Potential associations between the mite Varroa destructor and other stressors in honeybee colonies (Apis mellifera L.) in temperate and subtropical climate from Argentina

Accepted Manuscript Title: Potential associations between the mite Varroa destructor and other stressors in honeybee colonies (Apis mellifera L.) in temperate and subtropical climate from Argentina Authors: Agostina Giacobino, Pacini Adriana, Ana Molineri, Graciela Rodr´ıguez, Paola Crisanti, Natalia Bulacio Cagnolo, Julieta Merke, Emanuel Orellano, Ezequiel Bertozzi, Hern´an Pietronave, Marcelo Signorini PII: DOI: Reference:

S0167-5877(18)30110-7 https://doi.org/10.1016/j.prevetmed.2018.09.011 PREVET 4537

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Received date: Revised date: Accepted date:

9-2-2018 12-9-2018 12-9-2018

Please cite this article as: Giacobino A, Adriana P, Molineri A, Rodr´ıguez G, Crisanti P, Bulacio Cagnolo N, Merke J, Orellano E, Bertozzi E, Pietronave H, Signorini M, Potential associations between the mite Varroa destructor and other stressors in honeybee colonies (Apis mellifera L.) in temperate and subtropical climate from Argentina, Preventive Veterinary Medicine (2018), https://doi.org/10.1016/j.prevetmed.2018.09.011 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Potential associations between the mite Varroa destructor and other stressors in honeybee colonies (Apis mellifera L.) in temperate and subtropical climate from Argentina.

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*Giacobino, Agostinaa*; Pacini, Adrianaa, Molineri, Anaa; Rodríguez, Gracielab; Crisanti Paolab; Bulacio Cagnolo, Natalia c; Merke, Julietac; Orellano, Emanuelc; Bertozzi, Ezequielc; Pietronave, Hernánc; Signorini, Marceloa

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Consejo Nacional de Investigaciones Científicas y Técnicas, Instituto Nacional de

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Tecnología Agropecuaria EEA Rafaela. Address: Ruta 34 Km 227, Rafaela. Postal code:

Instituto Nacional de Tecnología Agropecuaria EEA Hilario Ascasubi, Ruta Nacional 3

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b

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2300, Santa Fe province, Argentina. Phone: +54 3492 440121

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Km 794, Postal Code 8142, Hilario Ascasubi (Buenos Aires), Argentina. Instituto Nacional de Tecnología Agropecuaria EEA Rafaela. Adress: Ruta 34 Km 227,

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Rafaela. Postal code: 2300, Santa Fe province, Argentina. Phone: +54 3492 440121

* Corresponding author e-mail:

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[email protected]; [email protected]

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Co-authors e-mail:

[email protected]

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[email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] 1

[email protected] [email protected]

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Abstract The presence of Varroa destructor in colonies of Apis mellifera is explained by the

interaction among a number of factors including beekeeping practices and surrounding environment features. The aim of this study was to evaluate the relative impact of

environment geographical region and beekeeping management on Varroa infestation

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levels throughout a year. A monitoring study was carried out during 2015 in north-central regions from Argentina, consisting of three sampling dates: 1) autumn survey before

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autumn acaricide treatment; 2) autumn survey after autumn acaricide treatment and 3)

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spring survey. During these visits, we collected samples for Varroa mites and Nosema sp.

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presence assessment and information concerning the apiary management practices

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during each period. Both regional location and beekeeping practices impact on V. destructor infestation level during the course of the year, but relative importance depend

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partially on the time of year when this was observed. Varroa infestation level is driven

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simultaneously by a wide-range of environmental factors (regional effect) and honeybee population dynamics. Additionally, colony life histories are also strongly affected by the

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management practices employed by beekeepers, especially regarding the Varroa mites control and the supplementary feeding. Complexity involving multiple factors interaction in

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socio-ecological systems like beekeeping is discussed.

Keywords: Apis mellifera; Varroa destructor; Environment; Beekeeping;

Introduction 2

The western honeybee (Apis mellifera L.) caught historically scientist and society attention because it is the most widespread managed pollinator (Potts et al., 2016). Thus, as honeybee information is well documented compared to other pollinators it is a suitable study model for the worrying global pollinator decline phenomenon (Biesmeijer et al.,

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2006; Goulson et al., 2015). More significantly, the possible spillover of pests and

pathogens between species (Graystock et al., 2018), particularly from A. mellifera to wild

pollinators (Fürst et al., 2014) or from commercially produced bumblebee colonies to other native and managed pollinators (Graystock et al., 2013), enhance the importance of

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understanding and controlling main drivers for honeybee colony losses.

Potential drivers for declines are grouped into pests and pathogens, environmental

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stressors (including apicultural mismanagement) and lack of genetic diversity (Potts et al

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2010). Additionally, interactions between drivers was proposed as one of the reasons for

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the recently observed honeybee colony losses (Potts et al., 2016).

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The mite Varroa destructor (Anderson and Trueman, 2000) is one of the main threats to

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worldwide apiculture as it was indicated as the leading cause of colony loss reported in numerous studies (Le Conte et al.,2010; van der Zee et al., 2015; Smart et., 2016;

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Steinhauer et al., 2018) and as a promoter of opportunistic viral infections (Tentcheva et al., 2004; Dainat et al., 2012, Kuster et al., 2014). Moreover, as mites reduce bee immune

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responses (Gregory et al., 2005; Abbo et al., 2017) and more importantly modulate several Viruses dynamics, it was suggested that this bee-Varroa-virus complex might lead to

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honey bee colony collapse (Mondet et al., 2016). Simultaneously, numerous factors may promote Varroa mites presence and dissemination including other pathogens presence (Bahreini and Currie, 2015), beekeeping practices and regional location which includes not only temperature and humidity conditions but also land-use, pesticide load, or resource availability (Boecking and Genersch, 2008; 3

Rosenkranz et al., 2010; Giacobino et al., 2014). In order to evaluate these potential interactions we studied the association between Varroa infestation level and secondary drivers like Nosema sp. presence, geographical region, (a combined group of environmental stressors) and main beekeeping management practices.

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A strong association between Nosema presence and Varroa infestation level was found in

different studies (Invernizzi et al., 2011; Mariani et al., 2012; Higes et al., 2013; Little et al., 2016), plus previous research of our own group (Pacini et al., 2016a; Giacobino et al., 2017). In addition, Little et al. (2016) listed a number of hypothesis explaining these relationships including increased susceptibility to Nosema sp. infection following V.

