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Tracing the origin of migratory pests using geochemical fingerprinting : application to European starling… K C, Upama 2017

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  TRACING THE ORIGIN OF MIGRATORY PESTS USING GEOCHEMICAL FINGERPRINTING: APPLICATION TO EUROPEAN STARLING IN THE OKANAGAN VALLEY OF BRITISH COLUMBIA, CANADA by Upama K C M.Sc., Kathmandu University, 2013 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF   MASTER OF SCIENCES in THE COLLEGE OF GRADUATE STUDIES   (Environmental Sciences)  THE UNIVERSITY OF BRITISH COLUMBIA  (Okanagan)  November 2017  © Upama K C, 2017ii  The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled:   TRACING THE ORIGIN OF MIGRATORY PESTS USING GEOCHEMICAL FINGERPRINTING: APPLICATION TO EUROPEAN STARLING IN THE OKANAGAN VALLEY OF BRITISH COLUMBIA, CANADA submitted by Upama K C in partial fulfillment of the requirements of the degree of Master of Science.   Dr. Jefferson Curtis, Irving K. Barber School of Arts and Sciences  Supervisor   Dr. Ian Walker, Irving K. Barber School of Arts and Sciences  Supervisory Committee Member   Dr. John Woods, Irving K. Barber School of Arts and Sciences  Supervisory Committee Member     University Examiner     External Examiner       iii  Abstract The European starling (EUST) (Sturnidae: Sturnus vulgaris L.) is an invasive bird in North America where it is an agricultural pest. In British Columbia (Canada), the EUST population increases in orchards and vineyards in autumn, coinciding with ripening fruits. Starlings also create damage in dairy farms and feedlots by eating and contaminating food, and spreading diseases. Damage can be partly mitigated by the use of scare deterrents. However, scare techniques mainly serve to divert flocks until they become acclimated. Large-scale trapping and euthanizing before they move to fields and farms is the most practical means of preventing damage, but requires knowledge of natal origin.  Within a small (20,831 km2), agriculturally significant portion of south-central British Columbia, the Okanagan Valley, I employed analyses of 21 trace elements in bone tissue to discriminate the spatial distribution of juvenile EUST and to reveal the geographic origin of the problem birds. Stepwise discriminant analysis of trace elements classified juveniles from 64-79 % accurately to their natal origin, including sites that are 12 km apart. The elemental fingerprint of juveniles collected in the same area was different in two consecutive years (2015 and 2016). In both years, the majority of problem birds (55% in 2015 and 79 % in 2016) caught in vineyards and orchards were derived from the North Okanagan. In contrast, 89% of problem birds caught at dairy farms and feedlots were not from Okanagan and 11% were local in 2015; 100% of problem birds were local in 2016. It is unlikely that starlings from outside the region were misidentified as Okanagan Valley starlings because the geochemical fingerprints of those outside of the valley are very distinct. Thus, elemental signatures can separate populations with a high degree of spatial accuracy within several 10s of km, yielding a promising tool for identifying the geographic origin of migratory birds even over small geographic scales. These findings suggested that further control of starlings in vineyards and orchards, be targeted to the northern and southern regions of the valley; control in dairy farms and feedlots will require an expansion of the trapping program outside the region.    iv  Preface  The work was conducted in accordance with the ethics training requirements of the Canadian Council on Animal Care (CCAC)/ National Institute Animal User Training (NIAUT) program certificate number 7252-15.                             v  Table of Contents Abstract ......................................................................................................................................... iii Preface ........................................................................................................................................... iv Table of Contents .......................................................................................................................... v List of Tables .............................................................................................................................. viii List of Figures ............................................................................................................................... ix Acknowledgements ....................................................................................................................... x Dedication .................................................................................................................................... xii 1 Introduction ................................................................................................................................ 1 1.1 Research Questions ................................................................................................................. 3 1.2 Hypothesis ................................................................................................................................ 3 1.3 Literature Review ................................................................................................................... 4 1.3.1 Starling Background .......................................................................................................... 4 1.3.2 Vectors Disease .................................................................................................................. 7 1.3.3 Starlings as Agricultural Pests ........................................................................................... 7 1.3.4 Population Trend of European Starlings in the Okanagan Valley ..................................... 8 1.3.5 Tracing Origin and Movement Pattern .............................................................................. 9 1.3.6 Isotope vs Trace Element ................................................................................................. 11 1.3.7 Trace Elements as Natal Origin Markers ......................................................................... 12 2 Study Area ................................................................................................................................ 14 2.1 Sampling Sites ....................................................................................................................... 14 3 Materials and Methods ............................................................................................................ 19 3.1 Sampling ................................................................................................................................ 19 3.2 Geochemical Analysis ........................................................................................................... 21 3.2.1 Sample Preparation .......................................................................................................... 21 3.2.2 Instrumental Analysis ...................................................................................................... 22 3.3 Statistical Analysis of Data ................................................................................................... 23 vi  3.3.1 Pre-treatment of Data ....................................................................................................... 23 3.3.2 Trace Element Multivariate Analysis .............................................................................. 25 4 Results ....................................................................................................................................... 28 4.1 Spatial Separation of Juvenile (source population) Birds in the Okanagan Valley ....... 28 4.1.1 Elemental Composition of Juveniles Within and Among locations ................................ 29 4.1.2 Grouping Juvenile Birds from Different Sites in the Okanagan Valley .......................... 29 4.2 Differences among Fingerprints with respect to Farming Activities ............................... 40 4.3 Temporal Analysis of Biogeochemical fingerprinting ....................................................... 43 4.3.1 Temporal Variation in Elemental Fingerprinting of Juvenile Birds between years ........ 43 4.3.2 Temporal Variation in Elemental Fingerprinting of Juvenile and Adult Birds within Years ......................................................................................................................................... 46 4.4 Identifying the Origin of Problem Birds in Fall ................................................................. 46 4.4.1 Identification of Likely Immigrant Birds ......................................................................... 46 4.4.2 Identification of the Source Population of Problem Birds in the Okanagan Valley ........ 50 4.5 Fingerprinting between Male Vs Female ............................................................................ 61 5 Discussion.................................................................................................................................. 63 5.1 Spatial Separation of Starlings ............................................................................................ 63 5.2 Temporal Variation in the Elemental Fingerprints ........................................................... 67 5.3 Assigning Problem Starlings to Natal Populations ............................................................ 69 5.4 Discussion for Management ................................................................................................. 70 5.4.1 Contribution to Problem Birds in Vineyard and Orchards .............................................. 71 5.4.2 Contribution of Problem birds on Dairy Farms and Feedlots .......................................... 71 5.5 Variation in Age and Gender ............................................................................................... 73 6 Application, Management and Future Directions ................................................................ 74 6.1 Implications for Management .............................................................................................. 74 6.2 Directions for Future Work in the Okanagan .................................................................... 75 6.3 Other Applications ................................................................................................................ 76 6.4 Challenges and limitations ................................................................................................... 76 7 Conclusion ................................................................................................................................ 79 vii  References .................................................................................................................................... 80 Appendices ................................................................................................................................... 94 Appendix A Juvenile and adult European starling (Sturnus vulgaris) .................................. 94 Appendix B Descriptive statistics of juvenile European starlings in 2015 ............................ 95 Appendix C Z-scored data of trace elements of Juvenile birds in 2015 ................................. 96 Appendix D Z-scored data of trace elements of Juvenile birds in 2016 ............................... 102 Appendix E Grouping of individual juvenile birds in cluster analysis in 2015 and 2016 .. 113 Appendix F GPS point of sampling sites in 2015 and 2016 ................................................... 114                     viii   List of Tables   Table 1 Number of samples (juvenile and adult) analysed in 2015 and 2016……………. 21 Table 2 Internal standards and concentration used in ICP-MS and ICP-OES …………… 23 Table 3 Classification results for juveniles to its predicted locations in 2015 and 2016…. 32 Table 4 Classification results for juveniles collected in relation to different farming…….                     activities………………………………………………………………………….. 42 Table 5 Identification of the origin of adult birds in the Okanagan Valley in 2015………. 53 Table 6 Identification of the origin of adult birds in the Okanagan Valley in 2016………. 54 Table 7 The origin of problem birds in Fall in the different farming activities………….... 60 Table 8 Multivariate comparison of elemental fingerprints of male and female starling….      62                              ix  List of Figures  Figure 1 The population trend of European starling in the Okanagan Valley …………… 9 Figure 2 Map of study area with sampling sites …………………………………………. 15 Figure 3 Map of major bedrock types in the northern and central Okanagan Valley……. 16 Figure 4 Map of major bedrock types in the southern Okanagan Valley ………………... 17 Figure 5 Distribution of juvenile birds caught in different locations in 2015..................... 30 Figure 6 Distribution of juvenile birds caught in different locations in 2016 …………… 31 Figure 7 Cluster analysis of juvenile birds in 2015 ……………………………………… 35 Figure 8 Cluster analysis of juvenile birds in 2015 with AU value……………………… 36 Figure 9 Cluster analysis of juvenile birds in 2016………………………………………. 38 Figure 10 Cluster analysis of juvenile birds in 2016 with AU value ……………………. 39 Figure 11 Distribution of juvenile samples derived from different farming    activities……………………………………………………………………….. 41 Figure 12 Cluster analysis of juvenile birds collected in 2015 and 2016………………… 44 Figure 13 Cluster analysis of juvenile birds 2015 and 2016 with AU value …………….. 45 Figure 14 Cluster analysis of juvenile and adult birds in 2015 ………………………….. 48 Figure 15 Cluster analysis of juvenile and adult birds in 2016 …………………………. 49 Figure 16 The source population sites that contribute to vineyard/orchards    in 2015………………………………………………………………………... 56 Figure 17 The source population sites that contribute to vineyard/orchards    in 2016…………………………………………………………………………. 57 Figure 18 The source population sites that contribute to dairy farms/feedlots                   in 2015…………………………………………………………………………. 58 Figure 19 The source population sites that contribute to dairy farms/feedlots                   in 2016…………………………………………………………………………. 59   x  Acknowledgements I would like to express my deepest gratitude and appreciation to my thesis adviser, Dr. Jeff Curtis, for trusting me and taking me as a graduate student; without his support and guidance, this research would not have been possible. I would like to thank Dr. Ian Walker for his valuable suggestions; prompt support and responses to graduate program related matters and also for sharing his wonderful pictures of starlings for my research presentations. I would also like to thank Dr. John Woods for his thoughtful suggestions and queries related to starlings. His suggestions helped in identifying solutions to various research-related puzzles. I would like to thank Mr. David Arkinstall for his patience, responding to my queries and providing help in the trace lab. I would like to thank Ms. Connie Beibert for her guidance in the earlier phases of the project. She created a friendly environment in the team and that made it easy for me to work with the starling control program team. Thanks also to Ms. Louise Corbeil and Ms.Tyrion Miskell for facilitation with the starling control program, and for the opportunities to present my research progress to the funding agencies and the team. I would also like to extend my gratitude to the starling control program team, especially Mr. Chris Blythe, Mr. Don Marshall, Ms. Joanne Bray, and Mr. Jym Talman for supplying samples for this research. I would like to thank Dr. Jennifer Tedman-Jones for her help with the Mitacs application. Thanks to the Christmas Bird Count team for providing starling population data for British Columbia. Thanks are also due to the faculty and staff of the Department of Earth, Environmental and Geographic Sciences, especially Ms. Janet Heisler and Mr. Stuart MacKinnon for their support and guidance during my graduate education. Thanks to Ms. Ashleigh Duffy and Mr. Kevin Kuemper for their support during field trips. Thanks to both Mr. Anthony Friesen and Dr. Janice Brahney for their guidance in the lab. Thanks to Yi Wang for her friendship and thoughtful suggestions during our timely long discussion on statistics.  I would like to acknowledge British Columbia Grapegrowers’ Association and Mitacs for funding this project. Additionally, this project was partially funded through the Agriculture Environment Initiative with funds sourced from Agriculture and Agri-Food Canada and the British Columbia Ministry of Agriculture, and administered by the Investment Agriculture Foundation of British Columbia. Without their support, I wouldn’t have initiated this work. A very special thanks to my husband, Dinesh Adhikary, who believes in me and has always xi  encouraged me to complete this work. Last, but not the least, I would like to thank my family for their trust, support, and encouragement.                  xii  Dedication I would like to dedicate this work to my family and my thesis advisor.                               1  1 Introduction  European starling (Sturnus vulgaris L.) is one of the most successful non-native species in North America [1]. A few hundred birds were introduced in New York City in 1890 [2–4]. Surviving descendant birds spread rapidly throughout North America in the following years [2,5] and have now grown to a population of many millions. The perception of starlings changed along with population size from a beautiful, robin size ‘Chunky’ songbird into an aggressive and costly pest.  EUST is listed as one of the three worst invasive birds in the World Conservation Union List of  ‘100 of the world’s worst invasive alien species’ [6]. According to the International Union for Conservation of Nature, its conservation status, is considered to be of least concern.  It is considered as an agricultural pest [7], especially in fruit crops such as cherries, berries, and grapes [8]. It is omnivorous in nature, foraging on insects, fruits, bird feeders and even trash [5,9]. Furthermore, it is a very good vector of several diseases, such as avian salmonellosis, chlamydiosis, and  histoplasmosis which are transmittable to humans and livestock [10,11].   EUST causes a huge loss for agriculture and dairy farms. Annually around $800 million USD worth of agricultural crops were destroyed by EUST in the United States, based on crop losses of $5/ha [12]. It is estimated around 15-20 tons per day of cattle feed is consumed by starlings in livestock facilities [2]. According to conservative estimates, over $4 million per year is lost to vineyards and tree fruit farms in the Okanagan-Similkameen region alone  [7].   In order to control its population and subsequent damage, the Starling Control Program (SCP) was initiated in the Okanagan-Similkameen region in 2003. Agricultural industries, environmental agencies, and regional districts supported the program. The SCP traps birds in various locations in the Okanagan Valley throughout the year. From 2003 to 2013 around 544,000 birds were trapped by the control program [13]. Although the control program has been trying to reduce the starling population by aggressively 2  trapping all year round, numbers appear to be increasing, especially in the fall season due to migration and natal dispersal. It is necessary to find the origin of these migrant birds to enhance program effectiveness  Various types of eradication techniques such as falconry, noise deterrents (propane cannons), electronic distress calls, bird netting, and visual repellents have been applied but none of these techniques has proven effective [9]. This is possibly because the origin of these pest birds was unknown and none of the applied techniques addressed the problem at the source. Thus, understanding the natal origin and movement pattern of migratory pests, like starlings, is important to develop successful pest management [5,14].  Biogeochemical markers such as stable isotopes [15–18] and trace elements analyses [18,19] are the most suitable methods to trace the origin and migration of such birds because the markers are indicators of the environment of origin. Other techniques, such as satellite transmitters and radio transmitters, are suitable to track those organisms which can easily carry them [15].  These approaches are generally inappropriate for small organisms in large numbers [15]. Moreover, these techniques are expensive in terms of equipment and time in the field. Therefore, new technologies such as stable isotopes and trace element analysis could yield a suitable alternative method.   I chose trace elements for my study, though stable isotope analysis is a more common approach to trace organisms’ dispersion and movements. Starlings can disperse long distances and retain stable isotopes and trace element signatures in tissues reflecting natal food[15], location, and habitat [18]. Retention time of signatures in tissues varies from several days to years depending on the type of tissue. These chemical signatures can be applied as tools to reconstruct animal movement pathways [20]. Isotope ratios in birds can be homogeneous for thousands of kilometres [21]. In contrast, the province of British Columbia (BC) is geochemically diverse across small spatial scales [22],  which makes trace elements potentially much more sensitive for tracking regional starling movements 3  [14]. Furthermore, trace elements can provide a higher specificity than isotopes because of the larger number of potential parameters [18].   I used EUST leg bone tissue to fingerprint natal origins of individual starlings. Bone tissues are very useful for origin analysis as the mineral phase in the bone matrix grows rapidly when an individual is a juvenile and then turns over very slowly [14]. Thus, by identifying EUST origin, appropriate management to reduce EUST populations could be implemented in focused areas to reduce the damage created by the starling.   1.1 Research Questions My research questions are 1. Do the trace element fingerprints of bone tissue of EUST differ with space (distinct geological areas) and time?  2. What portion of adult birds captured in the fall are likely immigrants from outside the Okanagan Valley? 3. Is the starling population trapped in summer on feedlots and dairy farms different than those destroying fruit in the fall? 4. Does the elemental fingerprints of male and female starlings differ within the same geographic region?   1.2 Hypothesis 1. Ho: Elemental composition of starling bones is independent of geographic region. Ha: Elemental composition of starling bones depends on geographic region 2. Ho: There is no difference in elemental composition in bone tissue of starlings among years from the same area.  