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

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

The trophic niche of synanthropic insectivorous bats (Kuhl's pipistrelle, Pipistrellus kuhlii) and their potential contribution to arthropod pest suppression

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

By Yuval Cohen

March 2018

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

The trophic niche of synanthropic insectivorous bats (Kuhl's pipistrelle, Pipistrellus kuhlii) and their potential contribution to arthropod pest suppression

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

By Yuval Cohen

Under the Supervision of Professors Carmi Korine and Shirli Bar David

Mitrani Department of Desert Ecology

Author's Signature ……………… … ……… Date 18/3/2018

Approved by the Supervisor … … .. Date 18/3/2018

Approved by the Supervisor ….. .… Date 18/3/2018

Approved by the Director of the School ……………… Date 18/3/2018

III

The trophic niche of synanthropic insectivorous bats (Kuhl's pipistrelle, Pipistrellus kuhlii) and their potential contribution to arthropod pest suppression

By Yuval Cohen

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

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

2018

ABSTRACT

Conservation Biological Control (CBC) attempts to support the management of agricultural pests by identifying, preserving and enhancing the efficiency of natural enemies. Synanthropic insectivorous bats, are abundant in urban and agricultural ecosystems and can potentially suppress pest irruptions through opportunistic feeding. Yet, few studies described their trophic niche to high taxonomic resolution or related their diets to ecosystem services and their possible role in CBC. Pipistrellus kuhlii, a synanthropic generalist insectivorous bat, forages intensively over agricultural fields, including cotton fields in Israel. Hence, it has potential as a biological pest suppression agent.

My working assumption is that synanthropic generalist insectivorous, such as P. kuhlii bats, regularly provide ecosystem services and are suitable natural enemies for CBC, particularly that P. kuhlii bats respond to pest irruptions with opportunistic feeding. I predicted the community of arthropods in the diet of P. kuhlii bats will vary in space and time.

Furthermore, that the diet of the bats will comprise agricultural pest species. I expected that cotton pests will be found in the diet of the bats and will be positively correlated to their abundance in the cotton fields. Additionally, I anticipated that the bats dietary niche breadth will narrow as a function of increasing dominate pest species found in the diets.

I located roosts of P. kuhlii bats near cotton fields in Emek-Hefer, Israel. I collected feces from the roosts throughout the cotton season (May - October 2016), and applied a molecular method, eDNA Metabarcoding, to process 133 fecal samples and identify taxa in the diet of P. kuhlii.

I recovered 145 molecular operational taxonomic units (richness of prey) and 621 prey items (frequencies of prey) in the diet of P. kuhlii bats. I assigned 67% of the prey items to species or genus. The diet of the bats varied across the sampling season and showed disparities between sampling roosts. Lepidopterans and Dipterans emerged as dominate prey orders.

Twenty-five agricultural pest species (32% of the total prey items), which target various agricultural crops, were found in the diet. Furthermore, potential disease vectors and nuisance arthropod pests were also identified in the diet. Yet, I found relatively low intraguild predation on arthropod natural enemies. Pink bollworm (Pectinophora gossypiella), a major cotton pest, showed the highest frequency occurrence of all prey taxa in the diet of P. kuhlii. It was found in diet at all sampling roosts, regardless of distance to cotton fields, and showed positive correlation with proxies of its abundances in cotton fields. This suggests that P. kuhlii consume

Pink bollworms with opportunistic feeding. Additionally, the bats dietary niche breadth narrowed with the increasing frequency occurrences of Pink bollworms in the diet.

My study emphasis the synanthropic trophic niche of P. kuhlii bats, highlights their ecosystem services and suggests that they function as holistic natural enemies of various deleterious arthropods in anthropogenic habitats. My findings especially demonstrate their potential for CBC programs to suppress cotton pests. The results possibly signify trophic interactions that occur across the wide-ranging distribution of P. kuhlii, with significant implications to economic concern that are still underappreciated. Further research directions should include quantifying the actual services of P. kuhlii, followed by attempts to preserve, enhance and communicate its contribution to pest suppression and sustainable agroecosystems.

ACKNOWLEDGMENTS

I would like to express my sincere gratitude towards my supervisors Prof. Carmi Korine and Dr.

Shirli Bar David, for giving me the opportunity to take part in this fascinating and fulfilling study. For always being supportive, patient and providing me the professional guidance I needed. For letting me express myself throughout the research, share my ideas freely and making my MSc experience one that I will remember positively. I want to express my appreciation to Prof. Thomas Gilbert from the University of Copenhagen (CGG: Center for

GeoGenetics) for his willingness to collaborate. My extreme gratitude goes to Dr. Kristine

Bohmann (CGG) for being a spectacular mentor, for her encouragement, guidance and professionality that had great contribution to my study. I am ever grateful to Martin Nielsen

(CGG) for being supportive, coaching me in the laboratory and helping me complete the final steps of the molecular process. I thank Christina Lynggaard (CGG) for reassuring me during the lab work. I salute the farmers and cotton pest scouts for supporting the study in various ways.

Practically, Haggai Medini from 'Israel Seeds' for his enthusiasm, commitment to the project and his innovate perspective on integrating bats in cotton pest management. Additionally, I want to thank Ariela Niv and Michael Axelrod from the Israel Cotton Board for their contribution to the study. I thank Giora Shoham and Nimord Shai and for sharing their insights and data on cotton pests and pesticide sprays with me. I am grateful to my dear friends Irene Stevens and

Efrat Dener for always willing to help and give their advice. Additionally, I want to express thanks to Stav Livne for her valuable suggestions for the statistical analysis. I want to thank my lab members for being supportive and encouraging. I want to thank my friends that helped me with the bat roost surveys. My greatest gratitude goes to my family Pini, Tali, Dan and Ron

Cohen and grandmother Nomi Levy for letting me follow me ambitions and being kind and encouraging throughout the way. Finally, I want to thank the bats for being such majestic and fascinating creatures that benefit us in tremendous ways.

My research was funded by Israeli Ministry of Agriculture and Rural Development. My work was supported through a scholarship of the Albert Katz International School for Desert Studies

Foundation.

VII

TABLE OF CONTENTS

Abstract……………………………………………… ...... ………………..…………..……….II

Acknowledgments……………………………………… ...... …….………………....…………V

Table of Contents………………...……………………… ... …….…….………..………...…VII

Table of Figures…………………………………… ...... …..………….IX

Table of Tables…………………………………...………… ...... ….…….….……...... X

1. Introduction……………………………………………… ...... ……….…………...…………1

1.1 Agroecology and Urban Ecology……………………………………………………..………1

1.2 Ecosystem Services and Biological Control……………………….…………..….………... .2

1.3 Conservation Biological Control……………………………..……...…………….…...... 2

1.4 The Diet of Insectivorous Bats………………………………….…………..….….…...... 4

1.5 Insectivorous Bats as Natural Enemies………………………….…………….…….………..9

2. Hypothesis and Predictions………………………………..………...……...…...... …..…...12

3. Methods and Materials……………………………………….………...... ….………….....13

3.1 Sampling Procedure……………………………………………………………...………… 13

3.2 Molecular Diet Analysis……………………………………………….………….....….…..16

3.3 Statistical Analysis and Data Visualization…………………………….…...……...……… 29

4. Results………………………………………...……………………….….………..….……. 34

4.1 Fecal Source…...……...……………………...……………………….….………..….……. 34

4.2 Efficiency of the Molecular Diet Analysis……....…………...……….….………..….……. 34

4.3 Spatio-temporal Variation in the Diet of P. kuhlii……...... ………...……………..….……. 37

4.4 Orders, Richness and Families of Arthropods in the Diet of P. kuhlii ……..……..….……. 41

4.5 Pest Species in the Diet of P. kuhlii…………..……………………………..……..….……. 45

4.6 Similarities in the Diet of P. kuhlii…..…………………………….…………………..……. 50

5. Disscusion………….…………………………………………………….………..…….…... 53

5.1 Metabarcoding the Diet of P. kuhlii……………………………………..…………..…..…..53

5.2 Spatial-temporal Trends in the Diet of P. kuhlii……………….….…………………...……..55

VIII

5.3 The Trophic Niche of P. kuhlii…………………………………………...... ……...... ……58

5.4 Pipistrellus kuhlii in Conservation Biological Control…………….……...……….……..…60

6. Conclusions………..…………………………………………………….…………..……….66

7. References ……………………………………………………………….…….………..…...67

8. Appendices……………………………………………………………….…….………..…...91

TABLE OF FIGURES

Figure 1 - Study Area ...... 14

Figure 2 - Metabarcoding workflow until sequencing ...... 17

Figure 3 - Metabarcoding workflow following sequencing ...... 17

Figure 4 - GEL electrophoresis results for tagged PCR ...... 24

Figure 5 - Operational Taxonomic Units (OTU) assignment ...... 35

Figure 6 - Sample coverage ...... 36

Figure 7 - Venn diagram for arthropod richness ...... 37

Figure 8 - Canonical analysis of principles coordinates (CAP) ...... 39

Figure 9 - Community similarities as a function sampling intervals ...... 40

Figure 10 - Assignment counts to arthropod orders ...... 41

Figure 11 - Percent frequencies occurrence of arthropod orders ...... 42

Figure 12 - Percent frequencies occurrence of arthropod orders, per sampling date ...... 43

Figure 13 - Shannon diversity measures ...... 44

Figure 14 - Percent frequency occurrence of Pink bollworm (regional)...... 48

Figure 15 - Percent frequency occurrence of Pink bollworm (local) ...... 48

Figure 16 - Dietary niche breadth (Levin’s measure) ...... 49

TABLE OF TABLES

Table 1 - DNA Extractions Summary ...... 18

Table 2 - qPCR Settings ...... 20

Table 3 - Tagged-PCR Settings ...... 22

Table 4 - OTU Assignment Confidence Criteria ...... 27

Table 5 - Agricultural Pest Species Found in the Diet ...... 45

Table 6 - Potential Nuisance Pests or Diseases Vectors Found in the Diet ...... 46

Table 7 - SIMPER Results ...... 50

APPENDICES

Appendix 1 - DNA Extraction Protocol…………………………………………………………88

Appendix 2 - Unconstrained Ordination; Temporal Variation.…………………………………89

Appendix 3 - Supplementary Table 1 - Pairwe comparisons for orders………..……………..…91

Appendix 3 - Supplementary Table 2 - Pairwise comparisons per sampling date...... 92

Appendix 4 - Supplementary Table 3 - List of taxa in the diet of the Pipistrellus kuhlii ...... 95

ADDITIONAL FILES

Additional File 1 - Krona plot of the diet of P. kuhlii

Additional File 2 - Krona plot walk-through

1. INTRODUCTION 1

1.1 Agroecology and Urban Ecology 2

In the recent decades humans have immensely altered the land surface of the world (Foley et al. 3

2005). Ever more natural habitats are replaced or fragmented by agricultural and urban land use 4

(Antrop 2004, Foley et al. 2005). As a result, wildlife is forced to adapt to rapid changes in their 5 environment. In these new rather homogenized anthropogenic habitats, some species thrive 6

(Altieri 1999, Shochat et al. 2006, Welch et al. 2012), but many species perish and cause rising 7 concerns regarding biodiversity losses (Letourneau 1999, McKinney 2006). Disturbingly, 8 protecting natural areas alone is apparently no longer sufficient to maintain sustainable and 9 diverse natural ecosystems on its own accord (Fischer et al. 2006, Robinson 2006). Thus, 10 research and conservation initiatives also direct efforts at anthropogenic habitats, to address how 11 they can support wildlife and maintain sustainable ecological processes. 12

Two relatively new disciplines that address ecological processes which operate within 13 human modified landscapes are agroecology and urban ecology (Alberti et al. 2003, Wezel et 14 al. 2009). While urban ecology is mostly restricted to urban areas (Mcintyre et al. 2000), 15 agroecology generally focuses on agricultural areas and their surrounding (Wezel et al. 2009). 16

Yet, the two disciplines share ecological processes that are often interdependent. For example, 17 insectivorous bats may use anthropogenic structures situated in urban settlements to roost 18 during the day, but spend their nights foraging at nearby agricultural fields (Barak and Yom- 19

Tov 1991, Maxinová et al. 2016). The wildlife which display increased populations and 20 expanded distribution ranges particularly associated with humans, and the altered environments 21 in their proximity, are categorized as synanthropic species (Johnston 2002, Francis and 22

Chadwick 2012). This includes a large group of species, which are categorized as pests due to 23 their negative effects on human wellbeing (Schal and Hamilton 1990, McIntyre 2000). On the 24 other hand, some synanthropic species may contribute to our well-being by suppressing pests 25 and their damages, and therefore should be considered beneficial species. Yet, their vital 26 services are not always straight forward to detangle and thus can often go unnoticed. 27

1.2 Ecosystem Services and Biological Control 1

The recognition that wildlife species can offer benefits to humans in the form of pest 2 suppression has motivated attempts to identify and utilize the services they deliver. Examples 3 for such efforts trace back to 305 a.d.; when Chinese farmers constructed bamboo bridges to 4 connect citrus trees and aid the efficiency of Weaver ants, Oecophylla smaragdina (Fabricius), 5 to suppress citrus pests (see: Van Mele (2008) and Orr and Lahiri (2014)). In more recent times, 6 the framework for similar practices attempting to employ natural enemies to suppress pests is 7 generally associated with biological control (Eilenberg et al. 2001). Biological control consists 8 of methods to suppress pest populations by using other organisms (Stern et al. 1959, Ehler 1998, 9

Kogan 1998). Biological Control methods typically depend on natural mechanisms such as 10 predation, parasitism, herbivory and diseases but involve active human management to support 11 these processes (Kogan 1998). 12

The identification and quantification of services provided by natural ecosystems, or 13 specific wildlife species, has been recognized at the Millennium Ecosystem Assessment (2005) 14 and termed ‘ecosystem services’, referring to the benefits humans derive from them (Costanza 15 et al. 1998). Although it is worthwhile to mention the debate whether the term also engulfs 16 services by ecosystems that are strongly governed by anthropogenic drives (David Saltz, 17 personal communication), such as urban or agricultural systems (Bolund and Hunhammar 1999, 18

Swinton et al. 2007). I will not refrain from using the term ecosystem services, in this context, 19 as it is wildly accepted in the literature and applied also to anthropogenic systems (Bolund and 20

Hunhammar 1999, Cleveland et al. 2006, Swinton et al. 2007, Boyles et al. 2011). None the 21 less, the services wildlife provide in agroecosystems, including biological pest suppression, play 22 a key role in Integrated Pest Management (IPM), which aims to achieve sustainable reduction in 23 the use of chemical pesticides (Stern et al. 1959, Kogan 1998). 24

1.3 Conservation Biological Control (CBC) 25

In recent decades biological control is undergoing a paradigm-shift, embracing sustainable 26 practices, demonstrated by the growing interest in Conservation Biological Control (CBC) 27

(Barbosa 1999, Straub et al. 2008). This method seeks to identify, conserve and enhance the 1 capacity of natural enemies to suppress agricultural pests via adjustments in agricultural 2 management with complementary habitat modifications and diversification of agroecosystems 3

(Barbosa 1999, Gurr and Wratten 2000, Sunderland and Samu 2000, Tscharntke et al. 2007). 4

Conservation Biological Control shares similarities with nature conservation practices, 5 including efforts to maintain biodiversity and habitat diversity (Letourneau 1999, Bianchi et al. 6

2006). Both fields devote efforts to promote natural processes and feedback mechanisms that 7 increase ecological sustainability (Letourneau 1999, Tscharntke et al. 2007), yet CBC 8 specifically aims to sustain efficient natural enemies and their functionality (Straub et al. 2008). 9

The fundamentals of CBC primarily rely on natural enemies that naturally occur in the 10 agroecosystem (Tscharntke et al. 2007), which are frequently dominated (numerically) by 11 generalist predators (Symondson et al. 2002, Welch et al. 2012). Generalist predators have 12 diverse diets, which often times reflect the abundance of available prey, and thus their efficiency 13 in suppressing specific pest species was questioned (Symondson et al. 2002). However, over the 14 years developments in theory and practice of biological control has gradually strengthen the 15 conception that generalist predators can effectively suppress pests (Murdoch et al. 1985, 16

Symondson et al. 2002, Stiling and Cornelissen 2005). It is now commonly acknowledged that 17 assemblages of generalist predators contribute immensely to this objective (Settle et al. 1996, 18

Symondson et al. 2002, Stiling and Cornelissen 2005). Symondson et al. (2002) showed that 19 assemblages of generalist predators significantly reduced pest abundance in 79 % of the cases 20 they reviewed. However, teasing apart the contribution of a specific natural enemy species is a 21 difficult task (Schmitz et al. 2001, Lang 2003, Maas et al. 2013). 22

Ehler (1990) identified important traits for efficient natural enemies including: (1) their 23 abilities to rapidly colonize and keep pace with temporal and spatial disruptions in the 24 agroecosystem, (2) their abilities to display temporal persistence in the agroecosystem, allowing 25 them to maintain their numbers even when pest populations are low and (3) their abilities to 26 rapidly exploit a food resource by opportunistic feeding, practically their capacity to stimulate a 27

quick and effective response to pest irruptions. These traits, which markedly emphasis the 1 sustainable qualities of natural enemies, have been found to be especially relevant for generalist 2 predators (Ehler 1998, Symondson et al. 2002). 3

The traits mentioned above are mostly generated by entomologists who established these 4 fundamentals based on the ecology of invertebrate natural enemies (Ehler 1998). Yet, larger 5 vertebrate predators such as insectivorous birds and bats, despite their potential to suppress 6 pests via CBC, have been rather overlooked (Kunz et al. 2011, Wenny et al. 2011, Maas et al. 7

2013, 2016). Until recently, relatively few studies have assessed the contribution of 8 insectivorous birds or bats to suppress arthropod pests (Maas et al. 2013, King et al. 2015, 9

Maine and Boyles 2015, Puig-Montserrat et al. 2015). Thus, it is still rather uncertain to what 10 extent invertebrate biological control theory and practice applies to larger vertebrate 11 insectivorous such as bats. 12

1.4 The Diet of Insectivorous Bats 13

Bats (Chiroptera) are an extremely diverse order of mammals with more than 1300 species, 14 spread across six of the world continents (Fenton and Simmons 2014). Their rich diversity 15 include species that feed on arthropods to those that feed on fruit, nectar, amphibians, fish, birds 16 and blood (Kunz et al. 2011, Fenton and Simmons 2014). Insectivorous bats species employ a 17 variety of foraging strategies, such as aerial-hawking, substrate-gleaning, perch hunting and 18 trawling to capture their arthropod prey (Norberg and Rayner 1987). They can apparently exert 19 significant impact on communities of arthropods, by reducing the densities of herbivorous 20 arthropods, as shown by Williams-Guillén et al. (2011) and Maas et al. (2013) in coffee and 21 cacao agroforestries. Both studies relied on night exclosures and attributed the changes in 22 arthropods communities to insectivorous bats, yet their findings did not provide conclusive 23 evidence of predation by bats. 24