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destructor infestation and vice versa. A negative effect of Nosema sp. infection on

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acaricide treatment efficacy was reported (Botías et al., 2012a) as well as on honeybee

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defense capability against V. destructor (Bahreini and Currie, 2015).

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The influence of different geographical regions on mites levels might be explained by climate conditions that impact directly on Varroa population dynamics (García Fernández

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et al., 1995; García Fernández, 1997; Kraus and Velthuis, 1997; Moretto et al., 1991) or

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indirectly via the host including honey bee brood amount and the defensive behavior of the bees (Currie and Tahmasbi, 2008; Rosenkranz et al., 2010). In addition, a pollen rich diet

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can compensate the deleterious effects of mite parasitization (Annoscia et al., 2017); nevertheless, in intensive agricultural systems there are large temporal variations in the

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availability (quantity, quality and diversity) of nutritional resources (Di Pasquale, et al 2016). Moreover, regarding honeybee exposure to neonicotinoid insecticides, Blanken et

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al. (2015) revealed that the interaction between V. destructor and Imidacloprid reduces the flight capacity of honeybees. The beekeepers normally managed the colonies in order to improve their annual honey yield. Managed colonies involves crowding bees into apiaries and use beekeeping practices that may facilitate mite transmission (Seeley and Smith, 2015; Peck et al, 2016) 4

which is revealed in the within-colony V. destructor diversity (Dynes et al., 2017). Strauss et al. (2016) hypothesized that mite populations grow in South African bees might be slower than in other countries given the absence of acaricide use. Besides chemical control, splitting the colonies when making nucleus may remove approximately one third of

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the Varroa mites in the parent colony (Charrière, et al., 2001). In addition, supplementary feeding management in the colonies especially during the autumn and before winter

season is crucial since malnutrition is probably one of the causes of immunodeficiency in honeybee colonies. (Alaux et al., 2010). In previous studies, we found that the kind of protein and carbohydrate diet, as well as monitoring the colonies and woodenware

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disinfection, help keeping lower Varroa infestation level during early autumn (Giacobino et

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al., 2014). Similarly, apiaries where queen replacement was performed, the risk of

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achieving an increased percentage of Varroa in late autumn and spring was lower

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(Giacobino et al., 2015; 2016a). Requeening is a key feature associated with global honeybee health (Tarpy et al. 2000; Invernizzi et al. 2006; Schneider and DeGrandi

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Hoffman 2008; Botías et al. 2012b). There is additional work needed by a beekeeper to

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compensate unanticipated environmental problems that might overcome and varies greatly between regions. In this sense, along with beekeeping practices, beekeepers background

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and operation size are strongly associated to colony losses (Kulhanek et al., 2017,

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Jacques et al., 2017).

We presumed that all these associations are no constant throughout the honey productive cycle (from autumn to the following spring) as honeybee colonies go through yearly cycles

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and both beekeeping management and the “regional effect” fluctuate seasonally. As well, colony demographics and specifically brood production and nutritional needs change throughout the year (DeGrandi Hoffman and Chen, 2015). The goal of this study was to evaluate the relative impact of the Nosema sp. presence, geographical region and beekeeping management on Varroa infestation levels recognizing (a) the main factors 5

affecting V. destructor infestation in honeybee colonies before and after acaricide treatment just before winter (b) the key drivers associated with spring V. destructor infestation in the same honeybee colonies.

Study design and description of the apiaries distribution

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Materials and Methods

A monitoring study was carried out from late February to early November 2015 in north-

central Argentina. Three sampling dates were set: 1) February-June autumn survey before autumn acaricide treatment (autumn BT from now on); 2) April-July autumn survey after

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autumn acaricide treatment (autumn AT from now on) and 3) September-October spring

survey. The overlapping of the sampling periods BT and AT is because the treatment date

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is highly influence by apiaries location, and some regions treat their colonies before others.

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The monitoring visit AT was planned 40-45 days after acaricide treatment beginning in

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those apiaries that were treated. It is important to highpoint that no all beekeepers treat

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their colonies, thus in these cases we follow the same visit schedule for nearby treated

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apiaries in order to have comparable results according to regional sampling dates. During these visits the honeybee inspectors (i) collected, by means of a questionnaire,

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detailed information about the apiary management practices during each period, (ii) took samples of honeybees for Nosema spp. and V. destructor assessment, and (iii) estimated

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visually the number of adult bees and number of cells with sealed brood, pollen, and

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honey according to the Liebefeld method (Imdorf and Gerig, 2001). Ninety-nine apiaries, owned by different beekeepers, were randomly chosen following stratified randomization procedures (computerized random numbers) (Moher et al., 2010).

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At the beginning of the study, six colonies were randomly selected within each apiary. If a colony collapsed in the course of the study, it was not replaced. The six geographical region to evaluate the “regional effect” were defined according to temperature and humidity of the phytogeographical regions of Argentina, (described in

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Burkart et al., 1999; Arzamendia and Giraudo, 2004). Additionally, we described the main land-use conventionally performed in each of these regions (Giorgi et al., 2008; Riveros, 2009) (Table 1; Figure 1). Lastly, we assessed the surrounding vegetation variable

(resource availability) by observational description of the landscape in the close vicinity of each apiary. We identified the presence of crops, forest or grassland or a combination of

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them in the surrounded area (2-3 km). We did not measured the pesticide load by itself,

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but we assumed that apiaries surrounded at least partially by cultivated areas had more

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risk of pesticide exposure than those surrounded by grassland or forest.

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Questionnaires describing beekeeping effect

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Autumn BT survey: participating beekeepers answered a questionnaire that included

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questions with reference to number of colonies, carbohydrates and protein diets, monitoring of mite levels in the colonies measured by the beekeepers, queen replacement,

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making nuclei, colonies migration and treatment against Varroa mites (if regularly treat their colonies or not). Autumn AT survey: during the second visit, beekeepers provided

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additional information concerning particular treatment concept during autumn 2015 (product, date, etc.). Spring survey: During the last visit, we asked beekeepers about

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winter management. Depending on the geographical region, the date for the spring evaluation varied between the apiaries, setting this visit immediately after nectar flow begins. A complete list of the explanatory variables derivate from all surveys and field data collection is available as supplemental material.