Ha: There is a difference in elemental composition in bone tissue of starlings among years from the same area.    4  3. Ho: Elemental composition of birds caught in dairy/feedlots is the same as that of EUST caught at nearby orchards/vineyards. Ha: Elemental composition of birds caught in dairy/feedlots is not the same as that of EUST caught at nearby orchards/vineyards. 4. Ho: Elemental composition is the same for male and female starlings from the same geographic regions. Ha: Elemental composition is different for male and female starlings from the same geographic regions.  1.3 Literature Review  1.3.1 Starling Background   1.3.1.1 History of Starling in North America European starlings are native to Europe, southwest Asia, and northern Africa. Several attempts were made to introduce the species in North America in the mid- to late-nineteenth century [2]. In 1890-91, a few pairs survived in New York City’s Central Park [2–4]. Surviving descendant birds spread rapidly westward [2,5] throughout North America. Starlings were first documented in British Columbia in 1945-46; the authenticated record was made by A. J. Braun who noted four of eight birds seen at Oliver in 1947 [23]. The first nest was found on 6 May 1952 at the Vernon Country Club by James Grant and it became a common breeding species in the open country and around human habitations in the short period between 1952 and 1956 [23]. Eventually, starlings successfully became established in southern British Columbia between 1952-1956 [23].  1.3.1.2 Identification The adult EUST is a robin-sized blackbird, with glossy black plumage, a long slender beak, pointed triangular wings and a short squared tail. Starling plumage colour differs with the season. In summer, the birds have a yellow bill and their plumage looks iridescent green and purple, especially around the head, neck, breast and back, with pale spots especially on the back. After an annual (pre-basic) moult, they look brown and the 5  white spots get larger and brighter and spread all over the body. These white spots wear away in spring to produce the glossy black appearance with small light spots. Juvenile birds are shaped like adults, but have a uniform dull greyish-brown colour and a black beak (Appendix A). Starlings can be easily distinguished from any other blackbirds (family Icteridae) by their yellow bill in spring. Generally, the native North American blackbirds have a long tail and rounded wings, which makes them distinct from starlings. Juvenile starlings have a long black bill and uniform greyish-brown plumage quite distinct from that of native juvenile blackbirds.   Some moulting characters provide information about age and sex in starlings. Generally, birds in the first year are rich in white spots. After the first pre-basic moult, when they retain light terminal spots, the geometric shape of the spots differs with the age of the birds. For instance, in the <1year bird, spots are heart-shaped while they are V-shaped with a long black line in the centre in adults (more than 1 year) [24]. Additionally, age can also be determined by skull ossification. This can be done by incising or by examining the skull through the transparent featherless skin behind the ear [24]. The area directly behind the orbit or the area over the posterior edge of the orbit is the last to ossify, generally around January [25], and even the youngest birds preserve cloacal bursae (of Fabricius) until December [25].   Male and female starlings can be distinguished by eye colour after about 6 weeks of age [26]. Males have a uniformly dark brown iris whereas the female has a lighter ring around the outer margin of the iris [25]. The colour of the ring in the eye can be yellow, cream whitish, greyish or light brown. The base of the bill (lower mandible) provides the difference between the sexes during the breeding season. The bluish (steel blue) base in males, and reddish (pale pink) base in females can be easily observed in spring when the bill is yellow in colour. However, around 3-7% of starlings have contradictory iris and bill colour [27]. Moreover, females usually have a slightly more spotted appearance than males.  6  1.3.1.3 Feeding and Foraging Behaviour Starlings are omnivorous, , foraging on insects, fruits, grain, etc. [9,28]. They also visit birdfeeders and trashcans, and often feed on grubs in lawns [1,29].  Starlings can remain solitary or in pairs during the breeding season. After the breeding season, they become gregarious, forming large flocks numbering to thousands, for foraging and roosting. Flocks are composed of juveniles and adults [30] and join other black birds in winter roosting [29].  1.3.1.4 Habitat, Migration, and Breeding Starlings are very adaptive and use a wide variety of habitats. They usually avoid, large undisturbed non-grassland habitats such as forest, chaparral, and deserts, etc. In contrast, they are the uninvited and notorious guests in urban (human settlement) areas, agriculture areas, grassland lands, or areas where food resources, nesting cavities, and water are plentiful.   They aggregate into large flocks (tens to hundreds of thousands in number) [31]. The migration pattern in European starlings varies regionally and individually. In some areas, most of the breeding individuals are sedentary [26,32]. Some individuals migrate in some years and not in others. Some juveniles migrate, but even their nest mates (siblings) might not [32].  Generally, spring migration occurs between mid-February to end of March and fall migration occurs from September to December  [32,33].   Starlings brood 1-2 times per year with a clutch size of 3-6 eggs per brood [5]. Around 90% of females brood twice in a year if they brood earlier in the season [32]. Hatching occurs usually after 11.5 to 12 days after incubation [34] and chicks fledge in 21- 22 days. Food fed to nestlings varies geographically, consisting mostly of soft-bodied invertebrates such as millipedes, crane fly larvae, orthopterans, lepidopteran larvae, and coleopterans [35–37].   7  1.3.2 Vectors Disease Starlings are a vector for several diseases [38] such as avian salmonellosis, chlamydiosis, and histoplasmosis [2,10]. These diseases can be transmitted to humans, poultry, and livestock. Chlamydophila psittaci  lives in the dried feces and may be inhaled causing chlamydiosis [2,11]. Moreover, cattle can be affected by Johne’s disease, known as paratuberculosis, through Mycobacterium avium Runyon. carried by starling [39].   Fecal matter of starlings can pass transmissible gastroenteritis (TGE) to swine. Though there is no direct evidence, it is believed that starlings were vectors for an outbreak of TGE in Nebraska during the winter of 1978-1979 which caused the loss of 10,000 swine in one month [2,40]. Histoplasmosis was reported in a manufacturing facility in Nebraska used by starlings [2]. Farmers working in the field, especially in blackbird and starling roosting sites, can be affected by histoplamosis. It is a common, sometimes serious, fungal disease [2,41,42]. The fungus Histoplasma capsulatum Darling can easily grow in soils under bird roosts. When inhaled its spores, can cause various illnesses, such as minor lung disease to tuberculosis. In extreme cases it can cause blindness or death [42]. Starlings can host several viruses, and its migratory nature aids in spreading disease across large distances [2].   1.3.3 Starlings as Agricultural Pests Attention to starlings is increasing because of its nuisance problems, especially agricultural damage, health hazards, and economic loss [43]. They are considered as an agricultural pest [14] to fruit crops  [43] such as grapes, olives, cherries, and grain. Starlings are considered pests for five main reasons.  First, they are very adaptive in nature and omnivorous in feeding habit (dietary generalist) [5] which makes them resilient and adaptive to extreme and diverse habitat conditions. Second, they brood several times in a year [36,37] with a 3-6 egg clutch size with 48-79% success [2]. Third, they lay eggs in other bird nests and intraspecific brood parasitism is common in starlings, which increases the success rate and also the number of chicks that fledge [44]. Fourth, natural predators, parasites and diseases are not sufficiently inhibitory to control the population. Finally, they migrate individually and in huge flocks for food and 8  survival. All these factors together make starlings very resilient, adaptive generalist birds. They maintain a population size of over 200 million in North America.   Moreover, starlings as a pest, cause economic, ecological and social problems. It causes a huge loss in agriculture and dairy farms. They fly in huge flocks and consume high value agricultural crops such as cherries, berries, grapes, apples [2]. It was estimated in 2000 that starlings destroyed more than $800 million USD worth of agricultural crops in North America [7]. According to conservative estimates, over $4 million per year is lost to vineyards and tree fruits in the Okanagan-Similkameen region [7]. They also create problems around livestock facilities, congregating at feeding troughs to eat and contaminating food and water sources in the process [43,45]. Additionally, starlings compete with native birds, especially with cavity nesting species such as woodpeckers, bluebirds, and flycatchers [2].  1.3.4 Population Trend of European Starlings in the Okanagan Valley  The population trend of starlings in the Okanagan Valley from 1974 to 2016 was analysed on the basis of number/party hours from Christmas bird count data. Based on the analysis, the population trend for starlings is increasing in the Okanagan Valley [46].   The population trend in Vernon (northern region) increases between 1996 to 2004 and then it gradually decreases till 2010 (Figure 1 A).  High inter-annual variance might be attributable to migratory birds in the area. Similarly, the population trend in Kelowna, is similarly variable, however, there is a huge population increase between 2002-2003, followed by a large decease 2003- 2004 (Figure 1 B). The observed decrease in the population after 2003 corresponds to the initiation of the starling control program.  Since 2003, large numbers of starlings have been trapped and euthanized [47] annually. In contrast, the population trend is increasing in both Penticton and Oliver-Osoyoos, southern region of the valley (Figure 1 C & D).  The reason for the increasing population in Penticton and Oliver-Osoyoos might be due to increased acreage of vineyards and cherry farms especially in the southern region of the valley.   9  A: Vernon  B: Kelowna             C: Penticton         D: Oliver-Osoyoos  Figure 1 The population trend of European starling in the Okanagan Valley. Number per partly hours of European starling in the different locations of the Okanagan Valley in last 42 years from 1974 to 2016. A: Vernon, B: Kelowna, C: Penticton and D: Oliver-Osoyoos.  Data Source: Christmas Bird Count, Bird studies Canada.   1.3.5 Tracing Origin and Movement Pattern  To understand animals’ ecology, it is necessary to know their movement patterns. For this, we need to track individuals or populations on a seasonal or annual basis. Tracking animals can be done either directly or indirectly. Direct tracing includes following individuals over time and space through remote-sensing techniques like radio transmitters, or individual tags such as leg rings and neck collars. The direct tracking methods require that an animal be captured and recaptured and is not feasible for use with 01020304050198619881990199219941996199820002002200420062010201220142016Number/Party HoursYear02040608010012014019861989199219951998200120042007201020132016Number/Party HoursYear02040608010019741978198219851988199119941997200020032006200920122015Number/Party HoursYear020406080100197919821985198819911995199820012005200820112014Number/Party HoursYear10  small animals and long distance travelers. Direct tracking of birds mostly relies on extrinsic markers. The most traditional extrinsic methods used in birds include leg bands [18], neck collars, patagial tags and plumage marking with dyes [15].  These techniques have a severe limitation, the low rate of recapturing marked individuals (return rates of banded birds) [48,49], and fail to produce significant information on a large scale [48]. The other extrinsic techniques such as radio transmitters and satellite transmitters are useful for those organisms which can carry them easily [15]. For example, satellite telemetry  has limitations for some species, like small birds traveling long distances [48,50,51]. Hence, these approaches are generally inappropriate for small organisms in large numbers and are very expensive [15].   To fill the gap created by these limitations, new approaches involving intrinsic markers have been introduced. The intrinsic methods involve using different animal tissues that can be related to geographic areas [15]. Indirect tracking of individuals can be achieved by using their intrinsic biological (genetic variation) or biogeochemical markers present in tissues (trace element/stable isotopes) [52–54]. The difference in elemental composition and morphological changes in migratory organisms are the basic concepts in using intrinsic markers [15].  Among various types of intrinsic markers, isotopes [55], trace elements and DNA [15,18,56] are the most commonly used. Stable isotopes and trace elements are the most rapidly evolving and promising tools for studying migratory connectivity [57] and tracing origins  [53] of birds. Unlike extrinsic markers, trace element and stable isotope analysis does not require marking and recapturing individuals and can work with large populations on broader scales of time and space. Hence, the most viable and economical way of tracing small animals is now by using intrinsic markers, i.e. tissues of organisms which reflect geographical areas [15].  Selection of tissue type for this kind of study is most challenging, because of the variation in the turnover rate of elements and isotopes in different tissues. When animals undergo  dietary modifications because of migration, metamorphosis, or any other reasons, the elemental composition of their tissues will begin to change to reflect their new diet [58]. Turnover time is a period over which elemental concentration in an animal tissue will 11  change because of a change in diet. In a particular tissue, the turnover rate of elements is a product of that tissue’s metabolic rate [59,60]. The isotopic turnover rate represents the combination of isotopic dilution through growth and replacement due to metabolic processes [61]. Recent diet is reflected in the tissue of an organism with rapid elemental turnover, whereas, tissues with slow turnover will reflect long-term dietary averages [60]. Animals, which move between distinct food webs, can hold the information of previous feeding locations for periods that depend upon the elemental turnover rates of the tissue of interest [60,62]. Tissues vary in their rate of isotopic/elemental change, with high turnover in tissues such as blood plasma and liver, moderate turnover rates in muscle, and low turnover rates in long-lived tissues like bone [15,58,62].  Moreover, metabolically active tissues like liver and blood have a more rapid turnover rate than less metabolically inert tissues like hair, bill, claw, feathers, horn, nail, and bone [58,62]. For example, turnover rates of carbon and nitrogen isotopes range from a half-life of a couple of days for blood, to a year or longer for bone [60,63–65]. One study of small passerine birds showed that claws keep the record of diet for two to five months [63].  1.3.6 Isotope vs Trace Element Stable isotope analysis has become a popular tool for identifying the geographical origin, migratory routes, and populations of birds [57]. Stable isotopes like hydrogen (deuterium, 𝛿D) [53,66], carbon (𝛿13C) [66], nitrogen (𝛿15N) [67], and strontium (𝛿87Sr) [66] have been analysed to trace the origin and movement pattern in various types of bird tissues such as bone, claws, feathers, and blood [53,66].  For example, the isotopic composition of hydrogen, carbon, and strontium of tail feathers from black-throated blue warblers (Dendroica caerulescens Gmelin) were analysed to identify its migratory population throughout its breeding range in North America [66]. The stable-hydrogen isotopes (𝛿 D) of feathers, claws, and blood were analysed from white-throated sparrows (Zonotrichia albicollis Gmelin) to determine their origins [68].  Moreover, stable isotopes of carbon and nitrogen in various bird tissues like breast muscles [69], and stomachs (Hobson, Piatt, & Pitocchelli, 1994) have been mostly used to identify bird trophic levels.  12  Stable isotopes are a more commonly used approach for tracing migratory connectivity and identifying origin of birds because isotopes vary by climate over large spaces. The trace element profile varies even among small to large regions whereas, stable isotopes composition varies by continental, regional or specific habitats [16,17]. Moreover, a comparative study done between stable isotopes and trace elements demonstrated that trace elements had a much higher classification resolution than the stable isotopes profiles in classifying birds at the continental level (Africa and Europe) [18]. The underlying geology of British Columbia and the Okanagan Valley is diverse across small spatial scales [22], which makes trace elements potentially much more sensitive for tracking regional starling movements [14] at scales where stable isotopes are practically  homogeneous. Furthermore, trace elements can provide a higher specificity than isotopes because of the larger number of potential parameters [18].   1.3.7 Trace Elements as Natal Origin Markers Trace element profiles of different tissues, such as feathers, can provide information about birds’ geographic origins [18,71]. This relies on the fact that the mineral profile in the tissues of organisms varies micro-geographically, and thus location has considerable effect on trace element composition [14,18,19,71]. Birds acquire trace element signatures in their tissues from their diet, and these signatures reflect the chemical composition of the area in which the tissue was generated, or keep the record of past feeding history as recorded in long turnover tissues. The use of trace elements in identifying bird origins started in the 1950s [72] on upland game birds. Since then, trace elements have been successfully used in different types of studies including tracing fine scale dispersal of birds [71] and identifying the origin of different avian species [72]. For example, trace elements have been successfully used to distinguish breeding populations of mallards (Anas platyrhynchos L.)[72]; identifying moulting areas for sand martin (Riparia riparia L.) [19]; and identifying the origin of white-crowned sparrows (Zonotrichia leucophrys Forster)  [71]. Thus, trace element geochemical fingerprinting has been successfully used as an intrinsic marker to reveal the origin and migratory patterns of bird species.  In order to develop a successful pest management plan for an aggressive pest, like starlings, understanding its natal origins is important [5,14]. Moreover, understanding 13  population dynamics over different seasons is crucial in the selection of effective pest control methods [73]. Thus, trace element analysis may provide a powerful technique for identifying the origin of starlings over relatively small spatial scales, like in the Okanagan Valley.                          14  2 Study Area British Columbia (BC) is the western-most province of Canada, and has high geological diversity in rock types [74] (Figure 2A & 2B). The Okanagan Valley in south-central British Columbia is about 200 km long and 20 km wide (Figure 2B and 2C). It lies in between the Columbia and Cascade Mountain ranges in the southern interior of BC. The valley bedrock is comprised of volcanic, sedimentary, metamorphic and intrusive rocks [22] (Figure 3 & 4). Since BC and the Okanagan Valley are geologically heterogeneous, potential starling population sources were identified from different areas in the valley, to compare with birds caught in the fall.   The valley is characterized by hot summers and mild winters with an average daily maximum temperature of 27.2 °C in July and a minimum average daily temperature of -5.7 °C in December and January [75]. Around 2000 hours of sunlight per year and 250-400 mm of precipitation are received in most of the valley [76]. There are several large to small lakes in the valley, which helps to moderate extreme temperatures. Due to climatic and geological suitability, the Okanagan Valley is a famous agricultural region. The valley is ideal for several tree fruits such as apples, peaches, and other soft fruits, like cherries, berries, and grapes, and livestock operations including both dairy farms and feedlots. For example, around 97.5 % of BC’s vineyard and 98% of the province’s apples are grown in the Okanagan-Similkameen region [14,77]. The cold short winters, warm summers, rural/urban interface, and availability of year-round food sources in the Okanagan Valley provide excellent starling habitat.    2.1 Sampling Sites  Thirteen major locations were identified for potential source populations on the basis of different underlying geology (Figure 3 & 4), drainage basins [14], survey data drawn from the North American Breeding Bird Surveys and Christmas Bird Counts, and major human settlement areas (suitable starling habitat) in the valley between 280 m to 520 m altitude. The thirteen locations (Salmon Arm, Mara, Hullcar, Armstrong, Vernon, Coldstream, Lumby, Kelowna, Summerland, Penticton, Keremeos, Oliver, and Osoyoos) are distributed at intervals along 200 km of the Okanagan Valley. Salmon Arm is 15  technically in the Shuswap Valley, but was included because of its proximity and connectivity to the Okanagan Valley. Within these thirteen major locations, 27 sites (sub-locations) were selected depending on the feasibility for trap installation (Figure 2C). These sites were placed within a 1-8 km radius within each location. Samples of Juvenile EUST were collected from all thirteen locations to build a geochemical library. Adult birds were collected from among major locations and also from one additional location, Okanagan Falls, depending on availability.   Figure 2 Map of study area with sampling sites. 2A. Map of British Columbia (BC) in Canada; 2B. Okanagan-Similkameen region in BC; 2C. Samplings sites in the Okanagan-Similkameen region. Black circles represent sampling sites for juveniles whereas, green circles represent sampling sites for adults. Green circles with black dots represent the sampling sites where both adults and juvenile were collected.     