Urbanization can alter the composition of arthropod communities. However, little is known about how urbanization affects ecological interactions. Using experimental colonies of the black bean aphid Aphis fabae Scopoli reared on Vicia faba L, we asked if patterns of predator-prey, host-parasitoid and ant-aphid mutualisms varied along an urbanization gradient across a large town in southern England. We recorded the presence of naturally occurring predators, parasitoid wasps and mutualistic ants together with aphid abundance. We examined how biotic (green areas and plant richness) and abiotic features (impervious surfaces and distance to town center) affected (1) aphid colony size, (2) the likelihood of finding predators, mutualistic ants and aphid mummies (indicating the presence of parasitoids), and (3) how the interplay among these factors affected patterns of parasitoid attack, predator abundance, mutualistic interactions and aphid abundance. Aphid abundance was best explained by the number of mutualistic ants attending the colonies. Aphid predators responded negatively to both the proportion of impervious surfaces and to the number of mutualistic ants farming the colonies, and positively to aphid population size, whereas parasitized aphids were found in colonies with higher numbers of aphids and ants. The number of mutualistic ants attending was positively associated with aphid colony size and negatively with the number of aphid predators. Our findings suggest that for insect-natural enemy interactions, urbanization may affect some groups, while not influencing others, and that local effects (mutualists, host plant presence) will also be key determinants of how urban ecological communities are formed. . . . . Keywords Aphid Predation Mutualism Parasitoids Trophic dynamics Introduction 2016). Fragmentation reduces populations of native plants (Benitez-Malvido 1998; Jules 1998; Williams et al. 2005), Urbanization is the defining feature of recent history; today leads to decreased connectivity between vegetation patches over 50% (>90% in developed countries) of people live in and existing patches tend to be smaller (Medley et al. 1995; urban environments (United Nations 2014). Urbanization is McKinney 2002) and therefore of reduced quality as habitat arguably the greatest anthropogenic transformation that eco- for many animal species (Bradley and Altizer 2007; Faeth logical systems experience, and while most studies of urban et al. 2011; Turrini et al. 2016). There are also some dramatic ecology focus on changes to the diversity and abundance of physical changes from increased densities of roads, buildings species inhabiting towns and cities, attention has only started and other sealed structures and microclimatic changes such as to focus on how assemblages of interacting species are formed the urban heat island effect (Bradley and Altizer 2007; Faeth in urban areas, and how this is affected by the intensity of et al. 2011). Together, these changes affect the likelihood of urbanization (Bennett and Gratton 2012; Quispe and encountering species at higher trophic levels (Faeth et al. Fenoglio 2015; Pereira-Peixoto et al. 2016;Turrinietal. 2005;Egerer et al. 2017). Understanding how such extreme anthropogenic habitat changes may affect patterns of ecolog- ical interactions is perhaps most tractable with arthropod mod- * Mark D. E. Fellowes el systems (McIntyre 2000;Bang and Faeth 2011), but exper- firstname.lastname@example.org imental studies in urban ecosystems are scarce. Urbanization has been shown to affect the structure of People and Wildlife Research Group, School of Biological Sciences, insect communities, resulting in dramatic changes in their University of Reading, Whiteknights, Reading, Berkshire RG6 6AS, UK abundance and richness (McIntyre 2000;Grimm etal. 2008; 406 Urban Ecosyst (2018) 21:405–417 Raupp et al. 2010;Gardineretal. 2014), most frequently With direct trophic interactions such as predation, one spe- leading to a loss of diversity (Kahn and Cornell 1989; cies has a negative effect on the other species, but in indirect Suarez et al. 1998; McKinney 2002; Shochat et al. 2004; interactions one species can also positively affect another spe- Sadler et al. 2006;Clark etal. 2007;Maguraetal. 2010; cies through intermediate levels in a trophic cascade (Halaj Uno et al. 2010;Bangand Faeth 2011; Bennett and Gratton and Wise 2001;Müller et al. 2005; Turrini et al. 2016). For 2012; Ramírez Restrepo and Halffter 2013). Few studies example, the presence of some species of honeydew- have considered how these changes influences the outcome collecting ants results in increased aphid numbers and also of ecological interactions at multiple trophic levels increased numbers of aphid parasitoids when protecting (Shrewsbury and Raupp 2006; Bennett and Gratton 2012; aphids from predators and incidentally also protecting parasit- Fenoglio et al. 2013; Pereira-Peixoto et al. 2016; Turrini ized aphids against predators and hyperparasitoids (Völkl et al. 2016). For example, abiotic environmental factors 1992;Kaneko 2002). Nevertheless, the most recognized