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Field data collection Adult honeybees were examined to diagnose the presence of Varroa mites in all the selected colonies. In each colony, approximately 250 honeybees were collected from both sides of three unsealed brood combs in a jar containing 50% ethanol. The mites were

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separated from the honeybees by adding a drop of soap, shaking for 20 s and pouring the jar content into a sieve with a mesh size of 2 mm (Dietemann et al., 2013). The mean

abundance of V. destructor on adult honeybees was calculated dividing the number of

mites counted by the number of honeybees in the sample to determine the proportion of

infested individuals and multiplying by 100 to obtain the percentage of infestation on adult

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bees per colony (Dietemann et al., 2013).

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In order to diagnose the abundance of Nosema spp. in all colonies, worker foragers

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honeybee samples were collected from the hive entrance (temporarily blocked) using a

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portable vacuum device (vacuum device was only used to speed up field sampling without

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damaging the bees). A minimum of 60 honeybees were collected and placed in labeled plastic flasks containing 60 ml of 96° ethanol. Spore suspensions were prepared by adding

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60 ml of distilled water to crushed abdomens of 60 randomly selected individuals of each

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colony. Nosema spp. spores/bee (transformed to log10) were determined using light microscopy 40X and haemocytometer. For each sample, the number of spores in 80

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haemocytometer squares (5 groups of 16 squares) was countered (Cantwell, 1970; Fries et al., 1984).

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Statistical Analysis The percentage of infestation with V. destructor per colony was obtained for the three season samples. Univariate analysis (with apiary as random effect) was conducted for selecting explanatory variables potentially associated with autumn BT, autumn AT and Spring level and those having P-value ≤ 0.15 were selected for multivariable analysis 8

(Dohoo et al., 1996). Only the explanatory variable with the lowest P-value was selected for the multivariate model when two of them may have explained similar results and were statistically associated (collinearity evaluation (Dohoo et al., 2003). Since we collected data on grouped colonies (apiaries) and the unit of analysis was the

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colony, we adjusted a final mixed-effects gamma-regression model with apiary as the random effect to evaluate the effect of the selected explanatory variables on: 1) the

autumn BT percentage of infestation with Varroa mites on adult bees 2) the autumn AT percentage of infestation with Varroa mites on adult bees 3) the spring percentage of

infestation with Varroa mites on adult bees. In all three models, a manually conducted

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backward elimination strategy was followed by removing one variable at a time with the

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highest P-value. With each variable removed from the model, the coefficient of significant

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variables was checked and if it resulted in more than 20% change in estimates, the

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variable was retained in the model to account for its confounding effect (Chowdhury et al., 2012). A Pearson chi square test (both categorical predictors) or Kruskal Wallis test (one

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continuous predictor and one categorical predictor) were performed with the aim to

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establish the relation between the significant variables and the confounded variables

Results

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included in the final models.

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From the 594 colonies (99 apiaries with 6 colonies each) originally proposed we obtained 560 samples well conserved for Varroa destructor analysis. Colony size and reserves

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condition along with diseases information per geographical region and per season are presented in Table 2. The mean V. destructor infestation in the colonies during autumn BT was 8.53 ± 9.80 % (n= 560) and during autumn AT was 1.58 ± 3.90 % (n= 523). Same colonies showed 1.06 ± 1.89 % (n= 444) Varroa infestation during following spring. Varroa infestation during autumn before acaricide treatment 9

Five out of the 22 potential explanatory variables were selected for the probable association with autumn BT infestation (selected variables had a significance value P< 0.15; Table 3). Geographical region, colony size and some management practices were significantly linked with Varroa infestation in the univariate analysis (Table 3). The final

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multivariable model revealed that geographical region and colony size (number of adult

bees in the colony) were associated with the infestation of V. destructor before acaricide treatment (Table 4). On the one hand temperate regions like south Buenos Aires and

central Santa Fe had the highest percentages of Varroa infestation (P= 0.003; Table 4). On the other hand, colonies with high levels of Varroa infestation were smaller than

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colonies with low levels (P= 0.045; Table 4). Geographical region confounded the effect of

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the variable “Carbohydrate diet period” therefore the last was retained in the model.

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Beekeepers from Santa Fe regions (south and central) and south Buenos Aires mentioned

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the requirement of feeding the colonies during autumn and other moments, whilst most of beekeepers from Chaco regions just fed their colonies during autumn exclusively (χ2 =

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275.10; P< 0.001). The apiary random-effect was significant (P< 0.001).

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Varroa infestation during autumn after acaricide treatment

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Eleven out of the 27 potential explanatory variables tested were selected after the univariate analysis to be included in the multivariable model (selected variables had a

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significance value P< 0.15; Table 5). However, as the variables “regular autumn treatment”, “acaricide product autumn 2015” and “treatment concept autumn 2015” were

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autocorrelated (P< 0.001); only “regular autumn treatment” was included in the final model (because it had the lowest P-value). Backward model started with nine variables and the final multivariable model revealed three variables associated with AT Varroa infestation level (Table 6). As expected, low levels of Varroa infestation were detected when a regular autumn treatment is employed in

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the apiary (P= 0.035). In addition, the efficacy of this treatment depended on the brood cells in the colonies when treatment began (P= 0.018) (Table 6). Although Nosema sp. abundance BT was significantly correlated with Varroa infestation level AT (P= 0.053) the size of this effect is technically null (calculated estimate is close to zero).