16   Figure 3 Map of major bedrock types in  the north and central Okanagan Valley Source: Okanagan Watershed, British Columbia (3sheets). Geological Survey of Canada, Open file 6398. http://geoscan.nrcan.gc.ca/starweb/geoscan/servlet.starweb?path=geoscan/fulle.web&search1=R=292220 Licence: http://open.canada.ca/en/open-government-licence-canada       17   Figure 4 Map of major bedrock types in  the southern Okanagan Valley Source: Okanagan Watershed, British Columbia (3sheets). Geological Survey of Canada, Open file 6398. http://geoscan.nrcan.gc.ca/starweb/geoscan/servlet.starweb?path=geoscan/fulle.web&search1=R=292220 Licence: http://open.canada.ca/en/open-government-licence-canada     18    Note: Different colour represents different bedrock geology in the Okanagan Valley Source: Okanagan Watershed, British Columbia (3sheets). Geological Survey of Canada, Open file 6398. http://geoscan.nrcan.gc.ca/starweb/geoscan/servlet.starweb?path=geoscan/fulle.web&search1=R=292220 Licence: http://open.canada.ca/en/open-government-licence-canada  19  3 Materials and Methods Trace elements have been used as biogeochemical markers to trace the origin of birds [18,19]. The different chemical concentrations in bone tissue provide a unique chemical profile for each individual bird, that constitutes that bird’s chemical signature, effectively providing a chemical fingerprint. Juvenile birds were sampled to characterise each source population at the sites where they were collected. Because the sampling was done soon after fledge (July 13 to August 14 in 2015 and June 23 to August 4 in 2016), the birds had yet not dispersed from their natal environment; thus, their bone tissue will reflect the elemental fingerprint of the sites where they were collected. Consequently, the library for fingerprinting different sampling sites was developed from the elemental signature of newly-fledged juvenile starlings sampled at those natal sites.   3.1 Sampling Sampling was performed in collaboration with Starling Control Program (SCP), British Columbia Grapesgrowers’ Association (BCGA). Since 2003 a team of professional trappers contracted by the BCGA, has been trapping and euthanizing starlings following the guidelines of the Canadian Council on Animal Care. I subsampled tarsometatarsus (bone tissue) commonly known as tarsus or metatarsus from the samples collected from SCP trappers in sealed and labelled plastic bags [71]. Tarsus is found in only the lower leg of birds and other some non-avian dinosaurs but is homologous to mammalian tarsal and metatarsal bones.    Although feather tissues have been commonly used in comparable studies, and although no specific studies have been done to measure trace element turnover rates in bird bones, I chose to use bone tissue to investigate 21 trace elements in juvenile European starlings.  Feather tissue is more convenient to use but I chose bone for the following reasons: first, the turnover rate of bone tissue is very slow compared to any other tissue [62]. For example, the turnover rate of human bone is around 2-3% per year [78]. The trace element turnover rate is slow and the biological half-life of metals in bone could be up to 30 years [79]. Second, an earlier study of six European starling tissues (bone, liver, heart, muscle, brain, and feather) in BC identified bone as the best tissue for tracing bird origins 20  [14]. Finally, turnover rates in tissues like blood plasma and liver are high in birds, medium in muscle, and lowest in bone [62]. For example, in birds, the half-life of liver carbon is 2.6 days, whereas, it is 173.3 days in collagen [60].  Each individual bird is considered as a sample. Altogether, 653 samples were collected in the spring/summer and fall/winter of 2015 and 2016, from 27 different sites representing 12 major locations within the valley. The sampling was done in major locations such as Kelowna, Mara, Salmon Arm, Hullcar, Armstrong, Vernon, Coldstream, Lumby, Summerland, Penticton, Okanagan Falls, Oliver, Osoyoos, and Keremeos in both years. The sites, however, vary with respect to the juvenile and adult sampling in both years depending on availability. In spring/summer (May-July) juvenile birds, were collected and in fall/winter (August-December) adult birds were collected (Table 1). Age of the birds was determined from their plumage [18,19]. Additionally, aging by plumage was validated by a chronology of skull development (skull ossification) in juveniles. Five randomly selected samples which were considered as juvenile through plumage, were dissected in skull to observe the skulling [80]. Birds moulting between juvenile and adult plumage are considered as new adults of that year and hereafter called “<1 year adults” to differentiate them from juveniles and 1+ year adults.   For this study, adult birds were considered to be the problem birds in the Okanagan Valley and were divided into two major problem categories on the basis of farming practice, i.e. problem birds in vineyards/orchards and dairy farms/feedlots. Dairy farms/feedlots are primarily located in the northern regions and vineyards/orchards are in the southern region of the valley.        21  Table 1  Number of samples (juvenile and adult) analysed in 2015 and 2016. Samples were collected in different seasons in two consecutive years. As juveniles were available only in breeding season, they were collected in spring/summer and adults were collected only in fall/winter. The number of juvenile samples is higher than adults because juveniles were used to develop the elemental fingerprint of each location. Year Type of Bird (Age) Spring/Summer Samples Fall/Winter Samples 2015 Juvenile 105 - Adult - 118 2016 Juvenile 310 - Adult - 120 Total Samples  415 238 Total number of Sample Analysed  653  3.2 Geochemical Analysis   3.2.1 Sample Preparation   I used a pair of leg bones (tarsi) of individual birds for the analyses. Samples were prepared by methods described by Norris et al. (2007) with some modification. Briefly, the bone was freeze-dried and cleaned by removing outer skin and marrow.  Bones were washed repeatedly with ultra-pure water (18 µS) [18,19,81], dried at room temperature (20 ° C) [18,19] and weighed [66,81]. The clean fragments of the bone sample, weighing between 30 mg to 80 mg each, were put separately into Teflon tubes [66,81]; 2 ml of trace element grade concentrated nitric acid was added to each tube, before being capped and placed on a 70-80 ° C hot plate [81] for 2 hours to dissolve completely. Samples were cooled afterwards to room temperature on a bench.  One milliliter of trace metal grade hydrogen peroxide was added to further digest the dissolved organic compounds [81], after which samples were evaporated on a hot plate at 80 to 90 °C [14,19]. After cooling, 2 ml of 1 % HNO3 with 1 ppb indium solution were added to each vial to dissolve the residue over a period of 2 hours.  Samples were transferred quantitatively with three washes of the HNO3 indium solution into acid-washed polyethylene centrifuge tubes to a 22  final volume of 10 mL [14,81]. Sample blanks were prepared in the same way for every set of digestions to monitor contamination [18,48,81]. The sample was further diluted with 1 ppb indium, 1% HNO3 (100X factor) for ICP-MS and 1ppm yttrium, 1% HNO3 (10 X and 5X) for ICP-OES (Table 2) to fall within the detection limits of the instruments.  3.2.2 Instrumental Analysis I measured 21 elements: aluminium (Al), silver (Ag), barium (Ba), calcium (Ca), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), molybdenum (Mo), manganese (Mn), lead (Pb), sulphur (S), scandium (Sc), selenium (Se), tin (Sn), strontium (Sr), vanadium (V), zinc (Zn), magnesium (Mg), sodium (Na), and potassium (K) in bone tissue. The elements were selected on the basis that they are present at levels high enough for reliable quantification and also used in fingerprinting various species of birds to identify origin and migratory movements [18]. Out of 21 elements the concentration of four elements, Ca, Mn, Na, and K, were analysed via a Thermo-Electron Corporation, iCAP 6000 Series XR Inductively Coupled Plasma-Optical Emission Spectrometer (ICP-OES), because of high concentrations in bone tissue [14,18,19]. The remaining 17 elements were analysed via a Thermo-Fisher Element XR sector field Inductively Coupled Plasma-Mass Spectrometer (ICP-MS) [14,71].  Elements were analysed in either low resolution or medium resolution mode to resolve polyatomic interferences. Each instrument was calibrated by external multi-element standards. Four different concentrations of multi-element standards were run to produce calibration curves [71]. Indium [81] and yttrium  [14] were used as an internal standard for ICP-MS and ICP-OES respectively to monitor and correct for instrument sensitivity drift, viscosity variations, sample density and to improve accuracy and repeatability (Table 2). The blank and reference solutions were run at every 20-sample interval throughout the analysis. Three replicate analyses were done for each sample. Relative Standard Deviation (RSD) of the analysis was between 1-2%.      23  Table 2  Internal standards and concentration used in ICP-MS and ICP-OES. Two distinct internal standards were used in the ICP-MS and ICP-OES analyses. The last row indicates the sample dilution factor for both instrumental analyses.    ICP-MS ICP-OES Internal Standard Indium (1000 µg/ml) Yttrium (1000 µg/ml) Internal Standard Concentration 1 ppb In, 1% HNO3, 1ppm Y, 1% HNO3 and 10 ppm Y, 1% HNO3 Dilution Factor 100X 5X; 10X  The 21 different elemental concentrations generated a library of chemical fingerprints for juvenile starlings derived from different sites spanning the Okanagan Valley. The origin of each problem (adult) bird collected in fall/winter season was identified by comparing its chemical fingerprint statistically with the library of juvenile fingerprints established in the breeding seasons.   3.3 Statistical Analysis of Data  3.3.1 Pre-treatment of Data The elemental concentration in the original tissue was calculated using the sample dilution factor and sample weight used for the analysis (Equation 1).   Equation 1:       Then, the elemental concentration was normalized to the concentration of calcium to minimize variability due to the collagen content of bones.  Most bone mass consists of type I collagen and apatite (a mineral composed mainly of calcium and phosphate) [82]. Element concentration in bone (mg/g) =Raw element concentration in diluted sample (mg/L) ∗  diluted volume (L)Amount of original sample used to make dilution (ml) * bone weight (g) Total volume of sample (ml) sample (L)  * 24  Other cations and anions easily substitute into the mineral matrix of apatite.  Thus, other elements, derived from the diet can be incorporated into bone to create a fingerprint, representing the food an individual has ingested [83]. The bone mineralization is analogous to the geological mineral [82] and is known as hydroxyapatite, Ca5(PO4)3OH [84,85].   Data was normalized with calcium for several reasons, including because 1) the collagen structure (the main protein) of bone bonds poorly with metals, 2) there can be variability in the composition of collagen with respect to amino acids, and 3) there can be variability in overall collagen content of bone, especially as the animal grows [85].  Since, the “mineral: collagen” ratio varies with age [85], Ca-normalization minimizes errors from age-dependent variability in collagen content. The bones are flexible when young because of greater collagen and less apatite content (lower mineral: collagen ratio), and are stronger but more brittle in adults because of lower collagen and greater apatite content (higher mineral: collagen ratio) [85–88]. Thus, the juveniles have more collagen and less apatite relative to total bone mass than adults and vice versa. Normalizing with calcium, therefore, removes this variability due to collagen content.    After calcium normalization, the data were then standardized via z-scoring (Equation 2). Z scoring was used to prevent individual elements from having a disproportionate influence on the grouping [89,90].  Equation 2:    Where, Zi is the z-score for element i X = concentration of the element i   µ= mean concentration of element i in all samples S = standard deviation of the concentration of element i in all samples     Si Zi = Xi -µi 25  Distributions of z-scored data were log-transformed to normalize the data to fulfil the multivariate normal distribution assumption before z-scoring. Some elements such as Mo, Ag, Cd, Sc, Co, and Se were not normally distributed even after log transformation, probably because minimum values were close to the detection limit Even after removing these elements from the analysis, the overall discrimination was increased by only 3% in 2015, in contrast, it does not make any difference in 2016. Therefore, I decided to use all the elements without log transformation for further analysis. Hence, the untransformed data were used for the final analysis of these elements.   Additionally, correction of subsamples was performed for inter-annual analysis of the fingerprinting of birds from the same location between years. The same sample analysis (n=20) was run in both years. A scatter plot with a trend line was developed from the analysis of the same sample in both years. The data from one year (2015 sample) was corrected by the equation (slope intercept correction) of the line.   3.3.2 Trace Element Multivariate Analysis  3.3.2.1 Multivariate Analysis of Variance (MANOVA) and Multiple Discriminant Analysis A multivariate analysis of variance (MANOVA) was used to analyse trace element profiles of juvenile bone tissue collected in different locations [90]. A multiple discriminant analysis [91] of juvenile samples was performed to discard elements (variables), which were little related to group distinction, and also to develop the predictive model of group membership based on trace elements (chemical profile of bone).  A discriminant function (equation) was developed based on the linear combination of the predictor variables (trace elements) that provides the best discrimination (correctly separating individual birds) among the predetermined groups [18,92,93]. The predetermined groups were the sites where the birds were caught. The stepwise method and Mahalanobis distance [91] were used for the analysis because of the large number of variables (20 different element concentrations). The stepwise method looks at each element (variable), one at a time, and determines which element is the best predictor of group membership, and ultimately generates the best set of variables to 26  predict group membership. Cross-validation assessed the success of the proper bird groupings via the discriminant function and rules.   3.3.2.2 Cluster Analysis Cluster analysis [94] was performed to evaluate the spatial separation and temporal variation of the elemental fingerprint of starlings. Cluster analysis allocates individual birds into groups (clusters) based on how similar they are in chemical composition. In order to determine the best method for cluster analysis, various methods such as single, complete, average linkage and ward.D were applied to the juvenile birds of 2015 for separating populations by sites. In single linkage clustering, the algorithm joins the two closest objects to form a cluster [94] while the complete linkage considers the farthest neighbour to form clusters [95,96]. The results from single and complete linkage algorithms produce similar clusters [94]. Unlike single and complete linkage, an average linkage considers the mean of distances between clusters and produces different clusters. Average linkage clustering calculates all pairwise dissimilarities between the elements in cluster 1 and cluster 2 and considers the smallest of these dissimilarities as the distance between the two clusters [97]. In Ward’s minimum variance method, the pair of clusters is chosen to merge at each step based on the optimal value of an objective function [98]. It minimizes the total within-cluster variance. The pair of clusters with minimum between-cluster distance is merged at each step and by repeating the process, the number of clusters decreases until it becomes one huge single cluster [98]. Though different methods produce different outcomes, cluster analysis is a descriptive technique that can help identify patterns in data and the choice of method depending on the type of outcomes that make more sense [89].   For my data, the ward.D method [98,99] with Euclidean distance measures was used for analysis, which provided better separation of individual birds with similar signatures and geographic location. Additionally, an average method with correlation distance was used to compute a test of significance of the clusters (by calculating the p-value of the cluster). The R package “pvclust”  [100] was used; it uses bootstrap resampling techniques to compute the p-value for each cluster. This method generates thousands of bootstrap 27  replications by randomly sampling elements of the data. For each of the clusters, Approximately Unbiased (AU) and Bootstrap Probability (BP) values were calculated. The AU probability values (p-values) are computed by multiscale bootstrap resampling where AU ≥ 95 % are considered to be strongly supported by the data, while BP corresponds to the frequency that the cluster is identified in bootstrap copies [100]. Both ward.D and average methods of cluster analysis were done to evaluate the spatial separation of the juvenile fingerprints in both years and to evaluate the effect of time on the elemental fingerprint of juveniles from the same location. Only the ward.D method was used for all birds (juvenile and adult birds) to identify immigrant birds in both the years and to see the effect of gender on bone geochemical fingerprinting within sampling sites.   Statistical analysis was conducted in SPSS ver. 24.0 and R statistical software version 3.3.1 [101] with the cluster [102] and pvclust [100] packages. Map projections of data were done in ArcGIS 10.4.                      28  4 Results  The results of trace elemental fingerprinting of starling bone tissue are interpreted in four major sections, 1) the spatial separation of the individual birds in space; 2) analysis of temporal variation in the geochemical fingerprinting of individuals from the same locations; 3) identification of the origin of adult starlings, and 4) effect of age, sex and farming activity on geochemical fingerprinting of starlings. The raw element concentration was first mass-normalized and then normalized by calcium before any data analysis. Bone apatite may ideally be regarded as pure calcium phosphate, with all other elements being contaminants. The “mineral:collagen” ratio varies with age [85], Ca-normalization minimizes errors from age-dependent variability in bone apatite by normalizing to the mineral (non-organic part of the bone).Thus, after Ca-normalization, Ca was discarded from consideration in subsequent analyses.   4.1 Spatial Separation of Juvenile (source population) Birds in the Okanagan Valley Juveniles were collected in early summer from different locations situated at distances from 12 to 190 km apart throughout the Okanagan Valley in 2015 and 2016. Juveniles were considered as potential source populations because they were collected in the breeding season, with little prior chance to disperse beyond their natal habitat. The sample size varied with location depending on bird availability in traps. In 2015, the juvenile trace element fingerprint library was developed from 105 juvenile samples collected from 10 different locations. The number of juvenile samples varied depending on availability at each location, Kelowna (n=20), Hullcar (n=10), Salmon Arm (n=10), Armstrong (n=9), Mara (n=9), Vernon (n=18), Oliver (n=10), Osoyoos (n=8), Penticton (n=6) and Keremeos (n=5) (Table 3).  In 2016, Summerland (n=19), Coldstream (n=20) and Lumby (n=15) were added to the juvenile library, along with samples from the nine earlier (2015) locations. In addition, to make the library more robust and representative, in 2016, new sub-locations (sampling sites) within an 8 km radius of some of the previous locations were added in the library.  Like in 2015, the sample size varied among sites and size also varied between years, 29  Kelowna (n=20), Salmon Arm (n=16), Hullcar (n=19, n=21; 3.6 km apart), Armstrong (n=20), Mara (n=20, n=21; 1.3 km apart), Vernon (n=20), Oliver (n=10 n=30; 4.7 km apart), Osoyoos (n=11, n=21, 6.5 km, apart) and Keremeos (n=6, n=21: 7.9 km apart) (Table 3). No juvenile sample could be collected from Penticton in 2016. In 2016, the juvenile trace element fingerprint library was developed from 310 juvenile samples collected from 12 different locations. Hence, two different elemental fingerprinting libraries consisting of 105 (2015) and 310 (2016) juvenile birds were developed for the two different consecutive years. The sample size numbers and locations change with the objective and type of analysis.    4.1.1 Elemental Composition of Juveniles Within and Among locations  The multivariate comparison of 20 elements in the juvenile population using MANOVA shows there is a significant difference in the trace elemental composition of juvenile bone among locations (2015: Wilks’ λ <0.0001, F=6.655, df= 180, 645.230, power=1, p<0.0001 and 2016: Wilks’ λ =0.001, F= 9.847, df= 228, 2048.568, power = 1, P<0.0001; Appendix B). Hence, the null hypothesis that elemental composition of starling bone is independent of geographic region is rejected. However, there is no significant difference in elemental composition among juveniles from the same locations (Appendix C and D). The descriptive summary of the elemental composition of juvenile bone in different locations is shown in Appendix B. Of the 20 elements, three elements (Co, Cd, and Ag) were sometimes below the detection limit.  4.1.2 Grouping Juvenile Birds from Different Sites in the Okanagan Valley A stepwise discriminant analysis of 20 trace elements based on the juveniles (n=105) collected at 10 different locations in 2015, provided four canonical discriminant functions with significantly high eigenvalues (>1), and canonical correlations (rc> 0.70), separating the correct grouping of juvenile samples (Figure 5) from respective sites (Wilks’ λ = 0.001, 0.11, 0.73, χ 2 = 633, 428, 247, p<0.0001). Out of 20 elements, 10 elements (K, Mg, Na, Ag, Cd, Ba, Sc, Cr, Cu, and Se) were used as the best predictor variables in discriminating group membership. The probabilities of correctly classifying juveniles to their respective locations using stepwise discriminant analysis are shown in Table 3. The table shows the predicted classification (group membership) using cross-validation from 30  different locations compared to the apparent or actual observation. The overall probability of correctly classifying juveniles was 79% (P<0.0001, Press’s Q; Table3).  Most misclassification occurred between very close sites such as between Osoyoos and Oliver (37.5% each, Table 3), between Penticton and Keremeos (33.3%, Table 3) and between Mara and Armstrong (33.3%, Table 3). Hence, almost all the misclassification occurs between sites that are very close together, with one exception, between Osoyoos and Vernon in 2015 (37.5% each, Table 3).   