indi- might interfere with biotic interactions, thereby modulating rect trophic interactions are top-down trophic cascades in the strength of the trophic effects on food webs (Ritchie which predators influence plants by feeding on herbivores, 2000; Preisser and Strong 2004; Turrini et al. 2016). thus reducing the consequences of herbivory (Schmitz et al. Mooney et al. (2016) investigated if variation in light avail- 2000;Shurin etal. 2002; Turrini et al. 2016). ability (shaded understory or open meadow) determines the Traditionally, research on trophic interactions and food abundance of the aphid Aphis helianthi feedingontheherb webs mainly focus on direct interactions such as predation Ligusticum porteri. Aphid numbers were higher in open or parasitism, therefore the importance of non-trophic, indi- meadows than in shaded environments. This pattern was rect, and facilitative interactions has been rarely taken into not due to the direct effects of light on aphid performance, consideration (Ohgushi 2008). Facilitative or positive interac- plant quality or interactions with natural enemies, but in- tions, like mutualisms, are rarely considered as potential fac- stead was due to an indirect effect mediated by a mutualistic tors affecting urban populations and communities (but see e.g. relationship with ants, which were more abundant in Thompson and McLachlan 2007; Gibb and Johansson 2010; meadows. If, as expected, insects and other arthropods do Toby Kiers et al. 2010), and it is claimed that this type of respond to habitat structure, then we can predict that there positive interaction plays an important part in the structuring will be differences not only in species assemblages, but also of some biological communities by providing refuge from on trophic dynamics and species interactions as habitat con- predation or competition (Stachowicz 2001). Conversely, it figuration changes with urbanization. is important to consider that mutualisms have formed over If we consider that urban wildlife is subject to multiple evolutionary time scales, and we do not know if mutualisms changes in abiotic conditions simultaneously, it is not sur- have evolved to be resilient enough to endure anthropogenic disturbances (Sachs and Simms 2006; Toby Kiers et al. 2010). prising that predicting the consequences of such changes for trophic processes and for direct and indirect species inter- Host-parasitoid interactions are also likely to be con- actions is highly challenging (Turrini et al. 2016). However, siderably altered in urban ecosystems. Here, plant re- a few trends have begun to appear. Urban areas are often sources for herbivorous insects and their parasitoids are characterized by reduced numbers of native vertebrate pred- spatially subdivided and embedded in a matrix of built ators (McKinney 2002; Shochat 2004), an increased abun- environment (Bennett and Gratton 2012;Fenoglioetal. dance of some urban-adapted species, which can potentially 2013). These conditions are particularly prone to altering lead to increased competition and displacement (Hostetler insect colonization and persistence, which may lead to and McIntyre 2001), altered behavior and phenology altered trophic interactions (Fenoglio et al. 2013). (Connor et al. 2002;Neiland Wu 2006), high densities of Parasitoid insects are important biological control agents herbivorous arthropods (Dreistadt et al. 1990; Hanks and of herbivorous insect populations and have been found Denno 1993; Tooker and Hanks 2000), lower numbers of to be negatively affected by urbanization at both local arthropod predators (Turrini et al. 2016)and lowernumbers and landscape spatial scales (Bennett and Gratton 2012; of parasitoids (Denys and Schmidt 1998; Bennett and Fenoglio et al. 2013). Parasitoids are specialist organ- Gratton 2012; Burks and Philpott 2017). All these changes isms closely associated with their hosts (Kruess and can potentially lead to altered trophic structure, and we must Tscharntke 1994). Consequently, they might present recognize that trophic dynamics cannot be understood higher sensitivity to environmental fluctuation and an- based only on our knowledge of species composition thropogenic disturbance in comparison to less special- (Shochat et al. 2006). This way, evaluating empirically ized species (Gibb and Hochuli 2002). Since some her- how trophic dynamics behave in urban environments may bivore pest populations are limited by top-down control help us to make some broad and useful predictions regard- by parasitoids (Hawkins and Gross 1992), a decrease in ing the effects that urbanization could have on multi-trophic parasitism or predation can favour pest outbreaks in interactions. these areas (Schmitz et al. 2000;Roslinet al. 2014). Urban Ecosyst (2018) 21:405–417 407 Even less frequently considered is how these different Methods ecological interactions (host-parasitoid, predator-prey, mutualisms) act together to affect the insect assemblages Study sites and habitat variables found in urban environments. Systems including differ- ent types of interactions and trophic groups have only Study