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Additionally, the variable “regular autumn treatment” confounded the effect of the following

variables retained in the model: geographical region, experience of beekeeping and apiary location. For instance, 40%, 15% and 13% of the beekeepers from semi-arid, transition

and humid Chaco, respectively, declared not to treat their colonies. On the contrary, 100% from south Santa Fe and Buenos Aires and 94% from central Santa Fe declared to treat

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regularly their colonies (χ2 = 100.43; P< 0.001). Besides, 75% of the not treated apiaries

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are owned respectively by less experienced beekeepers (χ2 = 37.04; P< 0.001). Apiary

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location was indirectly associated to Varroa infestation after treatment, because apiaries

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that are not regularly treated were casually located under perennial tress (89%; χ2 =77.77; P< 0.001). Other management variables retained in the model (queen replacement and

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colony splitting) are as well strongly associated to the regular acaricide treatment. Most of

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the beekeepers that declared to split their colonies also treat them regularly (91.5%; χ2 =12.88; P< 0.001) and 93.7% of the beekeepers that regularly treat their colonies replaced

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frequently their colonies queens (χ2 =11.21; P=0.001). Apparently, spurious associations were found between Varroa infestation levels AT and colony disinfection as despite being

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selected in the univariable analysis they were not significant in the multivariable model neither were linked to the finally significant variables. The apiary random-effect was

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significant (P< 0.001). Varroa infestation during spring Twenty out of the 31 potential explanatory variables tested were selected after the univariate analysis to be included in the multivariable model (selected variables had a significance value P< 0.15; Table 7). However, the variables “regular autumn treatment” 11

“acaricide product autumn 2015” and “acaricide treatment concept autumn 2015” were associated (P< 0.001). Therefore, only the “Acaricide product autumn 2015” was included in the final model as it has the lowest P-value. Similarly, only geographical region was included in the final model as it was associated with surrounding vegetation (P< 0.001).

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Mostly forest and crops or only crops surrounded apiaries from Santa Fe regions (south and central). On the contrary, 50% up to 85% of apiaries from south Buenos Aires and

Chaco regions (humid, transition and semi-arid) were located near forest and grassland vegetation.

Backward model started with 17 variables and the final multivariable model revealed four

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variables significantly associated with the Varroa infestation level during spring:

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Geographical region (P= 0.002); brood cells autumn AT (P= 0.001); Varroa infestation

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autumn BT (P= 0.003); and Varroa infestation autumn AT (P= 0.001) (Table 8). The

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geographical region confounded the effect of the adult honeybee population autumn BT since colonies from Semi-arid Chaco were smaller than the rest of the colonies (Kruskal

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Wallis 56.47; P< 0.001). Similarly, the effect of the carbohydrate diet (Sucrose

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syrup/Honey or HFCS) was also confounded by the regional effect since only some beekeepers from Central Santa Fe (11.8%) fed their colonies with HFCS (χ2 =57.30; P<

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0.001). Therefore, both variables were retained in the model despite the fact that they are

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not significant. The apiary random-effect was significant (P< 0.001). Discussion

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The land use and climate change, as well as exposure to pesticides and the management of pollinators and pathogens are the main anthropogenic factors associated with the decline of pollinators (reviewed in Potts et al., 2016). Here, we analyzed how some of these drivers are associated with each other. A better understanding of the interactions between multiple drivers is key for a realistic approach to the study of honeybee losses

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and other pollinator declines. Additionally, this sort of studies are the basis to define management options and control policies in food production. Since V. destructor is a main threat to honeybee colonies, we studied its association with

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other suggested stressors, including Nosema sp abundance, geographical region, and apicultural mismanagement. Both region and beekeeping practices impact on infestation

level during the course of the year. However, it seems that the relative importance of one or the other depend on the moment when this is observed.

The percentage of Varroa infestation at the beginning of the autumn before treatment was

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strongly influenced by geographical region and colony size. Regions characterized by

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longer nectar/pollen flows and a more diverse landscape had less Varroa mites than

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regions with short nectar/pollen flow and higher pesticide pressure (inferred from crops

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predominance). Similar results were found previously when Chaco and Santa Fe regions were compared (Giacobino et al., 2017). Including a more contrasting area in a cold

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temperate point (south Buenos Aires) just increased that difference. We found that during

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autumn and before the acaricide treatment, the smaller the colony the higher the infestation. On the one hand, the presence of high percentage of Varroa mites may

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damage honeybee adult population reducing the colony size (Amdam et al., 2004; Guzmán-Novoa et al., 2010; Rosenkranz et al., 2010). On the other hand, the Varroa

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population grows during spring and summer as the colony does, but in the autumn (when less brood is available) the large Varroa population causes high infestation rates

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(DeGrandi Hoffman and Chen, 2015). Both variables, geographical region and colony size influence the parasitism dynamics during autumn season. The mite population strongly depends on the colony development, which is closely associated to environmental conditions (Meixner et al., 2015).

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Similar to previous results, in this study we found that Nosema sp. abundance was associated with the percentage of mite infestation (Little et al., 2016; Pacini et al., 2016a; Giacobino et al., 2017). Still, the size of this effect on Varroa infestation AT was technically null probably because is more likely that Varroa infestation increase Nosema sp

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abundance than vice-versa.

Varroa regular treatment and number of brood cells BT (at the moment of the treatment application) are key to prevent high infestation level before winter season (autumn AT),

independently of the region. It is already known that successful chemical control of Varroa

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in the colonies is essential in temperate climates to ensure winter survival (Giacobino et

al., 2015, Smart et al., 2016). Unexpectedly, it seems also key in subtropical climate like

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Chaco regions as no geographical effect was detected on Varroa levels at the end of the

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autumn season (AT survey). On the one hand, contrariwise to presume, brood cells BT

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were not associated to different climatic regions as south Santa Fe (temperate) showed

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the highest amount of sealed brood cells followed by Humid and Transition Chaco. However, most regions (excepting semi-arid Chaco) showed a reduction of the 50% of the

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sealed brood cells from autumn BT to autumn AT. Semi-arid Chaco exhibited similar

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Varroa infestation level and sealed brood cells in both monitoring dates (before and after the treatment). Still, Varroa infestation AT was one of the highest compared to other

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regions, essentially because half of the beekeepers did not treat their colonies. On the other hand, south Santa Fe with the exceptionally high amount of sealed brood BT and AT

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(compared to other temperate regions) showed more than 1% of Varroa infestation AT despite all the beekeepers treat their colonies. Greater areas of pupating brood in a colony in autumn could contribute to higher late season mite loads (Smart et al., 2016). Treatment concept (timing and product) against Varroa combined to the brood presence might be key even for regions when environment is more “bee friendly” like Chaco subtropical regions.

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Moreover, there is a significant interaction between brood availability and Varroa inbreeding to consider when designing acaricide application schemes in apicultural management (Martin, 1998; Wilkinson and Smith, 2002; Rosenkranz et al., 2010;

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Beaurepaire et al., 2017). In the final model for Varroa infestation AT the regular acaricide treatment variable

confounded the effect of secondary factors, mainly other beekeeping practices. This is probably because more experienced beekeepers adhere to a management program. Experienced beekeepers keep the Varroa levels tolerable during autumn by regular

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treating the colonies (Kulhanek et al., 2017) along with the performance of subsidiary

practices like splitting the colonies and queen replacement (Giacobino et al. 2014). More

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importantly, the regions showed different Varroa infestation levels AT but it was due mostly

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to the proportion of beekeepers that regularly treat the colonies as regular acaricide

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treatment is more frequently performed in the temperate regions (south Buenos Aires and

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central and south Santa Fe) than in Chaco subtropical regions.

