryx in Israel. Animal Conservation 1: 245-252.

Sarrazin, F. and R. Barbault (1996). Reintroduction: challenges and lessons for basic ecology. TREE 11 (2): 474-478.

Seddon, P., K. Ismail, M. Shobrak, S. Ostrowski, C. Magin (2003). A comparison of derived population estimate, mark-resighting and distance sampling methods to determine the population size of a desert ungulate, the Arabian oryx. Oryx 37: 286.

Shenk, J. S., and M. O. Westerhaus (1994). The application of near infrared reflectance spectroscopy (NIRS) to forage analysis. In Forage Quality Evaluation and Utilization, 406-449. American Society Agronomy, Madison, Wl.

Shenk, J.S., and M.O. Westerhaus (1991). Population definition, sample selection and calibration procedures for near infrared reflectance spectroscopy. Crop Science 31: 469–474.

Shmida, A. and J. A. Aronson (1986). Sudanian elements in the flora of Israel. Annals of the Missouri Botanical Garden 73 (1): 1-28.

Spalton, J. A. (1999). The food supply of Arabian oryx (Oryx leucoryx) in the desert of Oman. J. Zoology (1999) 248: 433-441.

Spalton, J.A. et al., 1999. Arabian oryx reintroduction in Oman: successes and setbacks. Oryx 33: 168.

Stanley-Price, M., (1989). Animal re-introductions: the Arabian Oryx in Oman. Cambridge Univ. Press, Cambridge, U.K.

Stephenson, T. (2003). Physiological ecology of moose: nutritional requirements for reproduction with respect to body threshold conditions. Alaska Department of Fish and Game. Federal aid in wildlife restoration research final performance report, 1 July 1997-30 June 2003. Grants W-23-4 to W-33-1, project 7.16 Juneau, Alaska. Streicher, U.; Nadler, T., (2003). Re-introduction of pygmy lorises in Vietnam. Reintroduction News (Newsletter of the IUCN Reintroduction Specialist Group) 23: 37-40.

Tilley, J. M. A. & Terry, R. A. (1963). A two stage technique for the in vitro digestion of forage crops. Journal of the British Grasslands Society 18: 104-111.

V. Sanz and A.Grajal (1998). Successful reintroduction of captive-raised Yellow- Shoulderd Amazon Parrots on Margarita Island, Venezuela. Conservation Biology 12 (2): 430-441. 65

Walker, J.W., S.D. McCoy, K.L. Launchbaugh, M.J. Fraker and J. Powell (2002). Calibrating fecal NIRS equations for predicting botanical composition of diets. J. Range Management 55: 374–382.

Wrangham R. and P. Waterman (1981). Feeding behaviour of vervet monkeys on Acacia tortilis and Acacia xanthophloea: with special reference to reproductive strategies and tannin production. J Animal Ecology 50: 715-739.

Yearsley, J., B. J. Tolkamp and A. W. Illus. (2001). Theoretical developments in the study and prediction of food intake. Proceeding of the Nutrition Society 60: 145-156.