sites were located in Greater Reading, Berkshire recently started to be empirically examined (Halaj and (51°27’N, 0°58’W), a large town in southern England with Wise 2001; Lurgi et al. 2016). In this work we explore a population of 290,000, which covers an area of ca. 72 km . these interconnected biological interactions in an urban Twenty-eight experimental sites were selected, and sites environment. We used a study system which consisted of were selected in order to capture an approximate gradient experimental colonies of the herbivorous aphid Aphis from very urbanized environments in the town center to fabae Scopoli reared on an herbaceous plant species suburban areas located on the south, covering areas of (the dwarf broad bean Vicia faba L.) and their naturally carparks, churchyards, parks, private and community gar- occurring predators, parasitoid wasps and mutualistic dens and woodlands. Each study site was at least 110 m ants along an urbanization gradient in a large town in apart (Fig. 1). southern England. Land-use data for urbanization metrics were derived Study sites varied in the amount of impervious sur- from the Ordnance Survey MasterMap® Topography faces, green areas, plant species richness and position layer, which represents topography at a scale of 1:1250. on the urban gradient. Distance from the town center is This is subdivided into a number of themes: administra- a variable frequently used as a proxy for urban gradi- tive boundaries, buildings, heritage and antiquities, land, ents, as cities and towns frequently show gradients of rail, roads, tracks and paths, structures, terrain and height urbanization from their centers to their edges, and that and water. Using GIS techniques, 30-m-radius buffers the biotic and abiotic factors that can potentially affect were delimited from the sites where the experimental biological systems tend to follow and change as func- plants were located. This buffer size was chosen due to tion of this gradient, resulting from variation in human restrictions in access, and also due to limitations in sam- population density and intensity of activity (Deichsel pling effort for the estimation of plant diversity. 2006; Clark et al. 2007; Bang and Faeth 2011). The Reclassification of urbanization metrics was per- extent of impervious cover (paved surfaces, structures formed to give the proportions of area represented by such as buildings and roads) causes a variety of detri- the following habitat types within those buffers: green mental effects on arthropods (Morse et al. 2003;Sadler areas, which was composed of gardens and lawns with et al. 2006;Maguraetal. 2008; Bennett and Gratton ornamental plants, bushes, trees and shrubs; impervious 2014), and it is a stronger predictor of urbanization surfaces, which comprised of buildings (any building or gradients than broad classifications such as urban, sub- artificial structure made of concrete, brick or stone) and urban and rural areas (Ellis and Ramankutty 2008; byways (roads, roadsides, tracks or paths made of imper- Ramalho and Hobbs 2012;Savageetal. 2015). vious surfaces such as asphalt). This procedure was car- Variation in structure of green spaces within cities rep- ried using QGIS 2.8.1 (QGIS Development Team 2015). resents the availability of habitats for arthopods in gra- In addition to these habitat variables, plant species rich- dients of urbanization, however, green spaces within ness within a 30-m radius of the study sites was estimat- cities that present complex structures and higher plant ed during the experimental period by visually counting richness are thought to be of high quality as habitats for all plant morphospecies within the area surrounding each insects (Pauleit and Duhme 2000;Whitfordet al. 2001; experimental colony. This method is strongly correlated Turner et al. 2005). with species richness and it effectively captures variance Here, we report the results of a study asking a) if the between study areas, with the advantage of reduced sam- relative performance of aphid colonies (i.e. aphid popu- pling effort and increased effectiveness to achieve statis- lation numbers) was associated with urbanization; b) if tical power (Abadie et al. 2008; Schmiedel et al. 2016). the presence of natural enemies (insect predators, para- Distance to the urban center (m) was calculated from sitoids) and mutualists (ants) found on colonies was de- each study site to a point at the central area of the town termined by urbanization or aphid numbers; c) how bi- (Fig. 1). otic factors (the assemblage of natural enemies and mu- tualists, green areas, plant species diversity and aphid Study system and summer recording numbers) and abiotic factors (impervious surfaces, dis- tance from urban centre) act in concert to determine her- Black bean aphids Aphis fabae Scopoli were maintained in a bivore population sizes and the occurrence of their mu- monoclonal culture in the laboratory using plastic and mesh tualists and natural enemies. cages. Cultures were kept at a constant temperature of 20 ± 408 Urban Ecosyst (2018) 21:405–417 Fig. 1 Study site location (n =28) in Greater Reading, England. The x marks the point from which distance from the city center was calculated from each sampling location. Aerial image was obtained from the Ordnance Survey Edina MasterMap® 1 °C and 16:8 h L:D light regime at ambient humidity on Some colonies were lost during the four sampling pe- broad bean, Vicia faba L. (var. the Sutton dwarf). Three days riods (three colonies on the first sampling period, eight before being allocated to the study sites, three adults were colonies on the second sampling period, three colonies transferred from the culture and reared on 14–16 day old on the third sampling period and four colonies on the dwarf broad bean plants (18–22 cm in height) to allow new fourth sampling period), caused by poor plant health, colonies to become established. These plants were sown in herbivory of plants by snails and slugs, and also from pots with traditional potting compost (Vitax Grower, damage or theft by the public. This resulted in 94 obser- Leicester, England), and watered as required. After three days, vations for analysis. the established aphid colonies on broad bean plants were All counts of aphids, predators, ants, mummies and transferred to the 28 study sites. plant richness were either log-transformed or square Two days after experimental colonies were placed in the root-transformed to deal with extreme values and to stan- field, species and numbers of aphids, predators, ants and dardize and homogenize residuals (Crawley 2007;Zuur parasitized aphids (mummies) were recorded, and then sub- et al. 2009). To analyze aphid colony numbers we used a sequently every three days for five recording days providing linear mixed model fitted by reduced maximum likeli- a total of 17 days of sampling in the field. At the end of this hood using package nlme (Pinheiro et al. 2016), and as sampling period the plant/aphid-colonies were removed and fixed factors (explanatory variables) we used proportion replaced by new ones in the field. Sampling was repeated of impervious surfaces, plant richness, distance to the four times in 2015 (sampling period one: May 16, 20, 24, 28 town center, predator abundance, number of ants farming and June 1; period two: June 15, 19, 23, 27 and July 1; the colony, parasitized mummies and an interaction fac- period 3: July 16, 20, 24, 28 and August 1; period four: tor between ants and predator numbers. We accounted August 14, 18, 22, 26 and 30). for repeated sampling of colonies in sites through time by adding period as a random effect. We removed the Data analysis variable proportion of green areas from the set of explan- atory variables since it was highly correlated to the pro- All statistical analyses were carried out using R 3.1.2 (R portion of impervious surfaces (r = −0.92). Development Core Team 2014). To deal with the excess of zeros when modelling ants, The dataset consisted of the cumulative numbers of predators and parasitized mummies as response vari- predators, ants, aphids and aphid mummies of the five ables, we transformed these variables as factors (pres- counting events on each of the four sampling periods. ence or absence) and ran logistic regressions models Urban Ecosyst (2018) 21:405–417 409 with a binomial error distribution family (with canonical Results link logit) using the function ‘glmer’ of package lme4 (Bates et al. 2015), with period as a random effect and Study sites fitted by maximum likelihood (Crawley 2007). When modelling predators we used the proportion of impervi- Our study sites captured an urban gradient. The proportion of ous surfaces, plant richness, distance to the town center, impervious surfaces was negatively correlated (r = −0.46), aphid abundance, number of ants farming the colony, and plant diversity positively correlated (r = 0.61), with dis- and number of parasitized mummies as explanatory fac- tance fromtowncenter. Plantrichnessvariedfrom14to100 tors. When modelling ants as response variable, we used species (mean ± SE: 35.86 ± 3.42), proportion of impervious the proportion of impervious surfaces, plant richness, surfaces varied from 0 to 0.862 (mean ± SE: 0.425 ± 0.051) distance to the town center, predator abundance, aphid and proportion of green areas around study sites varied from numbers, and numbers of parasitized mummies. When 0.138 to 1 (mean ± SE: 0.526 ± 0.050) (Fig. 1). analyzing parasitized mummies as the response variable, we removed the first sampling period from the dataset Taxa recorded sincenomummies were foundonthisperiod(leaving69 observations in total), then we modelled this as a func- In total we observed 30,557 aphids, 146 predators, 660 ants and tion of the proportion of impervious surfaces, plant rich- 448 mummies on our experimental plants. The ants attending ness, distance to the town center, predator abundance, the aphid colonies were Myrmica rubra (L.) and Lasius niger aphid numbers, and number of ants. (L.). The predator guild comprised mainly of spiders Model selection was made by comparing all candidate (Arachnida; 59%) and hoverfly larvae (Diptera: Syrphidae; models using Akaike’s Information