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Varroa infestation during spring was associated with mites loads previously registered in the colonies (BT and AT), the presence of brood cells during autumn AT and the

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geographical region where colonies were located. On the one hand, overwintering with relative high levels of Varroa infestation AT combined with the presence of brood cells

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during a “soft” winter might result in larger mite loads at the beginning of the spring. On the other hand, here we found that geographical region was also a main driver for mite

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infestation in BT (and colony size) and a secondary driver for Varroa levels AT. Thus, colony dynamics, driven by environmental conditions, have a significant influence on Varroa infestation rates (Meixner et al 2015). In addition, carbohydrate diet (secondary driver) was associated with spring Varroa infestation because it was linked to the geographical region (as we previously mentioned) 15

and because it has an impact on the Varroa infestation level in earlier stages of the year (Giacobino et al., 2014; 2015; 2017). Similarly, the colony size in early autumn is another secondary driver for spring Varroa infestation as it was strongly associated with Varroa infestation in autumn BT, which is a main driver for spring loads. Thus, there is a complex

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interaction between geographical region and autumn nutritional management (Döke et al., 2015) that influence the Varroa mites loads during following spring. The meaning of the regional effect

Colonies survival is associated with multiple factors including climate (temperature and

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humidity), floral resources available according to land use, pathogen infestation pressure,

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and colony management all connected lastly to the apiary location (Büchler et al., 2014;

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Jacques et al., 2017). Additionally, temperature, humidity, and foraging conditions had a

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significant effect on honeybee defensive performance like hygienic (Moretto et al., 2006) and grooming behavior (Currie and Tahmasbi, 2008). Moreover, the “geographical factor”

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(region) comprises many practices associated with land use (e.g. pesticides and

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herbicides application) that have negatively affected beekeeping in the last years (Vandame and Palacio, 2010; Van der Sluijs et al., 2013). Thus, the regional effect is a

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combined group of stressors that compromised by themselves honeybee colonies survival

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but that are also associated with the presence of the main threat that is V. destructor. Beekeepers contribution

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Human actions are contributing factors to spread honeybee pathogens and intensify colony declines (Owen, 2017). Feeding the colonies properly, monitoring mite levels after treatment, and disinfecting the woodenware of hives are key practices for keeping lower V. destructor infestations (Giacobino et al., 2014). Additionally, according to Jacques et al.

16

(2017), key aspects preventing colony losses are linked to beekeeper background and management practices, like queen replacement (Giacobino et al., 2016b). Lastly, one question remaining is the relative importance of Africanized honeybee

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distribution within the environmental/regional effect on Varroa infestation rates as northern regions are closer to Brazil (De Jong, 1984; Rosenkranz, 1999; Abrahamovich et al.,

2007). The lineage C (C1 and C2j haplotypes) is predominant at the south and the African lineage A (A1 and A4 haplotypes) is predominant at the north, but both genotypes were

observed in A. mellifera populations from Argentina (Whitfield et al., 2006). In this study,

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we did not analyze the genotypes/haplotypes of the colonies sampled. However, Pacini et al. (2016b) found that samples from temperate regions were mainly from European-

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derived haplotypes while samples from subtropical regions were largely from African

A

derived haplotypes. In addition, these results are in line with studies from Brazil and

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Uruguay that showed that A1 haplotype is more prevalent in the north of Brazil while the

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frequency of A4 is higher in populations from Central and Southern Brazil and Uruguayan samples and C1 is present in the southern populations (Diniz et al., 2003; Collet et al.,

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2006). Though, the interaction between different lineage genotypes and the environment

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has not been evaluated yet. On the one hand, a strong environmental effect on Varroa loads was suggested in previous European and African studies (Meixner et al., 2014; Muli

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et al., 2015). On the other hand, as African/Africanized bees maintain mite numbers at low levels in their colonies (Strauss et a., 2014; Strauss et al., 2015) and tolerate other

A

diseases (Dietemann et al., 2009; Human et al., 2011; Strauss et al., 2013), haplotypes distribution might explained at least partially the differences in Varroa loads between Chaco (subtropical) regions and Santa Fe (temperate) regions. Moreover, Strauss et al. (2016) observed, in savannah honeybee colonies (A. mellifera scutellata), a strong sign for mite resistance in surviving bee populations since the reduced population growth was due

17

to the low reproduction of Varroa mites. This resistant trait, along with other factors including genetic diversity, allow that honeybee populations in Africa to tolerate the impact of global stressors like V. destructor presence (Pirk et al., 2016). Then again, further studies should be conducted, especially in Latin America, where A. mellifera genotypes

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might be differently distributed.

Here we found that main drivers for the Varroa infestation levels in honeybee colonies all

the way through the year varied between early autumn (BT), late autumn (AT) and spring. As previously revealed, the colony size and brood rearing, driven by environmental

conditions are key for mite reproduction and population growth during autumn and at the

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beginning of the spring (Meixner et al., 2014; DeGrandi-Hoffman and Chen, 2015).

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However, along with the brood rearing in autumn, chemical control before winter is a main

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driver for mite infestation level in late autumn. In addition, a number of secondary drivers

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(mainly beekeeping management practices) also contributed to maintain low levels of infestation AT and therefore to improve overwintering (Döke et al., 2015).