While a detailed dietary analysis is a fundamental prerequisite to understand the feeding 25 ecology and trophic interactions of any species. Studying the diet of insectivorous bats partakes 26 its share of difficulties (Boyles et al. 2013); since they are nocturnal, highly maneuverable and 27

elusive creatures (Marques et al. 2013, Fenton and Simmons 2014) that are, in many countries, 1 protected by law (Voigt and Kingston 2015). These factors rule out their anesthetization to 2 inspect gut contents, hinder visual observations of predation and complicate handling them to 3 obtain their feces. Yet, when feces are eventually obtained, whether by mist-netting of bats or 4 by collecting feces from underneath roosts sites, they can provide means to investigate their 5 diets (Whitaker 1998, Bohmann et al. 2011). 6

1.4.1 Morphological Identification of Prey in the Diet of Insectivorous Bats 7

Conventionally, studying the diet of insectivorous bats involve teasing apart their feces to 8 visually identify prey items according to their fragmented remnants (Whitaker 1998). Although, 9 studies that employed this method pioneered our understanding of the diet of insectivorous bats 10

(Whitaker 1998). The taxonomic resolution of data obtained by this method, typically allows 11 assignment of prey fragments to taxonomic order or family (Beck 1995, Whitaker 1998). 12

Furthermore, identifying soft bodied prey by visual identification, such as Lepidopterans moths 13 which are often major agricultural pests in their larva stage (Oerke 2006), is further disrupted as 14 they leave few visual traces in the feces, after bats handle and digest them (Whitaker 1998, 15

Whitaker et al. 2009, Razgour et al. 2011). For these reasons, detailed trophic interactions and 16 the food webs involving insectivorous bats are still largely underexplored to this day (Boyles et 17 al. 2013). Practically, evidence necessary to establish the contribution of many species of bats to 18 pest suppression is severely lacking (Boyles et al. 2013). 19

1.4.2 Molecular Identification of Prey in the Diet of Insectivorous Bats 20

Recent developments in molecular methods markedly; eDNA Metabarcoding (Taberlet et al. 21

2012), unleash great potential to identify species in the diet of insectivorous bats (Bohmann et 22 al. 2011, Razgour et al. 2011). Allowing high taxonomic resolution discerption of their diets, 23 which enables addressing various ecological questions (Pompanon et al. 2012). In 24

Metabarcoding a standardized DNA region (DNA barcode) is PCR amplified, amplicons are 25 then sequenced with next generation sequencing and aligned with a reference database for 26 identification according to sequence similarities (Hebert et al. 2003, Pompanon et al. 2012). 27

Metabarcoding the diet of insectivorous bats typically involves using a general arthropod primer 1 set, to amplify the DNA in their feces (Bohmann et al. 2011, Razgour et al. 2011), and thus few 2 a-priori assumptions are involved regarding the species expected in the diet (Zeale et al. 2011, 3

Bohmann et al. 2014, Gurr and You 2016). Although amplification is generally broad for 4 arthropod taxa, some limitations in amplification success does occur for certain orders of 5 arthropods (Clarke et al. 2014, Alberdi et al. 2018) 6

Contrary to traditional controlled feeding experiments or PCR essays designed to infer 7 regarding a single species in the diet of bats (McCracken et al. 2012), Metabarcoding permits 8 studying the spectrum of prey species in the diet of wild bats through their feces (Zeale et al. 9

2011). Therefore, it may be utilized to simultaneously identify important prey species with 10 significant financial and economic implications, i.e.; agricultural pests (Aizpurua et al., 2018; 11

Galan et al., 2018), disease vectors or nuisance pests (Gonsalves et al. 2013, Galan et al. 2018). 12

But also beneficial arthropods i.e.; natural enemies and pollinators, which their predation might 13 suggest possible dis-services by insectivorous bats (Zhang et al. 2007). Sampling feces of bats 14 non-invasively from aggregations at roosts, lend consistent sampling sources without 15 unnecessary handling of bats. (Bohmann et al. 2011, Vesterinen et al. 2016). Overall embracing 16 molecular techniques is becoming increasingly more practical in terms of costs-efficiency 17

(Pompanon et al. 2012, Bohmann et al. 2014, Gurr and You 2016), and holds vast potential to 18 disentangle important trophic interactions at appropriate taxonomic resolution. 19

1.4.3 Community and Spatio-temporal Variation of Arthropods and its Implications to the 20

Diet of Insectivorous Bats 21

Communities of arthropod experience turnovers in species composition over time and space 22

(McIntyre et al. 2001, Legendre and De Cáceres 2013, Jonason et al. 2014). The diversity of 23 arthropods within a given habitat, whether in natural forests (Summerville et al. 2003, Dodd et 24 al. 2008, Riley and Browne 2011) or urban gardens and agricultural fields (Royauté and Buddle 25

2012, Penone et al. 2013, Jonason et al. 2014, Egerer et al. 2017), is affected by various 26 dynamic mechanisms and can vary across spatial locations as well as throughout and across 27

seasons (Legendre and De Cáceres 2013, Egerer et al. 2017, Lewthwaite et al. 2017). 1

Environmental conditions that differ across spatial gradients or through time can underline the 2 mechanisms that drive these processes (McIntyre et al. 2001, Legendre and De Cáceres 2013, 3

Jonason et al. 2014, Lewthwaite et al. 2017). 4

For example, Jonason et al. (2014) monitored moth diversity in an agricultural field in 5

Germany from March to October and found that the species composition of moths varied over 6 the season, and that temperature was the most significant factor affecting the species richness of 7 moths. Egerer et al. (2017) found landscape intensification surrounding urban gardens was an 8 important predictor of the abundance and richness of arthropod communities, while its effects 9 differed depending on spatial scales. 10

As generalist predators, the diets of insectivorous bats can, to some degree, reflect 11 changes in the communities of arthropod in their foraging grounds (Belwood and Fenton 1976, 12

Rydell 1986, Dodd et al. 2012, Vesterinen et al. 2016). Considering this potential, 13

Metabarcoding their feces was proposed as a possible tool for biodiversity monitoring to survey 14 arthropods, termed 'biodiversity capsules' (Boyer et al. 2015), and to inform of invasive 15 arthropod species in their environment (Maslo et al. 2017). Anthropogenic land use can also be 16 mirrored in the diet of bats (Kunz et al. 2011, Voigt and Kingston 2015). For example, the 17 diversity of arthropods in the diet of common bent-wing bat (Miniopterus schreibersii), 18 decreased with the area of intensive agricultural fields in their foraging range (Aizpurua et al. 19

2018). Thus, suggesting their trophic niche is governed by prey associated with agricultural 20 production (Aizpurua et al. 2018). These findings are in accord with other studies (Clare et al. 21

2011, Alberdi et al. 2012, Salinas-Ramos et al. 2015), that emphasizes land cover and habitat 22 types are prominent factors affecting the diet of bats (Arrizabalaga-Escudero et al. 2015, Maine 23 and Boyles 2015). 24

In addition to spatio-temporal changes in prey abundance, other factors that might 25 stimulate intraspecific variation in the diet of insectivorous bats include specific nutritional 26 demands at different biological life-phases, i.e.; pregnancy, lactation and preparation for 27

hibernation or winter (Racey and Swift 1985, Kurta et al. 1989, Kunz et al. 1995, Levin et al. 1

2009, 2013). Supplementary factors that may affect how insectivorous bats take advantage of 2 their available prey can be related to gender dietary variation (Mata et al. 2016), and possible 3 shifts in foraging strategies and success throughout their ontogeny (Rolseth et al. 1994). 4

The diversity of prey (resource) utilization shown by an organism can be measured with 5

Levin's dietary niche breadth (Krebs 1989), and can alter through time (Andreas et al. 2012, 6

Gulka et al. 2017). For example, the trophic niche of western Barbastelle bats (Barbastella 7 barbastellus) narrowed in response to an increase in abundance of larger moths which it 8 selected during their peak abundance in summer months (June - August) (Andreas et al. 2012). 9

Decrease in dietary breadth and diversity in response to rewarding prey, are reported for other 10 insectivorous bats species (Jones 1990, Agosta et al. 2003). Other top predators also show 11 similar trends, for example, Gulka et al. (2017) reported seasonal trophic niche shifts (inferred 12 from stable isotopic ratios), in three marine predators (two sea-bird species and a humpback 13 whale) that substantially narrowed their dietary niche breadth in response to seasonal increase in 14 abundance of spawning capelin (Mallotus villosus) forage fishes. 15

Artificial inductions of resources in systems such as urban ecosystems and 16 agroecosystems can stimulate arthropod irruptions, i.e.; irruptions of nuisance pest in sewage 17 treatment facilities of Dipterans from the family Psychodidae (Coombs et al. 1996) or well 18 renowned agricultural outbreaks of insect pests (Berryman 1982). Generalist predators which 19 routinely forage in these environments are likely to respond to such fluctuations in prey 20

(Symondson et al. 2002), and indeed some insectivorous bats species are reported to take 21 advantage of these profitable opportunities (Leelapaibul et al. 2005, McCracken et al. 2012, 22

Puig-Montserrat et al. 2015, Taylor et al. 2018). However, their arthropod prey varies 23 considerably over time and space, and so does their diet (Clare et al. 2011, 2014, Brown et al. 24

2015, Puig-Montserrat et al. 2015, Salinas-Ramos et al. 2015, Vesterinen et al. 2016, Aizpurua 25 et al. 2018). Therefore, it is necessary to employ a sampling regime that accounts for variation 26 in their diets to detect and accurately characterize their trophic relations. 27

1.5 Insectivorous Bats as Natural Enemies 1

Insectivorous bats are voracious predators that can quickly exploit pests irruptions (McCracken 2 et al. 2012) owing to their capacity to consume large quantities of arthropod prey (Kurta et al. 3

1989), their mobile abilities to commute over large obstacles and distances (Vincent et al. 2011, 4

Roeleke et al. 2018) and their social tendency to transfer information on profitable foraging 5 grounds (Cvikel et al. 2015, Hügel et al. 2017). Bats can be exceptionally long-lived (Austad 6 and Fischer 1991, Wilkinson and South 2002) and can show high fidelity to their roost sites and 7 foraging grounds for many years, therefore they can be particularly suitable agents to focus on 8 in CBC. 9

Various species of insectivorous bats are known to forage in agroecosystems (Kunz et al. 10

2011), and have been linked to pest suppression in several agricultural industries, among them; 11 corn (Maine and Boyles 2015), rice (Puig-Montserrat et al. 2015), macadamia (Taylor et al. 12

2018), pecan (Brown et al. 2015) cacao (Maas et al. 2013) and particularly in cotton in the 13

United States (Cleveland et al. 2006) in addition to their potential in many more crops 14

(Aizpurua et al. 2018, Galan et al. 2018). McCraken et al. (2012), showed that Brazilian free- 15 tailed bats (Tadarida brasiliensis), a migratory insectivorous bat that roosts in colonies of up to 16 millions of individuals, can track and exploit a cotton pest, the American cotton bollworm's 17

(Helicoverpa zea), consuming them in high numbers. In another study Maine and Boyles (2015) 18 found that excluding insectivorous bats from plots within corn fields, was correlated to high 19 infestation rates by H. zea (which is also a corn pest) with significantly more damage to corn 20 yield, in comparison to control plots where bats could forage. 21

Boyles et al. (2011) quantified ecosystem services by insectivorous bats and estimated 22 they contribute more than $3.7 billion annually to pest suppression in cotton dominated 23 agroecosystems in the United States. Their estimates only include the reduced costs of pesticide 24 applications. Thus, they underestimate the cascading economic implications of using chemical 25 pesticides on the biodiversity in agroecosystems and on human health. 26

Overall, attempts to quantify ecosystem services by bats make bold assumptions (Boyles 1 et al. 2013), but they undertake a central role in highlighting their value. Yet, considering the 2 diversity of bats (Fenton and Simmons 2014), their wide-ranging ecosystem services is likely 3 mostly undiscovered (Kunz et al. 2011, Maas et al. 2016). Practically, the diet of many 4 synanthropic species of bats has not been examined in this context, and possibilities to identify, 5 preserve and perhaps enhance their services await. 6

1.5.1 Kuhl's pipistrelle (Pipistrellus kuhlii) 7

In my study I focus on a highly synanthropic species; Kuhl’s pipistrelle (Pipistrellus kuhlii), 8 aerial hawking insectivorous bats weighing 5-7 grams (Schnitzler et al. 1987, Korine and 9

Pinshow 2004, Berger-Tal et al. 2008). It is widespread in Africa, Europe and Asia (Bray et al. 10

2013, Ancillotto et al. 2016), and one of the most common bats in Israel (Yom-Tov and 11

Kadmon 1998). The bats are known to use anthropogenic structures as roosts, and are strongly 12 associated with urbanized and human disturbed habitats (Barak and Yom-Tov 1991, Ancillotto 13 et al. 2015, Maxinová et al. 2016). They typically forage in human modified habitats (Korine 14 and Pinshow 2004), usually within 2 km of their roost (Serangeli et al. 2012, Maxinová et al. 15

2016), consisting of; urban characteristics (Ancillotto et al. 2015), i.e.; near street lights (Barak 16 and Yom-Tov 1989, Polak et al. 2011), agricultural habitats (Kahnonitch 2015), water 17 reservoirs (Korine and Pinshow 2004) but also in forest edges (Charbonnier et al. 2014) and 18 other natural habitats (Korine and Pinshow 2004). 19

Pipistrellus kuhlii bats are described as generalist opportunistic predators (Feldman et al. 20

2000, Goiti et al. 2003). Goiti et al. (2003) suggest that they are opportunistic-selective forages, 21 which select certain prey groups at different times of the season (e.g.; Coleopterans in May and 22

Lepidopterans in September October). They inferred this from comparing (Ivlev's electivity 23 index) arthropods in the diet of P. kuhlii bats to arthropods monitored in their foraging areas at 24 corresponding times. Conventional methods to investigate the diet of P. kuhlii bats (Whitaker 25

1988), revealed ten different orders of arthropods they consume (Beck 1995, Feldman et al. 26

2000, Goiti et al. 2003). Across the studies Diptera was the most significant prey order (Beck 27

1995, Feldman et al. 2000, Goiti et al. 2003), while Lepidoptera and Coleoptera were also 1 consumed frequently in studies from the Basque country and Israel (Feldman et al. 2000, Goiti 2 et al. 2003). Additionally, Trichoptera frequently occurred in samples in one study across 3

Europe, and Hymenoptera (winged ants) were found to be the dominate prey in another diet 4 study from northern Galilee of Israel (Whitaker et al. 1994), but according to a very limited 5 sample size. In addition, Galan et al. (2018) used Metabarcoding to process three fecal samples 6 of P. kuhlii collected in France, and although their sample size was small they identified two 7 agricultural pest species in the diet. 8

Recently, P. kuhlii bats have been shown to forage intensively over cotton fields in Israel 9

(Korine et al., in preparation). Their activity, measured via acoustic monitoring, peaked in July 10

– September, when various cotton pests increase in the fields. Synchronous patterns in the 11 activity of P. kuhlii bats with pest abundances in the cotton fields, suggest that P. kuhlii increase 12 their foraging in response to pest irruptions. Namely of a major cotton pest from the order 13

Lepidoptera, the Pink bollworm (Pectinophora gossypiella) which can display up to four 14 lifecycles in a single cotton season (Niv 2013). Conclusive evidence of predation is required to 15 validate pest consumption and address the potential contribution of P. kuhlii bats to suppress 16 this pest. Preceding information of the ecology of P. kuhlii promotes describing their diets in 17 fine detail, practically in an agroecosystem that includes cotton fields, to assess their potential 18 contribution to pest suppression and relevance to CBC. 19

The objectives of my study are: (1) to describe the trophic niche of P. kuhlii in a rural 20 agroecosystem containing cotton fields, by identifying the arthropods in the bats diet using 21

Metabarcoding, while accounting for spatio-temporal variation in its diet; (2) to infer on the 22 services (and dis-services) it potentially provides and conclude accordingly the potential 23 suitability of this highly synanthropic bat to CBC. By focusing on a major cotton pest, the Pink 24 bollworm (Pectinophora gossypiella), I will specifically address if P. kuhlii displays 25 opportunistic feeding on pest irruptions, a trait that relates to efficient pest suppression by 26 generalist predators (Ehler 1998, Symondson et al. 2002). 27

2. HYPOTHESIS AND PREDICTIONS 1

My working assumption is that synanthropic generalist insectivorous, such as P. kuhlii 2 bats, regularly provide ecosystem services and are suitable natural enemies for 3

Conservation Biological Control. 4

Hypothesis I: As generalist predators the diet composition of P. kuhlii bats varies in space 6 and time. 7

Prediction: The community of arthropods identified in the diet of the P. kuhlii bats will show 9 variation among sampling roost sites and sampling dates. The community of arthropods in the 10 diet of bats will show decreasing similarities with increasing distance interval between sampling 11 roost sites and with increasing time interval between sampling dates. 12

Hypothesis II: Pest irruptions are an attractive food source that may prompt 14 opportunistic feeding by P. kuhlii bats. . 15

Prediction 1: The diet of P. kuhlii bats roosting in an agroecosystem (i.e.; contain agricultural 17 fields their 2 km foraging range), will include agricultural pest species 18

Prediction 2: The frequency of cotton pests in the diet of P. kuhlii bats will be positively 19 correlated with (proxies of) the abundance of Pink bollworms (P. gossypiella) pests monitored 20 in the cotton fields in the study area. 21

Prediction 3: The dietary niche breadth (Levin's measure) of P. kuhlii bats, per sampling date, 22 will narrow as a function of increasing frequency occurrence of dominate pest species in the diet 23 of the bats. 24

3. METHODS AND MATERIALS 1

3.1 Sampling Procedure 2

3.1.1 Study Area 3

I conducted field work in Emek-Hefer valley, Israel (Figure 1a). The climate of the region is 4

Mediterranean; winters are wet and cool (November - February), with an average 540 mm of 5 rain per year, while summers are dry and hot (June - September). Mean temperatures are 12°C 6 during January and 27°C during August (Ein HaHoresh Meteorological Station, Israel 7

Meteorological Service). The study area lies in the Mediterranean Sea coastal plain. The 8 topography is a shallow valley (elevation 0 – 50m) that originally contained patchy swamps, the 9 swamps were drained out in the early 1930's in order to make the land suitable for agriculture 10

(Manor and Hagali 2002). At this time the regions land use is dominated by agricultural 11 practices comprising: horticulture, pisciculture, orchards, pastures and annual crops. Cotton is 12 cultivated in region with more than 458 hectares of land dedicated to this sector. Additionally, 13 the regions consist of urban and rural residential areas, industrial areas and several sewage 14 treatment facilities. 15

3.1.2 Roost Survey 16

I surveyed the study area to locate roosts of insectivorous bats in proximity to cotton fields, to 17 collect fecal samples throughout the cotton season of 2016. In accordance to P. kuhlii strong 18 tendency to occupy man-made roosts (Yom-Tov and Kadmon 1998, Ancillotto et al. 2015), I 19 focused my search on artificial structures, with suitable crevices for these bats. I used 20

Geographical Information System (GIS) ArcGIS 10.2 software (ESRI, California, USA) to map 21 the locations of cotton fields in 2016 and define a buffer zone of 2 km radius around them 22