Figure 5 Distribution of juvenile birds caught in different locations in 2015 (distributed on the first two canonical discriminant functions, as calculated from 20 trace elements).   Discriminant analysis for juveniles (n=310) from 2016 followed. Unlike 2015, there were only 18 trace elements after Cd and S were discarded from the analysis. Cd was below the detection limit in most of the samples, while the calibration curve from standard solutions of sulphur was not strong enough to provide precise measures during the 2016 instrumental analysis. Also, unlike 2015, the number of locations was increased by 2 in 31  2016. The stepwise linear discriminant analysis produced three discriminant functions with significantly high eigenvalues (>1) (Wilks’ λ= 0.11, 0.036, 0.082, χ 2= 1336.305, 989.181, 741.834, p<0.0001) and canonical correlation (rc> 0.73) based on the thirteen elements as predictor variables. In 2016, 6 new elements (Sr, Sn, Al, V, Mn and Zn) were added to seven elements (K, Mg, Ag, Cd, Ba, Sc and Cu) used as discriminating predictor variables in 2015. The overall cross-validation indicates that, the source population was 63.9% correctly grouped with regard to trap sampling location (Figure 6, Table3; P<0.0001, Press’s Q;). Additionally, like in 2015, the highest rates of misclassification occurred principally between nearby sites such as Osoyoos and Oliver (25%, Table 3), Armstrong and Mara (25% Table 3), Vernon and Mara (30%), Lumby and Salmon Arm (20%, Table 3), Keremeos and Lumby (33.3%, Table 3), and Summerland and Armstrong (21%, Table 3). Thus, again, the highest degree of misclassification occurs between sites that are very close, with two exceptions in 2016.    Figure 6 Distribution of juvenile birds caught in different locations in 2016 (distributed on the first two canonical discriminant functions as calculated from 18 trace elements).32  Table 3  Classification results for  juveniles to its predicted locations in 2015 and 2016. The predicted group membership shows the percentage of correctly classified samples, based on the cross-validation function in the SPSS software package where, in 2015 63.9% and in 2016 79.0% of original grouped cases correctly classified.  Year Site Predicted Group Membership (%) Kelowna Hullcar Salmon Arm Armstrong Mara Vernon Oliver Osoyoos Penticton/ Summerland Keremeos No of Sample 2015 Kelowna 95 0 0 0 0 5 0 0 0 0 20 Hullcar 0 90 0 0 0 0 0 10 0 0 10 Salmon Arm 0 0 70 20 10 0 0 0 0 0 10 Armstrong 0 0 0 78 22 0 0 0 0 0 9 Mara 0 0 0 33 67 0 0 0 0 0 9 Vernon 0 0 0 0 0 100 0 0 0 0 18 Oliver 0 0 0 0 10 0 60 30 0 0 10 Osoyoos 0 0 0 0 0 37 37 25 0 0 8 Penticton 0 0 0 0 0 0 0 0 67 33 6 Keremeos 0 0 0 0 0 0 0 0 0 100 5     33  Year  Sites Predicted Group Membership (%) Kelo- wna Hull- car SalmonArm Arm-strong Mara Ver- non Oliver Osoyoos Penticton/ Summer land Keremeos Cold-stream Lumby No. of Sample  2016 Kelowna 70 0 0 15 0 0 10 0 5 0 0 0  20 Hullcar 0 77 0 0 12 0 5 0 0 0 5 0  40 Salmon Arm 0 0 69 6 6 6 6 0 0 6 0 0  16 Armstrong 5 0 0 60 25 0 0 0 10 0 0 0  20 Mara 0 7 0 5 73 0 0 0 2 0 12 0  41 Vernon 0 0 0 0 30 65 0 5 0 0 0 0  20 Oliver 2 0 2 8 2 0 68 0 18 0 0 0  40 Osoyoos 0 6 3 0 0 0 25 66 0 0 0 0  32 Summerland 0 0 0 21 0 0 0 0 79 0 0 0  19 Keremeos 4 0 0 11 0 4 4 11 0 29 4 33  27 Coldstream 0 45 0 0 5 0 0 20 0 0 30 0  20 Lumby 0 0 20 0 0 0 0 0 0 13 0 67  15  34  Thus, comparing the results from the two consecutive years, the sites were largely grouped (above 60%) correctly, with Osoyoos as an exception. Only 25% in 2015 and 45% in 2016 of the Osoyoos juveniles were correctly grouped by location. Mostly the misclassification pattern that occurred was among nearby locations and consistent between years. For example, a high rate of misclassification occurred between Oliver and Osoyoos, and Armstrong and Mara in both years. The inconsistency of misclassification at some sites between years is comparatively small. For example, birds from Kelowna show 5% group membership with Vernon in 2015; In contrast, it shows 15% with Armstrong, 10% with Oliver and 5% in Summerland in 2016 (Table 3).   Cluster analysis of juveniles shows similar results. The clusters (Ward. D method) grouped juveniles into different clusters, even separating individuals derived from locations ≤12 km (Figure 7) apart. Since there is no particular rule for choosing the height in the Ward.D method dendrogram, the rule of thumb height of 10 was applied. Around 33.3 – 100% of juveniles from the same locations were grouped together at the height of 10 (Appendix E). Approximately unbiased (AU) values obtained via the average method have been used to test the significance of the clusters. Ten major significant clusters (red boxes) with an AU p-value >0.95 separate juveniles from different geographic locations. Thus, the null hypothesis that elemental composition of starling bones is independent of geographic region, is again rejected at the significance level of 0.05 (Figure 8).  35   Figure 7 Cluster analysis of juvenile birds in 2015. Ward.D method was used for the analysis of 105 juvenile birds sampled in the Okanagan Valley in 2015, derived from the Euclidean distance using 20 elements. The distance provides a relative measure of how different the clusters are.  Most of the individual birds collected at the same site are clustered together, indicating the similarity within sampling sites and dissimilarity among sites. Dark blue colour represents the samples from the northern-most area and the colour fades as the location moves towards the south. 36    Figure 8 Cluster analysis of juvenile birds in 2015 with AU value. The average method was used to calculate the AU value of 105 juvenile birds sampled in different locations in the Okanagan Valley in 2015 where the height represents the correlation; same legend as preceding figure; dark blue colour represents samples from the northernmost sites and the colour fades as the location moves towards the south.  37  In 2016, a similar pattern of spatial separation of juvenile birds (n=310) within 12 km distance was observed, even with the additional locations and sub-locations (Figure 9). At height 10, 47-100% of juveniles from the same sites were grouped in one cluster (Appendix E). Again, the height of 10 was selected on the basis of the thumb rule. The percentage of clusters decreased with the decrease in height and increased with the increase in height. Approximately unbiased (AU) values obtained via the average method have been used to test the significance of the clusters. Several significant clusters (red boxes) with AU p-value >0.95 separate juveniles into the different geographic locations. Thus, the null hypothesis that elemental composition of starling bones is independent of geographic region, is also rejected with the significance level of 0.05 in 2016 juvenile samples (Figure 10). The grouping based on cluster analysis showed similar spatial separation of juvenile fingerprinting among geographic locations to those obtained by comparing MANOVA fingerprinting and discriminant analysis. Hence, MANOVA, discriminant analysis and cluster analysis of juvenile birds, collected in the breeding season, indicated the elemental composition (biogeochemical fingerprinting) of bone depended largely upon the micro-geographical location. 38    Figure 9 Cluster analysis of juvenile birds in 2016. Ward.D method was used for the analysis of 310 juvenile birds sampled in different locations in the Okanagan Valley, in 2016, derived from Euclidean distance of 20 elements. The level of distance provides a relative measure of how different the clusters are. Most of the individual birds collected at the same site clustered together, indicating the similarity within sampling sites and dissimilarity among sites. Dark blue colour represents samples from the northernmost area and the colour fades as the location moves towards the south. 39    Figure 10 Cluster analysis of juvenile birds in 2016 with AU value. Average method was used to calculate AU value of 310 juvenile birds sampled in different locations in the Okanagan Valley in 2016 where the height represents the correlation; same legend as preceding figure; dark blue colour represents the sample from the northernmost area and the colour fades as the location moves towards the south. 40  4.2 Differences among Fingerprints with respect to Farming Activities  Birds were collected from sites distributed among different farming activities such as cherry farms, vineyards, dairy farms, feedlots and grasslands in this study. To compare the effect of farming activity on fingerprinting I chose four distinct farming activities from four locations (Oliver and Penticton, feedlot; Osoyoos and Penticton, cherry; Penticton, vineyard, and, Keremeos, grassland) within the smallest spatial separation (40 km) possible. Comparison of juvenile bird fingerprints among the different farming activities (within 40 km, and based on 20 elements), showed significant differences in relation to farm land use (Wilks’ Lambda < 0.0001, F=7.491, df= 15.752, 60, p<0.001, Partial Eta squared 0.966, power = 1). Moreover, discriminant analysis of the 2015 juvenile sample’s trace elements generated three canonical discriminant functions with highly significant eigenvalues (>1), significant Wilks’ Lambda (p<0.001) and canonical correlation, (rc> 0.70), correctly grouping the juvenile samples (Figure 11). Cross-validation showed that the sample classification was 92.9% correct (P<0.0001, Press’s Q; Table4). Juvenile samples were grouped correctly (100%) with respect to the predetermined group membership, except for the vineyard/cherry farm in Penticton where 66.7% of the juveniles in the vineyard were grouped with feedlots from Oliver. The null hypothesis is rejected that the elemental composition of birds caught in dairy/feedlots is identical to that of birds caught at nearby orchards/vineyards in the same area. However, the rejection of the hypothesis is potentially due to the effect of space. Elemental fingerprints of juvenile birds were distinct even among some locations ≤12 km apart. In contrast, these farming activities were sampled within a 40 km radius. Hence it is likely that the fingerprinting effect could be because of space rather than farming activity.           41   Figure 11 Distribution of juvenile samples derived from different farming activities. Distribution of juveniles in relation to four different farming activities (vineyard, cherry farm, feedlot, and grassland) within a 40 km radius in the southern Okanagan Valley in 2015 using the first two canonical discriminant functions of a discriminant analysis calculated from 19 trace elements      42  Table 4  Classification results for juveniles collected in relation to different farming activities. The predicted group membership shows the percentage of correctly classified samples, based on the cross-validation function in the SPSS software package, where 92.9% of originally grouped cases are correctly classified. Samples were collected from sites representing four different farming activities within a 40 km radius, based in the Okanagan Valley.    Site Predicted Group Membership Total Feedlot, Oliver Cherry Farm, Osoyoos Feedlot, Penticton Grassland, Keremeos Vineyard/ Cherry Farm, Penticton Count Feedlot, Oliver 10 0 0 0 0 10 Cherry farm, Osoyoos 0 8 0 0 0 8 Feedlot, Penticton 0 0 2 0 0 2 Grassland, Keremeos 0 0 0 5 0 5 Vineyard/ Cherry Farm, Penticton 2 0 0 0 1 3 % Feedlot, Oliver 100 0 0 0 0 100 Cherry farm, Osoyoos 0 100 0 0 0 100 Feedlot, Penticton 0 0 100 0 0 100 Grassland, Keremeos 0 0 0 100 0 100 Vineyard/Cherry Farm, Penticton 67 0 0 0 33 100 43  4.3 Temporal Analysis of Biogeochemical fingerprinting     4.3.1 Temporal Variation in Elemental Fingerprinting of Juvenile Birds between years  Prior to comparing the fingerprints of 2015 and 2016 juveniles, a comparison was made of samples collected in 2015 but subjected to replicate analyses in the two different years (2015 and 2016). This would detect potential inter-annual analytical artifacts. The juveniles from two different sites (Kelowna (n= 9) and Keremeos (n = 7) were analysed in both years to address this potential systematic bias. The cluster analysis of the replicate fingerprints, clearly separated (100%) of the samples by year. Data were therefore corrected with a linear equation for each element, derived from the elemental concentration as obtained in the two different years. Even after the data correction, the results for the two years’ data clustered separately. Interestingly, the Kelowna and Keremeos fingerprints formed 4 different clusters at height=15. This shows a high degree of dissimilarity in the fingerprints between sample years but a high degree of similarity within each year.   The inter-annual variability in geochemical fingerprinting was also assessed by comparing fingerprints of juvenile birds from 2015 (n=76) with juveniles from 2016 (n=107).  Only sample sites common to both years were used to remove effects attributable to the additional sample sites. Samples for 2015 and 2016 were collected at the same season in each year (May-August) and from the same sites (number of sites =7; Osoyoos, Kelowna, Vernon, Hullcar, Keremeos, Mara, and Armstrong). While trying to maintain the season and site consistency, a compromise was necessary due to the availability of bird samples; thus, the number of birds analysed differed between the two years and also varied among sites. All the raw elemental concentrations from 2015 were corrected and standardized (z-scored) together with the 2016 data. Juvenile birds from 2016 clustered separately (100%) from 2015 juveniles; however, the different sites grouped in different clusters within each year (Figure 12). The Approximately Unbiased (AU) p-value > 0.95 for four major clusters, shows significantly distinct fingerprinting between the two years (Figure 12). Thus, the null hypothesis, that there is no difference in 44  the starling bone tissue elemental composition between years from the same area, is rejected with the significance level of 0.05 (Figure 13).     Figure 12 Cluster analysis of juvenile birds collected in 2015 and 2016. Ward.D method was used for the analysis of juvenile birds collected at the same sites in 2015 and 2016. Blue shading represents 2015 samples, and green shading indicates 2016 samples. 45   Figure 13 Cluster analysis of juvenile birds 2015 and 2016 with AU value. The Average method was used to calculate the AU value for juvenile birds collected at the same sites in 2015 and 2016. Blue shading represents 2015 and green shading represents 2016 birds. 46  4.3.2 Temporal Variation in Elemental Fingerprinting of Juvenile and Adult Birds within Years  The cluster analysis between juvenile and adult (<1 year and 1+ year) birds shows the majority of < 1 year adults grouped with the juveniles from the same sites. The < 1 year adults (n=20) were collected from three different sites: Vernon, Penticton, and Keremeos in 2015. For instance: 100% of Keremeos, 75% of Vernon and 45% of Penticton young adults grouped with the local juvenile source population at the height of 1-5. However, the remaining 25% of < 1 year adults from Vernon grouped with the juveniles from Oliver and 55% of young adults from Penticton grouped with juveniles from Vernon, Oliver, and Armstrong (Figure 14).    4.4 Identifying the Origin of Problem Birds in Fall  4.4.1 Identification of Likely Immigrant Birds The starling population increases in vineyards and orchards every fall.  Apart from local reproduction, the adult population increase each fall might be due to additional birds immigrating from other areas. Adults whose fingerprints do not match those in the Okanagan fingerprint library can be considered as likely immigrants. As a whole, adult birds (< 1year and 1+ year) in the valley are considered problem birds. To identify the likely immigrant birds, cluster analysis was performed.   Cluster analysis of all birds caught in 2015, including both juvenile (n=105), and problem birds (< 1 year adults (n=20), and 1 + year adults (n=98)) depicts two distinct clusters, separating most problem birds into one cluster and another cluster comprising a mix of the remaining problem birds and juvenile birds.  Around half of the problem birds (n=52, 44%) clustered separately, indicating chemical composition distinct from the Okanagan Valley birds (Figure 14). These problem birds were considered as likely immigrants to the valley. The majority of likely immigrants were found in the northern region of the valley especially in Coldstream (100%) and Lumby (100%). Moreover, 100% of Kelowna and Oliver (Feedlot) and 13% of Summerland birds were considered as likely immigrants. In contrast, in a 2016 cluster analysis of all birds, juvenile (n= 310) and 47  problem birds (n=120) did not form any defined clusters indicating that all problem birds in 2016 were likely local birds (Figure 15).48    Figure 14 Cluster analysis of juvenile and adult birds in 2015. Ward.D method was used for the analysis of juvenile and adult (n-223) birds collected in different locations in the Okanagan Valley in 2015, derived from Euclidean distance and 20 elements. The level of distance provides a relative measure of how different the clusters are.  Most of the individual birds collected at a site cluster together indicating the similarity within sampling sites and dissimilarity among sites. Dark blue colours represent samples from the northernmost area and the colour fades as the location moves towards the south. 49    Figure 15 Cluster analysis of juvenile and adult birds in 2016. Ward.D method was used for the analysis of juveniles and adult (n=430) birds collected in different locations in the Okanagan Valley in 2016, derived from Euclidean distance of 20 elements. The level of distance provides a relative measure of how different the clusters are.  Most of the individual birds collected at a site cluster together indicating the similarity within sampling sites and dissimilarity among sites. Dark blue colour represents sample from the northernmost area and the colour fades as the location moves towards the south.50  4.4.2 Identification of the Source Population of Problem Birds in the Okanagan Valley To identify the origin of problem birds in 2015, all the juvenile and problem birds (n= 223) were z-scored together and then the discriminant analysis of juveniles alone was performed to generate the appropriate discriminant functions. Discriminant analysis of the trace elements of all (juvenile and adult) samples that z-scored together provided the same four canonical discriminant functions as in spatial separation of juveniles with highly significant eigenvalues (>1) and cross-validation showing that 79% of the samples were correctly classified (P<0.0001, Press’s Q; Table 3). These four discriminant functions were then used to identify the origin of the remaining (n= 66, 56%) local problem birds in the Okanagan Valley. The first function explained 44.2 % of the total variance among locations. The second function explained 33.0%, and the third and fourth explained 11.7 % and 6.1 %, respectively of the total variance among locations. In order to classify objectively the problem birds with respect to origin, the value obtained from each function for each problem bird was normalized (ratio of sample/ratio of centroid) by the centroid value of each site. Thus, if an individual problem bird were to lie at the centroid for a particular site, its value with respect to that site would be one. Each bird’s probable origin was identified by how closely that bird’s normalized value, with respect to each site.  For 2016, there were no likely immigrant birds identified from cluster analysis. All the juvenile (n=105) and problem birds (n=120) were z-scored together before discriminant analysis. The stepwise discriminant function analysis generated three discriminant functions based on thirteen elements, and correctly assigned 63% of juveniles to predicted groups (preassigned group: the location where they were caught). The three discriminant functions were then used to identify the origin of problem birds (n=119) in 2016. The first function explained 32.3% of the total variance among locations. The second and third function explained 18.9% and 17.5 % of the total variance among locations. Like in 2015, the value obtained from each discriminant function for each individual was normalized by the centroid value of each site to objectively assign problem birds to sites of probable origin.  51   In 2015, most of the problem birds (46%) collected throughout the Okanagan Valley matched the 2015 Vernon fingerprint. For instance, the fingerprints of 44.4% of problem birds in Penticton, 20% in Keremeos, 100% in Okanagan Falls, and 33% in Summerland matched with the Vernon fingerprint (Table 5). The second highest (7.5%) contributors of problem birds in the Okanagan Valley were Keremeos, Osoyoos, Pentiction, and Hullcar. For some of the local problem birds, the discriminant function value was too high to assign a local juvenile source population. For such birds, I have categorized them as non-specific Okanagan problem birds. The origin could not be detected for 17 % of local problem birds in Summerland and these are thus considered as the non-specific Okanagan birds.   In 2016, most of the problem birds (46%) collected throughout the Okanagan Valley matched with the Salmon Arm juvenile fingerprint. For instance, the fingerprints of 33.3% of adults in Mara, 60% in Lumby, 93% in Vernon, 42.9% in Summerland, 89.5% in Penticton, 100% in Okanagan Falls, and 66.7% in Oliver matched with the Vernon fingerprint. The second highest source for problem birds in 2016 was Oliver (11.66%) (Table 6).  All the problem birds in Vernon were resident birds (from Vernon) in 2015, but in 2016, 93% were from Salmon Arm and 7% from Hullcar. Out of 27 adults in Penticton, the fingerprints of most of the adults match the Vernon fingerprint (44.4%), followed by Hullcar (14.8 %) and locally Penticton (14.8 %). The remaining 11.1% of adults matched with Keremeos and Osoyoos equally, and only 3.7 % of adults matched with Oliver and Pentiction. As in 2015, the 2016 fingerprints of adults birds in Penticton matches with Oliver (5%), Vernon (5.3%) and Salmon Arm (89.5%). All the adult collected in Okanagan Falls were matched with Vernon in 2015 and Salmon Arm in 2016.    In Kelowna, all the adults were immigrants in 2015 whereas in 2016, 93.3 % of them were from Salmon Arm and 6.7% from Keremeos. Similarly, all problem birds in Lumby were immigrants in 2015, but the 2016 adult fingerprints matched with Salmon Arm 52  (60%), Oliver (33%) and Hullcar (6.7 %). Moreover, all the adults in Oliver were immigrants in 2015; in contrast, elemental fingerprints from Oliver matched with Salmon Arm (66.7%), with Keremeos (20%), and with Oliver and Osoyoos (6.7 %) in 2016. The fingerprints of problem birds in Summerland were a mixture of local (67%) and immigrant (13%) birds. Among 67% of local birds, the origin of 17 % of adults could not be detected, although they are from the Okanagan (Table 5). In 2016, no non-specific Okanagan birds were detected.   