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Honeybee colonies located in a more favorable environment like Chaco regions have great

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potential for developing apiculture activities if they adapt and improve their beekeeping management strategies. On the other hand, places like Central and South Santa Fe

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depend exclusively on the efficiency of the management strategies applied since there is a

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hostile environment during most of the year. Conflicts of interest statement

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There are no conflicts of interest to be declared. Acknowledgements This study has been carried out with the financial support of the PNAPI Project Nº 1112042 and Specific Project Nº 1112042 “Estrategias multidisciplinarias para mitigar el 18

efecto del nuevo contexto ambiental y productivo sobre la colmena”, Instituto Nacional de Tecnología Agropecuaria. This research was also supported by a grant of ANPCyT, PICT 2016-1568. Agostina Giacobino and Ana Molineri are posdoctoral fellows and Adriana Pacini is doctoral fellow from the Consejo Nacional de Investigaciones Científicas y

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Técnicas (CONICET, Argentina). Dr. Marcelo L. Signorini is a Research Career Member from the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET,

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A

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Argentina)

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to infestation by the parasitic mite Varroa destructor. Parasitology 143, 374–387. Tarpy, D.R., Hatch, S., Fletcher, D.J.C., 2000. The influence of queen age and quality during queen replacement in honeybee colonies. Anim. Behav. 59, 97-101. Tentcheva, D., Gauthier, L., Zappulla, N., Dainat, B., Cousserans, F., Colin M.E., Bergoin, M., 2004. Prevalence and Seasonal Variations of Six Bee Viruses in Apis mellifera L. and 29

Varroa destructor Mite Populations in France. Appl. Environ. Microbiol. 70 (12), 7185– 7191. Vandame, R., Palacio M.A., 2010. Preserved honey bee health in Latin America: a fragile equilibrium due to low-intensity agriculture and beekeeping? Apidologie 41, 243-255.

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Van der Sluijs J.P., Simon-Delso N., Goulson D., Maxim L., Bonmatin J.-M., et al. 2013 Neonicotinoids, bee disorders and the sustainability of pollinator services. Curr. Opin. Environ. Sustain. 5, 293–305.

van der Zee, R., Gray, A., Pisa, L., de Rijk, T., 2015. An Observational Study of Honey

Bee Colony Winter Losses and Their Association with Varroa destructor, Neonicotinoids

U

and other risk factors. PLoS ONE 10(7): e0131611. doi:10.1371/journal.pone.0131611.

N

Whitfield, C.W., Behura, S.K., Berlocher, S.H., Clark, A.G., Johnston, J.S., et al., 2006.

A

Thrice Out of Africa: Ancient and Recent Expansions of the Honey Bee, Apis mellifera.

M

Science 314 (5799), 642-645.

Wilkinson, D., Smith, G.C., 2002. A model of the mite parasite, Varroa destructor, on

D

honeybees (Apis mellifera) to investigate parameters important to mite population growth.

A

CC

EP

TE

Ecological Modelling 148, 263-275.

30

Figure Captions

Figure 1. Apiaries location and distribution according to geographical regions of Argentina. 1) Map of Argentina with all the study regions included. 2) Detailed distribution of the

SC RI PT

apiaries from Semi-arid Chaco (grey circles), Transition Chaco (black stars), Humid Chaco (black squares), central Santa Fe (white circles) and south Santa Fe (black circles).3)

A

CC

EP

TE

D

M

A

N

U

Detailed distribution of the apiaries from south Buenos Aires (black triangles).

31

Table 1. Region characterization based on annual mean temperature and precipitation, land use and floral resources. Annual temperature (ºC)

Region

Annual precipitation (mm)

Climate

Main Land use

Nectar/Pollen Flow

Intermediate (three –four months)

15

600

Cold temperate

South Santa Fe

18

600-1100

Temperate

Soy, corn, and wheat

Central Santa Fe

17-18

800-900

Temperate

Humid Chaco

23

> 1200

Sub-tropical without dry season

TE

23

550-800

Sub-tropical with dry season

Semi-arid

U

Forest production

Short (less than three months) Intermediate (three –four months) Long (between 9 and 10 months) Long (between 9 and 10 months) Long (between 9 and 10 months)

A

CC

EP

Semi-arid Chaco

Dairy farms and wintering animals on alfalfa pastures Small farmstead, livestock or forest and rice production Cereals, oleaginous, and cottonseed crops mixed with livestock production

N

A < 1000

M

23 to 24

D

Transition Chaco

SC RI PT

South Buenos Aires

Rape, sunflower and onion crops mixed with livestock production on alfalfa pastures

32

Table 2. Regional data for Varroa infestation level, Nosema spp. abundance and colony strength estimation (adult bee population, number of brood, honey and pollen cells) during the three sampling periods (2015) in Apis mellifera colonies from Argentina.

Parameter

South Santa Fe (48 colonie s)

Central Santa Fe (102 colonie s)

Humid Chaco (77colo nies)

Transiti on Chaco (70 colonie s)

Semiarid Chaco (63 colonie s)

SC RI PT

Mom ent

South Buenos Aires (200 colonie s)

18365± 3139

18988± 4284

20101± 2534

18119± 3411

19862± 2946

16895± 3751

Brood*

19317± 9842

32769± 12450

22804± 17740

30018± 11759

27355± 10767

22715± 10323

Pollen*

6805± 4025

4877± 3614

2498± 2759

5893± 3954

9328± 5939

8195± 5211

Honey*

34322± 13151

19779± 8598

14507± 10808

21317± 8054

28160± 13138

20130± 10083

% Varroa

11.06 ± 11.09

8.08 ± 9.97

10.31 ± 11.58

5.66 ± 3.82

7.16 ± 7.99

3.08 ± 2.79

Nosema spores/bee

396259± 748132

992482 ± 1572635

624125 ± 1814140

46193 ± 263546

75873 ± 232626

79999 ± 354330

14965± 3634

16610± 4571

13816± 4196

17416± 3112

11122± 4680

15950± 3763

8856± 7579

19060± 10079

9500± 9380

16375± 11656

13454± 11004

20916± 7960

3379± 4157

2330± 2488

5892± 5085

5553± 3637

5815± 5375

21208± 11920

18543± 12026

27853± 11273

35200± 11626

20916± 11227

1.85 ± 3.63

0.81 ± 2.69

3.10 ± 5.20

0.99 ± 1.85

2.74 ± 3.41

CC

Brood*

Autumn (AT)

A

Pollen*

Honey*

% Varroa

Not measure d Not measure d 1.19 ± 4.31

N

A

M

D

EP

Adult bee population

U

Adult bee population

TE

Autumn (BT)