(Figure 1b), corresponding to a typical movement range of P. kuhlii from their roost (Serangeli 23 et al. 2012, Maxinová et al. 2016). Starting from March 2016 I inspected potential structures 24 that I could access within the defined area. I expanded my search area further from the fields by 25 roughly 1-2 km, in order to try to account for bats that possibly forage at further distances than a 26

2 km range (Serangeli et al. 2012, Maxinová et al. 2016) . 27

I searched for aggregations of fresh feces in structures that can potentially function as 1 roosts for P. kuhlii bats. When I found suitable aggregations, I visually examined if they 2 resemble P. kuhlii feces, by comparing them to a reference fecal sample which I obtained from a 3 confirmed roost of P. kuhlii bats in the south of Israel (Berger-Tal et al. 2008). I relied on fecal 4 visual characteristics; i.e.; color, shape, size as well as the presence of digested insects within 5 them. I confirmed the roosts were occupied by visual and audio observations at sunset. I used a 6 hand held acoustic bat detector (Pettersson, D100 Ultrasound Detector, Uppsala Science Park) 7 on heterodyne mode, to listen to the calls emitted by emerging bats. I set the detector to the 8 frequencies that match the calls that P. kuhlii characteristically emit: 35-40 kHz (Barak and 9

Yom-Tov 1989, Russo and Jones 1999). I verified echolocation calls were consistent in 10 frequency with those of P. kuhlii bats (Barak and Yom-Tov 1989). Furthermore, I used a 11 molecular method to determine the source of feces (see details below). 12

13 Figure 1 – (a) Emek-Hefer in Israel (b) Emek-Hefer study area featuring cotton fields locations 14 (2016) with a 2 km radius surrounding the fields. Roosts of bats located in the roost survey are 15 marked (roosts with a red icon were only temporarily occupied, while those with a black icon 16 were consistently occupied throughout the study). 17

3.1.3 Sample Collection 1

I used a non-invasive method to collect fecal samples and identify prey items in the diet of bats 2 with Metabarcoding. I positioned a clean plastic polyethylene sheet beside the walls where the 3 bats roost and returned the following days to collect fresh feces. I randomly collected fecal 4 pellets that aggregated on the plastic sheets, 10-20 collection tubes per roost per sampling event. 5

When fecal pellets on the plastic were in contact, I stored them together and eventually treated 6 them as one sample. Collection tubes contained 1-3 fecal pellets. I used a new polyethylene 7 sheet for every sampling event. Since the molecular method I applied is highly sensitive (Deiner 8 et al. 2017), potentially capable of detecting small traces of DNA in environmental samples. I 9 gave special emphasis to avoid cross-contamination between samples by using disposable 10 gloves and instruments at all times while collecting and handling the feces. 11

In order to preserve the DNA in the feces in sufficient quality I applied a two-step 12 storage protocol following recommendations in Nsubuga et al. (2004). In the field I stored the 13 feces immersed in 99% alcohol within a sterile 2 ml Eppendorf tube. 24-60 hours later, when I 14 returned to the university laboratory, I drained the alcohol with a disposable pipette and 15 transferred the fecal pellets to a new Eppendorf containing silica gel 1-3 mm granules (Merck, 16

CAS#1327-36-2, Darmstadt, Germany). Finally, I froze the collection tubes in -20 Cº, which 17 remained frozen for 2-6 months until I extracted DNA from the samples in November – 18

December 2016. The samples were defrosted for a few days during the time I transported them 19 to the laboratory facilities in the Centre for GeoGenetics, Natural History Museum of Denmark, 20

University of Copenhagen, Copenhagen, Denmark. 21

I collected feces at 9 sampling dates approximately every 19 (± 10 SD) days, starting 22 from when cotton seedlings emerged in late May, until the cotton was harvested in most of the 23 fields in the study area in mid-October, sampling dates are shown in Table 1. In total I collected 24

800 collection tubes (of 1-3 pellets) from seven different roosts, but only five roosts were 25 permanently occupied in all the sampling dates (Figure 1b) and included in the diet analysis 26

(Table 1). 27

3.2 Molecular Diet Analysis 1

3.2.1 eDNA Metabarcoding 2

I used eDNA Metabarcoding, to target arthropods in the fecal samples and identify them via 3 traces of DNA (Bohmann et al. 2011, Zeale et al. 2011, Taberlet et al. 2012). The primers that I 4 used are taxa-specific and target barcode regions (Hebert et al. 2003) in the DNA of arthropods 5 or mammals. These DNA regions are well preserved within species but show high variability 6 between species (Taylor 1996, Zeale et al. 2011, Taberlet et al. 2012). The two DNA regions 7 which I targeted are within the mitochondrial Cytochrome c oxidase subunit I (COI) for 8 arthropods (Hebert et al. 2003, Zeale et al. 2011) and the 16S ribosomal RNA for mammals 9

(Taylor 1996, Tillmar et al. 2013). I used high-throughput sequencing (next generation 10 sequencing) to process multiple samples simultaneously and identify multiple taxa (Pompanon 11 et al. 2012). The sequences obtained after sequencing were clustered into unique Operational 12

Taxonomic Units (OTU); which are sequences that share a defined similarity threshold. 13

Eventually I assigned OTU's to taxa according to their similarities with sequences in reference 14 databases in the BOLD: The Barcode of Life Data System (www.barcodinglife.org) or 15

GenBank (www.ncbi.nlm.nih.gov/genbank). 16

Identification of prey taxa to species level relies on the breadth and completeness of the 17 available DNA sequence references (Hebert et al. 2003, Moritz and Cicero 2004, Bohmann et 18 al. 2011). Since many species have yet to be added to the Barcode of Life Database 19

(Ratnasingham and Hebert 2007), especially in regions where less barcoding efforts are invested 20

(Burgar et al. 2014), implications for species and genus identification of prey taxa can arise for 21 species missing from the reference databases. However, assignment of OTU's to their correct 22 order and family is generally attainable, even if the prey species does not occur in the reference 23 database, due to relativity high shared similarities within orders and families of arthropod taxa 24

(Razgour et al. 2011, Alberdi et al. 2018). 25

I conducted the molecular laboratory work at the Centre for GeoGenetics (CGG), Natural 26

History Museum of Denmark, University of Copenhagen in collaboration with Prof. Thomas 27

Gilbert and his group, under the supervision of Dr. Kristine Bohmann. Martin Nielsen prepared 1 the samples in the final step before sequencing (library building) (Carøe et al. 2018), and 2 conducted the bioinformatics to generate operational taxonomic units which I assigned to taxa. 3

The CGG has strict international standards emphasizing good laboratory practice to generate 4 reliable results. Accordingly, I followed all standard procedures in the laboratories to reduce 5 contamination risks. I incorporated negative and positive controls at all the appropriate steps to 6 account for quality control and possible contaminations. 7

See below the molecular workflow (Figure 2), including the different steps up until 8 sequencing, details are provided in the following sections. 9

10 Figure 2 – Metabarcoding workflow until sequencing, see details in text. 11

3.2.2 Sample preparation 12

I pooled fecal pellets into unified samples before DNA extractions, combining pellets that were 13 collected at the same sampling roost site and date, to reduce costs and workload. Unified 14 samples contained 4.8 (± 0.6 SD) pellets and weighed 0.03 (± 0.01 SD) g', hereafter 'sample' 15

(Figure 2). In another study the same number of fecal samples (five) were found to be a 16

satisfactory number to represent the families of prey taxa that were consumed in nightly feeding 1 bouts, by another species of insectivorous bats (Whitaker et al. 1996). I processed three samples 2 per site per sampling date. However, when the number of feces (per roost) was not sufficient to 3 comprise three samples I only processed two samples (I encountered 3 such cases, see 4 extraction summary Table 1). 5

3.2.3 DNA Extraction 6

I extracted DNA from the samples with MoBio Power Fecal ® DNA Isolation kits (MoBio 7

Laboratories, CA, USA). I followed the manufacturer's protocol with minor modifications 8

(detailed protocol in the supplementary material: Appendix 1 – DNA Extraction protocol). In 9 each of the 'extraction session' (ca. 10-14 samples), I incorporated a negative control (without 10 any DNA template), hereafter 'extraction blanks'. I performed the complete extraction protocol 11 on the extraction blanks (as well as all the following steps of Metabarcoding), to account for 12 possible contamination. After extracting DNA from the samples, I diluted them in 100 μl Tris- 13

EDTA buffer solution and stored them in LoBind 1.5 ml Eppendorf tubes at -20°C. Altogether I 14 extracted DNA from 133 samples (approximately 648 fecal pellets) collected from 5 different 15 sites at 9 separate sampling dates (Table 1). Additionally, 13 extraction blanks were processed, 16 one for each extraction session. 17

Table 1 – Summary of the number of extractions per sampling date from sampling roosts that 18 were included in the diet analysis of Pipistrellus kuhlii. Samples contained 4.8 (± 0.6 SD) fecal 19 pellets and weighed 0.03 (± 0.01 SD) g', sampled at May – October 2016 in Emek-Hefer, Israel. 20

Roost May June June July July August August September October Name (I) (II) (I) (II) (I) (II) 21/5 4/6 18/6 9/7 28/7 19/8 26/8 9/9 17/10 A 3 3 3 3 3 3 3 3 3 B 3 3 3 3 3 3 3 3 3 C 2 3 3 3 3 2 3 3 3 D 3 3 3 3 3 3 3 3 3 E 3 3 3 3 3 3 3 3 2 21

3.2.4 Quantitative PCR screening 22

Quantitative Polymerase Chain Reaction (qPCR also known as Real time PCR) can be used to 23 quantify the copy numbers of DNA at different stages of the PCR (Shapiro and Hofreiter 2012, 24

Murray et al. 2015). This is achieved by incorporating fluorescent dye in the 'PCR-mix' (such as 25

SYBR Green) which binds to the double-stranded DNA. As the DNA is amplified in the qPCR 1 reaction the number of target molecules copies increases and this allows more fluorescent dye to 2 bind to them (Shapiro and Hofreiter 2012). A florescence sensor in the qPCR measures the 3 corresponding change in florescence intensity, at the different stages of the PCR reactions 4

(Shapiro and Hofreiter 2012). The correlation between florescence intensity and DNA copy 5 numbers allows real-time quantification of the PCR amplification. 6

I used qPCR to screen my samples and asses the following: 7

I. Amplification success – I screened a third of my samples (n = 45) (one sample per 8

roost for each of the sampling dates) with the two primer-sets (for arthropods and 9

mammals), and evaluated the efficiency of my DNA extractions for arthropod and 10

mammal DNA. I used two sets of primers; 11

'Zeale' arthropod primers: to amplify arthropods in the diet of the bats, this primer 12

set is frequently used in insectivorous bat diet studies (Zeale et al. 2011, Alberdi et 13

al. 2018). The primer set is designed to amplify a 157 base pair (bp) mini-barcode 14

region in the COI mitochondrial DNA (Zeale et al. 2011). 15

'16 S mammal' primers: to amplify mammal DNA and verify the source of the feces 16

, the primer set is designed to amplify a 92 bp mini-barcode region in the 17

mitochondrial ribosomal RNA (rRNA) of mammals (Taylor 1996, Tillmar et al. 18

2013). 19

I also used the results to consider how well the samples amplified by inspecting 20

the threshold cycle (Ct value), amplification slope and fluorescent signal (Murray et 21

al. 2015). According to the results, I choose an optimal cycle number for the 22

tagged-PCR (the following step to be described). 23

II. Inhibition – DNA extraction products of environmental samples contain high 24

concentration of inhibitory substances, that may impair the amplification of the 25

target region of DNA (Shapiro and Hofreiter 2012, Murray et al. 2015). In order to 26

assess for inhibition, I diluted aliquots from a third of my samples (representing 27

each site and date) with purified ddH2O at 1:5 and 1:10 ratios. In theory when 1

diluted samples consistently amplify earlier and with a steeper amplification slope, 2

it is best to consider dilution before applying PCR (Shapiro and Hofreiter 2012). 3

However, the majority of my samples amplified successfully and in the "correct" 4

order, i.e.; from the highest concentration to the lowest. Therefore, I proceeded with 5

non-diluted samples. 6

III. Contamination – I screened the extraction blanks and generated graphs of melting 7

(disassociation) curves, to visualize amplicons products according to their length. 8

Extraction blanks are not expected to generate amplification curves, as they should 9

not contain DNA templates. However, in some cases even blank samples can 10

amplify during the terminal cycles of PCR (in my case after cycle ~34), due to PCR 11

by-products such as hybridized primers (primer-dimers). Such hybridized primers 12

have a shorter length from my target regions. Thus, I could differentiate primer- 13

dimers from possible contaminations when inspecting the disassociation curves of 14

extraction blanks. My qPCR screenings indicated my negative controls were not 15

contaminated. 16

Additionally, the qPCR screenings showed arthropod and mammal DNA were successfully 17 extracted from the samples and provided inference for the optimal settings for tagged PCR. I 18 carried out the qPCR screening on an Agilent Technologies Stratagene Mx3005P qPCR 19

Thermocycler (Agilent Technologies, Santa Clara, CA, USA). I used a master-mix of reagents 20 and qPCR settings described in the Table 2. 21

Table 2 – qPCR setting used to screen samples to assess amplification success for arthropod and 22 mammal DNA from Pipistrellus kuhlii fecal samples collected in Emek-Hefer, Israel. 23

Zeale - Arthropod 16 S - Mammal Mitochondrial COI 16S Region Forward Primer AGATATTGGAACWTTATATTTTATTTTTGG CGGTTGGGGTGACCTCGGA Reverse Primer WACTAATCAATTWCCAAATCCTCC GCTGTTATCCCTAGGGTAACT Reagent

ddH2O 13.05 μl Buffer 10x 2.5 μl

MgCl2 (25 mM) 2.5 μl

dNTP (10 mM) 0.5 μl BSA 1.25 μl AmpliTaq Gold® 0.2 μl DNA Polymerase (5 U/µl) SYBR Green Dye 1 μl

Total master-mix 21 μl Primer (10 μM each) 1.5 μl Forward 1.5 μl Reverse DNA Template (1:1 / 1:5 / 1:10 1 μl dilutions) Total mix 25 μl 95°C for 10 mins; 40 cycles of 95°C for 10 mins; 40 cycles of qPCR settings 95°C for 15 s, 54°C for 30 s, 95°C for 15 s, 59°C for 30 s, 72°C for 30 s. 1°C melt curve 72°C for 30 s, 1°C melt curve Negative Controls master mix without master mix without DNA Template DNA Template 1

3.2.5 Tagged PCR 2

Tagged-PCR involves the coupling of short (5-8) nucleotide sequences (tags) to forward and 3 reverse primers, the tags are amplified with the target region and appear in the final sequence 4

(Binladen et al. 2007). By using unique tagged primers for each sample I could pool together 5 and simultaneously sequence amplicons from different samples, with the ability to trace back 6 which amplicon originated from which sample via bioinformatic filtering (Binladen et al. 2007, 7

Pompanon et al. 2012). I amplified three replicates for every sample, each with a unique tag 8 combination to allow their traceability. I only retained sequences that appeared in two or more 9 of the replicates to decrease false positive taxa identification (Schnell et al. 2015). 10

Throughout the process of Metabarcoding with tagged primers, tag combinations that 11 were not originally prepared can occasionally form due to PCR errors (Schnell et al. 2015). 12

These sequences may cause false positive taxa assignments therefore should be excluded from 13 the analysis. To aid the identification and exclusion of sequences with unused tag combinations, 14 so called "tag-jumps", I used distinctive matching tag combinations for forward and reverse 15 primers of every given replicate in the study, as recommended by Schnell et al. (2015). 16

Sequences that showed non-matching tags were excluded during the bioinformatics steps 17

(Zepeda-Mendoza et al. 2016). 18

I prepared a master-mix (in a DNA free designated laboratory) with reagents as 1 described in (Table 3). I distributed the mix in 96-well PCR plates, I assigned PCR negative 2 controls (with no DNA template) in each column in the 96-well plate in which samples were 3 inserted, hereafter 'PCR Blanks'. I also incorporated one PCR positive control per PCR plate. 4

The positive controls I used for the Zeale primers was ground beetle DNA (Pterostichus 5 melanarius), for the 16S Mammals primers I used Giraffe DNA (Giraffa camelopardalis). The 6 species I choose for the positive controls had previously been successfully amplified with the 7 same primer sets, with similar PCR settings, and are routinely used as positives in the CGG 8 laboratories. However, they are not found in my study area; therefore, I did not expect to find 9 their DNA in any of my field samples and thus they also provided extra control for cross 10 contaminations. I used the following settings for tagged-PCR (Table 3): 11

Table 3 - Tagged-PCR primers, master mix, setting and positive controls that were used to 12 amplify arthropod and mammal DNA from Pipistrellus kuhlii fecal samples collected in Emek- 13 Hefer, Israel. 14

Zeale - Arthropod 16 S - Mammal Mitochondrial COI 16S Region Tagged- AGATATTGGAACWTTATATTTTATTTTTGG CGGTTGGGGTGACCTCGGA Forward Primer Tagged-Reverse WACTAATCAATTWCCAAATCCTCC GCTGTTATCCCTAGGGTAACT Primer Reagent

ddH2O 16.175 μl 14.675 μl Buffer 10x 2.5 μl 2.5 μl

MgCl2 (25 mM) 2.5 μl 2.5 μl dNTP (10 mM) 0.5 μl 0.5 μl BSA 0.625 μl 0.625 μl AmpliTaq 0.2 μl 0.2 μl Gold® DNA Polymerase (5 U/µl) Total 22.5 μl 21 μl mastermix Primer mix 1.5 μl 1.5 μl Forward (10 μM each) Forward + Reverse 1.5 μl Reverse DNA Template 1 μl 1 μl Total mix 25 μl 25 μl Tagged-PCR 95°C for 10 mins; 30 cycles of 95°C for 10 mins; 32 cycles of 95°C settings 95°C for 15 s, 52°C for 30 s, for 15 s, 59°C for 30 s, 72°C for 7 m. 4°C cooling 72°C for 7 m, 4°C cooling Number of 133 x 3 (replicates) 45 x 3 (replicates) samples Positive Pterostichus melanarius - DNA Giraffa camelopardalis - DNA

Controls Negative No DNA Template No DNA Template Controls 1

3.2.6 GEL electrophoresis 2

I visualized 5 μl of the tagged-PCR amplification products stained with 2 μl ethidium bromide 3 dye, in 2% agarose gel. I used a 50 bp ladder to estimate the size of the amplicons and evaluate 4 if they fit the expected amplicon lengths. I photographed the GEL products with UV light and 5 roughly categorized the band intensity to account for amplification success and as a crude 6 measure of DNA concentration. I assigned bands to three categories: 'no band' for unsuccessful 7 amplifications (or negative controls) and 'faint band' or 'bright band' for successful 8 amplifications (see picture; Figure 3). I redid the tagged-PCR process with the same tags 9 originally assigned for replicates which did not amplify (samples with no band). Eventually all 10 replicates were successfully amplified, excluding one set of replicates from a single 11 unsuccessful extraction that I re-did with a new set of fecal samples. 12