The fingerprints of problem birds in Keremeos in 2015, were matched to local (Keremeos) (80%) and Vernon (20%) juveniles. The fingerprints of problem birds in Mara matched with Salmon Arm by 33.3%, Armstrong and Oliver by 20%, and Coldstream, Hullcar, Vernon and Keremeos by 6.7% in 2016. Overall, most fingerprints of problem birds in 2015 and 2016 match with the Vernon and Salmon Arm sources, at the northern end of the Okanagan Valley (Table 5 & 6).                   53  Table 5  Identification of the origin of adult birds in the Okanagan Valley in 2015  Location Total no. of birds per sites Immigrant Adults Local Adults Site of Origin No. of bird % Number % Number %  Vernon 4 - - 4 100 Vernon 4 100 Penticton 27 - - 27  100 Hullcar 4 14.8 Keremeos 3 11.1 Oliver 1 3.7 Osoyoos 3 11.1 Penticton 4 14.8 Vernon 12 44.4 Keremeos 5 - - 5 100 Keremeos 4 80 Vernon 1 20 Okanagan Falls 4 - - 4 100 Vernon 4 100 Summerland 30 4 13 26  87 Armstrong 3 10 Hullcar 1 3 Kelowna 2 7 Keremeos 1 3 Osoyoos 2 7 Penticton 1 3 Salmon Arm 1 3 Vernon 10 33 Non-specific 5 17 Kelowna 14 14 100 -     Oliver 15 15 100 -     Coldstream 10 10 100 -     Lumby 9 9 100 -     Total 118         54  Table 6  Identification of the origin of adult birds in the Okanagan Valley in 2016   Bird caught site Total no. of birds Origin Site No. of bird % Mara 15  Armstrong 3 20.0 Coldstream 1 6.7 Hullcar 1 6.7 Keremeos 1 6.7 Oliver 3 20.0 Salmon Arm 5 33.3 Vernon 1 6.7 Lumby 15  Hullcar 1 6.7 Oliver 5 33.3 Salmon 9 60.0 Vernon 15 Hullcar 1 6.7 Salmon Arm 14 93.3 Hullcar 2  Mara 1 50.0 Summerland 1 50.0 Kelowna 15  Keremeos 1 6.7 Salmon 14 93.3 Summerland 21  Hullcar 3 14.3 Kelowna 1 4.8 Mara 2 9.5 Oliver 4 19.0 Salmon Arm 9 42.9 Summerland 1 4.8 Vernon 1 4.8 Penticton 19  Oliver 1 5.3 Salmon Arm 17 89.5 Vernon 1 5.3 Blue Mountain 3 Salmon Arm 3 100.0 Oliver 15  Keremeos 3 20.0 Oliver 1 6.7 Osoyoos 1 6.7 Salmon Arm 10 66.7 Total   120   55  4.4.2.1 Identifying the Origin of Problem Birds in Vineyards/Orchards and Dairy Farms/Feedlots To make the identification easier from a management perspective, the problem birds collected in fall were divided into two sectors, first, problem birds collected in vineyard and orchard and second, those collected at dairy farms and feed lots. Most dairy farms/feedlots are located in the northern region and vineyards/orchards prevail in the southern region of the valley. In both years, the majority of problem birds (2015:55%, 2016:79%) caught in vineyards and orchards were derived from the North Okanagan. Specifically, the highest contributor was Vernon (41%) in 2015 and Salmon Arm (67%) in 2016 (Figure 16 & 17; Table 7).  The remaining 45% of problem birds in 2015 were a mixture of local and immigrant birds (29% southern local, 2% central); and in 2016, the remaining 21% were southern locals (19%) and 2% central. In 2015, 8% of problem birds in vineyards were from the Okanagan (but their origin could not be more closely defined) and 6% of problem birds were immigrants. However, all the problem birds collected in 2016 were assigned to source populations (Table 7). Lumby and Coldstream were the only two sites in the fingerprint library that did not contribute problem birds to the vineyards and orchards in both years in the valley.   56    Figure 16 The source population sites that contribute to vineyards/orchards in 2015. Heat map with the source population sites that contributed to the vineyards and orchards in 2015. The origin of the source population of problem birds caught in vineyards and orchards, farms and feedlots, identified from discriminant analysis.          57    Figure 17 The source population sites that contribute to vineyards/orchards in 2016. Heat map with the source population sites that contributed to the vineyards and orchards in 2016. The origin of the source population of problem birds caught in vineyards and orchards, and farms and feedlot was identified using discriminant analysis.    In contrast, the 2015 problem birds caught on dairy farms and feedlots were largely immigrants (89%); 11% were local; and all the problem birds were local in 2016 (Table 7). Specifically, Vernon contributed 11% of problem birds on dairy farms and feedlots in 2015. However, in 2016, most of the problem birds were from Salmon Arm (60%) followed by Oliver, Osoyoos and Keremeos (Figure18 & 19; Table 7). Lumby did not contribute to the problem birds on dairy farms and feedlots in either year. Similarly, the south Okanagan locations, such as Osoyoos and Penticton, did not contribute to the dairy farm and feedlot problem in the valley.    58    Figure 18 The source population sites that contribute to dairy farms/feedlots in 2015. Heat map with the source population sites that contributed to the dairy farms and feedlots in 2015. The origin of the source populations of problem birds caught in dairy farms and feedlots, were identified from discriminant analysis.           59    Figure 19 The source population sites that contribute to dairy farms/feedlots in 2016. Heat map with the source population sites that contributed to the dairy farms and feedlots in 2016. The origin of the source population of problem birds caught in dairy farms and feedlots, were identified from the discriminant analysis60  Table 7  The origin of problem birds in Fall in the different farming activities  Year  Vineyard and Orchard Dairy farm and Feedlot Northern  % Central % Southern % Non-specific Okanagan (%) Immigrant (%) Northern % Southern % Immigrant (%) 2015 Armstrong 5 Kelowna 3 Keremeos 12 8 6 Vernon 11 - - 89 Hullcar 8   Oliver 2   Salmon Arm 2   Osoyoss 8   Vernon 41   Penticton 8   Total 55  2  29 8 6  11   89 2016 Hullcar 5 Kelowna 2 Keremeos 5 - - Armstrong 6.5 Keremeos 2 - Mara 4 Oliver 10 Coldstream 2 Oliver 17 Salmon Arm 67 Osoyoos 2 Hullcar 7 Summerland 2 Vernon 4 Summerland 2 Mara 2 Salmon Arm 60 Vernon 2 Total 79  2  19    79  21   61  4.5 Fingerprinting between Male Vs Female  Based on trace element composition, there is no significant difference between male and female birds in most of the sites except for Armstrong and Osoyoos. However, there is a significant effect of sites in male and female trace element composition in bone tissue (Table 8). Hence the null hypothesis “elemental composition is different for male and female starlings from same geographic regions” is rejected at the 0.05 significance level and 95% confidence interval.   62  Table 8 Multivariate comparison of elemental fingerprints of male and female starling.  The test of significance by pairwise comparison of male and female starlings in different valley locations on MANOVA on the basis of elemental fingerprints of starling bone tissue. The results of power of analysis for non-significant tests is provided at the a=0.05 level. Question Site Farming Activities Answer Test Does sex have an effect on the trace element composition of bone of juvenile birds? Keremeos Grassland No Wilks’ Lambda = 0.24, F (2,18) = 4.610,  p= 0.193 Partial Eta squared 0.976, power = 0.250 Osoyoos Cherry farm Yes Wilks’ Lambda = 0.004, F (2,18) = 28.1,  p< 0.05 Partial Eta squared 506.04, power = 0.774 Oliver Vineyard No Wilks’ Lambda = 0.267, F (11,18) = 1.677,  p= 0.192 Partial Eta squared 0.733, power = 0.533 Armstrong Landfill Yes Wilks’ Lambda = 0.000, F (1,18) = 1136,  p< 0.05 Partial Eta squared 1, Power = 0.968 Hullcar Dairy farm No Wilks’ Lambda = 0.061, F (2,18) = 1.722,  p=0.430 Partial Eta squared 0.939, Power=0.130 Mara Dairy farm No Wilks’ Lambda = 0.41, F (2,18) = 2.62,  p=0.312 Partial Eta squared 0.959, Power =0.169 Salmon Arm Dairy farm No Wilks’ Lambda = 0.003, F (1,14) = 25.5, p=0.154, Partial Eta squared 0.997, Power =0.257 Does sex have an area of effect in the chemical composition? Mix sites  Yes Wilks’ Lambda = 0.002, F (108,729) = 13.068,  p<0.001, Partial Eta squared 0.642, Power =1 63  5 Discussion 5.1 Spatial Separation of Starlings The Okanagan Valley trace element fingerprint library was developed from juvenile starling bone tissue. Juveniles best represent the locations of source populations in the valley because they are collected in the breeding season, before the birds have had an opportunity to disperse widely. To make the library more representative, juveniles were collected from almost all the Okanagan cities/towns, and lands used for different agricultural activities, such as vineyards, orchards, dairy farms, feedlots, grasslands, and landfill sites.  I hypothesized that the chemical composition of juvenile bird bone tissue was independent of site. Based on the information derived from discriminant and cluster analyses, it was determined that the majority of juveniles collected bore similar fingerprints within sites, but these fingerprints differed among the sites. Therefore, the null hypothesis was rejected.   The elemental composition of most juvenile birds varied little at a particular site. Similarly, elemental analysis of tail feathers of nestlings and other juvenile sand martins (Riparia riparia L.) from the same colony exhibited no significant differences [19]. Additionally, similarly consistent trace element fingerprint results have been found within sites for western sandpiper (Calidris mauri Cabanis) feathers [81]. This suggests, as with feathers, the elemental composition of bone depends on colony micro-geographic position. Especially for juveniles and nestlings, the elemental composition might represent the site where the nestlings’ food was collected [19] and/or where the juveniles fed.   The similarity in the elemental composition of juveniles collected within sites could be due to the similarity in phenotypic quality of adults, such as breeding at the same site, and use of communal or cooperative breeding (feeding by multiple adults) [18,103]. This type of adult behaviour could yield similar nestling chemical profiles during bone formation. 64  Moreover, a high degree of similarity in feeding and foraging habitat of juveniles in the colony could be factors maintaining the similarity of juvenile bone trace element composition in a colony. Since most juveniles collected within sites were chemically similar, I can assume that these juveniles had not dispersed. Thus, the juvenile fingerprint library adequately represents the fingerprints of source populations at particular sites in the valley.  However, a few juvenile birds caught within a site do not match with the majority of juveniles at that site, but match with bird fingerprints from other sites. For example, note the misclassification rate among Oliver and Osoyoos juveniles. The misclassifications may have arisen due to very similar agricultural practices and soil types at these two sites. Additionally, the dissimilarities noted in a few birds within a particular location (Oliver and Osoyoos) might be due to differences in food choice or due to early dispersal and could be due to geological similarity. Starlings are generalist birds with extremely diverse diets. Their diet varies geographically, seasonally, and with age [37]. The diversity of food options from invertebrates to fruits for juveniles’ increases in spring and summer and that could yield differences in elemental composition of bone tissue in individual birds within a colony. Moreover, the fingerprint differences seen in some birds could be due to short distance dispersion. Starling movement behaviour varies regionally and individually [26,32]. Additionally, most misclassifications were evident between nearby locations such as between Oliver and Osoyoos, and Mara and Armstrong. Therefore, the geological similarities of sites may yield similar fingerprints between sites.   Resolving populations in space revealed, distinct trace elemental signatures within the 160 km length of the Okanagan Valley. This spatial separation of starling populations via bone tissue trace elemental fingerprinting is comparable to feather tissue results obtained on other species, allowing successful separation of bird populations at scales ranging between 10s of kms to continental distances. Both local dispersion and long distance migration of birds have been distinguished in the trace elemental profiles of feather tissue. For example, white-crowned sparrow (Zonotrichia leucophrys  Forster) samples collected from four dialect populations were distinguished within a 400km stretch [71]. 65  Similarly, sand martin (Riparia riparia L.) and barn swallow (Hirundo rustica L.) populations were separated within and between continents (i.e., across Europe and between European and African moulting sites) [18,19]. The shortest distance separating sites with distinct chemical signatures in my study was 12 km (between Vernon and Armstrong landfill). Similarly, distinct trace element signatures have been detected in western sandpiper (Calidris mauri Cabanis) feathers from two sites less than 3 km apart [81] in the USA.   I detected 64- 79 % differentiation in juvenile starlings among sites. This is similar to the degree of differentiation reported for shearwaters (Calonectris Cory & Oustalet) 75-89.9% [104] and lower than that in white-crowned sparrows 100%  [71]. There is no specific threshold to assume the best magnitude for spatial separation. Using the rule of thumb, I assume overall 60% of discriminating power through cross-validation would provide reliable separation of group membership. The degree of spatial separation in juvenile fingerprints was 79% in 2015 (with 10 different locations) and the magnitude decreased to 63.9% in 2016 (with additional locations excluded). Adding three new locations in 2016 decreased the differentiation to 63.9%. The decreased differentiation in the juvenile populations might be because of increased elemental variability arising through the addition of new nearby sites. This indicates that space has an effect on the magnitude of separation. A similar decreasing trend in correct classification was observed in shearwater (Calonectris Cory & Oustalet) feather elemental profiles when the sample size was randomly reduced by 20% prior to formulating the discriminant functions [104].   The dissimilarity of chemical composition between locations within the Okanagan Valley may be due to either geologic [20], or irrigational [71] factor, or both. The underlying geology of the Okanagan watershed is very diverse within small spatial scales [22]. For instance, bedrock underlying the southern Okanagan is heterogeneous, made up of volcanic, metamorphic, plutonic, and sedimentary rocks, and overlain by glacial sediments (Figure 3 & 4) [22].  66  Similarly, soil chemistry is non-uniform over the landscape [105]. Water, mineralogy and soil microbiology affect soil chemistry. Solutes present in soil water can be derived from weathering of rocks and minerals locally or could have come from water (precipitation and irrigation). The trace elements move from minerals to soil water [106] where they are taken up by plants. In turn, plants form the base of the food web, eventually accumulating in animal tissues, forming a unique marker. The trace element availability in soil depends on various factors like soil pH, humidity, porosity, clay, and humidity [107,108].   Unlike irrigation water, precipitation probably contributes little to bone element composition because it is deionized water. The chemical composition of precipitation differs regionally depending on local sources such as lakes, surface water, etc. Generally, precipitation is poor in ions unless it becomes contaminated by pollution or dust. The elements that I have analysed in bone tissue are negligibly volatile. The concentration of the elements I have analysed are very-low in precipitation relative to soil water, groundwater, and surface water [109,110]. The major sources of irrigation water in the Okanagan Valley are surface and groundwater (mostly streams draining the highland plateau lakes). The regional chemical composition of surface water is influenced by geology and climate [111]. Thus, the spatial variability in precipitation occurs over a huge scale, but the irrigation water could be variable at small scales. Hence, the variability in chemical composition of soils and irrigation water could both contribute to the elemental composition of starling foods.    Trace element composition in bone tissues can yield a very specific biogeochemical marker. In this study, I have included 21 elements. Out of 20 elements analysed, fifteen elements (K, Mg, Na, Ag, Cd, Ba, Sc, Cr, Sr, Sn, Al, V, Cu, Mn, Zn, and Se) were used in a discriminant analysis that correctly classified juveniles to predetermined source populations. Some elements such as Mg, Cd, Cu, Ba, Sn, and Sr, are divalent cations, like Ca; and the molecular size varies but most of them are similar to the size of Ca. Therefore, they can substitute for Ca in apatite relatively easily. Similarly, some of them are not abundant in nature. For example, in a mineralized component of bone, Pb substitutes for Ca in hydroxyapatite with a turnover rate ranging from 1-8% per year 67  depending on the type of bone tissue [112].  Monovalent (e.g. Na and K) and multivalent elements (e.g. Al, Mn) create potential gaps in apatite and are probably less enriched in bone relative to divalent elements. Consequently, the shortlisted elements from the discriminant analysis are a mixture of those elements, which could easily be present in bone, and could vary in space.   5.2 Temporal Variation in the Elemental Fingerprints Trace elemental fingerprints of juveniles collected at the same location, around the same season in two consecutive years, were largely different (Figure 12 & 13). Although the habitat was similar, the dissimilarity in the elemental signature of juveniles from the same site might be due to environmental or analytical variability.   First, observed differences could be caused by environmental variability from sampling at different times in different years. Environmental variability can lead to different fingerprinting due to variability in various environmental factors such as temperature and precipitation; and food availability. Annual average temperature of Kelowna (at UBCO) was 10.53 oC in 2015 and 10.17 oC in 2016. Similarly, the average annual precipitation of Kelowna (at UBCO) was 24.95 mm in 2015 and about 28.17 mm in 2016. In 2015, the annual average temperature was slightly higher and precipitation was lower than in 2016.  Annual variations in temperature and precipitation in the valley can directly affect the irrigation system and indirectly affect starling food availability. This variability in temperature and precipitation between years can have some effect on seasonal food availability.  For instance, wet years might yield different insects than dry years; and wet years may yield large natural fruit crops whereas in dry years irrigated fruit may prevail. Changes in foraging behaviour by the generalist starlings due to changes in food availability could have produced different elemental fingerprints. Therefore, the combined influence of various environmental factors could potentially create different starling fingerprints between years, even at the same sites.  Second, analytical variability could yield fingerprint variations due to differences in sample preparation and sample analysis. Although, I tried to keep the sample preparation 68  identical between years, small differences might contribute to this. Variation in sample analysis could have led to variation in measured element concentrations likely due to differences in instrument performance and calibration of internal standards between years. Inter-annual differences in the sample analyses were assessed by reanalyzing 2015 samples with the 2016 samples, and correcting for possible analytical variability via slope and intercept adjustments. Differences in the fingerprints remained, suggesting that both sample preparation and analytical differences may have contributed to the analytical variability.   Thus, for starling, the elemental fingerprints are specific to a given year. This result is in contrast to the consistency of results obtained for feather trace elements in individual sand martins (Riparia riparia L.) [19]. The inter-annual differences in trace element fingerprints in my study might be because of differences in spatial scale, type of bird, and tissue used. My study area was smaller than that used for sand martins.   Starlings are generalists in terms of foraging behaviour, whereas swallow and martins are very specific in their diet. Being a generalist, starlings feed on extremely diverse foods depending on habitat, prey type, season and availability [37]. Consequently, inter-annual differences in diet could easily lead to differences in elemental fingerprints.   Inter-annual similarities in geochemical fingerprints were mostly detected in analyses of feather tissues. Elements partition differently into different tissues. Elements are deposited from plasma into bone according to their apatite affinity. The composition and structure of the feather and bone matrices is very different. The bone matrix is a composite material of collagen and bioapatite [85] whereas the principal constituent of a feather is a protein-like material known as keratin. On the basis of molecular structure, bone has a relatively much higher affinity for trace elements as compared to keratin. Furthermore, apatite incorporates many elements at much higher concentrations (parts per million) [85]. In contrast, the structure and chemistry of keratin do not favour complexation of trace metals. Furthermore, feather samples are easily contaminated from 69  water and dust. The concentrations and types of elements sequestered in these tissues can be very different. Thus, bone and feather assimilate and retain elements very differently.   5.3 Assigning Problem Starlings to Natal Populations The spatial resolution of the juvenile trace elemental fingerprints was high in the Okanagan Valley. I considered juveniles as the source population to characterize the particular sites where they were caught. I used the juvenile fingerprint library to assign a potential natal location to adult birds (referred to as problem birds hereafter) via discriminant functions (models). Adult birds collected in the fall in the Okanagan Valley were considered problem birds, and were potentially derived from both local and immigrant birds. The origins of some problem adults could not be identified and they were considered to be non-specific Okanagan birds; but are likely from sites within the valley, perhaps from intermediate areas between sampling locations.   Problem birds from both years were assigned to potential natal populations through discriminant analyses. The results can be compared to those of other studies, for example, in the marine environment, for the geographic assignment of shearwaters (Calonectris) to breeding colonies [104]; and in the terrestrial environment, to assigning non-local-dialect white-crowned sparrows (Zonotrichia) to populations that matched their song dialect (natal populations) [71] using feather trace elemental analysis and discriminant functions.   