Mean ± SD

33

1003974 ± 2370582

931951 ± 1848523

66025 ± 206846

153181 ± 445872

1388019 ± 4491399

Adult bee population

15576± 4406

14085± 3054

15561± 4776

15109± 4805

17966± 2924

18660± 2800

Brood*

25790± 13284

20862± 8491

25118± 15163

34867± 14664

21120± 6296

29668± 12868

Pollen*

16205± 11518

2807± 1912

2515± 2471

4914± 2976

9386± 3635

6411± 4698

Honey*

4036± 5007

17565± 7008

8363± 7135

11423± 10489

26986± 5299

17977± 9403

% Varroa

0.25 ± 0.6

1.73 ± 2.54

0.53 ± 1.36

2.26 ± 2.73

1.56 ± 1.90

2.12 ± 1.97

Nosema spores/bee

1250177 ± 2473499

440284 ± 760335

672228 ±111967 0

32826 ± 89620

122065 ± 362372

U

SC RI PT

Not measure d

1810911 ± 4629927

N

Spring

Nosema spores/bee

A

CC

EP

TE

D

M

A

*Cells

34

Table 3. Explanatory variables evaluated for potential association with Varroa destructor infestation level during 2015 autumn season before acaricide treatment (gamma

Carbohydrate diet period

Central Santa Fe

102

Humid Chaco

77

Transition Chaco

70

Semi-arid Chaco

63

South Buenos Aires No

200

Yes

536

No

193

Heat

8.08±9.97

10.31±11.58 5.66±3.82

P< 0.001

7.16±7.99 3.08±2.79

11.06±11.09 6.53±6.57

U

24

8.62±9.92

0.013

7.37±10.14

314

9.76±10.12

47 152

5.70±3.87 4.70±5.09

Other plus autumn

396

9.78±10.60

Continuous

560

-------

Chemical Autumn

P-Value

0.014

0.001 0.037

TE

Adult bee population autumn BT

48

N

Colony disinfection

Mean (S.D)

A

Splitting colonies

N

M

Geographical region

Level South Santa Fe

D

Variable

SC RI PT

distribution) in Apis mellifera colonies from Argentina.

A

CC

EP

BT: before autumn acaricide treatment

35

Table 4. Final multivariable logistic regression model (backward selection) for Varroa destructor infestation level during 2015 autumn season before acaricide treatment (gamma distribution) in Apis mellifera colonies from Argentina.

effects

Geographical region

5.175

Level

Estimate

0.403-0.860

<0.001

95% IC

P-Value

South Buenos Aires

-0.176

South Santa Fe

0.022

Central Santa Fe

-0.192

Humid Chaco

0.173

Transition Chaco

-1.229

-1.911-(-)0.545

------

------

-0.343

-0.905-0.219

------

------

-0.063

-0.124-(-)0.001

Autumn Carbohydrate

D

(Ref)

Adult bee

TE

Other plus

population

Continuous

EP

autumn(Ref.)

-0865-0.514

-0.487-0.630

M

Semi-arid Chaco

diet period

P-value

SC RI PT

Fixed

0.599

95% CI (1)

U

Apiary

Z

N

effect

Estimate

A

Random

-0.989-0.504 -0.654-1.001

0.003

0.232

0.045

CC

autumn BT

(1) 95% confidence interval

A

BT: before autumn acaricide treatment

36

Table 5. Explanatory variables evaluated for potential association with Varroa destructor infestation level during 2015 autumn season after acaricide treatment (gamma distribution) in Apis mellifera colonies from Argentina. Level South Buenos Aires South Santa Fe

N

40

1.85±3.63

Central Santa Fe

100

0.81±2.69

Humid Chaco

78

3.10±5.20

Transition Chaco

66

0.99±1.85

Semi-arid Chaco

52

2.74±3.41

Beekeeping experience

≤ 10 years

163

2.03±3.78

> 10 years

292

Queen replacement

No

162

Yes No

361

P-Value

SC RI PT

187

U

1.19±4.31

1.29±3.96

N

Geographical region

Mean (S.D)

A

Variable

2.32±5.01 1.25±3.24

0.008

0.065 0.097

23

5.40±6.31

523

1.41±3.67

182

1.68±3.77

291

1.18±3.84

Chemical

44

3.76±4.39

Direct sun

174

1.05±3.61

Perennial trees

180

2.44±4.26

No perennial trees

157

0.72±1.84

Mixed

12

7.73±10.25

No

40

4.92±4.85

Yes

477

1.27±3.69

Nosema sp. abundance BT

Continuous

523

--------

0.057

Brood cells autumn BT

Continuous

523

--------

0.012

No

4.72±3.89

Amitraz

35 233

Piretroids

196

1.12±2.70

Organics

40

3.58±5.95

No

35

4.72±3.89

Splitting colonies

M

Yes No Colony disinfection

D

TE

EP

Apiary location

Heat

A

CC

Regular Autumn treatment

Acaricide product autumn 2015

1.28±4.16

0.007

0.025

0.071

0.005*

0.013*

0.045* 37

Acaricide treatment concept autumn 2015

Recommended

290

1.38±3.71

Non recommended

185

1.42±4.08

BT: before autumn acaricide treatment

A

CC

EP

TE

D

M

A

N

U

SC RI PT

* Collinearity between regular autumn treatment, acaricide product and treatment concept autumn 2015.

38

Table 6. Final multivariable logistic regression model (backward selection) for Varroa destructor infestation level during 2015 autumn season after acaricide treatment (gamma

Random effect Estimate

Z

95% CI (1)

Apiary

4.085

4.805

2.717-6.143

<0.001

Fixed effects

Level

Estimate

95% IC

P-Value

Continuous

0.154

0.026-0.283

0.018

No

3.543

0.257-6.829

Yes (Ref.)

------

------

Continuous

<0.001

Beekeeping

≤ 10 years

-0.018

experience

> 10 years (Ref.)

Queen

No

M

distribution) in Apis mellifera colonies from Argentina.

autumn BT Regular

SC RI PT

Brood cells

0.035

replacement Splitting

Colony

A

CC

disinfection

Apiary location

Nosema sp. abundance

N

A

0.805 ------

0430

-0.741-1.602 0.471 ------

No

0.547

-3.193-4.267

Yes (Ref.)

------

------

No

-0.873

-3.105-1.359

Heat

-1.904

-4.254-0.446

Chemical (Ref.)

------

------

Direct sun

2.082

-0.989-5.153

Perennial trees

-0.145

-1.798-1.508

No perennial trees

0.671

-0.876-2.018

Mixed (Ref)

------

------

Continuous

<0.001

-0.001-0.001

Yes (Ref.)