Figure 3 – GEL electrophoresis results for tagged PCR. Categorization of pools according to 2 amplification intensity and success with Zeale arthropod primers used to amplify DNA from 3 Pipistrellus kuhlii fecal samples collected in Emek-Hefer, Israel. The samples are categorized 4 by color according to their sampling roosts, date and replicate. PCR BL = PCR Blanks, xBL = 5 Extraction Blanks. 6

3.2.7 Pooling and Purification 7

In order to processes samples simultaneously at comparable DNA concentrations (Schnell et al. 8

2015, Carøe et al. 2018). I pooled PCR amplicon products according to the intensity of their 9

GEL band. I pooled aliquots of 5 μl for samples with bright bands and 10 μl for faint bands. I 10 purified half (ca. 155 μl) of every amplicon pool with QIAquick PCR Purification Kit (Qiagen, 11

Chatsworth, USA) following the manufactures protocol. I measured the purified DNA 12 concentrations in a Qubit™ Fluorometer (Invitrogen, Carlsbad, USA). I diluted the pools to 13 similar concentration and used a TapeStation (Agilent Technologies, Palo Alto, USA) to 14 precisely assess the purification efficiency of the pools, to quantify the DNA concentration 15 within them and to calculate equimolar ratios of each amplicon pool for library preparation. 16

3.2.8 Library preparation for sequencing 1

Next generation sequencing platforms require enzymatic preparation of DNA and ligation of 2

DNA adapters for specific sequencing platforms (Meyer and Kircher 2010, Carøe et al. 2018). 3

Thus, amplicon pools were transformed into Illumina sequencing libraries with the novel 4

'single-tube preparation method' (Carøe et al. 2018) . Equal amounts of 100 ng from each pool 5 were end-pair repaired, followed by heat-inactivation of enzymes with their incubation for 30 6 min at 20 ºC followed by 30 min at 65 ºC in a thermal cycler. Blunt-end adaptors were then 7 ligated to double strand DNA, and pools were bead purified using streptavidin coated baits 8

(Beckman Coulter, Brea, USA). 9

The pools were index-PCR with a unique index for each pool. Indexes are unique short 10 nucleotide sequences (like tags), used to distinguish amplicon pools. In accordance to using 11 tagged-PCR, index-PCR allow pooling of multiple amplicon pools before sequencing (Schnell 12 et al. 2015, Zepeda-Mendoza et al. 2016). Eventually each sample had two unique tags – one for 13 their pool (index) and one for their replicate. This allowed traceability of the sequences to their 14 amplificon pool and specific replicate. 15

3.2.9 Sequencing 16

Sequencing was carried out on an Illumina HiSeq 2500 machine for 100 cycles at the Danish 17

National High-throughput Sequencing Center, Copenhagen, Denmark. 18

An overview of the steps after sequencing are shown in Figure 4, details are described in text in 19 the following sections. 20

Figure 4 - Metabarcoding workflow (following sequencing) and bioinformatic steps, see details 2 in text. 3

3.2.10 Bioinformatics 4

Metabarcoding studies generate "big-data" with millions of sequences that necessitate 5 processing via bioinformatic pipelines (Zepeda-Mendoza et al. 2016). The following steps 6 described in this section were conducted by Martin Nielsen (PhD candidate at the University of 7

Copenhagen). Initially the quality of sequences was assessed with FastQC Software (Babraham 8

Bioinformatics, UK), low-quality sequences were discarded, sequences were trimmed from 9 adapters and pair-end sequences were merged together (Figure 4). A designated toolkit, DAMe 10

(Zepeda-Mendoza et al. 2016), was used to perform the following manipulations on the dataset. 11

Sequences were sorted and traced back to their sample replicate according to their tags and 12 indexes. Sequences with non-matching tag combinations were discarded. The dataset was 13 scanned in order to identify and remove chimeras; artificial sequences that form when two or 14

more biological sequences merge together, with the algorithm UCHIME (Edgar et al. 2011). 1

Furthermore, sequences were filtered according to their expected bp length. 2

Following sorting of the sequences to their origins and initial quality control, sequences 3 occurring in 2 or more of the 3 replicates were retained, with a minimum sequence copy number 4 of 96. These conservative filtering thresholds should remove a large portion of erroneous 5 sequences, and control for false positives (Zepeda-Mendoza et al. 2016, Alberdi et al. 2018). 6

Filtering thresholds decisions were supported by the output of sequences from the negative and 7 positive controls. Finally, the remaining sequences were stripped from adapters, indexes, tags 8 and primers and clustered according to 97% similarity, to be collapsed into Operational 9

Taxonomic Units (OTU's). 10

3.2.11 Taxonomic Assignment 11

I aligned the OTU sequences against a reference sequences database. For mammal amplicons 12

(16S mammal primers) I used GenBank (www.ncbi.nlm.nih.gov/genbank/) nucleotide 13 collection database. For arthropod amplicons (COI Zeale primers) I used Barcode Of Life Data 14

Systems V4 (BOLD Systems; http://www.boldsystems.org), 'Species Level Barcode Records' 15 as well as the 'All Barcode Records on BOLD' when no matches were attained with the previous 16 database mentioned (Ratnasingham and Hebert 2007). 17

When an OTU sequence showed 100% similarity to a single reference species in the 18 reference database, I assigned it to that species. To conclude when sequences matched to several 19 species or had lower similarities in comparison to reference sequences. I used a criteria's for 20

OTU assignment that is commonly used for insectivorous bat diet studies (Razgour et al. 2011), 21 and modified it following Kruger et al. (2014) and Galan et al. (2018). 22

Table 4 – Confidence criteria for Operational Taxonomic Units (OTU) Assignment which were 23 obtained via Metabarcoding the fecal samples of Pipistrellus kuhlii collected in Emek-Hefer, 24 Israel. OTU's were compared to reference sequences in the BOLD database and GenBank. 25

Confidence Percent Description Level Similarity 1a >99% Solid species match 1b >98% Probable species match Match to several species – assignment to a single species that contains the study 2 >98% area in its distribution

3 >98% Multiple matches – assignment to the highest shared taxonomic resolution for the matches, i.e.; genus or family or order 4 >97.4% Multiple matches or low similarities (<98%) – assignment to shared order 1 I excluded OTU's that did not conform with the criteria from further analysis, as well as those 2 that clearly do not contain the study area in their distributions. 3

3.2.12 Categorizing species of human concern in the diet of P. kuhlii 4

I classified agricultural pest species in the diet, and the crop (or industry) they are affiliated with 5 relying on peer reviewed published literature (i.e.; Robinson (2005)), as well as the Hebrew 6

University online database of Plant Pests of the Middle East 7

(http://www.agri.huji.ac.il/mepests/pest/). I also extracted information from peer reviewed 8 literature regarding potential diseases vectors, nuisance pests and beneficial natural enemies, to 9 classify additional species I recovered in the diet of the bats. I did not categorize prey species 10 that might provide pollination services, due to difficulties to attain and extract such information 11 on nocturnal insects from the literature (Macgregor et al. 2015). 12

3.2.13 Pest Monitoring and Pesticides Application 13

Pests are monitored in the cotton fields in Israel by professional pest scouts, who obtain weekly 14 estimates of their infestation rates (Niv 2013). Monitoring is routinely executed in order apply 15 specific pesticide treatments targeting specific pest species, only when potential damage to 16 crops surpasses economic injury level (Kogan 1998, Niv 2013). The pest scouts use syntactic 17 sexual pheromone baited traps to attract Pink bollworm male moths to indicate their presence in 18 the fields (Kehat and Dunkelblum 1993). Additionally, egg and larva infestation rates are 19 monitored by inspecting randomly selected cotton capsules from the fields. While pesticides are 20 applied when Pink bollworms surpass the economic threshold, approximately 2% cotton balls 21 infested with Pink bollworm eggs in 100 capsules inspected per 10 - 20 hectares of cotton field 22

(Niv 2013). Therefore, to some extent the area sprayed (i.e.; in hectare) can provide a proxy of 23 the abundance of Pink bollworm at a given timeframe. That is a larger area sprayed corresponds 24 to a larger area in which Pink bollworm populations surpassed the 2% economic injury 25 threshold. I used the area sprayed according to pest monitoring (or the number of capsules 26

infested in a field, when data was available), as a proxy for the abundance of Pink bollworms in 1 the fields and the number of moths available as prey for bat. In support, a recent study found a 2 strong correlation between the number of cotton capsules infested and the number of male Pink 3 bollworm moths trapped in pheromone traps at corresponding cotton fields before pesticides 4 were applied (Carrière et al. 2017). Typically, monitoring for Pink bollworms takes place at 5

June – September when Pink bollworm populations are known to increase and when the cotton 6 crops are most susceptible (Niv 2013). 7

3.3 Statistical Analysis and Data Visualization 9

3.3.1 Sample Coverage interpolation and extrapolation curves 10

I used Chao’s measure of Sample Coverage (Chao and Jost 2012, Chao et al. 2014) to assess my 11 sampling efficiency and estimate the number of undetected species in the diet of the bats. I 12 addressed this question according to diversity from all samples in the study pooled together. 13

Sample coverages ranges between 0 – 1, a value of 1 indicates all species of the detectable 14 estimated richness in the diet have been identified in the sampling process (Chao and Jost 15

2012). In order to compute rarefaction and extrapolation curves based on sample size and 16 corresponding to species richness, I used a nonparametric richness estimator, for frequency 17 incidence based data (Chao et al. 2014). To compute the sample coverage, rarefaction and 18 extrapolation sampling curves I used iNEXT online, an R-based interactive software (Hsieh et 19 al. 2016), I exported the results and plotted them. 20

3.3.2 Multivariate analysis and community visualization 21

I used multivariate ordination methods to reduce dimensionality in the data and visualize 22 patterns in the communities of arthropods in the diet of the bats. Ordination procedures are 23 classified as either unconstrained, for example metric multidimensional scaling (also known as 24 principal coordinate analysis or PCO) , or constrained, for example canonical analysis of 25 principal coordinates (CAP) (Anderson and Willis 2003). Unconstrained procedures of 26 ordination do not rely on a-priori hypotheses but attempt to decrease dimensions in the data by 27

minimizing residual variance or a stress function. Yet, in unconstrained ordinations certain 1 patterns in the entire data cloud can occasionally disguise real patterns of differences among 2 groups (Anderson and Willis 2003). On the other hand, constrained ordinations rely on a-priori 3 hypothesis when generating a plot and therefore can overcome limitations to reveal real patterns 4 in the data cloud that might be concealed otherwise (Anderson and Willis 2003). 5

I used CAP to inspect my data when unconstrained ordinations failed to visually 6 discriminate trends according to sampling date. CAP is an ordination procedure that allows any 7 distance or similarity index to be used, and is often used for complex ecological data (Anderson 8 and Willis 2003). The number of axes (m) in this procedure is chosen to minimize 9 misclassification error of samples to their constrained group, or by the minimum residual sum of 10 squares. The calculation of misclassification error is obtained with a "leave-one out" procedure 11

(Anderson and Willis 2003). In this procedure a single sample is taken out, while the analysis is 12 performed on the rest of the samples. The "left out" sample is then classified to the canonical 13 space generated without it. If the sample is grouped with its original group, the classification is 14 determined successful. Repeating the procedure across all samples generates a percent 15 classification rate, thus combined with permutations the ‘leave-one-out’ procedure can allow a 16 test of hypotheses using canonical test statistics. 17

I used Krona Tools (Ondov et al. 2011) to plot a multi-layer chart of the taxonomic 18 hierarchical data of the OTU’s assigned to taxonomic ranks in the bats diet. Krona charts allows 19 complex datasets to be explored and interpreted rather objectively. The plot is available as an 20 additional file (Additional File 1 – Krona plot of the diet of P. kuhlii) with a brief explanation 21 how to browse through the multi-layers (Additional File 2 – Krona plot walk-through). 22

In order to visualize disparities in taxa richness (unique OTU's) of species between the 23 sampling roosts, I generated a Venn diagram (Venn 1880) displaying the taxa richness (unique 24

OTU's) exclusive to a specific site or a group of sites, using 'DrawVenn' tools provided in the 25 website ‘Bioinformatics and Systems Biology’, Ghent University 26

(http://bioinformatics.psb.ugent.be). 27

I used ANOVA-like test statistics based on a matrix of resemblances (similarities indices), 1 calculated between pairwise pairs of samples, to compare the community of arthropods at 2 different sampling dates or sites (Anderson Walsh 2013). To test for significance differences in 3 similarities among groups of samples, I performed an Analysis of Similarities (ANOSIM). A 4 non-parametric statistical test using ranked distance or similarity between pairs of samples or 5 variables (Anderson and Walsh 2013). I calculated similarities of community composition 6 among pairs of samples using Jaccard index, a suitable index for presence absence data 7

(Anderson et al. 2011). ANOSIM generates a statistics 'Global R', which represents the 8 separation among groups. An R value of 1 indicates perfect separation between the levels of the 9 factor, while R value of 0 indicate no separation between levels of the factor. I defined groups 10 by sampling dates or by sampling roost site. I performed 9999 permutations to determine the 11 significance of the test and separation between the sampling groups with regards to their taxa 12 composition. Additionally, I ran a PERMANOVA test to detect differences in community 13 composition among the groups and determine if the centroids and variation of sample 14 similarities are significantly different between the groups (Anderson and Walsh 2013). I defined 15 sampling date as a fixed factor and sampling roosts as a random factor and checked for an 16 interaction between the two factors. ANOSIM and PERMANVOA tests were computed in 17

PRIMER-E Software v.6 (Clarke 1993). 18

To address my specific predictions that the community of arthropods in the bats diet will 19 show decreasing similarities with increasing distance interval between sampling sites. I 20 measured the psychical Euclidean distance (meters) between the different sampling sites using 21 the ‘Measure tool’ in ArcGIS ArcMap v. 10 (ESRI, Redlands, CA). I pooled the OTU’s 22 identified in each site and created a pairwise Bray-Curtis similarities index, suitable for 23 abundance data (Anderson et al. 2011), for each unique combination of sites. I plotted the 24 pairwise similarities against the distance intervals and calculated a simple linear regression, 25 using JMP 10.0.2 (SAS Institute Inc., Cary, USA), to test the strength and significance of the 26 regression. Additionally, I summed the OTU’s identified in each date and created pairwise 27

Bray-Curtis similarities for all date combinations. I calculated the time interval between the 1 sampling dates (in days) and plotted the pairwise similarities against the time intervals to 2 compute a linear regression and test the strength and significance of the regression using JMP 3

10.0.2 (SAS Institute Inc., Cary, USA). 4

In order infer regarding dietary diversity across time I checked for statistical differences 5 between sampling dates in the average number of unique OTU's found within a sample (across 6 all five roosts) using one-way ANOVA in SPSS (SPSS Inc., Chicago, USA). I also calculated 7

Shannon Diversity measures using the 'diversity' function included in the library 'vegan' (Dixon 8

2003) in the software R (R Core Team 2013), for the sum of taxa found in in the diet at each of 9 the sampling dates. To compare between the frequencies of different prey orders found in the 10 diet, I calculated their percent frequency occurrences in all the samples combined as well as in 11 each of the sampling dates separately. I compared pairwise couples of the portions of the orders 12 in the diet and tested the equality in proportions of paired samples using McNemar test, which is 13 suitable test for paired binomial proportions (Fagerland et al. 2013). 14

3.3.3 Pink bollworm in the diet of P. kuhlii 15

I calculated the percent frequency occurrences of Pink bollworm in the diet of the bats at each 16 sampling date (across all sites). I checked for correlations of these value and the proxies of Pink 17 bollworm abundance in the cotton fields. On a regional scale, I used the percent frequency 18 occurrences at all five sites pooled together and correlated them with the area of cotton fields 19 sprayed in the week before each sampling date (May - September). For a local inference, I 20 correlated the percent frequency occurrences of Pink bollworms in the diet of bats roosting in 21 roost site E with the number of cotton capsules infested monitored in a nearby cotton field. 22

However, since the sampling dates for pest monitoring and bat diets were not synchronized, I 23 compared data obtained within the same week. I computed the correlations using SPSS 24

Statistics Software (SPSS Inc., Chicago, USA) with one-sided Person correlations, assuming 25

Pink bollworm frequencies in the diet increase with their abundances found in the cotton fields. 26

I used Levin’s measure (Krebs 1989) of niche breadth to quantify bat resource use of 1 arthropod prey at different sampling dates (in all five sites together) with the equation: 2

1 퐵 = 3 ∑푃2푖

Equation 1 - Levin’s measure of niche breadth 4

Where, B is Levin's measure of niche breadth, P is the proportion of prey i found in all samples 5 attained in the same date. I used linear regression to assess how niche breadth is affected by the 6 number of Pink bollworm identified at a given sample date. To assess how Pink bollworms 7 consumption affects the similarities in the community of arthropods in the diet. I used 8

‘similarity percentages’ routine (SIMPER) (PRIMER-E Software v.6) to calculate the 9 contribution of each taxa to the similarities among samples at a given date. I used the SIMPER 10 results to compare the relative contribution of additional taxa, beside Pink bollworms, to 11 similarities in the diet of the bats at different sampling dates. 12

4. RESULTS 1

4.1 Fecal source 2

The samples which were amplified with mammal primers, were all (45/45) found positive for 3

P. kuhlii DNA, supporting the roost sites are occupied by P. kuhlii bats and that the fecal 4 samples were excreted by them. A single sample was also positive for black rat (Rattus rattus) 5

DNA besides P. kuhlii. However, I did not exclude the sample from the diet analysis, since the 6 rat DNA could result from external contamination (i.e.; the rat touched the fecal pellets before 7 collection). Furthermore, the 3 arthropod OTU's found in these sample were all found in 8 additional samples, and 2/3 of them were found in samples positive only for P. kuhlii, therefore 9

I did not suspect the arthropods was consumed by a black rat. 10

An additional indirect indication for the sample source, from 8 samples in my study, 11 included recovering arthropod OTU's that were assigned to the bat bug; Cacodmus vicinus, 12 which is a host-specific ectoparasite associated with P. kuhlii bats and was likely consumed 13 when bats were grooming (Quetglas et al. 2012). I collected these samples at three different 14 roost sites. 15

4.2 Efficiency of the molecular diet analysis 16

4.2.1 Taxonomic resolution 17

Of the 133 DNA extractions I conducted, 132 extractions (>99%) produced arthropod DNA that 18 was successfully amplified, sequenced and assigned to arthropods at different taxonomic ranks 19 according to the confidence criteria. Following bioinformatic filtering, the sequences were 20 clustered to generate 168 unique molecular OTU’s of which 145 were assigned to different 21 taxonomic ranks; 60 to species, 23 to genus, 23 to family, 39 to order and the remaining 23 were 22 not assigned and excluded from further analysis. The average number of OTU’s retained per 23 sample was 5 ± 2 SD (OTU richness within sample ranged from 1 to 10). The total arthropod 24

OTU’s assigned to taxonomic ranks amounted to 621 prey items. To clarify; an OTU can appear 25 in multiple samples and thus the number of prey items (or OTU abundance) is higher than the 26 number of unique OTU’s (richness). Hereafter, I use prey items and abundance interchangeably 27

and unique OTU’s and richness interchangeably. The majority of prey items were assigned to 1 high taxonomic ranks of species or genus (67% of the prey items - 416/621), the remaining prey 2 items were assigned to a family or order (33% of the prey items - 205/621) (Figure 5). 3