Problem birds having trace element fingerprints distinct from the Okanagan populations were consider immigrant birds. These immigrant birds were first detected through cluster analysis.  Immigrant bird fingerprints display a chemical composition distinct from Okanagan Valley juveniles. It is unlikely that starlings from outside the region were misidentified as Okanagan Valley starlings because the geochemical fingerprints of all populations sampled outside of the valley are very distinct. A study done in 2013 illustrates this, with Okanagan starling fingerprints being very distinct from those of surrounding regions, including Quesnel, Washington state, and Grand Forks [14].   70  Moreover, some problem birds, though they are likely from the valley, could not be assigned to sites of natal origin. The discriminant function values for these birds were too different from the centroid values for natal locations included in the fingerprint library. That might indicate that they are from areas between my sampling locations. Also, there is little information about the distribution and relative abundance of trace elements across the landscape [48,72]. Thus, the natal grounds of problem birds in the Okanagan Valley were identified through discriminant function analysis. The results provide evidence that bone tissue elemental fingerprints of migratory pest starlings can be used to identify the natal origin of starlings with high spatial resolution (10s of km). The technique can be very effective in an area like the Okanagan Valley with highly diverse geology.  This study indicates that the trace element profiles of closely spaced terrestrial passerine bird populations, like starlings’, can be distinguished from each other [71] and used to trace the origin of individual birds to natal sites [14]. This study also demonstrates the robustness of using trace elements in bone to track small-scale movements with a high degree of temporal and spatial precision. The selected trace elements may provide the best marker for tracing the origins of starlings and similar birds. Additionally, this technique of using trace element composition can be applied to other regions with diverse geology to trace the origin and movement of other pest species.   5.4 Discussion for Management  To make the findings easier to interpret from a management perspective, the problem birds collected in fall were divided into two sectors, first, problem birds collected in vineyard and orchard and second, those collected at dairy farms and feed lots. The origin of problem birds was interpreted in geographic space and for two land uses. Birds were categorized spatially into three main regions corresponding to Okanagan Valley regional districts as northern, central and southern. Major land-use categories analysed were vineyards, orchards, dairy farms and feedlots.    Around 96% of BC’s soft fruits, apples, and grapes are grown in the south-central Okanagan, whereas livestock operations such as dairy farms and feedlots, grains and forage crops predominate in the north Okanagan [47,77]. Thus, vineyards and orchards 71  are dominantly located in the southern region; and dairy farms and feedlots are dominantly located in the northern region of the valley.   5.4.1 Contribution to Problem Birds in Vineyard and Orchards The problem bird contribution to vineyards and orchards in the southern part of the Okanagan Valley was mostly from the northern end of the valley in both years. It is likely that the birds raised in the northern region move to the southern part of the valley for the change of food (fruits) in the fall. During winter, dairy farms and feedlots provide, adequate shelter and warmth and a diverse supply of food and water [113]. But in the fall, birds might move south for the fruit. The remaining problem birds in vineyards and orchards are a mixture of local (southern) and immigrant birds. This implies that the population increase in the south (vineyards/orchards) in the fall is likely due to migration especially from the north or movement of local resident birds due to foraging behavior. Specifically, locations, such as Salmon Arm, Vernon, Hullcar, and Armstrong are the major contributors of problem birds in vineyards and orchards. Thus, trapping from Vernon to Salmon Arm likely reduces the problem bird population in vineyards and orchards.   A relatively minor portion of the problem birds in vineyards and orchards were immigrant and non-specific Okanagan birds. The non-specific Okanagan birds are those birds that are from the valley.  Their elemental fingerprint does not match to a specific site fingerprint because they have a signature that is intermediate between sites. In contrast, immigrant birds are those problem birds that originate outside of the valley. Because immigrant and non-specific Okanagan adults contribute minimally to the problem population, investing resources in identifying their origin may be inconsequential.    5.4.2 Contribution of Problem birds on Dairy Farms and Feedlots The problem birds on dairy farms and feedlots were a mixture of immigrants (from outside of the valley) and local birds. The majority of the problem birds in the northern region were immigrants, indicating that birds from other (Northern BC) regions likely 72  enter the northern end of the valley. The high immigrant population in the north Okanagan is likely attributable to the higher number of dairy farms and feedlots, and spatial proximity to unsampled populations. Several past studies have noted that the flocks of starlings increase in number and concentrate on dairy farms and feedlots in winter [113,114].   Problem birds in dairy farms and feedlots (Lumby and Coldstream) were 89% immigrant birds in 2015. The higher number of immigrant birds might be due to the easily accessible food, water, and shelter for birds. In contrast, in 2016, problem birds were from local northern and southern region source populations, and this could reflect a seasonal dispersion as explained above. Therefore, the inter-annual contribution of immigrants is higher in dairy farms and feedlots in the north than in vineyards and orchards in south.   Thus, the problem birds that appear in vineyards and orchards in the south are a portion of the local and immigrant birds that are coming from the northern region of the valley; however, adults in the north are replaced by immigrants from outside of the valley. Lumby is the only site that did not contribute problem birds to either farming activity. This indicates that either these sites are not actually contributing or they are very well controlled. However, 20% of juvenile fingerprints from Lumby matched with Salmon Arm, and this might be because they are geochemically similar, and it is also possible that the juveniles might move within a smaller spatial scale. Hence, the chance of future contributions to the bird problem from these sites cannot be ruled out.   The predicted group membership of juvenile birds, as inferred from discriminant analysis, reveals that some birds disperse locally within the Okanagan Valley. The problem bird contribution at sites encompassing both farming activities suggests seasonal dispersion of starlings within and among regions of the valley, and also outside of the valley. It is observed that around one-third of female starlings nest in the same box in consecutive years and the number of problem birds increases in vineyards and orchards every year with the ripening of fruit [7]. The intermixing of a certain percentage of 73  juvenile fingerprints among locations supports this concept of internal dispersion within and between regions in the valley. For example, the fingerprints of some juvenile birds from Osoyoos and Oliver match best with Keremeos and Summerland indicating internal dispersion within the southern region. Similarly, matches with Salmon Arm, Mara and Hullcar in the northern region indicate dispersion among regions in the valley. Hence, a certain number of starlings are circulating to and fro, from north to south, and seasonally within the Okanagan Valley (Table 3). Additionally, the presence of immigrant adults in the fall indicates dispersion of birds from outside of the valley; thus, the starlings are a mobile pest creating problems in relation to both farming activities via their seasonal dispersion.    5.5 Variation in Age and Gender The trace element fingerprints of juvenile and most <1 year adults collected at the same sites were similar; however, most of the 1+ year adult fingerprints were different. This similarity between juvenile and <1 year adults arises because they are from the same population (flocks) and use analogous feeding and foraging habitat [19]. The differences between juvenile and 1+ year adults, however, is consistent with inter-annual variations in the juvenile fingerprints. Also, the composition of bone tissue varies with tissue age [84]. For instance, the mineral content of bone increases with age [82].   There was no difference in the elemental signature between genders. This result is comparable to results obtained using elemental analyses of sand martin (Riparia riparia L.) [19], and seabird [115] feathers, where the elemental composition of feathers did not vary by gender.      74  6 Application, Management and Future Directions  6.1 Implications for Management  This study reveals the comparative contribution from different sites to the problem birds in vineyards/orchards and dairy farms/feedlots in the Okanagan Valley through trace element fingerprinting. Vernon and Salmon Arm are sites that are over-represented in the problem population, whereas sites like Coldstream, Summerland, and Okanagan Falls are are under-represented. Sites in the southern valley do not contribute a large proportion of the problem birds, suggesting adequate control at present.   Some sites, especially Lumby, do not contribute to problem populations.  It is possible that starlings from the Lumby area do not migrate to vineyards and feedlots, or that control of starlings in Lumby by trapping is highly effective. Thus, to cut costs, trapping at Lumby stations could potentially be curtailed since they do not contribute to the problem directly in vineyards and orchards (Table 7).   To further control problem birds, trapping should focus on sites in Salmon Arm, Vernon, and Hullcar in the northern region; and Oliver, Osoyoos and Keremeos in the southern region (Table 7). Only 6% of problem birds in vineyards and orchards were immigrants, and efforts to identify their origin, and control such an insignificant contributor to the problem would likely not be cost effective.  Hence, the findings suggest that further starling control for southern vineyards and orchards, be primarily targeted at the northern and southern regions of the valley.    In contrast to vineyards and orchards, starling control for dairy farms and feedlots could focus on the Salmon Arm and Vernon populations in the north; and Oliver and Osoyoos in the south (Table 7).  However, because a high proportion of problem birds in dairy farms and feedlots were immigrants to the region, expanding trapping and sampling outside the region may be required.   75  6.2 Directions for Future Work in the Okanagan The main objective of this study was to identify the origin of problem birds in the Okanagan Valley during fall. I measured geochemical fingerprints at a small spatial scale in the Okanagan Valley. Success at this small scale is very encouraging and suggests that the approach could easily be used at a larger spatial scale regionally, and likely elsewhere, given sufficient variability in underlying geochemistry. A key advantage of expanding sampling outside of the Okanagan Valley would be the resultant greater certainty when identifying immigrants, as well as populations that may share similar geochemical signatures to those at Okanagan Valley sites.     Since I had samples from two consecutive years I also analysed the temporal variability of trace element fingerprinting. Temporal variability was larger than spatial variability, probably because I was pushing the limits of spatial resolution to identify source populations.  It may be possible to maintain the spatial resolution and enhance temporal resolution in future by minimizing analytical artifacts between years.  This could be accomplished by preparing and analyzing all samples in a single batch, and by minimizing differences in collection date among samples.  Because there were temporal differences in the fingerprints, it is not recommended to use the discriminant functions outside the year and region that the functions were derived.  Thus, future source population identification should be accompanied by contemporaneous sampling of the source populations.    Also, I used bone tissue for my analysis for two main reasons. First, it provides greater resolution of spatial separation than other tissues due to slow turnover rate. Second, I had access to an abundance of euthanized samples. It would not be feasible to use bone tissue for live-captured and especially protected or endangered or threatened species. In this situation, feathers would be a suitable alternative tissue to use in this type of research [14].    76  6.3 Other Applications  Besides identifying bird origins, the trace elemental signature could be used in several other ecological studies of birds, including matching breeding populations with overwintering populations [72], tracking fine-scale dispersal [71], identifying migratory connectivity [18], identifying moulting areas [19], etc. Trace element fingerprinting and source identification has applications also for other migratory wildlife, and in food authentication studies [116]. The concepts are similar using chemical markers present in food (meat, fruits, vegetables, juice, etc.) tissues to infer the location where they were produced.  Trace element fingerprinting techniques have greatly contributed to resolving problems pertaining to the authentication and adulteration of foods, including  beverages, vegetables, fruits, meats, dairy products and cereals [107,117]. For example, multi-elemental fingerprinting analysis was performed to identify the geographic origin of ciders from England, Switzerland, France, and Spain [116], and rice from different cultivated areas, such as Australia, Japan, and the USA [118]. This example supports the concept that multi-elemental fingerprinting can be used to identify the sources of food products across large spatial scales. This is possible for a wide range of other commodities including beverages such as wine [106,119], tea [120,121], juice; vegetables such as onions [122], potatoes [123]; honey [124]; dairy and animal products, such as cow milk, meat and fish [125]; and oil products like olive oil [126], and pumpkin seed oil [127]. Thus, trace element fingerprints have proven to be a very strong tool for identifying geographical origin and adulteration issues for a wide range of naturally occurring foodstuffs [121].     6.4 Challenges and limitations  In spite of having very wide applications of the technique, there are two major challenges in using this technique in any study. First, the species of interest should be migratory and the region of interest should have geological diversity at scales relevant to the movements of individuals and populations.  Trace element fingerprinting is most suitable in geologically diverse areas because fingerprints can vary micro-geographically [19]. Thus, this technique is a strong tool to trace origins or track the migratory movement of 77  organisms when the dispersion/movement happens between distinct geological areas or ecosystems.    Trace element profiling is more difficult for wide-ranging, continuously distributed species as the number of potential source areas is vast. If the organism is large-scale migratory in nature, then trace elements alone might not be sufficient to track their origin. Also trace elements alone would not be sufficient for tracking the origin of migratory organisms that undertake large-scale movements and forage in diverse geochemical environments. However, the combined use of trace elements and isotopes could help track continental-scale movement patterns. For example, both trace elements and stable isotopes were used to identify the moulting grounds of sand martins in Europe and Africa [19].   Second, the choice of a tissue type which best preserves a suitable temporal record of past feeding [15] is another challenge in these studies.  This is important because the turnover time of elements varies among different tissue types. Turn-over time is a period over which trace element concentrations in animal tissue will change because of changes in animal diet.   In birds, geochemical fingerprints are often derived from feather samples.  Limitations to feather use include the loss of information when the feathers are moulted seasonally. Additionally, feathers are an organic matrix not well suited to the accumulation of trace elements from dietary sources. Finally, they are easily contaminated by mineral dust.    The use of bone tissue provides very promising results for identifying the origins of adult European starlings. But, there are a few limitations that accompany the bone tissue technique. Firstly, this technique might be limited to certain species because of the sampling method. Bone sampling generally requires destructive sampling; thus, it might not be applicable to endangered or protected species. Secondly, the chemical signature might change with the average lifetime of the tissue. Since the turnover rate of the bone 78  collagen and apatite is very slow [53,128], the signature might preserve for several years on average.   When animals undergo certain dietary modifications because of dispersion, metamorphosis, or any other reason, the elemental composition of bone will begin to change to reflect their diet at rates determined by the turnover times [58]. This was minimized by using juveniles in my study, to more confidently assign source populations.  Age 1+ adult birds were less confidently assigned to source populations because temporal variability is relatively large compared to spatial variability, and a portion of their life history might be spent outside the region as they continue to turnover elements in proportion to differences in regional geochemistry.  Thus, there is potential to wrongly assign the origin of 1+ adults in this study due to fingerprinting averaging in birds, created though the mixing of trace elements from several areas over time.                    79  7 Conclusion  European starlings are mobile pests and a huge problem in the Okanagan agriculture industry. For more than a decade a starling control program has worked to mitigate EUST populations by trapping throughout the valley. Despite trapping, the problem persists. By identifying the origin of problem birds and the sites contributing most to the problem birds, the starling control program can focus on the most problematic sources. Trace element fingerprints have been successfully used as an intrinsic marker to trace the origin of problem birds in the Okanagan Valley. This research identified the origin and the sites that contribute most to the problem. Most of the problem birds in vineyards and orchards were identified as local birds; very few were immigrants. In contrast, in dairy farms and feedlots, most of the problem birds were identified as immigrants to the valley. This study reveals that trace element fingerprints, a natural environmental tracer, can track the origin of mobile organisms like starlings. Thus, by identifying the origin of problem birds in the valley, this research provides important detail regarding the sites contributing to the problem, allowing managers to make more informed decisions about how and where to concentrate control efforts.   Besides identifying the origin of problem birds, this study also shows that trace elements can provide high spatial resolution, distinguishing populations within several tens of kilometers of each other. In regions with highly diverse geochemistry, like the Okanagan Valley, trace elements can be extremely useful for tracking dispersion and movement patterns in organisms. 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PLoS ONE. 2007;2:3–7.    