0.053

-0.068-0.051

------

EP

colonies

-0.001-0.001

------

D

abundance

TE

Nosema sp.

U

Autumn treatment

P-value

0.774

0.265

0.433

0.053

39

region

-1.716

-4.455-1.023

-1.440

-3.381-0.510

1.328

0.993-3.850

Humid Chaco

-2.245

-5.572-1.083

Transition Chaco Semi-arid Chaco (Ref.)

-0.007

-2.597-2.583

------

------

0.133

SC RI PT

Geographical

South Buenos Aires South Santa Fe Central Santa Fe

(2) 95% confidence interval

A

CC

EP

TE

D

M

A

N

U

BT: before autumn acaricide treatment

40

Table 7. Explanatory variables evaluated for potential association with Varroa destructor infestation level during 2015 beginning of spring season in honey bee colonies (gamma distribution) in Apis mellifera colonies from Argentina.

Carbohydrate diet Carbohydrate diet

0.26 ± 0.60

41

1.74 ± 2.54

Central Santa Fe

93

0.54 ± 1.36

Humid Chaco

63

2.27 ± 2.13

Transition Chaco

45

1.57 ± 1.90

Semi-arid Chaco

46

2.13 ± 1.97

≤ 10 years

153

1.47 ± 2.16

> 10 years

232

0.71 ± 1.59

Professional

167

*

Other Sucrose syrup/Honey

0.97 ± 1.82

427

1.11 ± 1.91

11

0.06 ± 0.17

110

1.42 ± 2.06

Other plus autumn No

328

0.97 ± 1.83

23

2.72 ± 2.36

Yes

421

0.98 ± 1.82

No

160

1.33 ± 2.10

Heat

233

0.56 ± 1.32

Chemical

46

2.40 ± 2.41

Direct sun

153

0.64 ± 1.58

Perennial trees

153

1.49 ± 2.04

No perennial trees

126

1.16±1.99

Mixed

12

0.068 ± 0.14

No

59

1.20 ± 1.95

2:1 Syrup

259

1.31 ± 2.13

1:1 Syrup

12

2.4 ± 1.81

Nectar flow

78

0.39 ± 0.77

Nectar flow + Syrup

36

0.10 ± 0.2

No

35

2.16 ± 2.27

Yes

403

0.93 ± 1.81

TE

Splitting colonies

EP

Colony disinfection

A

CC

Apiary location

Carbohydrate feeding pre-winter

Regular Autumn treatment

0.70 ± 1.34

233

HFCS Autumn

P-Value

SC RI PT

156

D

period

South Buenos Aires South Santa Fe

U

Scale of beekeeping

Mean (S.D)

N

Beekeeping experience

N

A

Geographical region

Level

M

Variable

< 0.001**

0.072 0.002 0.122 0.058 0.048

0.009

0.088

0.007

0.072***

41

No

128

0.99 ± 1.99

Yes

306

1.09 ± 1.86

Forest and grassland

265

0.99 ± 1.78

Forest and crops

117

1.5 ± 2.23

Crops Between 22000/17600 Between 17599/11000 Less than 11000

52

0.53 ± 1.49

328

0.98 ± 1.87

77

1.18 ± 1.84

8

1.51 ± 3.4

Brood cells autumn BT

Continuous

-------

-------

0.136

Adult bee population autumn AT

Continuous

-------

-------

0.043

Brood cells autumn AT

Continuous

-------

35

2.58 ± 2.16

204

0.55 ± 1.39

154

1.22 ± 1.98

Organics

32

2.32 ± 2.67

Acaricide treatment concept autumn 2015

No

35

2.58 ± 2.16

Recommended

241

1.03 ± 1.95

Non recommended

155

0.82 ± 1.62

Varroa infestation autumn BT

< 3%

282

1.07 ± 1.81

≥ 3%

149

1.06 ± 2.03

< 1%

322

0.61 ± 1.34

≥ 1%

106

2.44 ± 2.58

No Amitraz Piretroids

EP

TE

Acaricide product autumn 2015

M

A

-------

D

Adult bee population autumn BT

CC

Varroa infestation autumn AT

0.087

0.158**

SC RI PT

vegetation

U

Surrounding

N

Varroa Monitoring

0.098

0.038

0.001***

0.09***

0.015

< 0.001

A

HFCS: High fructose corn syrup. BT: before autumn acaricide treatment AT: after autumn acaricide treatment *

Beekeeping is not the main economical income of the family.

**Collinearity between geographical region and surrounding vegetation

42

A

CC

EP

TE

D

M

A

N

U

SC RI PT

***Collinearity between regular autumn treatment and acaricide product and treatment concept autumn 2015

43

Table 8. Final multivariable logistic regression model (backward selection) for Varroa destructor infestation level during 2015 beginning of spring season in honey bee colonies (gamma distribution) in Apis mellifera colonies from Argentina.

Random effect Estimate

Z

95% CI (1)

Apiary

2.77

4.84

1.85-4.16

<0.001

Fixed effects

Level

Estimate

95% IC

P-Value

1.83

0.24-3.42

0.75

-0.40-1.90

Geographical

Humid Chaco Transition

region

Chaco Semi-arid Chaco South Buenos

SC RI PT

U

Fe

2.14

0.81-3.47

N

Central Santa

2.31 2.59

0.002

0.20-4.24

A

Fe

M

South Santa

P-value

(------)

1.05-4.13

(------)

D

Aires (Ref.)

diet

TE

Sucrose

Carbohydrate

syrup/Honey

0.07 (------)

Between 22000/17600

0.88

-0.067-1.84

Between 17599/11000

1.10

0.13-2.07

Less than 11000 (Ref.)

(------)

(------)

Continuous

0.29

0.13-0.47

< 3%

-0.49

-0.83 - (-0.17)

≥ 3% (Ref.)

(------)

(------)

< 1%

-0.94

-1.48 - (-0.39)

≥ 1% (Ref.)

(------)

(------)

EP

CC A

Brood cells autumn AT Varroa infestation autumn BT Varroa infestation autumn AT

-0.20-4.99

(------)

HFCS (Ref.)

Adult bee population autumn BT

2.40

0.072

0.001 0.003

0.001

44

(1) 95% confidence interval HFCS: high fructose corn syrup

A

CC

EP

TE

D

M

A

N

U

SC RI PT

BT: before autumn acaricide treatment AT: after autumn acaricide treatment

45