Figure 5 - Operational Taxonomic Units (OTU) of arthropods identified in the diet of 5 Pipistrellus kuhlii via molecular analysis (Metabarcoding) of fecal samples (n = 132) from 6 Emek-Hefer, Israel. The chart shows prey items (OTU abundance) assigned per taxonomic rank. 7 The numbers of unique OTU’s (richness) per taxonomic rank (in brackets) are also presented in 8 the chart. 9

4.2.2 Sample coverage of the diet of P. kuhlii 1

The sample coverage for arthropods species detected in the diet of bats in all samples in the 2 study (n = 132) reached a coverage rate of 0.89 (Figure 6). Sample coverage to sampling units 3 curve showed a tendency to stabilize indicating an appropriate sampling effort. According to 4 extrapolations procedures, the sample coverage that was observed corresponds to 145 unique 5

OTU’s of the 198 estimated richness in the diet of P. kuhlii. 6

0.8 observed 0.6 sample coverage (132,0.891)

0.4

Sample Sample coverage 0.2

0 1 51 101 151 201 251 301 Number of sampling units

interpolated observed extrapolated 7

Figure 6 Observed sample coverage (observed) for arthropods detected in the diet of 8 Pipistrellus kuhlii bats via molecular analysis (Metabarcoding) of fecal samples (n = 132) from 9 Emek-Hefer, Israel. Interpolation (solid line segment) and extrapolation (dashed line segments) 10 of sample coverage as a function of sampling units produced according to Chao’s measure of 11 sample coverage using iNEXT software online version. 12

4.3 Spatio-temporal variation in the diet of P. kuhlii 13

The composition of prey items in the diet of P. kuhlii differed significantly between sampling 14 dates (ANOSIM Global R = 0.372, PERMANOVA Pseudo. F8, 87 = 2.932, p = 0.0001) and roost 15 sites (ANOSIM Global R = 0.296, PERMANOVA Pseudo. F4, 87 = 2.728, p = 0.0001). 16

Furthermore, the interaction between sampling dates and roost sites had a significant effect on 17 the community of arthropods in the samples (PERMANOVA Pseudo. F32, 87 = 1.263, p = 18

0.0001). 19

4.3.1 Spatial variation in the diet of P. kuhlii 1

The species of arthropods detected in the diet of bats varied among sampling roost sites. To 2 clarify, the arthropods in the diet were either detected in a single roost site (exclusive to one of 3 the roost site), or else appeared in the diet in more than one of roost sites. The sum of species 4 richness that were exclusive to a single roost (any of the five roosts) was 85 (Figure 7: the 5 peripheral numbers), corresponding 59 % of the species richness in the diet. The remaining 6 species were detected in at least two roost sites (Figure 7). However, the nine-species found in 7 all roost sites (Figure 8: the center of the diagram), were among the ten most abundant prey 8 items (in terms of the number of samples they appeared in) in the diet of the bats. In fact, these 9 nine species amount to 260/621 of the total prey abundance in the study and make up 42 % of 10 the prey items found in the diet of the bats. 11

Figure 7 - Venn diagram of the arthropod richness in the diet of Pipistrellus kuhlii bats detected 14 by a molecular methods (Metabarcoding) at five different roost sites in Emek-Hefer, Israel. The 15 peripheral numbers in the diagram represent the number of species exclusive to a single roost 16 site. The internal numbers represent the sum of species exclusive to a group of roost sites 17

matching the shapes the number overlays (i.e.; the number 9 in the center of the diagram 1 represents 9 species of arthropods that were detected in all five roost sites). 2

The community similarities (calculated with Bray-Curtis resembles indices) of arthropods in the 3 diet among the different roost sites did not decrease with increasing distance (in meters) 4

2 between the roosts sites (R = 0.084, F1, 8 = 0.736, p = 0.416). 5

4.3.2 Temporal variation in the diet of P. kuhlii 6

In a CAP analysis with sampling date as a constrained factor, samples of the same sampling 7 date cluster together, representing similarities in the diet of the bats as a function of time across 8 the sampling roosts (Figure 8). In total 43/132 (32.58 %) of the samples were correctly 9 classified to their sampling date. A significant portion of misclassifications were a result of a 10 successive sampling dates (42/89) indicating similarities between successive sampling dates. 11

Figure 8 - Canonical analysis of principles coordinates (CAP) of Jaccard similarities for 2 arthropods identified in the fecal samples of Pipistrellus kuhlii sampled at nine sampling dates 3 (May – October) in Emek-Hefer, Israel. CAP1 and CAP2 explained 90 % and 81 % of the 4 variation respectively in the grouping of samples. 43/132 (32.58%) of the samples were 5 correctly classified to their sampling date in a “leave-one-out” procedure. 6

Accordingly, the community similarities (Bray-Curtis) of the arthropods identified in the diet 7

(across all five sampling roosts) at different dates significantly decreased with increasing time 8

2 interval between sampling dates (R = 0.54, F1, 34 = 39.961, p < 0.01) (Figure 10). 9

70 y = -0.24x + 49.8

60 R² = 0.54

Curtis) - 50

10 Community similarities (Bray 0 0 20 40 60 80 100 120 140 160 Interval in sampling time (days) 1

Figure 9 - The relationship between community similarities (Bray-Curtis) of arthropods in the 2 diet of Pipistrellus kuhlii among pairs of sampling dates as a function of their time interval 3 (days). The diet was sampled at May - October 2015 in Emek-Hefer, Israel. 4

4.4 Orders, richness and families of arthropods in the diet of P. kuhlii 5

Nine different orders of arthropods were identified in the diet of P. kuhlii (Figure 10). The 6 majority of prey items identified in the diet (98.7%) belong to four orders; Lepidoptera, Diptera, 7

Coleoptera and Hemiptera (Figure 10). Lepidopterans showed the highest number of unique 8

OTU's assigned to taxa (richness) as well as the highest number of prey items (abundance) 9 assigned to taxa, followed by Dipterans, Coleopterans and Hemipterans (correspondingly) 10

(Figure 10). 11

300

Richness Abundance

250

200

150 Counts

100

Figure 10 – Richness (unique OTU's) and abundance (number of prey items) of order of 2 arthropods identified in the diet of Pipistrellus kuhlii bats by molecular analysis 3 (Metabarcoding) in fecal samples (n = 132) collected from Emek-Hefer, Israel. 4

Lepidoptera and Diptera were the dominate orders found in the diet, with 87% of the 6 samples in the study containing Lepidopteran prey items followed by 77% containing Dipterans 7

(Figure 11). While, Coleoptera was found in 43% of the samples and Hemipterans in 28% of the 8 samples. The remaining five orders in the diet (including: Neuroptera, Mantodea, Araneae, 9

Orthoptera and Hymenoptera) were found in 5% of the samples in the diet. Pairwise 10 comparisons for frequencies occurrences of arthropod orders in the diet (that are shown in 11

Figure 11) were significantly different in McNemar's statistical test for proportions of paired 12 orders (Supplementary Table 1). 13

1 0.9 0.8 0.7 0.6 0.5 0.4

0.3 Frequency occurrence

% % 0.2 0.1 0 Lepidoptera Diptera Coleoptera Hemiptera Other Orders 1 Figure 11 - Percent frequencies occurrence of arthropod orders identified in the diet of 2 Pipistrellus kuhlii bats by molecular analysis (Metabarcoding) in fecal samples (n = 132) 3 collected from Emek-Hefer, Israel. Other orders include: Neuroptera, Mantodea, Araneae, 4 Orthoptera and Hymenoptera. All pairwise comparisons of portions were significantly different 5 McNemar's statistical test for proportions of paired orders, see Supplementary Table 1. 6 7

The frequency occurrence of orders varied across the sampling dates (Figure 12). Dipterans 8 showed the highest portions in May - June (1) with equal portions to Lepidopterans in July (1) – 9

July (2), and Lepidopterans showed the highest portions thereafter from July (2) – October 10

(Figure 12). However, pairwise comparisons of the difference in portions of Lepidopterans and 11

Dipterans, within sampling dates, only show a significantly higher portion of Dipterans in May 12 and Lepidopterans in August (2) and September (see Appendix 3 - Supplementary Table 2 for 13 all pairwise comparisons). Other arthropod orders (including: Neuroptera, Mantodea, Araneae, 14

Orthoptera and Hymenoptera) were only consumed in July (1) – August (2) (Figure 12) and in 15 relativity low portions in comparisons to the main prey orders (Appendix 3 - Supplementary 16

Table 2). 17

1 0.9 0.8 0.7 0.6 0.5 0.4

0.3 Frequency occurrence

% % 0.2 0.1 0

Diptera Lepidoptera Coleoptera Hemiptera Other orders 1

Figure 12 - Percent frequencies occurrence of arthropod orders, per sampling date, identified in 2 the diet of Pipistrellus kuhlii bats by molecular analysis (Metabarcoding) in fecal samples 3 collected from Emek-Hefer, Israel between May – October 2015. Other orders include: 4 Neuroptera, Mantodea, Araneae, Orthoptera and Hymenoptera. 5

4.4.1 Prey diversity in diet of P. kuhlii 6

Shannon diversity measures of the diet of bats (for all prey per sampling date pooled together) 7 peaked in July (2) (Figure 13), from July (2) the diversity decreased until October (Figure 13). 8

Although, the average number of species (unique OTU's) found within a sample 4.6 (± 0.66 9

SD), did not show a significant difference across the sampling dates (one-way ANOVA F8, 123 = 10

1.581, P = 0.137). 11

3.6

3.2

ShannonDiversity 2.8

2.4

Figure 13 – Shannon diversity measures for the total prey taxa in the diet of Pipistrellus kuhlii 2 bats identified by molecular analysis (Metabarcoding) in fecal samples collected from Emek- 3 Hefer, Israel. 4

4.4.2 Families of the main insect orders in diet of P. kuhlii 5

The taxa of prey found in the diet of P. kuhlii are represented by 51 different families, but 6 notably certain insect families appeared in greater frequencies than others. For example, of the 7

16 families in the order Lepidoptera, 52% of the prey items are comprised of only two families; 8

Gelechiidae (29%) and Cosmopterigidae (23%), which consist of important agricultural and 9 storage pest species (Robinson 2005). In the order Diptera 13 families were identified while 10

69% of the prey items in this order are comprised of three families; Chironomidae (26%), 11

Psychodidae (23%) and Limoniidae (20%), which are mostly associated with moist and aquatic 12 habitats (Robinson 2005). 13

4.5 Pest species in the diet of P. kuhlii 14

Twenty-five species of agricultural and stored food pests were identified in the diet of P. kuhlii. 15

Together they amount to 192 prey items, corresponding to 53% (192/365) of the prey items 16 assigned to species and 31% (192/621) of the prey items assigned to arthropod taxa in the study. 17

The list of pests found in the diet of the bats (Table 5) includes major agricultural pest species 18 such as: The pink bollworm (Pectinophora gossypiella), Angoumois grain moth (Sitotroga 19 cerealella), Diamondback moth (Plutella xylostella), Tomato leaf miner (Tuta absouluta), 20

Citrus blossom moth (Prays citri) and more (Table 1). The pest species identified in the diet 1 were predominantly affiliated with damages to cotton, citrus, storage food and vegetable crops 2

(Table 5). 3

Table 5 - List of agricultural pest species identified in the diet of Pipistrellus kuhlii via a 4 molecular method (Metabarcoding) of fecal samples (n = 132) in Emek-Hefer, Israel. 5 Information in the table includes the number of samples positive for each arthropod species (# 6 of samples), the common names of the pest species, and their main pest affiliation. Sampling 7 dates shows the dates at which the species was identified in the diet: 1 – May, 2 – June (I), 3 – 8 June (II), 4 – July (I), 5 – July (II), 6 – August (I), 7 - August (II), 8 – September and 9 – 9 October. Roost sites list the sampling site (A – E, All = all roosts) at which the species of pest 10 was identified. All the species in the list showed 100% sequence similarities to BOLD reference 11 species and were assigned according to confidence criteria (1a) (Tuta absoluta and Ceratitis 12 capitate were assigned according to confidence criteria (2) (see methods)). 13

# Main Common Sampling Roost of Family Species Pest Name Dates Sites Samples Affiliation 41 Gelechiidae Pectinophora Pink bollworm Cotton 6 - 9 All gossypiella 35 Cosmopterigidae Anatrachyntis simplex False pink Cotton, Millet 5 - 9 All bollworm 31 Gelechiidae Sitotroga cerealella Angoumois Stored food 2 - 9 All grain moth 24 Cosmopterigidae Anatrachyntis badia Florida pink Citrus 2 - 8 A, C, D, E scavenger 9 Plutellidae Plutella xylostella Diamondback Brassicaceae 2, 4, 5, 7 A, B, D, E moth 8 Gelechiidae Tuta absoluta Tomato leaf Tomato 1,2,3,5,9 A, B, D miner 7 Praydidae Prays citri Citrus blossom Citrus 1,3,4,5,6, C, D, E moth 7 7 Pentatomidae Nezara viridula Green stink bug Polyphagous 1 - 6 A, B, C 6 Crambidae Spoladea recurvalis Beet webworm Cotton 6 - 8 A, B, D 4 Gracillariidae Phyllocnistis citrella Citrus leaf Citrus 3, 4 A, C miner 3 Ptinidae Lasioderma serricorne Cigarette beetle Stored food 5 - 7 E 2 Tephritidae Ceratitis capitate Mediterranean Polyphagous 1, 3 A fruit fly 2 Anthribidae Araecerus fasciculatus Coffee bean Stored food 5, 6 E weevil 2 Nolidae Earias insulana Egyptian stem Cotton 6, 8 A, B borer 2 Crambidae Chilo partellus Spotted stalk Maize 6, 8 B borer 1 Crambidae Duponchelia fovealis European Polyphagous 1 B pepper moth 1 Anthomyiidae Delia platura Seed corn Fabaceae 2 A maggot 1 Bedelliidae Bedellia somnulentella Sweet potato Sweet potato 4 E leaf miner 1 Crambidae Herpetogramma Grass Turf 4 B licarsisalis webworm 1 Ptinidae Stegobium paniceum Drugstore Stored food 4 E beetle 1 Pyralidae Pyralis farinalis Meal moth Stored food 5 C 1 Pyralidae Cryptoblabes gnidiella Honeydew Citrus 5 E moth 1 Lygaeidae Nysius graminicola Unknown Polyphagous 3 A

1 Lygaeidae Nysius binotatus Unknown Polyphagous 9 C 1

Pests species were identified in the diets of bats at all five roosts sites (Table 5), and across the 2 sampling dates (Table 1). A positive increase was found in the total number of pest prey items, 3

2 per sampling date, as the season progressed (R = 0.6, F1, 8= 10.814, p < 0.01). The full list of all 4 assigned taxa in the diet is provided in the supplementary martial: Appendix 3 - Supplementary 5

Table 3). Additionally, the diet and species that were consumed may be explored interactively 6 using the Krona plot (Additional File 1). 7

4.5.1 Nuisance pests, diseases vectors and natural enemies in the diet of P. kuhlii 8

Additional prey with human concern that were found in the diet of P. kuhlii include prey items 9 that can potentially cause nuisance or carry diseases. For example, Dipterans from the families 10

Psychodidae (Drain flies) and Chironomidae (midges), that may cause clogging in sewage 11 treatment facilities, or Typhaea sp. (hairy fungus beetle) which are potential diseases vectors of 12

Salmonella and more prey items presented in (Table 6). 13

Table 6 - List of arthropod taxa that are potential nuisance pests or diseases vectors identified in 14 the diet of Pipistrellus kuhlii via a molecular method (Metabarcoding) of fecal samples (n = 15 132) in Emek-Hefer, Israel. Information in the table includes the number of samples positive for 16 the arthropod taxa (# of samples), the common names of the pest taxa, and their nuisance or 17 disease affiliation. Taxa with missing species account for multiple matches to BOLD reference 18 sequences and therefore inconclusive assignment to a single species and assignment to lower 19 taxonomic rank (according to criteria (3); see methods). 20

# of Common Nuisance Disease vector Family Genus Species Samples name affiliation affiliation

52 Chironomidae - - Midges nuisance by their allergic rhinitis presence, clogging and bronchial in sewage asthma treatment facilities 45 Psychodidae - - Drain fly Clogging in sewage Nosocomial treatment facilities infections 17 Mycetophagidae Typhaea - Hairy fungus - Salmonella beetle Enterica and Campylobacter 14 Culicidae Clux - Mosquito Blood sucking West Nile Virus, Rift Valley Fever 21

Potential beneficial natural enemies were also identified in the diet, the number of prey items in 22 the diet which belong to such groups amount altogether to 21 representing 3 % of the total prey 23 items including: 14 prey items from the Carabaide beetle family, two spiders (Ero 24

quadrituberculata, Neoscona subfusca), three Lacewings (Chrysoperla pudica and two 1

Chrysoperla sp.), a single Praying mantis (Miomantis sp.), and a single parasitoid wasp 2

(Venturia canescens). 3

4.5.2 Pink bollworm in the diet of P. kuhlii 4

The Pink bollworm (P. gossypiella) was the most abundant prey item in the diet of P. kuhlii and 5 was identified in 41/132 of the samples (Table 5). They emerge in the diet at July (2), when 6 flowers of cotton develop into capsules. Pink bollworms increase in frequency occurrences in 7 the diet as the cotton developed, reaching 93% of the samples (27/29) in September – October 8

(Figure 14). Pink bollworms were identified at all five roost sites regardless of their distance to 9 cotton fields, they did not show significant differences in frequencies occurrence at the different 10 roosts (ANOVA F1, 4 = 2.21, p = 0.23). 11

The frequency occurrences of Pink bollworm in the diet of bats, roosting is roost site E, 12 was positively correlated (Pearson R = 0.81, p < 0.01 (1 - tailed)) with the increase of Pink 13 bollworms abundance (inferred from cotton capsules infested) monitored in a nearby field 14

(Figure 15). Additionally, at the regional scale, the increase in frequency occurrences of Pink 15 bollworm in the diet of bats, across all roost sites, was positivity correlated with the total area of 16 cotton fields at which pesticide treatments targeting Pink bollworms were applied, during the 17 week prior to sampling bat feces (Pearson R = 0.86, p < 0.01 (1 - tailed)) (Figure 14). 18

100% 400 Pesticides Targeting Pink bollworm Pink Targeting Pesticides

350 with (hectare) Sprayed Area Total 80% diet 300

250

60% P. P. kuhlii's 200

40% 150

% Freq. Freq. % of Occurrence 100

20% Pink bollworm in 50

0% 0

Figure 14 - Percent frequency occurrence of Pink bollworm (Pectinophora gossypiella) found in 4 the diet of Pipistrellus kuhlii bats at five roosts sites in the study area (in blue – primary Y axis), 5 in relation to a proxy of Pink bollworm abundance in the cotton fields in the study area. The 6 sprayed area is calculated according to cumulative area of cotton fields in the study area (Emek- 7 Hefer, Israel) that were sprayed during week prior to sampling the diet of bats. The two 8 measures showed a significant correlation (Pearson R = 0.86, p < 0.01 (1 - tailed)). 9