94  Appendices  Appendix A Juvenile and adult European starling (Sturnus vulgaris); Photo Credit: Dr.Ian Walker      95  Appendix B Descriptive statistics of juvenile European starlings in 2015 The sample sizes vary between locations  Element K  Mg  Na  Sr  Mo  Ag  Cd  Sn  Ba  Pb  Location Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Armstrong 0.18 0.64 0.38 1.01 -0.28 0.46 0.17 1.04 -0.56 0.80 -1.30 0.72 -1.30 0.54 0.07 0.93 0.78 1.38 0.07 1.09 Hullcar 1.00 0.61 0.25 0.65 0.29 0.65 0.50 0.64 -0.25 0.51 0.46 0.77 0.05 0.92 0.06 0.81 1.04 1.67 -0.35 0.15 Kelowna -0.93 0.29 -1.39 0.45 -0.84 0.64 -0.02 0.88 0.61 0.90 0.37 0.49 0.96 0.14 -0.99 0.45 0.19 1.02 0.40 1.75 Keremeos 1.68 0.75 0.84 0.37 2.02 1.33 -0.58 0.43 0.69 1.45 0.82 0.29 0.47 0.29 0.33 0.64 -0.60 0.46 -0.42 0.11 Mara -0.57 0.68 0.75 1.05 -0.74 0.70 -0.13 0.84 -0.55 2.18 -1.63 0.70 -1.53 0.65 0.22 0.82 -0.24 0.70 -0.28 0.17 Oliver -0.40 0.28 -0.25 0.41 -0.09 0.44 -0.25 0.74 0.15 0.63 0.10 0.81 0.41 0.93 -0.13 1.28 -0.48 0.58 0.26 0.52 Osoyoos -0.02 0.49 -0.36 0.41 0.09 0.28 -0.33 0.32 0.44 0.24 0.47 0.65 0.77 0.20 -0.41 0.50 -0.38 0.47 -0.06 0.37 Pentiction 0.98 0.70 0.72 0.86 1.92 0.51 0.89 2.92 0.10 0.29 0.90 0.45 0.15 0.28 0.74 1.04 -0.30 0.30 0.90 1.87 Salmon Arm 1.13 1.00 0.54 0.97 0.06 0.82 0.42 0.51 -0.86 0.61 -1.05 0.45 -1.25 0.37 -0.06 0.53 0.15 0.72 -0.25 0.25 Vernon -0.52 0.61 0.36 0.54 0.05 0.64 -0.35 0.56 0.00 0.32 0.59 0.38 0.26 0.26 0.87 0.99 -0.44 0.39 -0.30 0.17 Element Al  S  Sc  V  Cr  Mn  Co  Cu  Zn  Se  Location Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Armstrong 0.25 0.55 0.40 1.38 0.22 0.32 0.90 1.42 -0.31 0.01 -0.36 0.94 0.28 0.28 -0.10 0.95 0.11 1.11 1.01 0.46 Hullcar 1.49 2.35 0.38 0.90 0.61 0.45 -0.04 0.42 -0.25 0.06 0.00 0.91 0.42 0.42 0.14 0.86 0.53 0.84 0.44 0.63 Kelowna -0.44 0.22 0.38 1.52 0.16 0.41 -0.08 0.81 -0.15 0.18 -0.31 0.68 -0.10 0.15 -0.96 0.42 -0.49 1.51 -1.12 0.58 Keremeos -0.24 0.34 0.25 0.40 -3.11 0.84 -0.30 0.25 -0.20 0.10 1.68 1.25 1.93 3.34 0.55 0.76 0.72 0.74 -2.17 1.07 Mara 0.10 0.75 -0.48 0.52 0.13 0.34 0.57 1.51 -0.29 0.05 -0.64 0.65 0.14 0.12 0.07 0.81 -0.19 0.50 0.80 0.33 Oliver -0.46 0.20 -0.53 0.47 -0.30 0.79 -0.40 0.31 -0.30 0.02 -0.54 0.90 -0.46 0.35 -0.11 1.16 -0.21 0.61 0.04 0.52 Osoyoos -0.47 0.19 0.21 0.34 0.05 0.81 -0.29 0.58 -0.28 0.02 -0.08 0.64 -0.59 0.13 -0.39 0.48 -0.16 0.57 0.16 0.23 Pentiction -0.39 0.27 -0.36 0.54 -0.81 2.00 -0.53 0.32 -0.17 0.15 1.06 1.29 0.32 0.72 0.88 1.11 0.24 1.03 -0.11 1.14 Salmon Arm 0.57 0.87 0.03 1.14 0.28 0.18 0.80 1.63 1.81 2.60 -0.51 0.51 0.76 0.76 -0.21 0.52 0.24 1.24 0.82 0.50 Vernon -0.17 0.41 -0.36 0.52 0.43 0.32 -0.46 0.37 -0.01 0.55 0.65 0.76 -0.87 0.30 0.91 0.95 0.07 0.44 0.18 0.34  Note: SD: Standard Deviation 96  Appendix C Z-scored data of trace elements of Juvenile birds in 2015   Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZS ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe Kel1 -1.01 -1.61 -1.54 1.68 0.96 0.96 1.06 -1.12 1.37 -0.24 -0.44 2.79 0.57 -0.27 0.42 -0.21 0.11 -1.02 2.63 -1.07 Kel2 -0.83 -1.31 -2.22 1.19 0.41 0.68 0.95 -0.92 -0.43 -0.48 -0.47 0.53 0.18 -0.56 -0.21 -0.06 0.00 -0.81 -0.32 -1.64 Kel3 -0.73 -2.06 -1.06 -0.46 -0.16 0.92 0.88 -1.64 -0.61 -0.64 -0.69 -1.68 0.47 -0.88 -0.25 -1.18 -0.23 -1.55 -2.81 -1.08 Kel4 -0.98 -1.49 -2.59 -0.25 0.28 0.78 0.91 -1.36 0.46 -0.30 -0.61 -0.81 0.29 -0.68 -0.23 -0.80 -0.13 -1.22 -1.57 -1.22 Kel5 -1.10 -2.48 -1.14 -0.45 -0.26 0.49 0.88 -1.09 -0.35 0.56 -0.64 -1.48 0.17 -0.63 -0.25 -0.87 -0.26 -1.08 -1.43 -1.46 Kel6 -1.12 -1.35 -0.67 0.43 0.80 0.57 1.07 -1.46 1.30 1.17 -0.48 -0.24 0.36 -0.43 -0.29 -1.14 -0.03 -1.41 -1.41 -1.00 Kel7 -0.94 -1.48 -0.62 -0.87 -0.17 0.68 0.94 -1.62 -0.87 -0.30 -0.57 -0.73 0.35 2.78 0.08 -1.42 -0.03 -1.56 -1.86 -0.51 Kel8 -1.08 -1.45 -0.46 -1.22 -0.08 0.25 0.77 -1.06 -1.14 -0.34 -0.67 -1.61 -0.03 -0.63 -0.14 -0.98 -0.26 -0.87 -2.11 -1.44 Kel9 -1.14 -1.46 -0.86 -1.39 -0.23 0.20 0.95 -0.98 -1.27 -0.26 -0.63 -1.33 -0.02 -0.68 -0.28 -0.67 -0.26 -0.78 -2.41 -1.29 Kel10 -0.66 -0.92 -0.58 -0.12 3.05 0.49 1.22 -0.99 0.79 7.29 -0.10 1.77 0.01 0.37 -0.09 0.39 -0.13 -0.95 1.35 -1.03 Kel11 -1.11 -1.01 -0.66 0.06 0.66 0.77 0.95 -0.87 -0.47 -0.36 -0.62 0.70 0.06 -0.59 -0.27 -0.23 -0.26 -0.93 -0.44 -1.38 Kel12 -0.09 -1.28 -0.08 0.45 0.56 -0.16 1.00 -0.20 1.66 1.14 0.03 2.21 -0.46 0.82 -0.21 0.84 0.03 -0.15 1.95 -1.51 Kel13 -1.05 -1.80 -0.74 -0.27 0.37 -0.05 0.94 -0.87 0.28 0.03 -0.32 1.05 -0.06 -0.16 -0.17 0.11 -0.27 -0.90 -0.58 -1.61 Kel14 -1.03 -1.30 -0.87 -1.06 -0.13 -0.33 0.75 0.07 -0.66 -0.10 -0.57 -0.27 -0.57 -0.44 -0.26 0.17 -0.13 0.04 -0.72 -2.21 Kel15 -1.24 -0.77 -0.43 0.88 1.80 0.12 0.88 -1.23 1.22 -0.36 -0.50 2.60 0.18 0.33 -0.13 -0.02 -0.13 -1.23 1.53 -1.04 Kel16 -1.21 -1.84 -0.61 0.55 1.35 0.37 1.07 -1.49 0.27 1.93 -0.15 2.43 0.37 0.21 0.19 0.23 0.08 -1.56 0.77 -0.81 Kel17 -1.18 -0.42 -0.54 -0.04 2.19 0.00 1.31 -1.09 2.52 0.02 -0.41 2.04 0.07 -0.08 -0.23 0.77 -0.13 -1.11  0.25 -0.74 97  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZS ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe Kel18 -0.51 -1.10 -0.40 -1.18 -0.19 -0.23 0.93 -0.84 -0.40 -0.39 -0.39 0.75 -0.13 -0.38 -0.17 0.20 -0.13 -0.77 -0.43 -1.21 Kel19 -1.01 -1.41 -0.56 1.27 0.01 -0.48 0.94 -0.73 -0.12 -0.16 -0.49 -0.09 -0.11 0.39 -0.12 -0.14 -0.13 -0.79 -0.64 -0.92 Kel20 -0.53 -1.21 -0.09 0.33 0.92 1.39 0.76 -0.35 0.19 -0.17 -0.02 -1.01 1.43 -0.16 -0.29 -1.17 0.27 -0.58 -1.63 0.74 V1S1 1.23 0.27 1.18 -0.34 -0.03 1.29 0.77 -0.63 -0.46 -0.56 0.44 -1.09 0.84 -0.13 -0.14 -1.30 0.26 -0.58 -1.09 0.31 V1S2 1.59 0.55 0.25 0.17 -0.12 1.15 0.77 0.10 -0.02 -0.45 2.59 0.03 0.50 -0.32 -0.27 -0.51 0.20 0.26 0.42 -0.01 V1S3 1.07 1.19 0.66 1.82 0.39 1.24 0.63 -0.39 2.26 -0.46 -0.24 0.59 0.66 -0.53 -0.30 -0.08 0.30 -0.26 0.23 0.09 V1S4 0.00 1.02 -0.19 0.27 0.13 0.71 0.87 0.30 -0.50 -0.43 1.48 1.33 0.46 -0.12 -0.27 0.28 0.38 0.46 0.87 2.00 V1S5 1.10 -0.37 0.73 0.71 0.32 0.86 0.75 0.31 0.25 -0.34 1.74 1.25 0.34 0.41 -0.14 1.16 0.37 0.43 0.68 0.23 V1S6 0.97 0.92 -0.84 -0.17 -0.25 0.94 0.78 1.35 -0.18 -0.16 1.54 1.40 -0.01 -0.05 -0.27 1.42 0.50 1.52 1.49 -0.25 V1S7 0.33 -0.57 -0.33 0.11 -1.22 -0.35 -0.99 1.35 -0.03 -0.28 -0.05 -0.42 1.73 0.30 -0.28 0.51 1.57 1.40 1.01 0.33 V1S8 1.68 -0.26 1.13 1.11 -0.36 -0.71 -1.34 -0.10 2.41 -0.04 7.56 1.13 0.41 0.69 -0.24 0.26 0.35 -0.02 1.75 0.96 V1S9 1.75 0.13 0.12 0.79 -0.49 -0.24 -0.81 -0.99 2.31 -0.42 0.01 0.18 0.63 -0.67 -0.26 -1.11 0.11 -1.07 0.40 0.26 V1S10 0.28 -0.40 0.19 0.48 -0.88 -0.31 -0.89 -0.72 4.36 -0.34 -0.19 -0.58 0.54 0.01 -0.29 -0.67 0.13 -0.79 -0.43 0.46 V4F1 2.26 0.92 0.19 0.86 -0.48 -0.36 -0.80 -0.70 1.51 0.27 -0.04 1.32 0.54 0.60 2.13 -0.55 1.06 -0.90 1.68 0.33 V4F2 2.11 0.78 0.16 0.17 -1.46 -1.47 -1.47 0.52 -0.13 -0.05 2.08 1.15 0.07 1.04 1.02 0.06 0.61 0.43 1.83 1.80 V4F3 0.94 0.25 0.52 0.75 -1.39 -0.92 -1.13 -0.23 0.81 -0.34 1.37 -1.29 0.34 0.61 -0.26 -0.93 0.18 -0.37 -1.01 0.99 V4F4 -0.20 0.87 -0.85 0.64 -1.36 -1.74 -2.10 1.06 1.01 -0.02 0.33 -0.44 -0.07 -0.17 0.07 -0.10 0.27 0.84 0.11 0.09 V4F5 -0.19 1.09 -0.68 1.32 -1.07 -1.06 -1.20 -0.05 0.27 -0.45 -0.23 -0.30 0.30 0.36 2.21 -0.94 0.82 -0.17 -0.18 0.50 V4M2 0.11 2.41 -1.13 0.40 -0.96 -0.62 -1.19 -0.26 -0.38 -0.39 1.07 -0.92 0.26 0.62 3.21 -0.90 1.23 -0.45 -1.05 0.50 98  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZS ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe V4M3 0.58 0.76 -0.43 -0.09 0.51 -0.70 -1.04 -0.34 -0.52 -0.39 -0.56 -1.02 0.39 -0.41 0.07 -1.28 0.16 -0.49 -0.84 0.75 V4M4 1.67 -0.90 0.43 0.30 -0.32 -1.64 -1.51 0.27 -0.25 -0.24 0.71 1.80 0.19 5.25 8.36 0.20 2.61 0.06 1.87 1.18 V4M5 1.92 -0.90 1.31 0.32 -1.20 -1.07 -1.00 -0.39 -0.35 -0.44 1.27 0.85 0.36 0.54 1.63 -0.01 0.48 -0.37 0.95 1.10 V4M1 2.11 0.15 1.08 -0.51 -0.90 -0.88 -1.06 -0.47 -0.50 -0.50 -0.32 -0.89 0.45 -0.39 -0.32 -0.64 0.15 -0.63 -0.95 1.03 V8F1 0.39 -1.40 -0.11 -0.34 -0.43 -0.44 -0.69 -1.03 -0.52 -0.43 -0.20 0.24 0.65 1.07 -0.31 -1.03 0.23 -1.17 -0.38 1.94 V8F2 0.36 -0.64 0.17 -0.35 -0.90 -0.78 -0.93 -0.59 0.12 2.90 0.48 -1.16 0.42 0.33 -0.31 -0.91 0.14 -0.76 -0.90 0.61 V8F3 -0.40 -0.20 -0.51 -1.12 -1.32 -2.66 -1.49 1.67 -0.84 0.16 0.79 -0.83 -0.34 2.92 -0.28 1.13 1.02 1.60 0.24 0.59 V8F4 1.62 0.19 0.65 0.54 -0.95 -1.40 -1.36 0.30 1.51 -0.19 0.25 1.73 0.21 1.82 -0.29 0.12 0.21 0.00 1.28 0.82 V8M1 -0.55 1.31 -0.92 2.10 -1.17 -2.26 -2.57 1.51 3.38 -0.03 1.37 0.96 -0.24 3.13 -0.30 1.15 0.30 1.35 2.17 0.59 V8M2 -0.23 1.31 -0.48 0.23 1.03 -1.14 -1.27 -0.35 0.06 -0.49 -0.01 -1.15 0.27 -0.54 -0.31 -1.14 0.17 -0.49 0.26 1.07 V8M3 0.13 0.91 -0.56 0.35 0.49 -0.93 -0.95 -0.49 0.72 -0.40 -0.32 1.86 0.34 0.15 -0.32 -1.09 0.15 -0.60 -0.88 0.79 V8M4 0.43 0.33 -0.44 -1.05 -1.12 -0.84 -1.07 -0.21 0.28 -0.46 0.04 -0.44 0.44 -0.30 -0.32 -0.98 0.15 -0.55 -1.27 1.28 V8M5 -0.10 1.61 -0.32 1.17 -0.64 -1.27 -1.38 -0.15 2.34 -0.43 -0.13 2.38 0.21 -0.46 -0.32 -0.47 0.18 -0.24 0.48 1.44 V6M1 -0.13 1.91 0.38 -0.62 -1.62 -1.89 -1.76 0.80 -0.35 -0.05 1.31 -0.03 -0.09 0.41 -0.32 -0.35 0.24 0.42 0.05 1.25 V6M2 -0.28 1.05 -0.86 1.43 -1.03 -1.62 -1.50 0.04 1.30 -0.35 -0.06 0.37 0.20 0.43 -0.32 -0.60 0.21 -0.05 -0.03 0.94 V6M3 -1.19 2.32 -1.81 -0.96 -1.32 -2.33 -2.19 1.11 -0.73 -0.22 0.16 -0.25 -0.11 -0.34 -0.31 0.07 0.07 0.96 -0.33 0.85 V6M4 -1.37 1.04 -1.52 -0.96 -1.31 -1.88 -1.70 0.42 -0.74 -0.32 -0.25 -0.21 -0.05 0.03 -0.33 -0.21 0.05 0.40 0.25 0.35 V6F1 -1.05 0.12 0.01 0.28 -0.70 -1.04 -0.99 -0.36 -0.93 -0.38 -0.46 -0.94 0.43 0.54 -0.32 -1.24 0.01 -0.58 -0.25 0.23 V6F2 -0.52 1.39 -0.32 0.47 -1.93 -2.95 -2.77 1.62 0.08 -0.13 -0.21 -0.58 -0.52 0.21 -0.16 0.32 0.10 1.47 0.32 1.11 99  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZS ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe V6F3 -0.60 -0.25 -0.54 -0.70 -0.94 -0.82 -0.92 -0.74 0.31 -0.51 -0.46 -1.35 0.42 -0.15 -0.30 -1.45 0.00 -0.89 -0.82 0.93 V6F4 0.87 -0.12 -1.04 -0.67 5.17 -1.12 -0.95 -0.38 -0.64 -0.09 1.43 -0.80 0.39 4.49 -0.25 -1.38 0.31 -0.37 -1.12 0.84 V6F5 -0.85 -0.76 -0.92 0.57 -1.25 -1.04 -1.00 -0.55 -0.44 -0.51 -0.52 -0.58 0.48 -0.46 -0.28 -0.93 0.29 -0.78 0.20 0.70 N1bF1 -1.28 0.02 -0.98 -1.02 0.28 0.66 0.31 0.96 -0.77 -0.38 -0.39 -0.29 0.46 0.42 1.53 1.07 -0.51 0.93 0.24 0.13 N1bF2 -0.60 0.37 -1.01 0.06 -0.14 0.66 0.36 0.54 -0.27 -0.39 0.00 -0.06 0.53 -0.69 -0.32 0.66 -1.02 0.67 -0.31 0.08 N1bF3 -0.47 -0.38 -0.04 -0.59 -0.20 0.08 -0.08 2.44 -0.12 -0.10 -0.11 -0.04 0.14 -0.43 -0.29 1.51 -1.31 1.98 0.62 -0.05 N1bF4 -0.25 0.70 0.04 -0.49 -0.06 0.64 0.28 0.35 0.02 -0.37 -0.37 0.14 0.58 -0.71 -0.05 -0.17 -0.81 0.34 -0.34 0.29 N1bF5 0.57 0.21 0.89 -0.09 1.06 0.61 0.53 0.22 -0.44 -0.45 -0.38 0.16 0.61 -0.09 1.26 0.65 -0.30 0.33 0.27 0.31 N1bF6 -1.22 0.21 -0.27 -0.60 -0.08 0.21 0.26 0.94 -0.68 -0.25 -0.37 -1.10 0.40 -0.63 -0.33 0.68 -0.92 1.13 -0.39 -0.03 N1bF7 -1.35 1.38 -0.13 -0.56 0.07 0.16 0.18 1.62 -0.61 -0.24 0.17 0.02 0.29 -0.65 -0.29 1.48 -0.99 1.50 0.38 0.58 N1bM1 -0.54 1.39 0.71 -0.27 0.01 0.53 0.69 -0.10 -0.53 -0.56 -0.60 0.22 0.70 -0.72 -0.31 0.09 -0.74 -0.01 -0.19 0.66 N1bM2 -0.92 0.35 -0.42 0.17 0.07 0.24 0.28 0.87 -0.19 -0.26 -0.27 -0.11 0.42 -0.45 -0.24 0.82 -0.91 1.14 0.08 0.11 N1bM3 -0.67 0.76 0.55 0.33 0.13 1.17 0.37 0.41 -0.80 0.08 -0.45 -0.25 1.07 -0.81 -0.05 0.61 -0.83 0.55 -0.36 0.15 N1bM6 0.41 0.59 0.15 0.55 0.07 0.93 0.10 0.52 0.61 -0.27 -0.38 -0.35 0.51 -0.74 -0.26 0.45 -1.03 0.60 -0.26 -0.56 N1bM7 -0.32 0.50 0.13 -1.00 -0.13 1.03 0.26 0.53 -0.59 -0.33 0.00 -0.97 0.43 0.35 0.40 0.14 -0.66 0.53 -0.27 0.03 N4F1 -0.71 -0.52 0.37 -1.03 -0.08 0.94 0.55 0.88 -0.52 -0.23 -0.16 -1.21 0.28 -0.64 -0.31 0.30 -0.71 1.07 0.03 0.32 N4M1 0.65 -0.16 1.60 0.67 -0.04 1.17 0.47 -0.53 -1.13 -0.52 -0.57 0.04 0.72 -0.79 -0.29 -0.43 -0.67 -0.52 -0.25 0.25 N1BM4 -0.02 0.21 -0.23 -0.09 -0.14 0.64 0.35 0.78 -0.51 -0.41 -0.32 0.12 0.38 -0.74 -0.26 0.31 -0.85 0.66 0.19 0.17 N1BM5 -0.95 0.68 -0.64 -0.24 -0.62 -0.20 -0.46 3.86 -0.05 0.02 -0.02 -1.37 -0.55 -0.09 -0.31 2.90 -1.65 3.96 1.03 -0.38 100  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZS ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe N1BM8 -0.55 -0.37 0.16 -0.86 -0.19 0.65 0.17 0.23 -0.77 -0.40 1.22 -0.72 0.47 -0.40 0.18 0.05 -0.64 0.38 -0.12 0.33 N4F2 -1.13 0.60 0.02 -1.18 -0.08 0.55 0.15 1.21 -0.54 -0.38 -0.13 -0.67 0.28 -0.46 -0.28 0.51 -1.10 1.09 0.91 0.87 P1F1 0.89 1.34 2.06 -0.90 -0.22 0.33 0.16 0.96 -0.80 0.22 -0.55 -0.81 0.30 -0.71 -0.15 0.37 -0.13 1.13 0.09 0.54 P1F2 1.71 1.40 2.49 -0.51 0.11 1.42 0.43 -0.82 -0.13 0.50 -0.70 -1.08 1.16 -0.93 -0.28 -1.06 -0.13 -0.82 -1.22 0.51 P2M2 -0.08 0.59 1.71 0.85 -0.14 1.05 0.19 0.81 -0.07 -0.23 -0.15 -0.07 0.34 -0.67 -0.31 0.75 -0.13 0.68 -0.16 0.46 P4F1 1.28 -0.65 1.47 -0.70 -0.03 0.42 -0.12 2.30 -0.12 4.66 0.03 -0.24 -0.08 -0.49 -0.11 1.97 -0.13 2.38 0.89 0.94 J-PD1 0.45 0.12 1.31 -0.12 0.41 1.29 0.47 0.13 -0.13 0.53 -0.50 -0.39 -2.59 -0.40 -0.27 2.02 0.95 0.31 0.00 -1.28 J-PD2 1.64 1.49 2.49 6.71 0.50 0.86 -0.21 1.08 -0.55 -0.28 -0.46 0.42 -3.96 -0.01 0.09 2.30 1.48 1.63 1.82 -1.80 J-K2F1 1.55 0.25 1.52 -0.02 0.53 1.26 0.80 -0.37 -0.35 -0.49 -0.59 0.21 -2.06 -0.48 -0.27 0.31 0.94 -0.38 0.31 -0.96 J-K2F2 2.04 1.25 2.07 -0.94 3.23 0.85 0.68 0.28 -1.14 -0.27 0.21 0.28 -3.10 0.11 -0.28 1.86 7.87 0.77 1.31 -1.35 J-K2F4 1.61 0.77 2.13 -0.82 -0.13 0.91 0.27 -0.16 -0.93 -0.57 -0.46 -0.34 -2.62 -0.50 -0.24 0.80 0.21 0.04 -0.40 -2.31 J-K2F5 0.59 0.97 0.36 -0.90 -0.09 0.55 0.51 0.71 -0.59 -0.42 -0.38 0.80 -3.50 -0.34 -0.02 1.90 0.28 0.70 1.21 -2.60 J-K2M1 2.63 0.95 4.04 -0.24 -0.11 0.55 0.08 1.20 0.02 -0.36 0.01 0.31 -4.27 -0.28 -0.21 3.55 0.34 1.60 1.17 -3.65 O2F1 -0.85 -0.66 -0.72 -0.69 -1.46 -1.53 -1.71 0.62 -1.03 0.02 -0.45 -1.32 0.08 -0.44 -0.30 -0.22 0.05 0.33 0.09 0.98 O2F2 -0.31 -0.62 0.72 1.60 -0.24 -0.58 -0.85 -0.73 -0.54 -0.19 -0.65 -0.52 0.51 0.34 -0.32 -1.34 0.00 -0.88 -0.75 0.60 O2F3 -0.13 0.04 0.18 -0.60 0.27 1.05 0.94 -0.60 -0.54 0.44 -0.43 -0.44 0.50 -0.60 -0.31 -0.42 -0.54 -0.35 -0.37 0.27 O2F4 -0.57 0.29 -0.04 0.25 0.33 0.64 0.93 -0.46 -0.39 0.90 -0.58 -0.39 -0.16 -0.63 -0.32 -0.37 -0.55 -0.29 -0.34 -0.47 O2F5 -0.63 -0.30 -0.41 -0.43 0.53 0.66 0.93 -0.46 -0.68 -0.23 -0.50 -0.45 -0.33 -0.64 -0.32 -0.62 -0.41 -0.35 -0.34 -0.11 O2M1 -0.26 -0.86 -0.62 -0.91 -0.04 -0.73 0.47 3.23 -0.85 1.06 0.04 -1.34 -2.30 -0.23 -0.28 1.55 -1.21 2.98 1.00 -0.90 101  O2M2 -0.01 0.28 0.34 -0.73 0.61 0.47 0.97 -1.05 -0.48 0.80 -0.58 -0.38 -0.16 -0.61 -0.27 -1.35 -0.37 -0.91 -0.78 -0.04 O2M3 -0.13 -0.45 -0.16 -0.43 0.49 0.74 0.99 -1.16 -0.79 -0.27 -0.57 -0.47 -0.04 -0.58 -0.32 -1.70 -0.47 -1.11 -0.94 0.09 O2M4 -0.49 -0.31 0.11 -0.61 0.56 0.12 0.72 -0.28 1.08 -0.29 -0.34 -0.11 -0.56 -0.19 -0.29 -0.32 -0.57 -0.18 -0.25 0.14 O2M5 -0.66 0.12 -0.29 0.03 0.44 0.12 0.73 -0.41 -0.56 0.37 -0.57 0.14 -0.48 -0.41 -0.25 -0.63 -0.55 -0.31 0.53 -0.12 O4F1 0.52 -0.74 0.27 -0.60 0.17 0.05 0.94 -0.66 -0.53 -0.29 -0.07 -0.20 -0.37 -0.54 -0.27 -0.47 -0.53 -0.49 -0.70 0.04 O4F2 -0.65 -0.40 0.17 -0.19 0.87 -0.24 0.77 0.25 -0.07 0.71 -0.46 0.05 -0.85 -0.53 -0.26 0.21 -0.62 0.29 0.82 -0.06 O4F3 0.63 -0.49 0.19 0.17 0.29 -0.13 0.92 -0.17 -0.28 0.16 -0.53 -0.10 -0.69 -0.60 -0.27 -0.05 -0.57 -0.24 0.04 0.10 O4M1 -0.19 -0.68 0.03 -0.41 0.12 0.06 0.85 -0.70 -1.20 -0.46 -0.62 0.17 -0.45 1.11 -0.27 -0.65 -0.53 -0.57 -0.50 -0.08 O4M2 -0.20 -0.33 0.03 -0.51 0.49 0.32 0.98 -1.15 -0.71 -0.13 -0.70 0.02 -0.14 -0.21 -0.27 -0.81 -0.36 -1.13 -0.93 0.00 O4M3 0.45 0.25 0.50 -0.01 0.51 1.30 0.59 -0.56 -0.11 0.01 -0.51 0.57 1.34 -0.67 -0.26 -0.07 -0.66 -0.65 0.23 0.53 O4M4 -0.48 0.25 -0.46 -0.82 0.56 1.29 0.67 -0.61 -0.49 -0.21 -0.49 0.37 0.94 -0.54 -0.31 -0.09 -0.66 -0.55 -0.35 0.39 O4M5 -0.28 -0.75 -0.02 -0.28 0.50 1.08 0.43 0.29 0.35 -0.29 -0.37 0.80 0.62 -0.32 -0.31 1.25 -0.79 0.26 0.14 0.38              102  Appendix D Z-scored data of trace elements of Juvenile birds in 2016   Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe J16-V2F1 1.97 0.56 1.97 -0.4 0.01 0.57 -0.2 1.1 -0.6 -0.2 0.56 0.46 -0 0.84 3.12 -0.4 0.64 0.05 0.31 J16-V2F2 0.42 0.08 0.43 -1.1 -0 0.57 -0.2 1.2 -0.5 -0.3 0.63 0.54 -0.3 -0.2 1.28 -0.5 0.56 -1 -0 J16-V2F3 0.54 0.54 0.79 0.89 -0 0.56 -0.1 0.64 1.49 -0.2 0.62 0.48 0.11 -0 1.74 -0.6 0.37 -0.6 0.79 J16-V2M1 -0.2 1.97 1.55 -0.3 -0 0.58 -0.2 1.46 -1.1 -0.2 1.47 0.41 2.01 -0 1.73 -0.4 0.84 0.14 0.87 J16-V2M2 1.28 0.25 0.73 -0.4 -0 0.56 -0.1 2.09 -0.7 -0.2 -0.2 0.49 0.55 0.01 2.39 -0.6 1.41 -0 1.28 J16-V2M3 1.65 0.23 1.1 -0.2 0.05 0.54 -0.2 1.35 -0.7 -0.2 -0.1 0.44 0.9 0.54 5.5 -0.5 0.77 0.13 0.44 J16-V2M4 -0.2 1.51 1.12 1.23 0.03 0.59 -0.1 0.73 -0.9 -0.3 -0.1 0.47 0.07 0.23 8.63 -0.6 0.68 3.73 1.52 J16-V1F1 1.86 0.62 1.04 -0.3 0.1 0.65 -0.2 1.59 0.04 -0.2 -0.2 0.41 1.1 0.01 4.82 -0.1 0.9 0.5 1.54 J16-V1F2 -0.1 0.62 -0.1 -0.3 0.14 0.64 0 2.43 -1.2 -0.2 -0 0.36 0.95 0.46 4.47 0.32 1.34 -0.5 0.71 J16-V1F3 0.62 1.22 0.95 1.71 0.04 0.6 0.1 1.19 0.8 -0.3 -0.2 0.45 -0.4 1.9 4.29 -0.3 0.85 0.29 1.98 J16-V1F4 0.3 2.75 1 1.42 0.03 0.63 0.24 2.12 0.33 -0.2 0.19 0.39 0.87 0.49 3 0.14 1.99 0.32 2.24 J16-V1F5 0.22 1.45 0.87 0.33 0.01 0.54 0.35 -0.1 -1 -0.2 0.33 1.97 -0.3 -0 -0.7 0.04 0.79 0.23 -0.3 J16-V1F6 0.21 0.34 0.45 -0.5 0.01 0.47 0.09 -0.1 -0.8 -0.2 3.55 0.58 2.92 0.02 -0.7 -0.1 0.66 -0.2 0.22 J16-V1F7 1.86 1.64 2.03 -0.2 -0 0.37 -0 -0 0.42 -0.2 0.74 0.44 0.07 0.59 -0.3 0.31 1.07 0.33 -1 J16-V1M1 0.57 4.05 0.55 0.22 -0 0.41 -0 0.03 -0.2 -0.2 0.04 0.33 -0.1 -0.1 -0.4 0.35 1.31 0.05 -1.2 J16-V1M2 2.62 0.59 1.96 0.55 -0 0.34 0.04 -0.3 -0.7 -0.3 14.5 0.39 0.29 -0 -0.7 -0.1 0.78 -0.1 -0.7 J16-V1M3 2.41 0.84 1.65 -0.4 -0.1 0.34 0.01 -0.1 -0.3 -0.2 1.09 0.31 -0.3 -0.2 -0.5 -0.1 0.77 0.19 0.01 J16-V1M4 -0.3 1.01 -1.1 0.42 -0.1 0.38 -0 -0.1 -1.4 -0.2 -0.1 0.43 -0.4 -0.2 -0.7 -0.4 0.68 -0.6 -0.8 J16-V1M5 3.37 0.65 2.09 -0.9 -0.1 0.38 0.01 -0.1 -0.4 -0.3 1.19 0.36 1.24 0.07 -0.2 -0.1 0.75 -0.2 0.42 J16-V1M6 -0.2 0.03 0.01 -1.5 -0.1 0.42 0.03 -0.3 -1.2 -0.2 -0.4 0.41 2.69 -0.5 -0.4 0.32 0.83 -0.5 -0.1 J16-Kel1 -0.5 0.29 0.1 -0.4 -0.1 0.42 0.47 -0.2 -0.9 -0.2 -0.2 0.39 0.42 -0 0.21 0.15 0.8 1.18 -1.2 J16-Kel2 0.21 1.85 0.83 -0.7 -0.1 0.37 0.58 -0.2 0.15 -0.1 -0.1 0.35 0.13 -0.2 0.45 -0.1 0.87 0.64 0.51 J16-Kel3 -0.6 2.71 0.58 -1 -0 0.46 1.15 -0 0.04 2.4 0.18 0.33 0.23 -0 0.32 0.07 1.1 2.03 0.67 J16-Kel4 0.14 1.92 -0.4 -0.2 0.2 0.35 1.41 0.34 1.89 0.11 0.21 0.38 3.72 1.55 0.2 2.37 3.91 1.38 -0.8 J16-Kel5 -0.2 1.16 0.21 -0.6 0.41 0.36 0.84 0.65 -0.1 -0 0.16 0.35 0.72 2.66 0.48 2.9 4.55 1.34 0.04 J16-Kel6 -0.6 1.42 -0.3 0.11 0.22 0.59 1.41 0.37 1.47 0.21 0.11 0.3 1.27 0.9 0.1 2.81 4.29 2.27 -0.2 J16-Kel7 -0.1 0.24 0.03 -0.8 -0.1 0.38 0.89 -0.3 -0.1 4.4 -0.1 0.49 0.81 0.07 -0.3 -0.3 0.6 1.68 0.41 103  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe J16-Kel8 0.11 1.49 -0.5 -0.6 -0.1 0.44 0.66 -0.1 0.09 0.67 0.22 0.36 1.71 0.07 0.03 0.02 0.81 2.53 -1 J16-Kel9 -0.7 0.63 -0.9 -0.2 -0.1 0.47 0.08 -0.3 -0.2 -0.2 -0.2 0.44 -0.2 -0.2 0.97 -0 0.62 2.66 -0.8 J16-Kel10 -0.6 1.34 -0.3 0.14 -0.1 0.42 0.9 -0.2 -0.3 1.57 0.01 0.43 0.82 0.15 0.09 0.1 0.64 1.18 -0.4 J16-Kel11 -0.6 0.96 -0.4 1.31 -0.1 0.39 7.99 -0.2 0.03 -0.2 0.08 0.38 5.17 -0.1 -0.1 0.99 0.87 4.13 -0.3 J16-Kel12 -0.4 0.34 -0.3 -1 -0 0.56 4.97 0.87 -1 -0.2 -0.2 1.19 3.9 -0.2 -0.5 0.44 0.49 3.04 2.01 J16-Kel13 -0.8 2.37 -1 1.48 -0 0.51 9.85 1.44 1.32 -0.2 -0.1 0.74 4.91 -0.3 -0.2 1.13 0.94 6.41 3.7 J16-Kel14 -0.3 1.54 -0.3 -0.4 0.02 0.51 -0.1 1.2 -0.3 -0.1 -0.3 0.55 -0.3 -0 -0.4 0.34 0.85 0.95 0.76 J16-Kel15 -1 0.89 -0.8 -0.6 0.03 0.47 0.01 2.47 -0.1 -0.1 2.79 0.35 0.1 3.6 -0.6 -0.1 2.16 0.11 0.04 J16-O1F1 -1.3 0.04 0.26 1.71 -0 0.32 9.59 3.9 -1 -0.2 0.17 0.23 7.78 0.1 -0.3 1.5 2.39 7.35 -2.6 J16-O1F2 -0.6 -0.4 0.33 0.18 -0 0.43 -0 1.65 -1 -0.1 -0.2 0.38 -0.5 -0.1 -0.5 -0.4 0.98 -0.4 4.24 J16-O1F3 -0.5 -0.1 -0.5 0.64 -0 0.4 1.33 3.65 0.3 -0.2 0.98 0.26 0.83 0.38 -0.3 0.18 2.42 0.6 3.31 J16-O1F4 -0.9 -0.2 0.57 0.69 -0 0.34 -0.2 2.39 -1 2.9 0.84 0.35 -0.4 0.62 -0.3 -0.3 1.61 0.48 0.98 J16-O1F5 -1 -0.9 -0.1 -0.3 -0.1 0.37 -0.2 1.76 -1.3 -0.2 0.22 0.3 0.02 -0.2 -0.3 -0.5 1.04 -0.2 -0.5 J16-O1F6 1.38 0.4 1.18 0.57 0.02 0.48 -0.1 1.43 -1 0.22 0.03 0.32 -0.3 -0 -0.2 -0.4 0.96 0.08 -1.5 J16-O1F7 0.2 -0.7 0.56 0.21 -0 0.46 -0.2 1.11 -1 -0.2 -0.2 0.46 -0.2 0.38 0.34 -0.5 1.2 -0.3 0.12 J16-O1F8 -0.3 -0.5 -0.8 0.42 -0 0.51 0.1 1.29 1.19 0.19 -0.2 0.35 1.51 -0.1 -0.3 -0.5 1.32 -0.3 2.41 J16-O1M1 0.11 0.32 0.3 -0.4 -0.1 0.46 -0.2 1.19 0.54 -0.2 -0.1 0.37 -0.4 -0.3 -0.1 -0.5 0.72 -0.2 1.36 J16-O1M2 -0.7 -0 0.23 -0.5 -0 0.42 -0.1 1.09 -0.5 0.05 -0.2 0.35 0.14 -0.4 -0.2 -0.5 0.72 1.02 -1.4 J16-O1M3 0.19 1.44 0.04 -0.8 -0 0.42 -0 1.05 -0.6 0.72 0.57 0.35 -0.5 2.03 0.05 -0.4 1.41 0.67 1.47 J16-O1M4 0.24 1.45 1.02 2.14 -0 0.44 -0 1.53 -0.7 -0.1 0.53 0.28 -0.5 0.33 -0 -0.5 1.34 0.44 -0.5 J16-O1M5 -0.3 0.74 0.8 0.08 0.02 0.37 -0 1.45 -0.3 -0.1 0.52 0.29 -0.6 0.12 0.11 -0.3 1.15 0.33 2.79 J16-O2M1 0.95 0.99 0.34 0.09 -0 0.39 -0.1 2.07 0.89 0.64 0.47 0.26 -0.6 -0.3 -0.2 -0.1 1.25 0.34 -0.9 J16-O2M2 -0.4 1.88 -0.7 0.17 -0.1 0.31 -0.1 2.81 0.37 0.97 0.35 0.15 -0.6 -0.1 0.02 -0.2 2.24 0.67 0.01 J16-O2M3 0.09 0.71 0.54 1.82 -0 0.45 -0.2 1.29 0.37 -0.2 0.4 0.33 -0.5 -0.3 -0.1 -0.5 1.04 0.17 0.56 J16-O2M4 1.64 0.5 0.42 -0.2 0.16 1.13 0.88 2.82 -0.2 0.77 -0.2 4.87 -0.5 -0.2 -0.1 0.01 1.41 0.37 -1.3 J16-O2M5 0.52 1.5 0.9 -0 -0 0.57 -0.1 0.99 -0.5 -0.2 -0.2 0.91 -0.5 -0.2 -0.1 -0.5 0.72 0.07 -0.1 J16-O2M6 0.91 1.08 0.09 -0.5 0.02 0.51 -0 0.84 -0.6 0.68 -0.2 0.94 0.71 -0.3 -0.2 -0.7 0.55 -0.5 -1.6 J16-O2M7 0.32 0.24 0.83 0.06 -0 0.51 -0.1 1.34 -0.9 0.02 -0.1 0.84 -0.4 -0.1 -0.5 -0.4 0.85 -0.2 -1.1 J16-O2F1 -0.4 0.18 -0 -0.7 -0 0.51 -0 1.35 -0.8 0.26 -0 0.74 -0.2 0.41 -0.3 -0.3 0.88 0.32 -1.1 104  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe J16-O2F2 -0.5 0.01 -0.3 -0.5 0.05 0.55 -0 1.75 -1 -0.1 -0 0.75 -0.5 -0.3 -0.4 -0.5 0.99 -0.5 1.15 J16-O2F3 -0.8 0.39 -0.8 -0.7 -0 0.51 -0.1 1.79 -0.5 0.09 -0.2 0.74 -0.6 -0.3 -0.1 -0.6 2.56 -0.5 -2 J16-O2F4 -0.6 0.71 0.27 -0.4 -0 0.48 -0.1 1.49 -1.4 -0.1 -0.1 0.64 -0.5 -0.4 -0.2 -0.3 0.9 -0.4 -0.8 J16-O2F5 -0.7 0.23 -0.3 3.64 0.01 0.48 -0.1 1.1 -0.7 -0.1 -0.3 0.64 -0.4 -0.2 -0.4 -0.5 0.76 0.01 -1.1 J16-O2F6 -0 0.87 0.3 -0.3 0.01 0.49 -0.1 0.88 -0.9 0.13 -0.2 0.72 -0.5 -0.1 -0.4 -0.5 0.71 -0.2 -1.7 J16-O2F7 -0.2 -0.1 0.28 0.22 -0 0.51 -0 0.51 0.37 -0.1 -0.3 0.64 -0.5 -0.2 -0.2 -0.7 0.42 0.17 -0.8 J16-O2F8 0.06 0.21 0.36 -0.4 -0 0.48 -0 1.6 0.47 0.18 -0.2 0.75 -0.5 -0.4 -0 -0.2 0.99 0.9 0.99 J16-O2F9 0.11 1.21 1.49 0.69 -0 0.51 -0 0.77 -0.4 -0.1 -0.3 0.72 -0.5 -0.3 -0.4 -0.6 0.68 0.04 -1.1 O2F10 -0.5 -0.4 -0 -0.8 -0 0.51 -0 0.76 -1.1 -0.2 -0.3 0.72 -0.3 -0.3 -0.4 -0.8 0.6 -0.7 -1.7 J16-O5F1 -0.6 0.52 -0.8 3.12 0.02 0.54 -0.1 0.79 -0.8 -0.3 -0.3 0.64 -0.6 0.15 -0.2 -0.7 0.7 -0.4 -0.1 J16-O5F2 -0.8 -0.2 -0.7 -1 -0 0.48 -0 1.34 -1 0 -0.1 0.64 -0.5 1.25 -0.5 -0.5 1.21 -0.3 -2 J16-O5F3 -1 -0.4 -0.5 0.81 -0 0.47 -0 0.66 -0.7 -0 -0.3 0.71 -0.5 -0.5 -0.4 -0.6 0.42 0.35 0.22 J16-O5F4 -1 -0.4 -1 -0 -0 0.48 -0 1.4 0.1 0.14 -0.2 0.74 2.23 0.04 -0.3 -0.4 0.94 -0.4 -1.8 J16-O5F5 -0.9 -0.4 -0.9 5.46 -0 0.51 -0.1 1.63 1.54 -0.2 -0.2 0.64 -0.5 0.47 0.03 -0.4 1.22 -0.2 -2.1 J16-O5F6 -0.7 -0 -0.1 -0.5 -0 0.45 -0.1 1.1 -1.3 -0.1 0.09 0.74 -0.5 -0.2 -0.2 -0.1 1.67 -0.1 -0.9 J16-O5F7 -0.6 -0.7 -0.3 -0.1 0.41 0.41 -0.8 3.19 -0.7 0.06 -0.2 0.27 -0.5 0.21 -0.3 -0.5 1.06 -1.1 0.64 J16-O5F8 -0.2 0.68 -0.2 0.08 -0.3 0.25 -0.8 0.16 -0.1 0.08 -0.2 -1.2 -0.6 -0.2 -0.3 -0.8 0.77 0.1 0.12 J16-O5F9 -0.7 0.36 -1.2 -0.1 -0.4 0.27 -0.8 0.11 0.13 -0.2 -0.3 -1.5 -0.6 0.03 -0.3 -1.1 0.83 -1.4 0.59 JO5F10 -0.9 0.86 -0.4 -0.7 -0.4 0.26 -0.7 -0.1 -0.9 -0.2 -0.2 -1.3 -0.5 -0.4 -0.5 -1.1 0.7 -0.6 -0.3 O5F11 -0.3 -0.2 -0.7 0.59 -0.4 0.27 -0.7 0.06 -0.9 0.51 -0.2 -1.5 -0.5 -0.5 -0.5 -0.8 0.94 -1.1 -0.3 J16-CurF1 -0.8 0.65 -0.3 -0.7 -0.3 0.29 -0.7 -0.2 -1 -0.3 -0.3 -1.3 -0.6 -0 -0.4 -0.8 0.63 -1.2 0.96 J16-CurF2 -0.4 1.1 0.79 -0.2 -0.3 0.25 -0.7 -0.6 -0.9 -0.3 -0.3 -1.3 -0.2 0.1 -0.7 -0.8 0.69 -0.9 0.23 J16-CurF3 -0.5 1.91 0.35 -1.2 -0.4 0.21 -0.8 -0.4 -0.1 -0.3 -0.3 -1.6 -0.7 -0.1 -0.5 -0.7 0.82 -0.6 0.25 J16-CurF4 -0.8 0.77 -1 0.06 -0.4 0.24 -0.7 -0.8 0.72 -0.3 -0.4 -1.3 -0.4 -0.5 -0.5 -0.8 -0.6 -1 -0.3 J16-CurF5 -0.1 1.8 0.99 -1.5 -0.4 0.19 -0.7 -0.4 -1.2 -0.3 -0.3 -1.5 -0.4 -0.4 -0.5 -0.9 0.62 -1 0.01 J16-CurF6 -0.9 1.05 -1 -0.1 -0.4 0.18 -0.8 -0.8 0.01 -0.1 -0.1 -1.7 -0.6 -0.5 -0.3 -0.9 -0.5 -0.3 1.12 J16-CurF7 -0.9 1.02 -1.4 0.88 -0.5 0.09 -1 0.18 1.77 -0.3 -0.3 -2.1 -0.5 -0.3 0.22 -0.8 1.11 -1 0.43 J16-CurF8 -0.5 1.01 0.41 0.81 -0.4 0.14 -0.8 -0.3 0.25 -0.2 -0.1 -1.8 -0.6 -0.3 -0.4 -0.8 0.88 -0.9 0.4 J16-CurF9 -0.3 1.77 -0.6 0.17 -0.3 0.22 -0.7 -0.4 -0.2 -0.3 -0.3 -1.3 -0.4 -0.2 -0.4 -0.9 0.65 -1 0.27 105  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe CurF10 -0.2 1.06 -0 0.24 -0.4 0.09 -0.9 -0.1 0.45 -0.3 -0.2 -2.1 -0.3 0.88 -0.1 -0.9 1.09 -0.6 1.69 CurM1 -0.9 0.96 -0.1 -0.8 -0.5 0.1 -1.1 0.25 -1.2 -0.3 -0.3 -2.6 -0.4 -0.4 -0.5 -0.9 1.25 -0.9 -0.2 CurM2 -1.2 1.44 -1.7 1.02 -0.4 0.17 -0.8 -0.2 1.77 -0.3 -0.3 -1.6 -0.7 -0.6 -0.2 -1 0.73 -1.2 0.46 CurM3 -1.2 0.77 -1.8 0.26 -0.5 0.13 -1 -0.8 0.73 -0.3 -0.2 -2.3 -0.6 -0.4 -0.1 -1 -0.5 -1.2 -0.4 CurM4 -1.3 1.14 -1.6 -0.6 -0.5 0.16 -0.9 -0.7 0.12 -0.2 -0.2 -2.1 -0.6 1.12 -0.4 -0.6 0.27 -1.4 -0.8 CurM5 -1 1.08 -1.1 1.27 -0.5 0.11 -1 -0.7 3.05 -0.3 -0.2 -2.3 -0.4 -0.4 -0.2 -1 -0.4 -1 1.35 CurM6 -0.9 1.65 -1 0.93 0.02 0.69 0.19 0.71 1.43 -0.3 0.15 1.77 -0.4 -0.4 -0.2 -0.4 0.92 -0.8 2.43 CurM7 -1.1 0.82 -1.5 0.03 -0 0.48 -0 1.03 0.05 -0.3 -0.3 0.91 -0.3 -0.1 -0.3 -0.5 0.85 -1.1 0.95 CurM8 -1 1.46 -1.2 0.17 -0 0.54 -0.1 0.34 0.58 -0.3 -0.4 0.9 -0.3 -0.2 -0.5 -0.5 -0.5 -1 -0.4 CurM9 -0.6 0.15 -0.1 -1.2 -0 0.5 -0.1 -0.2 -0.4 -0.3 -0.4 0.77 -0.5 -0.4 -0.3 -0.7 -0.6 -1.6 -0.1 CurM10 -0.3 0.47 0.18 -0.6 -0 0.49 -0 0.14 1.19 -0.2 -0 0.66 -0.2 -0.3 -0.4 -0.4 -0.3 -0.9 0.48 CurM11 -0.4 0.81 0.1 -0.9 -0 0.5 -0.1 0.02 -1.3 -0.2 -0.4 0.64 0.06 -0.3 -0.3 -0.4 -0.5 -1.1 0.24 StobF1 0.29 0.62 0.24 -1.3 0.02 0.5 -0.1 -0.1 -1 -0.3 -0.3 0.65 -0.4 1.03 -0.7 -0.6 -0.1 -1.3 -0 StobF2 0.08 -0 0.55 -0.5 -0 0.42 -0.1 0.58 -0.3 -0.2 0.66 0.55 0.77 0.14 -0.6 -0.2 -0 -0.9 0.54 StobF3 -0.4 1.06 -0.3 1.24 -0 0.46 0.02 0.45 0.39 -0.3 -0.2 0.56 -0.5 0.26 -0.2 -0.3 -0.3 -1.2 1.66 StobF4 -0.4 0.8 -0.7 0.57 -0 0.49 -0 0.4 0.09 -0.3 -0.2 0.61 0.13 -0.4 -0.7 -0.4 -0.4 -0.6 0.75 StobF5 -0.1 0.36 0.49 0.21 -0 0.48 0.33 -0 0.12 -0.1 -0.3 0.59 -0.2 1.22 -0.4 -0.5 -0 -0.5 -0 StobF6 -0.1 0.61 -0.1 -1.2 -0 0.49 -0 0.12 -0.8 -0.3 -0.3 0.6 -0.2 -0.2 -0.7 -0.4 -0.5 -0.9 0.8 StobF7 0.26 0.6 0.46 0.19 -0 0.48 0.03 -0.2 -0.9 -0.3 -0.3 0.65 -0.2 -0.3 -0.4 -0.6 -0.4 -0.5 -0 StobF8 0.52 1.09 1.57 -0.9 -0 0.51 0.03 0.09 -0.6 -0.3 -0.3 0.58 -0.2 -0.3 -0.4 -0.8 -0.5 -0.9 0.42 StobF9 0.37 1.07 1.22 -0.8 -0 0.5 -0.1 0.21 0.57 -0.2 -0.2 0.54 -0.5 -0.3 -0.2 -0.7 -0.5 -0.7 -0.2 StobF10 0.16 1.42 0.49 -1.3 -0 0.52 0.07 0.08 -1 -0.3 -0.3 0.54 -0.2 -0.1 -0.8 -0.8 -0.5 -0.6 0.09 StobF11 -0.7 1.69 -0.8 -0.8 0.12 0.49 -0.1 0.31 0.15 -0.3 -0.1 0.54 -0.2 -0.2 -0.6 -0.6 -0.2 -1.1 0.31 StobM1 -0.3 1.69 -0.4 -1 -0 0.48 0.07 0.02 -0.1 -0.3 -0.3 0.59 0.59 -0.1 -0.5 -0.7 -0.4 -0.6 0.14 StobM2 1.76 1.09 1.71 -1.3 -0 0.5 -0 0.1 -1 -0.3 -0.1 0.57 -0.2 -0.5 -0.6 -0.8 -0.6 -1 1.23 StobM3 0.1 0.73 0.62 -0.7 -0 0.46 -0.1 0.02 0.27 -0.3 -0.4 0.54 -0.1 0.83 -0.8 -0.7 -0.3 -0.9 0.5 StobM4 -0 0.03 0.86 -1 1.01 0.57 -0.1 -0.9 0.3 0.38 -0.4 1.76 -0.4 -0.4 -0.4 2.29 -0.5 0.06 0.07 StobM5 1 1.48 2.19 -0.7 -0.1 0.36 -0.1 -1 -0.5 -0.2 -0.4 0.55 0.22 -0.4 -0.3 2.95 -0.6 0.04 0.2 StobM6 -0.3 0.9 0.09 0.19 -0.1 0.37 -0.1 -1 -0.1 -0.3 -0.4 0.43 -0.3 -0.4 -0.1 2.61 -0.6 0.21 0.76 106  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe StobM7 -0.1 1.45 0.33 -0.4 -0.1 0.37 -0.1 -1 0.48 -0.2 -0.4 0.41 -0 -0.5 -0.3 2.07 -0.6 0.12 0.3 StobM8 0.62 0.39 0.79 -1.1 -0 0.36 0.02 -1 -0.9 -0.3 -0.4 0.47 4.21 -0.3 -0.5 3.47 -0.3 0.29 0.37 StobM9 -0.6 0.04 0.52 -1.3 -0.1 0.35 -0.1 -1 -0.9 -0.3 -0.4 0.32 1.95 -0.5 -0.6 0 -0.6 -0.3 0.38 StobM10 0.57 0.93 1.67 -0.6 -0 0.33 -0.1 -1 -0.8 -0.2 -0.4 0.37 -0.4 -0.3 -0.4 4.24 -0.3 0.26 0.19 GraceF1 -1.5 0.01 -2.7 -0.1 -0 0.26 -0.1 -0.9 -0.7 -0.3 -0.4 0.27 -0.4 -0.2 -0.2 5.97 -0.2 -0 -0.4 GraceF2 -1.1 0.65 -0.9 2.48 -0.1 0.35 -0.1 0.06 0.72 -0.3 -0.4 0.38 -0 -0.2 -0.5 -0.4 -0.6 -0.4 0.43 GraceF3 -1.3 1.25 -0.9 3.16 6.22 0.28 0.09 0.74 2.19 -0.2 1.3 0.42 -0.3 4.81 0.2 0.9 0.2 0.96 1.24 GraceF4 -1.3 0.62 -1.9 0.76 -0.1 0.26 -0.1 0.65 -0.5 -0.3 -0.3 0.18 -0.6 -0.1 -0.5 -0.5 -0.6 0.04 0.96 GraceF5 -1.2 0.77 -0.8 -0.2 -0.1 0.32 -0.1 0.14 -0.2 -0.3 -0.4 0.35 -0.2 -0.3 -0.6 -0.7 -0.5 -0.1 0.37 GraceF6 -1.2 0.16 -1.3 -0.4 -0.1 0.26 -0.1 0.3 -1.3 -0.3 -0.1 0.4 -0.1 -0.5 -0.6 -0.7 -0.4 -0.3 0.62 GraceF7 -1.3 0.63 -1.1 -0.3 -0 0.31 -0.1 0.34 0.45 -0.2 -0.4 0.36 -0.6 0 -0.4 -0.5 -0.7 0.19 0.01 GraceF8 -1.3 -0.1 -0.9 0.1 -0.1 0.27 -0.1 0.27 -0.3 -0.2 -0.3 0.35 -0.5 -0.4 -0.5 -0.2 -0.7 0.06 1.26 GraceF11 -1.3 0.69 -0.1 0.04 -0.1 0.22 -0.1 0.31 -0.4 -0.3 -0.2 0.3 0.25 0.52 -0.5 -0.7 -0.3 0.15 0.49 GraceM1 -1.1 0.41 -1.3 -0.5 -0.1 0.31 -0 -0 -1.4 -0.3 -0.3 0.5 0.13 -0.1 -0.3 -0.7 -0.4 -0.1 0.62 GraceM2 -1.3 -0 -2.7 -0.1 -0.1 0.23 -0.1 0.36 -0 -0.3 -0.3 0.35 -0.1 -0.4 -0.5 -0.6 -0.6 0.02 1.15 GraceM3 -1 0.2 -0.5 -0.3 -0.2 0.45 0 -0.4 -0.9 -0.3 -0.3 0.3 0.82 4.06 -0.7 -0.1 -0.2 -1.1 -0 GraceM4 -1.3 0.89 -1.9 0.17 -0.3 0.36 -0 -0.5 -0.5 -0.3 -0.4 -0.7 -0.3 -0.5 -0.6 -0.2 -0.5 -0.7 -0.4 GraceM5 -1.5 0.29 -2.8 -0.2 -0.4 0.3 0.04 -0.4 1.49 -0.3 -0.3 -1.6 -0.6 -0.4 -0.2 0.14 -0.5 -1.1 -1.3 J16-GraceM6 -1.2 1.07 -2.1 -0.2 -0.4 0.32 -0.1 -0.5 1.08 -0.3 -0.2 -1.3 -0.6 -0.3 -0.1 -0 -0.6 -1.1 0.09 J16-GraceM7 -1.2 0.56 -1.9 1.13 -0.4 0.29 -0.1 -0.5 -0.4 -0.3 -0.4 -1.4 -0.6 -0.5 0.04 -0 -0.6 -0.8 -1 J16-GraceM8 -1.1 2.04 -1.1 0.78 -0.4 0.25 -0.1 -0.5 2.99 -0.3 -0.3 -1.5 -0.7 -0.4 -0.2 0.12 -0.6 -0.4 -0.7 J16-GraceM9 -1.1 -0 -0.3 0.41 -0.3 0.31 -0.1 -0.6 -0.5 -0.3 -0.3 -1.2 0.97 -0.4 -0.6 -0.1 -0.5 -0.8 -0.6 GraceM10 -1.2 -0.1 -0.7 -0.2 -0.3 0.31 0.12 -0.6 -0.5 -0.3 -0.3 -1.2 0.76 -0.1 -0.5 -0.1 -0.6 -0.5 -0.7 J16-CR1 -0.6 0.01 -0.3 -0.1 -0.3 0.33 -0 -0.7 0.26 -0.1 -0.3 -1.1 -0.3 -0.4 -0.5 -0 -0.4 -0.7 -0.8 J16-CR2 -0.5 1.31 0.61 -0.9 15.6 0.3 0.36 -0.4 0.04 -0.2 -0.3 -1.4 -0.4 3.94 0.28 0.28 -0.4 -0.5 -0.1 J16-CR3 -0.3 0.46 -0.2 -0.8 -0.1 0.33 -0.1 -0.6 -1 -0.3 -0.3 -1.2 -0.5 -0.4 -0.6 0.22 -0.6 -1.4 -0.9 J16-CR4 -0.7 1.24 0.04 -0.9 -0.3 0.32 -0.1 -0.6 -0.3 -0.3 -0.3 -1 -0.2 0.77 -0.3 0.13 -0.5 -0.9 -0.7 J16-CR5 -0.9 -0 -0.4 -0.4 -0.3 0.35 -0.1 -0.7 -0.4 0.04 0.37 -1 -0.4 -0.6 -0.3 -0.2 -0.6 -0.9 -1.1 J16-CR6 -1 -0.1 -0.5 0.02 -0.3 0.34 0.17 -0.6 0.69 -0.2 -0.2 -0.9 0.1 -0.2 -0.5 -0.2 -0.6 -1 -0.3 107  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe J16-CR7 -0.7 0.6 -0.7 -1 -0.3 0.31 -0.1 -0.6 0.47 -0.3 -0 -1.3 -0.1 -0.3 -0.5 0.12 -0.6 -0.8 -0.8 J16-CR8 -1.1 1.62 0.68 0.45 -0.3 0.32 -0.1 -0.6 0.53 -0.2 -0.2 -1.4 -0.4 -0.5 -0.5 0.18 -0.6 -0.9 -0.5 J16-CR9 0.15 0.35 0.4 -0.5 -0.3 0.35 -0.1 -0.6 -0.3 -0.2 -0.3 -1.2 -0.4 -0.5 -0.6 0.13 -0.6 -1.3 -0.6 J16-CR10 -1.1 0.06 -0.9 0.44 -0.3 0.34 -0 -0.7 -0.4 -0.2 -0.3 -1.1 -0.3 -0.2 -0.6 -0.1 -0.5 -1.4 -1.1 J16-CR11 0.81 0.93 1.4 0.91 -0.3 0.35 -0.1 -0.7 0.94 -0.1 -0.3 -0.9 -0.1 -0.4 -0.5 -0 -0.6 -0.5 -0.5 J16-CR12 0.62 0.76 1.25 -1 -0.3 0.33 -0.1 -0.6 -0.1 -0.3 -0.1 -1 -0.5 -0.3 -0.6 -0.1 -0.4 -1.2 -0.4 J16-CR13 0.14 -0.7 0.17 -0.7 -0 0.56 0.13 0.03 -0.8 -0.3 -0.3 1.48 -0.2 -0.4 -0.2 -0.4 -0.6 -1.1 0.11 J16-CR14 0.66 -0.9 0.31 -1.2 -0 0.55 -0.1 -0 0.64 -0.3 -0.3 0.7 0.07 -0.3 0.52 -0.3 -0.6 -0.9 0.01 J16-CR15 0.69 0.65 1.57 -0.5 -0.1 0.56 -0.1 0.12 0.58 -0.3 -0.3 0.65 -0.5 -0.4 0.73 -0.2 -0.6 -0.6 -0.3 J16-CR16 1.36 0.16 1.42 -0.3 -0.1 0.53 -0.1 0.15 1.27 -0.3 -0.3 0.61 -0 -0.4 0.49 -0.3 -0.6 -0.6 -0.8 J16-CR17 0.41 -0.5 0.04 -0.8 -0.1 0.54 -0.1 -0 0.27 -0.3 -0.3 0.54 -0.5 -0.4 0.46 -0.3 -0.6 -1 0.36 J16-CR18 0.48 -0.7 0.73 0.17 -0 0.55 -0.1 0.2 -1.1 -0.1 -0.3 0.48 0.24 -0.4 -0.1 -0.1 -0.6 -0.5 0.09 J16-CR19 0.7 0.33 1.69 1.21 -0.1 0.52 -0.1 0.07 1.26 -0.2 -0.3 0.5 -0.3 -0.4 0.76 -0.3 -0.6 -0.2 -0.4 J16-CR20 0.14 -0.7 -0.2 -1.3 -0.1 0.61 -0.2 0.34 -0.9 -0.2 -0.1 0.45 -0.3 -0.3 0.08 -0.1 -0.6 -1.3 0.48 J16-O6F1 -0.2 -0.7 -0.1 -0.3 -0.1 0.53 0.01 0.17 -0.2 -0.2 -0.3 0.52 -0.3 -0.5 -0.1 -0.2 -0.6 -1 -0.8 J16-O6F2 1.13 -1.1 0.44 -0.1 -0.1 0.54 -0.2 0.51 -0.7 -0.1 -0.3 0.45 -0.3 0.19 -0.1 -0.1 -0.6 -1.1 -0.3 J16-O6F3 -0 -1.2 -0.1 -0.4 -0 0.53 -0.1 -0.1 -0.8 0.07 -0.4 0.57 -0.6 -0.4 -0.4 -0.4 -0.6 -0.8 -0.4 J16-O6F4 -0.2 -1.2 -0.5 -0.2 -0 0.56 -0 0.41 0.68 -0.1 -0.2 0.45 -0.6 -0.4 0.53 -0.1 -0.6 -0.6 -0.4 J16-O6F5 0.31 -1.3 0.29 -0.8 -0 0.54 -0.1 0.18 -0.7 -0.2 -0.3 0.55 -0.5 -0.3 0.53 -0.3 -0.6 -0.8 -0.5 J16-O6F6 1.37 -0.4 0.59 -0.6 -0 0.53 -0 0.17 -0.3 -0.2 -0.4 0.49 -0.5 -0.4 1.45 -0.2 -0.6 -0.8 -0.4 J16-O6F7 0.29 -1.6 -0.1 -0.7 -0.1 0.58 -0 0.43 -0.7 -0.2 -0.3 0.45 3.12 -0.2 1 -0.1 -0.6 0.18 -0.6 J16-O6F8 0.48 -0.9 0.33 -0.3 -0 0.59 -0.1 0.19 -0.5 0.11 -0.3 0.51 -0.3 -0.4 0.32 -0.4 -0.6 -0 -0 J16-O6F9 -0.1 -1.1 0.24 -0.2 -0 0.59 -0.1 0.08 -0.5 -0.1 -0.3 0.56 -0.3 -0.2 0.25 -0.4 -0.5 -0.1 -0.4 O6F10 0.03 -1.9 -0.7 -0.3 -0 0.53 -0.1 0.13 1.75 -0.3 -0.3 0.52 -0.4 -0.2 0.21 -0.4 -0.6 -1 -0 J16-O6M1 4.77 -1.1 2.14 0.63 -0 0.51 -0.2 0.52 1.17 8.61 -0.3 0.53 -0.1 -0.4 0.48 -0.1 -0.2 -0.6 1.62 J16-O6M2 0.81 -0.5 0.3 -0.3 -0 0.57 -0.2 0.33 -0.9 -0.1 -0.2 0.47 -0.3 -0.3 -0 -0.1 -0.5 -0.5 0.86 J16-O6M3 3.42 -0.7 2.17 -0.1 -0 0.67 -0.2 0.91 -0.7 -0.3 -0.3 3.51 -0.5 -0.4 -0.2 -0.2 -0.6 -0.8 1.68 J16-O6M4 4.49 -0.5 2.81 -0.1 -0 0.52 -0.1 0.18 0.04 0.41 -0.4 0.72 -0.5 -0.4 0.13 -0.4 -0.6 -1 -0.7 J16-O6M5 5.02 -1.7 3.36 -0.3 -0 0.52 -0.1 -0 -0.5 7.56 -0.2 0.51 -0.1 -0.4 0.99 -0.5 -0.6 0.05 -0.9 108  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe J16-O6M6 2.21 -1.3 1.84 0.31 0.04 0.54 -0.2 0.52 -0.4 0.02 -0.3 0.43 -0.5 -0.3 0.73 -0.3 -0.6 -1 -0.6 J16-O6M7 5.55 -0 3.75 0.2 -0 0.53 0.04 0.5 0.17 10.7 -0.3 0.39 -0.4 -0.4 1.83 -0.2 -0.6 0.22 -0.9 J16-O6M8 1.56 0.16 1.58 0.81 -0.1 0.5 -0.1 0.31 -0.1 -0 -0.3 0.44 -0.6 -0.4 0.95 -0.4 -0.6 -1 -0.6 J16-O6M9 3.67 -1.3 2.25 -0.3 -0.1 0.51 -0.1 0.12 -0.2 -0.2 -0.3 0.46 -0.3 -0.5 0.23 -0.5 -0.6 -1.4 -0.3 O6M10 3.26 -1.1 2.18 0 -0 0.49 -0.1 -0 -0.6 0.74 -0.4 0.49 -0.4 -0.3 0.53 -0.6 -0.6 -1.2 -0.5 O6M11 1.42 -1.1 1.39 -0.7 -0.1 0.47 -0.2 0.39 -0.5 0.61 -0.3 0.43 -0.5 -0.1 -0.5 -0.4 -0.5 -1.1 -0.7 J16-K3F1 -0.4 -0.8 -1.3 -0.5 -0.1 0.45 -0.2 0.51 -0.7 -0.3 -0.4 0.41 -0.5 -0.3 -0.1 -0.3 -0.6 -1.3 0.67 J16-K3F2 0.17 -0 0.07 -0.6 -0.1 0.33 -0.1 -1 -0.3 -0.2 -0.5 0.33 -0.6 -0.5 0.27 0.34 -0.6 0.66 -0.9 J16-K3F3 0.09 -1 -0.3 -0.5 -0.1 0.35 -0 -1 0.31 -0.3 -0.5 0.29 -0.6 -0.5 0.06 0.25 -0.6 -0.1 -0.4 J16-K3F4 -0.3 -0.6 -1.2 -0.4 -0.1 0.54 -0 0.02 1.55 0.15 -0.4 0.51 -0.5 -0.1 2.65 -0.7 -0.5 0.26 -0.4 J16-K3F5 -0.1 -1 -0.5 -1 -0.1 0.55 -0 0.57 1.31 -0.1 0.68 0.38 -0.2 0.83 1.81 -0.7 -0.3 -0.1 -0.1 J16-K3F6 -0.4 0.26 -0.7 -0.2 -0.1 0.52 -0.1 0.56 1.83 -0 -0.4 0.34 -0.6 0.61 2.89 -0.6 -0.3 0.73 -0.5 J16-K3F7 -0.3 0.26 -0 -0.8 -0 0.59 -0.2 0.79 1.02 -0.3 -0.3 0.38 -0.6 0.33 1.64 -0.7 -0.5 -0.1 -0.3 J16-K3F8 -0.1 -0.9 -0.9 -1.3 -0.1 0.49 -0 0.23 -0.1 -0.2 -0.4 0.4 -0.6 0.01 0.98 -0.8 -0.4 -0.3 0.07 J16-K3F9 -0.4 -0.6 -0.9 -1.4 -0 0.55 -0.1 0.22 1.48 -0 -0.4 0.38 -0.6 1 1.8 -0.8 -0.1 2.48 -0.3 J16-K3F10 -0.2 -0.7 0.2 -0.7 -0.1 0.53 -0.2 0.32 0.71 0.36 -0.4 0.4 -0.5 -0.5 0.57 -0.8 -0.6 0.02 -0.7 J16-K3M1 0.09 0.13 -0 -0.3 -0 0.48 -0.2 0.64 1.19 -0.3 -0.3 0.3 -0.3 -0.3 3.55 -0.7 -0.5 -0.2 -0.7 J16-K3M2 -0.2 -0.2 -0.3 -0.9 -0.1 -1.2 -0.2 0.82 -1.2 -0.3 -0.4 0.86 -0.5 0.02 -0.4 0.29 -0.6 0.24 0.13 J16-K3M3 -0.2 -1.3 -0.3 -1.1 -0.1 -2.4 -0.1 -0 0.42 -0.3 -0.4 -0.6 -0.2 -0.4 -0.5 -0.1 -0.6 -0.1 0.5 J16-K3M4 0.32 -0.9 0.51 -0.9 -0.2 -2.6 -0.4 -0.1 -0.7 -0.2 -0.3 -0.7 -0.4 -0.4 -0.5 -0.2 -0.6 0.39 1.21 J16-K3M5 0.22 -1.7 -0.4 -0.9 -0.1 -2.9 -0.4 0.02 -0.8 0.3 -0.4 -1 -0.6 -0.4 -0.7 -0.2 -0.6 -0.7 0.27 J16-K3M6 -0.2 -2.3 -1.2 2.09 -0.1 -3.6 -0.4 0.23 0.96 -0.3 -0.4 -1.3 -0.4 1.01 -0.4 0.09 0.03 1.29 0.68 J16-K3M7 -0.1 -1.7 -0.1 1.08 -0.2 -3.1 -0.3 -0.1 -0.6 -0.2 -0.4 -0.9 -0.5 -0.4 -0.6 -0.1 -0.7 0.48 1.96 J16-K3M8 -0.5 -1.6 -1.2 -1 -0.2 -4.1 -0.4 0.28 -0.2 -0.2 -0.4 -1.4 -0.5 -0.1 -0.6 0.07 -0.6 0.24 0.41 J16-K3M9 0.29 -1.1 0 -0.7 -0.2 -4 -0.4 0.14 0.07 -0.3 -0.4 -1.2 -0.6 -0.5 -0.5 -0.3 -0.7 -0.2 1.57 K3M10 0.03 -1.3 -0.3 -0.8 -0.1 -3.3 -0.2 -0.1 0.08 -0.3 -0.4 -0.9 -0.6 -0.6 -0.7 -0.5 -0.7 -0.3 1 K3M11 0.44 -0.3 -0.2 -0.9 -0.2 -3.9 -0.3 0.06 0.43 -0.2 -0.4 -1.2 -0.5 0.56 -0.3 -0.3 -0.7 0.92 -0.1 J16-Hl1 -0.2 -1.1 -0.3 -0.3 -0.1 -3.3 -0.4 -0 0.42 -0.3 -0.1 -1.1 -0.6 -0.2 -0.5 -0.5 -0.6 0.12 0.33 J16-Hl2 -0.3 -0.3 0.5 -1 0.32 -3.6 -0.3 0.05 0.06 -0.3 -0.3 -1.1 4.26 -0.5 -0.6 -0.4 -0.6 0.04 1.92 109  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe J16-Hl3 -0.2 -0.7 0.15 -1.7 -0.1 -3.2 -0.2 -0 -1.6 -0.3 -0.3 -0.7 -0.5 -0.4 -0.7 -0.3 -0.5 -0.7 1.33 J16-Hl4 -0.3 -0.8 -0.1 -1.6 -0.2 -3.3 -0.3 -0.1 -1.9 -0.3 -0.3 -0.8 -0.6 -0.5 -0.9 -0.6 -0.6 -0.4 -0.1 J16-Hl5 -0.1 1.19 1 1.75 -0.1 -4.4 -0.4 0.13 0.96 -0.3 -0 -1.3 -0.6 -0.3 -0.7 -0.4 -0.6 0.55 1.19 J16-Hl6 -0.3 -1 -0.5 -1.5 -0.1 -4.4 -0.4 0.31 -1 -0.3 -0.2 -1.1 -0.6 -0.3 -0.8 -0.4 -0.4 0.53 1.05 J16-Hl7 -0 -0.5 0.07 -0.9 -0.2 -4 -0.4 -0 0.18 -0.3 -0.3 -1 -0.2 0.05 -0.6 -0.2 -0.5 0.15 0.45 J16-Hl8 -0.2 -0.8 0.41 0.05 -0.2 -4 -0.4 0.02 0.06 -0.2 -0.4 -1 -0.4 0.08 -0.7 -0.5 -0.5 -0.4 1.52 J16-Hl9 -0.3 -0.8 -0.2 -1 -0.1 -4.2 -0.4 0.01 -0.3 -0.2 -0.2 -1 -0.5 -0.5 -0.7 -0.5 -0.6 -0 0.41 J16-Hl10 -0.4 -0.7 -0.4 -0.7 -0.2 -4.1 -0.4 -0 -0 -0.3 -0.3 -0.9 -0.5 -0.5 -0.8 -0.6 -0.7 -0.1 0.07 J16-Hl11 0.01 0.26 1.23 0.7 -0 -0 -0.1 0.6 -0.3 -0.2 -0.3 2.37 -0.6 -0.4 -0.7 -0.5 -0.7 0.26 0.06 J16-Hl12 0.4 0.07 1.03 -0.9 -0.1 -0.8 -0 0.72 -0.2 -0.3 -0.3 0.82 -0.3 -0.1 -0.6 -0.7 -0.6 0.44 0.7 J16-Hl13 -0.4 -1.1 -0.2 -0.9 -0.1 -0.4 -0.1 0.22 -0.3 -0.3 -0.3 0.67 -0.3 -0.3 -0.8 -0.6 -0.6 -0.4 -0.1 J16-Hl14 -0.1 0.7 0.8 -0.1 -0.1 -0.7 -0.1 0.4 0.58 -0.3 -0.3 0.6 -0.6 0.1 -0.8 -0.6 -0.5 0.27 1.21 J16-Hl15 -0.3 -1.1 -0.5 -0.6 -0.1 -0.7 -0 0.5 -0.9 -0.3 -0.2 0.59 -0.1 -0.2 -0.8 -0.5 -0.6 -0.2 0.85 J16-K2M1 1.26 -0.4 1.46 -1.4 -0.1 -1 -0 0.66 -1 -0.1 -0 0.57 -0.6 0.08 -0.7 -0.4 -0.6 -0.5 0.87 J16-K2M2 2.95 -1.6 2.37 -0.5 0.02 -1.1 -0.1 3.53 -0.3 -0.1 0.31 0.55 -0.5 6.61 -0.7 4.18 8.37 0 0.39 J16-K2M3 1.88 0.02 3.37 0.05 -0.1 -0.8 -0 0.32 -0.1 -0.3 -0.2 0.65 -0.4 -0.1 -0.7 3.11 0.18 0.47 0.24 J16-K2M4 0.81 -2.3 0.88 -0.6 0.03 -0.8 -0.1 0.63 -0.9 -0.2 0.47 0.55 -0.4 11.2 -0.7 3.23 0.91 0.41 2.14 J16-K2M5 0.65 -2.1 0.37 -0.9 -0.1 -1.1 0.06 0.53 -0.6 -0.2 -0.3 0.7 -0.5 -0.4 -0.7 3.52 -0.7 0.03 3.11 J16-K2F1 2.98 -1.3 2.38 -0.1 -0.1 -1 -0 0.83 -1.4 -0.2 -0.2 0.61 -0.6 -0.2 -0.7 3.25 0.03 -0.4 0 SchutF1 -0.1 -0.9 -0.5 -0.4 -0.1 -0 -0.1 0.31 -1.3 -0.3 -0.3 0.59 0.71 -0.3 -0.8 0.94 -0.6 -0 -0 SchutF2 -0.3 -1 -0.9 -0.2 -0.1 -0.4 -0.2 0.64 -1 -0.3 -0.1 0.7 0.06 -0.3 -0.7 1.35 -0.6 -0.2 0.29 SchutF3 -0.4 -0.9 -0.8 -0.7 -0.1 -0.5 -0 0.6 0.38 -0.2 -0.2 0.61 -0.3 -0.4 -0.6 1.44 -0.6 0.22 0.31 SchutF4 -0.3 -1.4 -0.7 0.02 -0.1 -0.3 -0 0.42 0.07 -0.2 -0.3 0.74 -0.3 0.39 -0.5 1.37 -0.5 0.69 -1.3 SchutF5 -0.7 -1.7 -0.5 -0 -0.1 -0.2 -0.2 0.29 -0.9 -0.3 -0.3 0.64 1.34 -0.4 -0.7 0.96 -0.6 -0.3 -1.2 SchutF6 -0.4 -0.8 -0.6 -0.3 -0.1 -0.3 -0.2 0.41 -0.8 -0.2 -0.2 0.6 -0.5 -0.5 -0.6 1.11 -0.6 -0.1 -1.2 SchutM1 0.11 0.69 0.71 0.44 -0 -0.4 -0.1 0.66 0.4 -0.2 -0.3 0.56 -0.4 0.5 -0.2 0.58 -0.4 1.8 -1.9 SchutM2 -0.4 -1.1 -0.7 -1.1 -0.1 -0.3 0.21 1.12 -0.6 1.01 5.41 0.72 0.99 -0.2 -0.5 0.31 4.55 0.04 -0.1 SchutM3 -0.3 -0.8 -0.1 0.35 -0 -0.4 -0.1 0.73 -0 -0.2 0.94 0.73 1.09 -0.4 -0.8 0.48 -0.3 0.57 0.15 SchutM4 -0.4 -0.2 -1.6 1.36 -0 -0.4 -0.1 0.78 0.09 -0.3 -0.3 0.7 0.21 0.64 -0.8 0.24 -0.2 -0.1 0.82 110  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe SchutM5 -0.4 0.59 0.61 0.06 -0.1 -0.4 -0.2 0.44 -1 -0.2 -0.3 0.6 0.24 0.56 -0.6 0.32 -0.2 0.03 -0.3 SchutM6 -0.5 -1.5 -0.3 -0.2 -0.1 -0.4 -0.1 1.14 -1.4 -0.2 -0.1 0.69 0.06 -0.3 -0.8 0.23 0.12 -0.1 -0.9 J16-SchutM7 -0.6 -1.2 -0.2 -0.4 -0 -0.3 -0.2 0.28 -0.9 -0.3 -0.2 0.71 1.09 -0.1 -0.8 0.18 -0.6 -0.7 0.47 J16-SchutM8 -0.3 -0.6 -1.7 1.23 -0 -0.6 0.01 0.88 -0.3 -0.3 0.13 0.67 0.23 -0.4 -0.7 -0.2 -0.4 -0.1 -0.9 J16-SchutM9 0.04 0.28 -0.9 0.37 -0.2 -0.1 -0.3 -1.1 0.09 -0.3 -0.1 -0 -0.1 0.39 0.4 -0.1 -0.6 0.26 -0.8 SchutM10 -0.4 -1 -0.4 -0 -0.1 -0.2 -0.1 -1.3 -0.7 -0.2 0.3 -1 0.47 -0.2 0.33 -0.4 -0.5 0.2 0.15 J16-O4 M1 1.05 -0.6 -0.7 1.53 -0.2 -0.5 -0.3 -1.3 0.83 0.86 2.78 -1.9 -0.4 0.12 0.33 -0.2 -0.5 0.81 1.58 J16-O4M2 -0.6 -1 0.18 -0.2 -0.2 -0.2 -0.2 -1.3 0.15 -0.2 -0.1 -1.2 -0.5 -0.3 -0.1 -0.5 -0.6 0.72 -0.3 J16-O4M3 -0.1 -1.2 -0.7 0.82 -0.2 -0.4 -0.2 -1.3 0.77 -0.2 0.78 -1.9 -0.5 -0.3 0.33 -0.3 -0.6 -0.1 -1.6 J16-O4M4 -0.1 -1.8 -1.4 -0.3 -0.2 -0.4 -0.2 -1.3 0.37 -0.1 0.18 -1.7 -0.6 -0.5 0.3 0.17 -0.6 0.25 -0.2 J16-O4M5 0.17 -0.9 0.75 1.08 -0.2 -0.6 -0.2 -1.4 -1.1 0.48 0.08 -2.2 -0.6 -0.6 -0.3 0.13 -0.6 0.29 2.07 J16-O4M6 -0.7 -1.9 -2 0.44 -0.2 -0.5 -0.2 -1.4 0.2 1.31 -0.4 -1.9 -0.4 -0.6 -0.2 0.13 -0.5 0.23 -1.4 J16-O4M7 1.1 -0.9 0.7 1.44 -0.1 -0.3 -0.2 -1.3 0.3 -0.2 0.08 -1.4 0.61 -0.5 -0.5 -0.3 0.13 -0 0.4 J16-O4M8 -0.3 -0.6 -0.6 1.99 -0.2 -0.3 -0.2 -1.3 -0.3 -0.3 0.02 -1.7 -0.6 -0.5 -0.1 -0 -0.5 0.21 -1.5 J16-O4M9 -0.1 -1.1 -0.1 -0.3 -0.1 -0.1 -0.2 -1.3 -0.5 -0.2 -0.1 -1 -0.6 -0.6 -0.5 -0.4 -0.6 -0.3 -0.5 J16-O4 M10 -0.1 0.65 0.96 1.48 -0.1 -0.4 -0 -1.3 0.35 -0.1 1.21 -1.7 0.73 0.48 0.24 1.05 -0.4 1.07 -0.1 J16-AlF1 -0.5 -0.9 -1.9 -0.2 -0.1 -0.1 -0.2 -1.2 1.41 -0.2 0.84 -1.3 -0.4 0.36 0.16 0.6 -0.4 -0.5 -0.1 J16-AlF2 0.13 -0.7 -0.5 -1.3 -0.1 -0.1 0.04 -1.2 -0.8 -0.2 0.16 -1.2 0.02 0.67 0.47 0.52 -0.5 0.07 -0 J16-AlF3 -0.2 0.74 0.2 -0.1 -0.2 -0.3 -0 -1.3 0.23 -0.2 1.33 -1.9 0.11 0.73 0.19 1.09 -0.4 1.11 -0.7 J16-AlF4 0.23 -0.4 -0 0.46 -0.2 -0.4 -0.1 -1.3 0.33 -0.1 0.29 -1.7 0 -0.1 0.6 0.39 -0.5 0.84 -0.3 J16-AlF5 -0.4 -1.1 -0.8 -0.2 -0.2 -0.3 -0.2 -1.3 -0.3 -0.2 0.31 -1.4 -0.2 -0.1 -0.2 -0.3 -0.6 -0.1 -0.7 J16-AlF6 0.13 0.01 0.48 -1.3 -0.1 -0.3 -0.2 -1.3 -0.4 -0.3 -0.3 -1.5 -0.3 -0.3 -0.2 -0.2 -0.6 0.56 0.48 J16-AlF7 -0.1 -1.2 -0.7 -0.8 -0.1 -0.4 -0 -1.3 -0.7 -0.2 -0.1 -1.5 -0.1 -0.4 -0.4 -0.3 -0.6 0.09 -0.2 J16-AlF8 -0.4 -1.1 -0.6 2.19 -0.2 -0.3 -0.1 -1.3 0.54 -0.2 0.4 -1.3 0.48 -0.4 -0 -0.3 -0.5 1.03 -0.7 J16-AlF9 -0.2 -0.1 0.44 -0.3 -0.1 0.47 -0.1 -1 -0.5 -0.3 -0.2 1.63 0.45 1.23 -0.5 -0.2 -0.6 0.1 -0.7 J16-AlF10 -0.3 0.45 0.16 -0 -0 0.27 -0.1 -1 0.02 -0.3 -0.1 0.62 -0.5 -0.4 -0.6 -0 -0.6 -0.5 -0.5 J16-AlM1 -0.2 -0.6 0.13 -0.4 -0.1 0.34 -0.1 -1.1 0.14 -0.3 -0.4 0.52 -0.6 -0.5 -0.1 -0.5 -0.6 -0.3 -0.6 J16-AlM2 0.11 -0.7 -0.1 -0.2 -0.1 0.37 -0.1 -1.1 0.46 -0.3 -0.2 0.52 0.51 -0.6 0.07 -0.5 -0.6 0.15 -0.4 J16-AlM3 0.02 0.56 0.64 -0.8 0.05 0.37 0.15 -1 0.51 -0.2 -0 0.39 0.06 -0.2 1.65 6.62 -0.1 1.67 0.02 111  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe J16-AlM4 0.13 -0.3 0.05 0.31 -0.1 0.37 -0.1 -1.1 -0.3 -0.2 0.25 0.5 1.59 -0.5 1.22 -0.4 -0.6 1.48 -0.8 J16-AlM5 0.08 -0.8 -0.1 -0.5 -0.1 0.33 0 -1 -0.3 -0.2 -0.1 0.56 1.26 -0.4 0.39 -0.3 -0.5 0.97 0.88 J16-AlM6 0.07 -1.2 -0.7 -0.7 -0.1 0.32 -0.1 -1 -0.2 -0.2 0.72 0.4 -0.3 -0.4 0.4 -0.3 -0.6 -0.3 -0.8 J16-AlM7 0.06 -0 0.25 0.38 -0.1 0.23 -0 -1 0.88 -0.3 1.1 0.47 1.74 -0.3 0.32 -0.3 -0.6 0.89 -0.9 J16-AlM8 -0.2 -0.7 -0.6 0.1 -0.1 0.35 -0.1 -1.1 0.15 -0.3 0.11 0.5 -0.2 -0.3 0.23 -0.5 -0.6 0.69 -0.7 J16-AlM9 -0.5 -0.9 -1.5 0.52 -0.1 0.16 -0 -0.8 1.92 -0.3 0.62 0.52 -0.2 0.05 0.15 -0.1 0.01 -0.3 -0.1 J16-AlM10 0.3 -1.3 -0.6 -0.5 -0.1 0.32 0.01 -1 0.3 -0.2 0.31 0.46 1.01 -0.5 0.32 -0.3 -0.6 0.15 0.29 J16-GrayF1 0.32 -0.5 -0.4 0.13 -0.1 0.22 -0.2 -1 -0.5 -0.3 -0.2 0.46 -0.2 -0.3 -0.1 -0.1 -0.6 -0.1 0.06 J16-GrayF2 -0.2 -0.3 0.06 -0.8 -0.1 0.17 -0.1 -1.1 0.23 -0.3 0.66 0.54 -0.3 -0.3 -0.3 -0.3 -0.6 0.24 -0.1 J16-GrayF3 -0.4 -0.1 0.77 0.21 -0.1 0.28 -0.1 -1.1 -1.1 -0.3 -0.3 0.42 0.61 -0.4 -0.4 -0.4 -0.6 0.86 0.51 J16-GrayF4 0.22 -0.5 -0 -0.9 -0 0.22 -0.1 -1 -0.4 -0.3 -0.4 0.41 0.97 -0.1 -0.5 -0.1 -0.6 0.52 -0.1 J16-GrayF5 -0.4 -1 -0.1 -0.5 0.57 0.25 -0.1 -1 0.54 -0.3 -0.3 0.41 0.45 0.44 -0.3 -0.2 -0.6 -0.4 -0.5 J16-GrayF6 -0.2 -0.7 -0.2 0.31 -0.1 0.28 -0.1 -1.1 0.01 -0.3 -0.4 0.59 0.05 -0.5 -0.6 -0.3 -0.6 0.59 -0.7 J16-GrayF7 -0 -0.6 -0.7 -0.1 -0.1 0.04 -0.2 -1 0.5 -0.3 -0.3 0.32 0.1 -0.4 -0.5 0.06 -0.6 0.37 -1.9 J16-GrayF8 -0.3 -0.6 -0.6 -0.7 -0.1 0.2 -0.1 -1 0.09 -0.2 -0.3 0.49 0.34 -0.3 -0.3 -0.4 -0.5 0.45 -1.2 J16-GrayF9 -0.4 -0.9 -0.5 2.96 -0.1 0.56 -0.1 -0.9 1.67 -0.3 -0.4 1.94 -0.4 0.03 -0.1 -0.1 -0.6 0.51 -0.7 J16-GrayF10 -0 -0.5 -0.1 -1.2 -0.1 0.39 -0.1 -1 -0.9 -0.3 -0.4 0.54 1.13 -0.4 -0.2 0.14 -0.6 0.71 -0.7 J16-GrayM1 -0.1 -1.2 -0.4 -0.7 -0.1 0.3 -0.1 -1.1 0.31 -0.3 -0.3 0.4 2.39 -0.4 0.01 0.26 -0.6 0.31 -0.9 GrayM2 -0.4 0.43 -0.5 2.38 -0.1 0.32 -0.1 -1 -0.3 -0.3 -0.4 0.45 0.33 0.07 -0 0.02 -0.6 0.85 -0.9 GrayM3 -0.1 -0.9 -0.4 0.29 -0.1 0.28 -0.1 -1 -0.8 -0.2 -0.4 0.5 -0.6 -0.5 -0.4 -0.1 -0.6 -0 -0.5 GrayM4 0.09 -0.9 0.05 0.08 -0.1 0.29 -0.1 -1.1 0.56 -0.2 -0.1 0.47 -0.3 -0.5 -0 0.47 -0.6 0.06 -0.7 GrayM5 -0.4 -0.4 -0.3 -0.3 -0.1 0.26 -0.1 -1 -0.2 -0.3 0.68 0.47 0.55 -0.3 -0.4 0.6 -0.6 0.06 -0.9 GrayM6 -0.2 -0.5 0.15 0.63 -0.1 0.34 -0.1 -1.1 -0.6 -0.3 -0.4 0.41 -0.3 -0.5 -0.4 0.9 -0.6 0.23 -0.8 GrayM7 -0 -0.9 -0.2 -0.1 -0.1 0.32 -0.1 -1.1 0.51 -0.1 -0.3 0.49 1.18 0.51 -0.5 0.15 -0.6 0.23 -1 GrayM8 0.13 -0.6 0.33 -0.5 -0.1 0.27 -0.1 -1 0.31 -0.3 -0.3 0.36 0.03 2.33 -0.3 0.47 -0.6 0 -1.4 GrayM9 -0.3 -1.1 -0.1 -1.1 -0.1 0.28 -0.1 -1.1 -0.8 -0.3 0.1 0.47 1.61 -0.3 -0.7 1.91 -0.6 0.27 -0.8 GrayM10 -0.3 -0.6 0.03 -0.6 -0.1 0.08 -0.1 -1 -0.7 -0.3 0.26 0.27 -0.2 0.57 -0.5 4.04 -0.5 -0.1 -1.7 J16-BB1 -0.5 -1.3 -0.1 0.48 -0.1 0.25 -0.1 -1 0.66 0.14 0.24 0.53 -0.6 -0.4 -0.2 0.52 -0.6 -0.1 -0.8 J16-BB2 -0.2 -0.6 0.18 0.31 -0.1 0.22 0.01 -1 1.43 1.02 0.06 0.47 -0.5 -0.2 -0.1 -0.1 -0.6 0.36 -1 112  Code ZK ZMg ZNa ZSr ZMo ZAg ZCd ZSn ZBa ZPb ZAl ZSc ZV ZCr ZMn ZCo ZCu ZZn ZSe J16-BB3 -0.1 -0.4 -0.4 -0.1 -0 0.22 -0 -1 1.17 0.53 0.02 0.4 -0.6 -0.2 0.48 -0.1 -0.5 -0.1 -1.3 J16-BB4 -0.2 0.02 0.34 1.34 -0.1 0.26 -0.1 -1 1.69 0.55 -0.1 0.33 -0.5 -0.2 0.43 -0 -0.6 1 -0.4 J16-Kel16 -0.2 -0.1 0.35 0.6 -0.1 0.19 -0.1 -1.1 2.88 -0.2 0.33 0.46 -0.3 -0.3 0.87 -0 -0.6 0.87 -1.5 J16-Kel17 -0.1 -0.1 0.06 2.07 -0.1 0.14 -0.1 -1 3 -0.3 0.53 0.45 -0.6 -0.2 1.01 0.05 -0.6 0.17 -1.6 J16-Kel18 -0.3 -0.6 0.71 -0.7 -0.1 0.29 -0.1 -1.1 -0.3 0.02 0.07 0.47 -0.5 -0.5 0.27 -0.1 -0.5 0.83 -1.5 J16-Kel19 -0.1 -0.3 0.85 -0.6 -0 0.31 -0 -1.1 -0.4 -0.2 0.5 0.39 -0.5 -0.4 0.01 -0.1 -0.5 0.72 -1.3 J16-Kel20 0.07 -0.8 0.56 -0.3 -0.1 0.07 -0.2 -1.1 -0.2 -0.2 0.86 0.19 -0.1 0.29 0.28 0.31 -0.5 3.27 -0.3 J16-SL1 -0.2 -0.9 -0.7 0.37 -0.2 -0.2 -0.2 -1.3 0.36 -0.1 -0.2 -1.2 -0.6 0.26 -0.1 0.44 -0.5 0.25 0.66 J16-SL2 0.04 0.96 1.78 0.74 -0 -0.2 -0.1 -1.3 0.82 -0.2 -0.2 -1.3 -0.4 0.36 0.02 0.29 -0.5 0.58 -0.2 J16-SL3 0.09 -0.9 -0.4 0.63 -0.1 -0.2 -0.1 -1.3 -0.1 -0 -0.3 -1.3 -0.6 0.02 -0.1 0.36 -0.5 -0 0.02 J16-SL4 0 -1.8 0.05 0.35 -0.1 -0.2 -0.2 -1.3 1.05 0.13 -0 -1.4 -0.7 -0.5 -0.1 -0.3 -0.6 0.04 0.44 J16-SL5 -0.3 -0 -1.5 2.23 -0.1 -0.2 -0.2 -1.3 7.67 -0.1 -0.3 -1.4 -0.5 -0.6 0.67 -0.3 -0.6 0.01 0.35 J16-SL6 -0.2 -0.5 -1.6 2.21 -0.1 -0.2 -0.2 -1.3 1.09 -0 -0.2 -1.6 -0.6 -0.5 0.23 -0.1 -0.6 -0.5 2.27 J16-SL7 0.26 -1 -2.4 2.86 -0.2 -0.5 -0.2 -1.4 4.09 0.45 0.34 -2.4 -0.7 -0 0.82 0.32 -0.6 -0.3 1.77 J16-SL8 0.06 -0.7 -0.1 0.94 0.73 -0.2 -0.1 -1.3 -0.6 -0.2 0.26 -1.3 -0 -0.5 -0 -0.4 -0.6 -0.2 -0 J16-SL9 -0.1 -0.5 -0.4 0.47 -0.2 -0.1 -0.2 -1.3 1.42 0.68 0.09 -1.3 -0.6 -0.5 0.12 -0.3 -0.6 0.37 1.38 J16-SL10 0.14 -0.6 -1.5 1.25 -0.2 -0.1 -0.1 -1.3 0.83 -0.2 -0.4 -1.4 -0.7 -0.6 -0 -0.4 -0.6 0.21 -0.5 J16-SL11 0.09 -0.7 -0.3 2.31 -0.2 -0.2 -0.1 -1.3 1.38 0.06 -0 -1.5 -0.7 -0.6 0.14 -0.3 -0.6 0.22 0.34 J16-SL12 0.06 1.45 -0 2.16 -0.2 -0.4 -0.2 -1.4 1.35 -0.3 -0.2 -1.7 -0.7 -0.3 0.19 -0.2 -0.6 0.86 0.3 J16-SL13 -0.1 -0.8 -0.7 2.75 -0.2 -0.2 -0.1 -1.3 1.04 -0 0.05 -1.7 -0.7 -0.3 0.27 -0.3 -0.6 -0.8 -0.2 J16-SL14 0.13 -1.5 0.35 0.47 -0.1 -0.3 -0.2 -1.3 0.13 1.27 -0.4 -1.4 -0.6 -0.5 0.09 -0.3 -0.6 -0.2 0.74 J16-SL15 0.17 0.34 -1.4 3.49 4.28 -0.4 -0.3 -1.4 2.26 -0.1 0.13 -2.2 -0.7 -0.6 0.79 -0.1 -0.5 0.27 -0.2         113   Appendix E Grouping of individual juvenile birds in cluster analysis in 2015 and 2016 Height= 10    Site 2015 2016 Total number of bird Number birds in cluster (H=10) % Total number of bird Number birds in cluster (H=10) % Kelowna 20 20 100 20 17 85 Hullcar 10 8 80 40 31 77.5 Salmon Arm 10 5 50 16 12 75 Armstrong 9 7 77.78 20 10 50 Mara 9 5 55.56 41 37 90.24 Vernon 18 16 88.88 20 17 85 Oliver 10 7 70 30 30 100 Osoyoos 8 6 75 32 15 46.87 Penticton 6 2 33.33 - - - Keremeos 5 5 100 27 14 51.85 Summerland - - - 19 13 68.42 Okanagan Falls - - - 10 8 80 Coldstream - - - 20 14 70 Lumby - - - 15 14 93.33         114   Appendix F GPS point of sampling sites in 2015 and 2016        Juvenile Sampling Sites Juvenile and Adult Sampling Sites Location Code Latitude(N) Longitude(W) Location Code Latitude(N) Longitude(W) Osoyoos O4 49.05 119.51 Oliver O2 49.13 119.56 Keremeos K3 49.24 119.82 Kelowna Kel 49.92 119.36 Hullcar V1 50.50 119.25 Penticton P2 49.39 119.60 Salmon Arm V4 50.66 119.37 Vernon N4 50.22 119.30 Mara V6 50.67 119.08 Summerland P8 49.62 119.67 Armstrong V8 50.45 119.17 Keremeos K2 49.19 119.74 Vernon N1 50.37 119.28 Hullcar V3 50.48 119.28 Penticton P1 49.45 119.59 Lumby N5 (LM) 50.22 119.01 Penticton P4 49.52 119.55 Coldstream CR 50.22 119.19 Oliver O7 49.17 119.58     Oliver O8 49.17 119.58 Adult Sampling Sites Osoyoos O1 49.13 119.57 Location Code Latitude(N) Longitude(W) Oliver O6 49.01 119.43 Blue Mountain P3 49.32 119.53 Armstrong N1B 50.38 119.25 Summerland P7 49.59 119.73 Armstrong V10 50.37 119.26     Mara V7 50.68 119.07     

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