100% 30 Number of Cotton Capsules infestedCapsules Numberof Cotton

25 by Pink bollworms by Pink

67% 20 bats(roost Site E)

15 P. P. kuhlii 33% 10

% Freq. % Occurrence Pink of bollworm inthe diet of 0% 0

Figure 15 - Percent frequency occurrence of Pink bollworm (Pectinophora gossypiella) in the 11 diet of Pipistrellus kuhlii bats from roost E (Gan-Shmuel, Israel) (in blue – primary Y axis) in 12

relation to the average number (n = 5) of cotton capsules infested with Pink bollworm in 100 1 cotton capsules inspected per 10 - 20 hectares of cotton field (in pink – secondary Y axis). 2 Seven sampling events of both measures sampled within the same week in June – September 3 2015 in Emek-Hefer, Israel are shown in the plot. The two measures showed a significant 4 correlation (Pearson R = 0.81, p < 0.01 (1 - tailed)). 5

Levin’s dietary niche breadth of the bats (per sampling date) significantly decreased with the 7

2 frequency occurrence of Pink bollworms found in the diet (R = 0.78, F1, 8 = 7.3, p < 0.001) 8

(Figure 16). 9

35 y = -0.2x + 28.7 30 R² = 0.78

20 nichebreadth 15

dietary 10

5 Levins Levins

0 0 25 50 75 100 % Frequency occurrence of Pink bollworms in the diet 10

Figure 16 - The dietary niche breadth (Levin’s measure) of Pipistrellus kuhlii bats as a function 11 of frequency occurrence of the cotton pest Pink bollworm (Pectinophora gossypiella) moths in 12 its diet in the corresponding sampling date. Samples were taken from May- October 2015 at 13 2 Emek-Hefer, Israel. The two measures show a negative linear regression (R = 0.78, F1, 8 = 7.3, p 14 < 0.001) 15

4.6 Similarities in the diet of P. kuhlii 16

The similarities in the community of arthropods found in the diet of the bats at the different 17 sampling dates (across sampling roosts), increased as the season progressed. Pink bollworms 18

(G. gossypiella) alone explained 70% of the similarities in diet in September and 76% of the 19 similarities in October, as inferred from SIMPER analysis of sampling date groups (across all 20 five roost sites) (Table 7). Pink bollworms account for contributions to similarities in the diet, 21

(per sampling date) higher than any other species consumed throughout the entire sampling 22 season (Table 7). A secondary cotton pest the false pink bollworm (Anatrachyntis simplex) 23 contributed to the majority of similarities in August (I-II) (Table 7). Earlier in the season (May 24

– July (1), similarities were mostly explained by Dipterans from families; Chironomidae 1

(Kiefferulus brevibucca), Limoniidae (Symplecta pilipes) and Psychodidae (i.e.; Psychoda 2 alternate). 3

Table 7 - SIMPER results listing the similarities within date samples (across all sampling sites), 4 and the taxa that contribute to similarities (90% cutoff). 5 Average abundance (Av.Abund) = number of samples positive for OTU/total samples per date), 6 Contribution to date similarities (%Contrib) = percent contribution cumulative similarities 7 (%Cum). Note: species with a higher percent contribution than 50% are underlined (i.e.; the 8 Pink bollworm (Pectinophora gossypiella in September and October). 9

Group May Average similarity: 13.33

Av. % % Order Family Genus Species Abund Contrib Cum.

Dip. Chironomidae Kiefferulus brevibucca 0.5 31.1 31.1 Dip. Limoniidae Symplecta pilipes 0.4 24.2 55.2 Dip. Chironomidae 0.4 14.3 69.5 Dip. Culicidae Culex 0.3 7.7 77.2 Col. Mycetophagidae Typhaea 0.3 7 84.2 Hemi. Lygaeidae 0.2 5.1 89.3 Dip. ` 0.1 2.4 91.6

Group June(1) Average similarity: 9.22

Av. % % Order Family Genus Species Abund Contrib Cum. Dip. Limoniidae Symplecta pilipes 0.5 45.1 45.1 Dip. Chironomidae 0.2 6.9 52 Lepi. Coleophoridae Coleophora therinella 0.2 6.7 58.7 Lepi. Cosmopterigidae Anatrachyntis badia 0.2 5.6 64.2 Hemi. Cimicidae Cacodmus vicinus 0.2 5.2 69.4

Col. Cerambycidae Trichoferus fasciculatus 0.2 5.2 74.5 Dip. Psychodidae Psychoda alternata 0.1 4.1 78.7 Dip. Psychodidae 0.1 3 81.6 Lepi. Gelechiidae Sitotroga cerealella 0.1 3 84.6 Dip. Drosophilidae Drosophila simulans 0.1 2.3 86.9 Lepi. 0.1 2.3 89.2 Col. Carabidae Ophonus rufibarbis 0.1 2.1 91.2

Group June(2)

Average similarity: 8.24

Av. % % Order Family Genus Species Abund Contrib Cum. Dip. Limoniidae Symplecta pilipes 0.4 40.8 40.8 Dip. Chironomidae Kiefferulus brevibucca 0.3 22.9 63.6 Dip. Psychodidae 0.2 8 71.7 Col. Cerambycidae Trichoferus fasciculatus 0.2 6.5 78.2 Lepi. Gelechiidae Sitotroga cerealella 0.1 4.6 82.8

Lepi. Praydidae Prays citri 0.1 3.3 86.1 Lepi. Cosmopterigidae Pyroderces Argyrogra-mmos 0.1 2.9 89 Lepi. Gracillariidae Phyllocnistis citrella 0.1 2.6 91.6

Group July(1) Average similarity: 10.70 Av. % % Order Family Genus Species Abund Contrib Cum. Dip. Psychodidae 0.3 22.9 22.9 Lepi. Plutellidae Plutella xylostella 0.3 14.7 37.6 Dip. Chironomidae Kiefferulus brevibucca 0.3 13.2 50.9 Lepi. 0.3 8.4 59.2 Dip. Limoniidae Symplecta pilipes 0.3 8.1 67.4

Lepi. Gelechiidae Sitotroga cerealella 0.3 7.8 75.1 Hemi. Lygaeidae 0.3 7.4 82.5 Col. Carabidae 0.2 6.9 89.4 Lepi. Cosmopterigidae Anatrachyntis badia 0.1 2 91.4

Group July(2) Average similarity: 11.56 Av. % % Order Family Genus Species Abund Contrib Cum. Dip. Chironomidae Kiefferulus brevibucca 0.5 30.1 30.1 Lepi. Cosmopterigidae Anatrachyntis badia 0.3 13.8 43.9

Dip. Limoniidae Symplecta pilipes 0.3 12.8 56.6 Lepi. 0.3 8.4 65 Lepi. Cosmopterigidae Anatrachyntis simplex 0.2 5.8 70.7 Col. Mycetophagidae Typhaea 0.3 5.7 76.4 Hemi. Lygaeidae 0.2 4.5 80.9 Lepi. Gelechiidae Sitotroga cerealella 0.2 3 83.9 Col. 0.1 2.4 86.2 Lepi. Erebidae Pericyma 0.1 2.4 88.6

Col. Chrysomelidae 0.1 1.8 90.4

Group August(1) Average similarity: 19.19 Av. % % Order Family Genus Species Abund Contrib Cum. Lepi. Cosmopterigidae Anatrachyntis simplex 0.6 38.1 38.1 Dip. Psychodidae 0.5 23.4 61.5 Lepi. Gelechiidae Pectinophora gossypiella 0.4 12.9 74.4 Col. Chrysomelidae 0.3 6.6 81 Lepi. Gelechiidae Sitotroga cerealella 0.3 5.8 86.7 Lepi. 0.2 4.3 91.1

Group August(2) Average similarity: 27.64 Av. % % Order Family Genus Species Abund Contrib Cum.

Lepi. Cosmopterigidae Anatrachyntis simplex 0.7 33.9 33.9 Lepi. Cosmopterigidae Anatrachyntis badia 0.5 21.1 55 Lepi. Gelechiidae Sitotroga cerealella 0.5 15.8 70.7

Lepi. Gelechiidae Pectinophora gossypiella 0.5 14.2 85 Dip. Psychodidae 0.3 3.8 88.7

Lepi. Crambidae Spoladea recurvalis 0.2 2.8 91.6

Group September Average similarity: 36.00 Av. % % Order Family Genus Species Abund Contrib Cum. Lepi. Gelechiidae Pectinophora gossypiella 0.9 69.9 69.9

Lepi. Cosmopterigidae Anatrachyntis simplex 0.5 18.5 88.4

Lepi. Gelechiidae Sitotroga cerealella 0.3 6.7 95

Group October Average similarity: 33.96 Av. % % Order Family Genus Species Abund Contrib Cum. Lepi. Gelechiidae Pectinophora gossypiella 0.9 75.8 75.8 Lepi. Cosmopterigidae Anatrachyntis simplex 0.4 7.1 82.9

5. DISSCUSION 1

By incorporating innovative molecular methods my study sheds light on the trophic niche of P. 2 kuhlii. I identified prey taxa in the bats diet to high taxonomic resolution and accounted for 3 regional and seasonal spatio-temporal variation in its diet. Practically, my study highlights the 4 occurrence of multiple monetary significant pest species, which were consumed by these highly 5 synanthropic insectivorous bats. I found potentially notable ecosystem services to the cotton 6 industry, with possible indications that P. kuhlii supports pest suppression of Pink bollworm 7 moths. As inferred from the high occurrences of Pink bollworms in the diet, and by the notion 8 that bats displayed opportunistic feeding on these moths and narrowed their dietary niche 9 breadth in relation to their occurrence in the diet. My findings suggest P. kuhlii bats are 10 potentially suitable candidates for conservation biological control of more than a few 11 agricultural pest species, and can possibly support the suppression of additional nuisance pests 12 and disease vectors while they show relatively low intraguild predation of arthropod natural 13 enemies. 14

5.1 Metabarcoding the Diet of P. kuhlii 15

In my thesis I present a comprehensive study to describe the diet of P. kuhlii to high taxonomic 16 resolution and relate the findings to its synanthropic diet and ecosystem services. My results 17 strength the notion from Galan et al. (2018) findings, which recently found agricultural pest 18 species in the diet of P. kuhlii (but only processed three fecal samples). I show that non-invasive 19 molecular methods can be used effectivity to identify, via fecal samples, multiple prey species 20 in the diet of these urban dwelling predators. Although I did not compare different methods of 21 sample collection or preservation, as often suggested for DNA studies from non-invasive 22 materials (Renan et al. 2012). The two-step sample storage method, which I employed 23 following Nsubuga et al. (2004) recommendations, was resourceful as nearly 100 % of the DNA 24 extractions in my study yielded arthropod and mammal DNA that were assigned to taxa (note: I 25 only attempted to amplify a third of the samples with mammal primers). 26

My study represents the first Metabarcoding attempt to describe the diet of 1 insectivorous bats in the Middle East region, and to best of my knowledge one of few that 2 attempted to target arthropods taxa from eDNA material in the region. Thus, it was uncertain to 3 what degree the public barcode reference databases for arthropods (i.e.; the Barcode of Life 4

Database or Gene Bank) could be utilized to accomplish high resolution taxa identification in 5 my study region (genus or species). However, despite the relatively conservative bioinformatic 6 filtering and strict OTU assignment criteria that I employed, high taxonomic description of the 7 prey taxa was achieved. Consisting of 83 unique arthropod OTU's that were assigned to species 8 or genus and account for more than 67 % of the prey items retained in the study. I attained a 9 high sample coverage (0.89) for my sampling effort, and according to an extrapolation 10 procedure of the data, I identified most of the detectable diversity in the diet of the bats. 11

Furthermore, sample coverage extrapolations indicated that increasing the sample size by 12 double (to n = 264) would only slightly improve the sample coverage to 0.95 (by 0.06 ± 0.022). 13

For the primary purposes of my study this would probably not justify the cost or efforts. Since 14 mostly rare or already identified prey taxa would be added to the diet. 15

Lepidopterans and Dipterans emerged as the most important prey groups in my 16 analysis, with Coleopterans and Hemipterans as additional significant prey groups but to a 17 lesser extent. These results should be treated with some caution, since Clarke et al. (2014) report 18 that Zeale arthropod primers, which I used in my study, exhibit a PCR-amplification bias to 19 amplify certain arthropod orders with better efficiency, especially favoring Lepidoptera and 20

Diptera. Although, I identified six other orders in the diet, their portions in the diet (and possibly 21 those of additionally unidentified orders) may be underrepresented (Razgour et al. 2011, Clarke 22 et al. 2014, Piñol et al. 2015, Alberdi et al. 2018). Furthermore, primer mismatches can 23 theoretically cause a similar bias within orders, in which certain families or species possibly 24 may not amplify at the same efficiency (Piñol et al. 2015). With this said, most the orders in the 25 diet of the of P. kuhlii bats, according to previous indications of their diets (Beck 1995, Feldman 26 et al. 2000, Goiti et al. 2003), can be amplified with Zeale primers (Zeale et al. 2011). Thus, my 27

results likely represent the majority of their prey. While future studies to investigate the diet of 1

P. kuhlii could employ additional arthropod primer sets, e.g.; those discussed in Alberdi et al. 2

(2018), to account for prey that may have been undetected in my study. However, the species 3

that I assigned to taxa, according to the criteria I used (Razgour et al. 2011, Galan et al. 2018) in 4

which only OTU's with high similarities (>98%) to reference sequences were assigned to 5

species, and those with considerably low similarities (<97.4%) were excluded from the 6

analyses, should provide rather confident assignment for prey. 7

5.2 Spatial-temporal Community Trends in the Diet of P. kuhlii 8

As can be expected from generalist predators (Symondson et al. 2002, Rutz and Bijlsma 2006, 9

Randa et al. 2009, Peers et al. 2012), the community of arthropods in the diet of the bats 10

significantly varied by sampling date and (to a lesser extent) by location, possibly due to 11

variation in prey abundance. This could suggest that variation in the diet of P. kuhlii bats, and 12

their arthropod prey in my study area, occurs more distinctively over time than in space, 13

however the significant interaction between the factors (PERMANOVA test) indicate an added 14

effect of the two factors on the community composition of arthropods in the diet. 15

The diet of P. kuhlii bats in my study seems to shift gradually over the sampling dates as 16

the season progressed. My inference relies on the "leave-one-out" procedure (CAP analysis), in 17

which a relativity high number of samples were correctly classified to their sampling dates, yet 18

many of the samples were assigned to successive dates. These findings reflect common prey 19

between successive sampling dates. Furthermore, similarities in the community of arthropods 20

between dates showed a linear decrease according to their intervals in sampling time. In fact, 21

these results may indicate that the sampling interval accounted quite well for temporal variation 22

in the bats diet. In contradiction to strong turnovers in species composition which could suggest 23

that the sampling interval was too long. Yet, my results suggest this was not the case in my 24

findings. 25

My results emphasize the need to sample the diet of insectivorous bats with a sampling 26

regime that specifically accounts for temporal variation, as for other generalist predators 27

(Dalerum and Angerbjörn 2005). For instance, if the diet of bats in my study was only sampled 1 early in the season (May), then Dipterans and Coleopterans would emerge as the dominate prey, 2 while sampling later in the season would propose Lepidopterans as the most important order in 3 the diet (August (I), September). With this said, insectivorous bats can respond quickly to short- 4 term increases in arthropod densities (Levin et al. 2009, Charbonnier et al. 2014), thus it is 5 possible that even though my sampling was quite frequent, important prey items that show short 6 term activity peaks could be absent from my findings. For example, winged ants in summer 7 nuptial flights, that swarm for a short time period, were found in the diet of P. kuhlii in a 8 previous study in Israel (Whitaker et al. 1994). However, major prey sources in my study that 9 persist across time, or within roosts, are probably accounted for and emphasized by their high 10 frequency abundances and by their contribution to similarities in the diet. 11

The degree of community similarities in the diet between sampling roosts was not 12 explained by their physical distance, it may be that the lower number of sampling sites could not 13 provide sufficient statistical inference, or that additional factors come into play and drive spatial 14 variation in the diets. For instance, agricultural land use in the surroundings of sampling roosts 15 was associated with lower dietary diversity in a the diet of other species of insectivorous bats 16

(Clare et al. 2011, Aizpurua et al. 2018). Likewise, variation in land use around my sampling 17 locations, could potentially drive differences in diet of bats between the sampling roosts. 18

Especially when foraging ranges between sampling locations do not overlap and consist of 19 different land uses and specific prey sources (i.e.; those that can support high insect densities 20 associated with sewage treatment facilities, specific agricultural crops or food-storages 21

(Robinson 2005)). Indeed, the richness (unique OTU's) in the diet of bats in my study show 22 disparities between sampling sites, with many taxa only found in a single roost site. But these 23 differences in richness may overemphasizes the role of rare taxa, by reflecting their presence 24 and absence without accounting for their frequency occurrences in the diet. In fact, many of the 25 shared taxa between roosts were arthropods that show high frequency occurrences in the diet. 26

The diversity of the diet of P. kuhlii decreases towards the end of the sampling season 1 and is mostly associated with Lepidopterans mainly moths which belong to the families 2

Gelechiidae and Cosmopterigidae. These include the four most frequently abundant pest species 3 in the diet, found in at least four of the five sampling roosts (Table 5). Since the abundance of 4 moths available as prey for the bats probably varies across the sampling season (Jonason et al. 5

2014), their increased availability seems to dictate a large portion of the variation in the diets of 6 the bats across time and space. However, the dietary shifts may also be explained by different 7 nutritional and biological demands such as pregnancy and lactation or preparation for 8 hibernation (Racey and Swift 1985, Levin et al. 2009, 2013). 9

Environmental factors which may drive variation in the diet of the bats in the study, can 10 potentially be investigated in future analysis and should include seasonal changes in; 11 temperature, wind speed and changes in primary resources production (which can be inferred by 12 normalized difference vegetation index [NDVI] (Creech et al. 2016)), which are known to drive 13 changes in arthropod activity and their communities (Adams et al. 1995, Jonason et al. 2014, 14

Sweet et al. 2015). Other factors that can be addressed with regards to spatial anthropogenic 15 factors include the distance and presence of land use at different spatial scales around the 16 sampling roosts i.e.; the presence of specific crops or land use near the roost (Tabashnik et al. 17

1999, Dodd et al. 2008, Egerer et al. 2017). In support of these explanatory direction, one of the 18 most abundant prey items in the diet; the Angoumois grain moth (Sitotroga cerealella) a major 19 storage pest (Robinson 2005), was mostly found in the diet at two sampling roosts located in the 20 walls of cow-feed granaries. Similarly, other storage pests including hairy fungus beetles 21

(Typhaea sp.), the cigarette beetle (Lasioderma serricorne) and the drugstore beetle (Stegobium 22 paniceum) were also mostly associated with these two roosts. Thus, it seems the location of 23 sampling and its surrounding land use can possibly explain variation in the diet of the P. kuhlii 24 bats and how their diet is shaped. 25

5.3 The Trophic Niche of P. kuhlii 1

My findings are in accord with results from previous conventional morphological methods to 2

study the diet of P. kuhlii (Beck 1995, Feldman et al. 2000, Goiti et al. 2003). Though, my 3

results accentuate the importance of Lepidopterans in comparisons to all previous studies, 4

possibly as a result of lower detection rates in conventional diet analysis (Whitaker 1998), or 5

high molecular detection rates of Lepidopterans with Zeale primers (Clarke et al. 2014). 6

Previous studies mostly emphasized the importance of Dipterans in the bats diet. However, 7

considering that 87% of my samples contained Lepidoptera DNA and 76% contain Diptera 8

DNA, with many families and species in my findings assigned to both these orders. I believe my 9

results truly emphasize the important role of both these orders in the trophic niche of P. kuhlii, 10

and are not a pure artifact of the molecular method. 11

My results show Lepidopterans are consumed throughout the season and are accountable 12

for most of the similarities in the last sampling dates. These findings are in accordance with 13

Goiti et al. (2003) inferences, they suggested that P. kuhlii apparently selected Lepidopterans 14

during September – October. Although, the overall frequency abundance of Lepidopterans in 15

their study was considerably lower than Dipterans, even at the sampling dates in which they 16

were selected, which only matches my results for the first sampling date. They also found 17

Psychodidae (Dipterans), which were common prey in the diet for bats in my study, were 18

negativity selected which might suggest they are not a preferred prey source. 19

When comparing additional prey orders in my results to those that frequently occur in 20

previous conventional studies of P. kuhlii's diet, I found lower incidences of Hymenoptera in 21

relation to all previous studies (Whitaker et al. 1994, Beck 1995, Feldman et al. 2000, Goiti et 22

al. 2003), maybe as a result of primer bias against this order (Razgour et al. 2011, Clarke et al. 23

2014). Additionally, I did not recover prey from the order Trichoptera (caddisflies), a relatively 24

common prey of P. kuhlii in one study (Beck 1995). This arthropod order can potentially 25

amplify with Zeale primers (Alberdi et al. 2018), but are reported as pollution intolerant 26

indicators (Bonada et al. 2006). Since bats are excellent bioindicators of various environmental 27

aspects of their habitats (Jones et al. 2009), the complete absence of Trichoptera prey taxa from 1 my findings may indicate degraded aquatic habitats that are subject to anthropogenic impact in 2 my study region, i.e.; Hadera and Alexander rivers (Barinova et al. 2011), or specifically within 3 the foraging grounds that bats were using. In general, variation in prey groups in comparisons to 4 previous conventional studies can to some extent emerge from methodical differences (Razgour 5 et al. 2011), but is also likely to be a product of prey availability and different foraging 6 preferences that shape the diet of P. kuhlii bats in the rural agroecosystem that I studied. 7

5.3.1 Synanthropic Tendencies of P. kuhlii 8

The synanthropic tendencies of P. kuhlii bats, established in previous studies (Barak and Yom- 9

Tov 1989, Korine and Pinshow 2004, Ancillotto et al. 2015) match well with my roost survey, 10 in which multiple anthropogenic bat roosts were identified and occupied throughout my 11 sampling season. Furthermore, P. kuhlii strong ties to habitats affected by man (Korine and 12

Pinshow 2004) can now be associated with its trophic niche, one which according to my 13 findings, is dominated by numerous synanthropic arthropod species which the bat resourcefully 14 exploits (i.e.; agricultural pests, food storage pests and sewage fauna; drain flies, midges and 15 mosquitos). The wide-ranging distributions of many of its prey species, which are adapted to 16 resources and harborages around humans (Robinson 2005), might resolve the rather high 17 taxonomic detection rates I achieved through public molecular reference databases. 18

The prevailing share of synanthropic prey species in the diet of P. kuhlii can suggest that 19 its high densities in urban habitats (Ancillotto et al. 2015) and range expansion (Sachanowicz et 20 al. 2006, Ancillotto et al. 2016), is accelerated by exploiting already familiar prey resources. For 21 example, Dipteran families; Chironomidae and Psychodidae were abundant prey items in the 22 diet which have cosmopolitan distribution (Robinson 2005). These insects which are associated 23 with polluted and degraded aquatic habitats, i.e.; sewage treatments or pisciculture facilities, 24

(Robinson 2006), explained a large portion of similarities in the diet early in the sampling 25 season. This may suggest that anthropogenic aquatic foraging grounds are a major source of 26 prey during these times, when Lepidopteran cotton pests were in low abundance. 27

Many of the agricultural pests found in the diet have wide-ranging distributions and are 1

notoriously invasive species (CABI, 2018. Invasive Species Compendium), which expand their 2

range following the crop they exploit (Desneux et al. 2010, Zalucki et al. 2012, Byers and 3

Naranjo 2014). For example, two serious vegetable pests which I found in the diet of the bats; 4

the tomato leafminer (Tuta absoluta) and the diamondback moth (Plutella xylostella) (Desneux 5

et al. 2010, Zalucki et al. 2012). From an insectivorous bats perspective, the surplus of these 6

abundant food sources are a valuable prospects to exploit, especially during times when they 7

have high nutritional demands such as lactation and pregnancy (Leelapaibul et al. 2005, 8

McCracken et al. 2012, Charbonnier et al. 2014, Puig-Montserrat et al. 2015). The numerous 9

pests I identified in the diet of P. kuhlii bats proposes they regularly provide important 10

ecosystem services to humans that share habitats with them. 11

5.4 Pipistrellus kuhlii in Conservation Biological Control 12

Conservation Biological Control primarily relies on natural enemies that naturally occur in the 13

agroecosystem (Tscharntke et al. 2007). Thus, synanthropic bats such as P. kuhlii which show 14

temporal persistence in the agroecosystem, with relatively local foraging ranges and sedentary 15

roosting behavior (Barak and Yom-Tov 1991, Maxinová et al. 2016) are likely suitable 16

candidates for CBC. In addition, their opportunistic tendencies to exploit artificial resources and 17

foraging grounds (Korine and Pinshow 2004, Ancillotto et al. 2015) indicate they may respond 18

positively to various adjustments in agricultural management or habitat modifications. 19

However, modifications should attempt to preserve and enhance their efficiency, while also 20

keeping in mind the overall sustainability of the agroecosystem. 21

Effective generalist's predators that can suppress pests in temporary agroecosystems 22

should show temporal persistence with a capacity to maintain their diet when pest populations 23

decay (Symondson et al. 2002). The temporal variation in the diet of the bats in my study, 24

indicate they sustain themselves while cotton pests are in low abundances by maintaining a 25

relativity diverse diets that includes many Dipteran nuisance pests and additional agricultural 26

and storage pests. Though, their activity monitored in cotton fields proposes that they frequently 27

visit the fields throughout the cotton season, even when pest populations are low (Korine et al., 1 in preparation). Thus, they can accomplish another important trait for natural enemies that 2 requires opportunistic feeding habits allowing them to quickly exploit attacks by resurgent pests 3

(Symondson et al. 2002). Accordingly, my findings indicate that P. kuhlii bats display 4 opportunistic feeding behavior with regards to cotton pests, and maybe towards additional pests 5 which show temporal peaks in their diets. 6

In order to enhance the activity of bats in the fields and their potential contribution to pest 7 suppression, some possibilities can be explored. These include; constructing artificial water 8 sources near the targeted fields, to provide suitable drinking source for foraging bats and to 9 increase their presence in the fields. Additionally, erecting suitable artificial roosts near the 10 fields may support bat population densities (Flaquer et al. 2006), and also increase their activity 11 in the target fields (Boyles et al. 2013, Brown et al. 2015, Puig-Montserrat et al. 2015). For 12 example, artificial roosts near rice paddies in El Ebro Delta, Spain (Flaquer et al. 2006, Puig- 13

Montserrat et al. 2015), provide suitable roosting habitats for thousands of Soprano pipistrelle 14 bats (Pipistrellus pygmaeus) which contribute to pest suppression of rice borer moths (Chilo 15 supressalis) (Puig-Montserrat et al. 2015). 16

However, my study area consists of intensive agricultural practices in which chemical 17 pesticides and herbicides are used (Niv 2013), and the consequences for the health of bats which 18 forage and roost at these affected habitats should be addressed (Jones et al. 2009, Kunz et al. 19

2011). Thus, before attempting to enhance the activity of P. kuhlii in the agricultural fields, it 20 would be wise to examine the implications of intensive agricultural practices on their health 21

(O’Shea 2009, Kunz et al. 2011), and attempt to adjust agricultural practices to preserve their 22 valuable services and reduce toxicity to them. 23

Additionally, to assess the efficiency of a modification in the agroecosystem. It would be 24 necessary to quantify the actual contribution of bats to suppress pests (Boyles et al. 2013), 25 before and after a manipulation is employed. Attempts to quantify the contribution of bats to 26 cotton pest suppression in my study area, can include night time bat exclosures in the crop fields 27

(Maas et al. 2013, 2016, Maine and Boyles 2015). Alternative methods include modeling the 1 economic contribution of the bats using data of their activity in the fields, the portion of pests in 2 their diets, and their capacity to consume pests with the life history traits of the pest and the 3 costs it imposes for farmers (Taylor et al. 2018). While additional factors that should be taken 4 into consideration are when and where the pests were consumed (Federico et al. 2008, 5

Wiederholt et al. 2017), and the implications of intraguild predation by the bats. 6

Much of the debate regarding the contribution of generalist predators to pest suppression, 7 involves uncertainties with regards to the magnitude and the effect of intraguild predation 8

(Symondson et al. 2002, Lang 2003, Furlong 2015). Although it is considered difficult to 9 identify intraguild predation, at least within arthropods (Brodeur and Boivin 2006), my findings 10 provide insights to these interactions and suggest low frequencies of intraguild predation by P. 11 kuhlii bats, with relativity few of their prey items categorized as potential natural enemies. Most 12 of the potential natural enemies found in the diet were generalist predators themselves (e.g.; 13

Carabidae beetles). These arthropods can also take part in intraguild predation and cannibalism 14 themselves (Currie et al. 1996), therefore the actual implications of their predation and 15 relevance to biological control and crop yields are hard to infer (Brodeur and Boivin 2006). 16

However, the consequences of my observations should be examined since they probably 17 regularly occur for insectivorous bats, i.e.; many spiders were found in the diet of several 18 species of insectivorous bats (Galan et al. 2018). Yet, their dis-services were not addressed. 19

My study was primarily designed to address the potential of P. kuhlii bats to suppress 20 cotton pests. In accordance, my results indicate their diet contains many arthropods associated 21 with damages to cotton. Nevertheless, their diet shows high occurrences of additional pest 22 species, which damage citrus, storage commodities and vegetables among other crops. These 23 findings imply that the bats can function as holistic natural enemies, and that focusing on them 24 in conservation biological control initiatives can potentially benefit several industries within an 25 agroecosystem simultaneously. Moreover, it can contribute to suppress storage and nuisance 26 pests as well as disease vectors. Nevertheless, the high occurrences of Pink bollworms in the 27

diet and the all-around notion of their relevance to the diet of the bats in the study are especially 1 promising for CBC in cotton fields. 2

5.4.1 Pipistrellus kuhlii as a Natural Enemy of Pink bollworm 3

My results demonstrate that Pink bollworm moths, one of the most destructive pests of cotton 4 worldwide which is found virtually in all cotton growing regions of the world (Ingram 1994, 5

Byers and Naranjo 2014) (CABI, 2018. Invasive Species Compendium), are a primary food 6 source for P. kuhlii bats that roost near cotton fields. Pink bollworm moths were found in high 7 frequencies abundances in all the roosts I sampled, regardless of their distance to the cotton 8 fields. Suggesting the bats exploit the abundances of these pests as they irrupt, and that P. kuhlii 9 bats possibly aggregate in the cotton fields in the relevant times. The frequency occurrence of 10

Pink bollworms in the diet show strong positive correlation with proxies of their abundance in 11 cotton fields, on the regional level (across all five sites) and at the local level (comparing only 12 roost E to a nearby cotton field). This supports the notion that P. kuhlii bats respond to pest 13 irruptions within their foraging grounds with opportunistic feeding. 14

Pink bollworms and also a secondary cotton pest species the false pink bollworm, 15

Anatrachyntis simplex (Chamberlain 1993), are known to target cotton plants when flowers and 16 cotton capsules develop on the crop (late July in the study region) (Niv 2013), until the crop is 17 harvested (Byers and Naranjo 2014, Carrière et al. 2017). Interestingly, Shannon diversity of the 18 bats diets (for all five roosts together) decreases (from August (1)) in parallel to these times. 19

Accordingly, the niche breadth of P. kuhlii bats also narrows from August to October, and 20 shows a strong response to the increasing frequency occurrence of Pink bollworms in the diet. 21

While the average number of unique OTU's, found per sample, did not show significant 22 differences in richness across the date groups. Suggesting the bats eat are exploiting specific 23 prey sources. 24

Increasing similarities in the diet (inferred from SIMPER analysis) within sampling date 25 groups, complement the notion that bats decrease their dietary diversity and narrow their niche 26 breadth in response to Pink bollworms irruptions. The highest similarities in the last sampling 27

dates are explained by a few species that are associated with cotton, and were consumed in all 1 the sampling roosts. This proposes that the P. kuhlii bats, which show advantages in groups 2 hunting (Barak and Yom-Tov 1989), may aggregate to forage at cotton fields due to pest 3 irruptions at these times. Although, my sampling method at roost sites does not allow me to 4 infer exactly where the pests were captured. The relativity local foraging range of P. kuhlii bats 5

(Maxinová et al. 2016) and the activity of bats in the cotton fields that was previously monitored 6

(Korine et al., in preparation) suggests predation probably takes place in the cotton fields or 7 their vicinity. 8

Although I report opportunistic feeding on Pink bollworms, previous findings which 9 inferred selectivity by P. kuhlii bats on moths at late summer months (Goiti et al. 2003, Andreas 10 et al. 2012) could essentially imply that bats in my study are actively selecting the two cotton 11 pests. Even though I cannot establish this argument without data of the available alternative 12 prey. However, a narrowed niche breadth is generally associated with specialized resource use 13

(Peers et al. 2012), and was observed in other species of insectivorous bats at certain times of 14 the season with increased preferred prey abundances (Jones 1990, Andreas et al. 2012). This 15 may suggest such behavior also occurs in relation to pest irruptions in the agroecosystem I 16 studied. The overall notion that P. kuhlii bats are exploiting cotton pest irruptions, agrees with 17 acoustic analysis of bat activity over cotton fields in the study area (Korine et al., in 18 preparation). In which, bats increase their activity in the fields at similar time periods in the 19 season, and show an uncharacteristic (to this species) (Barak and Yom-Tov 1989) peak in night 20 time activity roughly around 2 – 4 am (Korine et al., in preparation), when Pink bollworms are 21 most active (Lukefarr et al. 1957, Lingren et al. 1989). 22

Preceding my study, the capacity of the P. kuhlii bats to consume Pink bollworm moths 23 was assessed in a controlled feeding trial, the results show that an adult bat can consume on 24 average 96 (± 46 SD) with a maximum of 188 Pink bollworm moths per sitting (in one night) 25

(Korine et al., in preparation). Yet, due to high energy demands of flying and aerial foraging 26

(McGuire and Guglielmo 2009, Shen et al. 2010) this impressive abundances may even 27

underestimate the capacity of wild bats. In my results I cannot directly infer regarding the 1 abundance of Pink bollworms within each sample (Razgour et al. 2011, McCracken et al. 2012). 2

Yet, their high frequency occurrence in the diet suggest that samples found positive are 3 probably a result of the contribution of multiple individuals of Pink bollworms moths which 4 were consumed. However, their predation very late in the cotton season (i.e.; October) might not 5 have significant economic benefits for the farmers and therefore their actual contribution to pest 6 suppression and crop yields should be examined in future studies. 7

Nevertheless, the predation of arthropods by bats can definitely show cascading effects 8 on crops (Williams-Guillén et al. 2008, Maine and Boyles 2015, Maas et al. 2016). Even though 9

Pink bollworms exert their damage to cotton in their larval stage (Tabashnik et al. 2005). The 10 activity of bats in the fields and the consumption of adult moths that emerge, or may arrive, in 11 the cotton fields can prevent them from mating or laying eggs on the cotton plants (Cleveland et 12 al. 2006, Federico et al. 2008). Previous findings relate similar agroecological trophic relations 13 between bats and moth pests to reduced damage to cotton, corn and additional crops, with 14 increased yields that generate economic benefits (Cleveland et al. 2006, Maas et al. 2013, Maine 15 and Boyles 2015). Pink bollworms emerging capacities to develop resistance to pesticides as 16 well as to Bt (Bacillus thuringiensis) cotton (Osman et al. 1991, Dhurua and Gujar 2011), 17 underscores the relevance of my findings which identify P. kuhlii bats as potentially valuable 18 natural enemies that can contribute to Pink bollworm sustainable suppression via CBC. 19

6. CONCLUSIONS 1

My study illustrates the synanthropic trophic niche of P. kuhlii bats and highlights their key role 2 in delivering ecosystem services, while my findings indicate they show relativity low 3 frequencies of intraguild predation. My results suggest that P. kuhlii bats are especially suitable 4 candidates for CBC and can function as holistic natural enemies that can benefit diverse 5 agricultural industries. I found strong indication that P. kuhlii bats may potentially contribute to 6 pest suppression of Pink bollworm moths, a serious problem for cotton farmers around the 7 world (Ingram 1994). I propose that CBC initiatives that account for possible negative 8 implications on the bats health (Kunz et al. 2011, Boyles et al. 2013) and regard for their 9 potential dis-servicers (Zhang et al. 2007), can provide a suitable approach to attempt to 10 enhance their contribution to sustainable pest suppression. My results can encourage 11 conservation initiatives to preserve insectivorous bat roosts and their activity in agroecosystem 12 and urban environments. 13

My results possibly represent trophic interactions that occur in large scales with 14 significant implications to economic concern across the wide-ranging distributions of P. kuhlii 15 bats (Juste and Paunović 2016). Communicating the findings of my study, among other recent 16 discoveries that accentuate the role of bats as vital ecosystem service provides (Cleveland et al. 17

2006, Boyles et al. 2011, Puig-Montserrat et al. 2015, Aizpurua et al. 2018, Galan et al. 2018, 18

Taylor et al. 2018) can help raise their reputation in the public eye (Boyles et al. 2011, 2013, 19

Kunz et al. 2011, López-Baucells et al. 2018). My findings can help balance the prevalent 20 cultural prejudice regarding bats, which imposes societal limitations that hamper efforts for their 21 conservation (López-Baucells et al. 2018). Practically amplified for synanthropic bats which 22 often come in contact with humans (Voigt and Kingston 2015). Whereas my findings support 23 there are positive implications for humans that share their habitats with synanthropic 24 insectivorous bat. This notion can very well signify the case for additional urban dwelling 25 predators and synanthropic species, which benefit us in ways that are still underappreciated but 26 justify conservation efforts (Gangoso et al. 2013, O’Bryan et al. 2018). 27

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8. APPENDICES 1

8.1 Appendix 1 – DNA Extraction protocol 2

1. I added fecal pellets (4.8 ± 0.6 SD, weighing 0.03 ± 0.1 SD gram) to the PowerBead Tubes 3 containing silica beads and beads solution. 4

2. I added 60 μl of C1 solution and briefly vortexed them. 5

3. I heated the tubes for 15 minutes at 65 ºC in an incubator. 6

4. I used a Qiagen TissueLyser II to "bead-beat" the samples for 10 minutes at frequency 1/20. 7

5. I centrifuged the tubes at 13,000g for 3 minutes. 8

6. I transferred the supernatant (400-500 μl) to new 1.5 ml tubes. 9

7. I added 250 μl of C2 solution to the tubes and by briefly vortexed them. 10

8. I incubated the tubes for 10 minutes at 4 ºC. 11

9. I centrifuged the tubes at 13,000g for 1 minute. 12

10. I transferred the supernatants (up to 600 μl) to new 1.5 ml tubes. 13

11. I added 200 μl of C3 solution and vortexed the tubes. 14

12. I incubated the tubes for 10 minutes at 4 ºC. 15

13. I centrifuged the tubes at 13,000g for 1 minute. 16

14. I transferred the supernatants (up to 750 μl) to new 1.5 ml tubes. 17

15. I added 1200 μl of C4 solution and briefly vortexed the tubes. 18

16. I loaded 650 μl of the mixes onto spin filters and centrifuged them at 8,000g for 1 minute. 19

17. I discarded flowthroughs and repeated the steps 16 and 17 until loading all the mix. 20

18. I added 500 μl of C5 solution and centrifuged the tubes at 13,000g for 1 minute. 21

19. I discarded flowthroughs and centrifuged the columns again at 13,000g for 1 minute. 22

20. I placed the spin filters in new 2 ml collection tubes and added 50 μl of EB buffer to the 23 filters. 24

21. I incubated the tubes for 15 minutes at 37-40 ºC. 25

22. I centrifuged the tubes at 13,000g for 1 minute and transferred the DNA extracts to 1.5 ml 26 low-bind tubes. 27

8.2 Appendix 2 - Unconstrained Ordination of the community of arthropods in the diet 1

of P. kuhlii 2

Unconstrained ordination Princaple component ordination (PCO) plots failed to emphasize 3 significant differences (found in PERMANOVA and ANOSIM tests) between groups of 4 samples according to date (Supplementary Figure 1). Thus, I used a constrained ordination 5

(CAP) to attempt to unmask the differences, as recommended by Anderson and Willis (2012) . 6

Plots were generated using PRIMER-E Software v.6 (Clarke 1993). 7

Resemblance: S7 Jaccard 40 DateName May

) June(1) n

o 20 i

t June(2)

a i

r July(1)

l July(2)

a 0

t August(1)

f August(2) o September

% -20 9

. October

(

2 O

C -40 P

-60 -40 -20 0 20 40 60 PCO1 (11.5% of total variation) 8

Supplementary Figure 1 - Unconstrained princaple component ordination (PCO) of Jaccard 9 similarities for arthropods identified in the fecal samples of Pipistrellus kuhlii sampled at nine 10 sampling dates (May – October) in Emek-Hefer, Israel. PCO1 and PCO2 explain 11.5 % and 11 6.9 % of the variation respectively. 12

8.3 Appendix 3 - Portion of samples containing arthropod orders in the diet of P. kuhlii 13

I compared the frequency occurrences of the main orders in the diet (Lepidoptera, Diptera, 14

Coleoptera, Hemiptera and all other orders pulled together). The results show that Lepidoptera 15 show the highest frequency occurrences followed by the orders as in Figure 11. The notion from 16

the graph is verified with significant differences in pairwise comparisons (Supplementary Table 1

2). 2

Supplementary Table 1 – Pairwise comparisons of frequency occurrence of the main orders in 3 the diet (all samples in the study n = 132) of the Pipistrellus kuhlii bats, in Emek-Hefer, Israel. 4 Orders compared are shown in the first column, Difference represents the difference in 5 frequency occurrence in the diet of the two orders compared, McNemar test statistic represents 6 McNemar test statistic, used to tests the equality of proportions of paired samples. Other orders 7 include: Neuroptera, Mantodea, Araneae, Orthoptera and Hymenoptera. 8

Compare Difference P-Value McNemar test statistic

Diptera - Lepidoptera -0.114 0.037 4.356

Coleoptera - Lepidoptera -0.447 0.000 46.082

Coleoptera - Diptera -0.333 0.000 28.891

Hemiptera - Lepidoptera -0.598 0.000 66.857

Hemiptera - Diptera -0.485 0.000 52.224

Hemiptera - Coleoptera -0.152 0.006 7.521

Other - Lepidoptera -0.826 0.000 107.009

Other - Diptera -0.712 0.000 86.490

Other - Coleoptera -0.379 0.000 41.397

Other - Hemiptera -0.227 0.000 20.024

The portions of samples (per sampling date) containing the main arthropod orders in the study 10 were compared using McNemar test statistic to test for significance differences (Supplementary 11

Table 2). Significant differences were found between the different orders in the diet, with 12 temporal variation in their relative importance. 13

Supplementary Table 2 - Pairwise comparisons for portions of samples, per sampling date, 14 containing an order of arthropod prey found in the diet of the Pipistrellus kuhlii bats, in Emek 15 Hefer, Israel between May - October. Orders compared are shown in the first column, 16 Difference represents the difference in frequency occurrence of the two orders compared, 17 McNemar test statistic represents McNemar test statistic, used to tests the equality of 18 proportions of paired samples. Other orders include: Neuroptera, Mantodea, Araneae, 19 Orthoptera and Hymenoptera. Significant differences (p < 0.05) are shown in bold. 20

May Compare Difference P-Value McNemar test statistic Diptera - Lepidoptera 0.500 0.023 5.143 Coleoptera - Lepidoptera 0.071 1.000 0.000 Coleoptera - Diptera -0.429 0.077 3.125

Hemiptera - Lepidoptera 0.000 1.000 0.000 Hemiptera - Diptera -0.500 0.046 4.000 Hemiptera - Coleoptera -0.071 1.000 0.000 June(1) Compare Difference P-Value McNemar test statistic Diptera - Lepidoptera 0.133 0.480 0.500 Coleoptera - Lepidoptera -0.400 0.041 4.167 Coleoptera - Diptera -0.533 0.013 6.125 Hemiptera - Lepidoptera -0.600 0.008 7.111 Hemiptera - Diptera -0.733 0.003 9.091 Hemiptera - Coleoptera -0.200 0.371 0.800 June(2) Compare Difference P-Value McNemar test statistic Diptera - Lepidoptera 0.000 1.000 0.000 Coleoptera - Lepidoptera -0.467 0.070 3.273 Coleoptera - Diptera -0.467 0.046 4.000 Hemiptera - Lepidoptera -0.467 0.070 3.273 Hemiptera - Diptera -0.467 0.023 5.143 Hemiptera - Coleoptera 0.000 1.000 0.000 July(1) Compare Difference P-Value McNemar test statistic Diptera - Lepidoptera -0.067 1.000 0.000 Coleoptera - Lepidoptera -0.333 0.131 2.286 Coleoptera - Diptera -0.267 0.134 2.250 Hemiptera - Lepidoptera -0.333 0.074 3.200 Hemiptera - Diptera -0.267 0.221 1.500 Hemiptera - Coleoptera 0.000 1.000 0.000 Other - Lepidoptera -0.800 0.001 10.083 Other - Diptera -0.733 0.003 9.091 Other - Coleoptera -0.467 0.046 4.000 Other - Hemiptera -0.467 0.046 4.000 July(2) Compare Difference P-Value McNemar test statistic Diptera - Lepidoptera -0.200 0.248 1.333 Coleoptera - Lepidoptera -0.333 0.074 3.200 Coleoptera - Diptera -0.133 0.683 0.167 Hemiptera - Lepidoptera -0.733 0.003 9.091 Hemiptera - Diptera -0.533 0.027 4.900 Hemiptera - Coleoptera -0.400 0.181 1.786 Other - Lepidoptera -0.800 0.001 10.083 Other - Diptera -0.600 0.016 5.818 Other - Coleoptera -0.467 0.046 4.000 Other - Hemiptera -0.067 1.000 0.000 August(1)

Compare Difference P-Value McNemar test statistic Diptera - Lepidoptera -0.286 0.134 2.250 Coleoptera - Lepidoptera -0.429 0.041 4.167 Coleoptera - Diptera -0.143 0.724 0.125 Hemiptera - Lepidoptera -0.643 0.008 7.111 Hemiptera - Diptera -0.357 0.131 2.286 Hemiptera - Coleoptera -0.214 0.371 0.800 Other - Lepidoptera -0.929 0.001 11.077 Other - Diptera -0.643 0.016 5.818 Other - Coleoptera -0.500 0.023 5.143 Other - Hemiptera -0.286 0.221 1.500 August(2) Compare Difference P-Value McNemar test statistic Diptera - Lepidoptera -0.400 0.041 4.167 Coleoptera - Lepidoptera -0.533 0.013 6.125 Coleoptera - Diptera -0.133 0.683 0.167 Hemiptera - Lepidoptera -0.933 0.001 12.071 Hemiptera - Diptera -0.533 0.027 4.900 Hemiptera - Coleoptera -0.400 0.041 4.167 Other - Lepidoptera -0.867 0.001 11.077 Other - Diptera -0.467 0.046 4.000 Other - Coleoptera -0.333 0.182 1.778 Other - Hemiptera 0.067 1.000 0.000 September Compare Difference P-Value McNemar test statistic Diptera - Lepidoptera -0.533 0.013 6.125 Coleoptera - Lepidoptera -0.733 0.003 9.091 Coleoptera - Diptera -0.200 0.371 0.800 Hemiptera - Lepidoptera -0.933 0.001 12.071 Hemiptera - Diptera -0.400 0.041 4.167 Hemiptera - Coleoptera -0.200 0.248 1.333 October Compare Difference P-Value McNemar test statistic Diptera - Lepidoptera -0.143 0.617 0.250 Coleoptera - Lepidoptera -0.857 0.001 10.083 Coleoptera - Diptera -0.714 0.004 8.100 Hemiptera - Lepidoptera -0.714 0.004 8.100 Hemiptera - Diptera -0.571 0.027 4.900 Hemiptera - Coleoptera 0.143 0.617 0.250

8.4 Appendix 4 - Taxonomic assignment of prey in the diet of P. kuhlii 1

Full list of OTU's retained and assigned to taxa according the confidence criteria, with the 2 number of prey items (the number of samples that were positive for each OTU) (Supplementary 3

Table 3). 4

Supplementary Table 3 - List of taxa in the diet of the Pipistrellus kuhlii bats identified via 5 Metabarcoding of its fecal samples (n = 132) collected in Emek-Hefer, Israel. 6 (#) = number of samples the OTU was present in, Conf. = confidence level of the assignment 7 (corresponding the confidence criteria, see methods), % = percent similarities to the Barcode of 8 Life Database reference sequences. 9

# Order Family Genus Species Conf. (%)

41 Lepidoptera Gelechiidae Pectinophora gossypiella 1a 100

39 Diptera Limoniidae Symplecta pilipes 1a 99.4

35 Lepidoptera Cosmopterigidae Anatrachyntis simplex 1a 100

32 Diptera Chironomidae Kiefferulus brevibucca 1a 100

31 Diptera Psychodidae 3 100

31 Lepidoptera Gelechiidae Sitotroga cerealella 1a 100

24 Lepidoptera Cosmopterigidae Anatrachyntis badia 1a 100

19 Lepidoptera 3 100

17 Coleoptera Mycetophagidae Typhaea 3 100

15 Diptera Chironomidae 3 100

14 Hemiptera Lygaeidae 3 98.1

12 Coleoptera Chrysomelidae 3 100 10 Coleoptera Cerambycidae Trichoferus fasciculatus 1a 100 9 Diptera 4 97.4 9 Lepidoptera Plutellidae Plutella xylostella 1a 100 9 Diptera Culicidae Culex perexiguus 1a 100

8 Lepidoptera Gelechiidae Tuta absoluta 2 100

7 Diptera Psychodidae 3 100 7 Hemiptera Cimicidae Cacodmus vicinus 1a 100 7 Lepidoptera Praydidae Prays citri 1a 100 7 Hemiptera Pentatomidae Nezara viridula 1a 100

6 Diptera 4 97.6 6 Lepidoptera Crambidae Spoladea recurvalis 1a 100

6 Hemiptera Cicadellidae Balclutha incisa 1a 100

6 Coleoptera Anthicidae 3 100

5 Lepidoptera 3 100

5 Lepidoptera Autostichidae Apethistis insulsa 1a 100

5 Lepidoptera Coleophoridae Coleophora therinella 2 98.0 5 Coleoptera Carabidae Ophonus rufibarbis 1a 100 5 Lepidoptera Tortricidae Bactra venosana 2 99.4 5 Lepidoptera Cosmopterigidae Pyroderces argyrogrammos 1a 99.4

5 Diptera Culicidae Culex 3 100

4 Lepidoptera 3 98.8

4 Lepidoptera 3 100

4 Coleoptera Carabidae 3 99.4 4 Lepidoptera Gracillariidae Phyllocnistis citrella 1a 100 4 Coleoptera Dytiscidae Rhantus suturalis 1a 100 3 Lepidoptera Pyralidae Cadra 3 100

3 Lepidoptera 4 97.9 3 Coleoptera Ptinidae Lasioderma serricorne 1a 100

3 Diptera 3 100 3 Diptera Psychodidae Psychoda alternata 1a 100

3 Diptera Limoniidae Gonomyia 3 100

3 Diptera Ulidiidae Physiphora 3 100

3 Diptera Chironomidae 3 100 3 Coleoptera Anthicidae Stricticollis tobias 1a 100

3 Diptera Tipulidae Tipula 3 100

2 Diptera Psychodidae 3 98.7 2 Lepidoptera Crambidae 3 100

2 Neuroptera Chrysopidae Chrysoperla 3 100

2 Lepidoptera Crambidae 3 100

2 Lepidoptera 3 100

2 Lepidoptera 3 98.9 2 Lepidoptera 3 100

2 Lepidoptera 4 97.8

2 Coleoptera 4 97.4 2 Coleoptera 4 97.4 2 Diptera Tipulidae Nephrotoma crocata 1b 98.7

2 Coleoptera Dytiscidae Laccophilus 3 100

2 Hemiptera Miridae Creontiades 3 100 2 Coleoptera Hydrophilidae Cercyon laminatus 1a 100 2 Lepidoptera Pyralidae Trachylepidia fructicassiella 1a 100

2 Diptera Tephritidae Ceratitis 3 100 2 Coleoptera Anthribidae Araecerus fasciculatus 1a 100

2 Lepidoptera Tortricidae 3 99.4 2 Lepidoptera Nolidae Earias insulana 1a 100

2 Coleoptera Mycetophagidae Typhaea 3 100 2 Diptera Muscidae Phaonia inenarrabilis 1b 98.7

2 Lepidoptera 3 100 2 Lepidoptera Crambidae Chilo partellus 1a 100 2 Lepidoptera Crambidae Duponchelia fovealis 1a 100

2 Hemiptera Miridae 3 100 2 Diptera Drosophilidae Drosophila simulans 1a 100

2 Diptera Tipulidae Tipula 3 98.7

2 Lepidoptera Erebidae Pericyma 3 98.7

1 Lepidoptera 3 100

1 Coleoptera 3 99.4 1 Lepidoptera 3 100

1 Coleoptera Carabidae Poecilus 3 100

1 Lepidoptera 3 100 1 Diptera Cecidomyiidae 3 100

1 Diptera Psychodidae 3 100

1 Lepidoptera 3 98.7

1 Coleoptera Ptinidae 3 100

1 Coleoptera Carabidae 3 99.3

1 Lepidoptera 3 100

1 Coleoptera Carabidae 3 100

1 Diptera Drosophilidae Zaprionus 3 100

1 Lepidoptera Noctuidae 3 100

1 Coleoptera Carabidae Selenophorus 3 98.1

1 Diptera Chironomidae 3 100

1 Diptera Psychodidae 3 100

1 Lepidoptera 3 98.9

1 Coleoptera Carabidae 3 98.7

1 Lepidoptera 3 99.0

1 Lepidoptera 3 100 1 Coleoptera 4 97.8 1 Diptera 4 97.7

1 Diptera 4 97.6 1 Hemiptera 4 97.4 1 Mantodea Mantidae Miomantis 3 100

1 Lepidoptera Gelechiidae Stenolechiodes macrolepiellus 1a 100

1 Lepidoptera Apatelodidae Apatelodes torrefacta 1b 98.7

1 Lepidoptera 3 100 1 Araneae Mimetidae Ero quadrituberculata 1a 100 1 Lepidoptera Gelechiidae Palumbina guerinii 1a 100

1 Hemiptera Cicadellidae Balclutha 3 100

1 Diptera Drosophilidae Drosophila 3 100

1 Lepidoptera Pyralidae Phycita diaphana 1a 100

1 Diptera Sepsidae Sepsis 3 100 1 Hemiptera Lygaeidae Nysius cymoides 2 100 1 Diptera Anthomyiidae Delia platura 1a 100 1 Lepidoptera Bedelliidae Bedellia somnulentella 1a 100

1 Hemiptera Cicadellidae 3 98.7

1 Lepidoptera Erebidae Cisthene picta 1b 98.1 1 Diptera Calliphoridae Pollenia pediculata 1a 100 1 Lepidoptera Crambidae Herpetogramma licarsisalis 1a 100 1 Lepidoptera Tischeriidae Tischeria dodonaea 1a 100

1 Diptera Muscidae Musca 3 100

1 Hemiptera 3 100 1 Hemiptera Miridae Deraeocoris punctulatus 1a 100

1 Lepidoptera 3 100 1 Hemiptera Lygaeidae Nysius graminicola 1a 100 1 Coleoptera Scirtidae Cyphon laevipennis 1a 99.4 1 Araneae Araneidae Neoscona subfusca 1a 100

1 Coleoptera Ptinidae Stegobium paniceum 1a 100

1 Diptera Chironomidae Chironomus transvaalensis 1a 100 1 Orthoptera Gryllidae Eumodicogryllus bordigalensis 1a 100 1 Lepidoptera Pyralidae Pyralis farinalis 1a 100 1 Hemiptera Lygaeidae Nysius binotatus 1a 100 1 Lepidoptera Geometridae Gymnoscelis rufifasciata 1a 100 1 Diptera Drosophilidae Drosophila hydei 1a 100 1 Lepidoptera Geometridae Scopula luridata 1a 100

1 Hymenoptera Ichneumonidae Venturia canescens 1a 100

1 Lepidoptera 3 100 1 Lepidoptera Pyralidae Cadra abstersella 1a 100 1 Lepidoptera Erebidae Araeopteron ecphaea 1a 100 1 Lepidoptera Cosmopterigidae Anatrachyntis vanharteni 1a 100 1 Neuroptera Chrysopidae Chrysoperla pudica 1a 100 1 Lepidoptera Momphidae Mompha epilobiella 1b 98.1 1 Lepidoptera Pyralidae Cryptoblabes gnidiella 1a 100 1