Criteria (Burnham 21%), aphid midges (Cecidomyiiidae; 7%), flower bugs and Anderson 2003) by developing a series of alternative (Hemiptera: Anthocoridae; 6%), ladybirds (Coleoptera: mixed effect models that include different combinations Coccinellidae; 3%) and smaller numbers (4%) of earwigs of the explanatory variables (Zuur et al. 2009,Table 1), (Dermaptera), harvestmen (Opiliones) and lacewings by fitting the full model with the set of all possible ex- (Neuroptera). planatory variables and taking out the least significant term on each step (Crawley 2007). We then ranked the Aphid abundance models according to AIC differences (Δ =AIC – i i AIC ,where AIC is the model i value and AIC is Model selection based on AIC differences revealed three mod- min i min the best model value). Models with Δ < 2 provide sub- el candidates (Δ < 2) for explaining variance on aphid num- i i stantial support for a candidate model, whereas values of bers, the first with predators, ants and parasitoids; the second Δ between 4 and 7 provide less support and Δ >10 with predators and ants and the third only with numbers of i i indicates that the model is unlikely. We also calculated ants farming aphid colonies (Table 1,models1,2,3). Akaike weights for all models, where these model However, Akaike weights indicated that the first and third weights are used to indicate the importance of a model, models are more likely to be the best models for explaining with increasing weights indicating the likelihood of a aphid numbers (Table 2), with ants farming the aphid colony particular model as the overall best model (Burnham the variable of the highest importance, being positively corre- and Anderson 2003). Aikaike weights can also be used lated with aphid increase (0.885 based on the sum of Akaike to calculate the relative importance of a variable by sum- weights within models with Δ <2) (Fig. 2). ming the Akaike weights of all models that include that variable (Burnham and Anderson 2003). Aphid predators We checked if collinearity could be a potential issue in our models through variance inflation factors (VIF) Based on AIC differences two models were selected as which is used as an indicator of multicollinearity in mul- candidates for explaining the presence of predators on tiple regression, with VIF values higher than 3 indicating aphid colonies (Table 1, models 4 and 5); however since that covariation between predictors may be a problem model 5 is 1.76 times more likely to be the best model (Zuur et al. 2007). All our VIF values were in the range (evidence ratio = 0.467/0.265) we chose this model as of 1.34–2.94. All response variables were checked for the overall best model. As explanatory factors, this model spatial autocorrelation through spline correlograms on included proportion of impervious surfaces, which nega- package ncf (Bjornstad 2015), and we did not find any tively determined predator presence; number of aphids, significant spatial structure in the response variables. We positively determining predator presence; and number of assessed the validity of all models by checking normal- ants farming the colony, which negatively influenced the ity, independence and homogeneity of model residuals. presence of aphid predators (Table 2,Fig. 3). 410 Urban Ecosyst (2018) 21:405–417 Table 1 Summary of model Model ID Response variable Explanatory variables/model AIC Δ W selection statistics for models i predicting aphid abundance, and 1 Aphids Predators + Ants + Parasitoid 141.02 0.000 0.347 presence/absence of predators, ants faming the aphid colonies 2 Aphids Predators + Ants 141.67 0.650 0.251 and parasitized mummies 3 Aphids Ants 141.40 0.380 0.287 4 Predators ImpSurf + PlantRich + Aphids + Ants 118.67 1.133 0.265 5 Predators ImpSurf + Aphids + Ants 117.53 0.000 0.467 6 Ants farming PlantRich + Aphids + Predators + Parasitoid 105.55 1.395 0.216 7 Ants farming Aphids + Predators + Parasitoid 104.16 0.000 0.434 8 Ants farming Aphids + Predators 105.83 1.673 0.188 9 Parasitoid DistCentre + Aphids + Ants 57.67 0.450 0.315 10 Parasitoid Aphids + Ants 57.22 0.000 0.395 Only models with substantial support (Δ < 2) are shown, and models highlighted in bold are considered the best model candidates and are further described on Table 2. Δ = AIC differences, calculated by subtracting the model with lowest AIC value from other model AIC values. W = Akaike weights, with higher weights indicating increased model importance; ImpSurf = proportion of impervious surfaces within 30 m buffers; PlantRich = plant species richness within 30 m; DistCentre = distance to each study site to the town centre Ants farming aphid colonies number of aphids (positive; Fig. 4a) and number of pred- ators (negative; Fig. 4b) as explanatory factors as the Three candidate models were selected based on AIC dif- number of parasitized mummies was not significant at ferences for explaining the presence of ants farming aphid α =0.05 (Table 2, model 7). colonies (Table 1, models 6, 7, 8). Model 7 (Table 2), with number of aphids, predators and parasitized Parasitoid attack mummies as explanatory factors, seemingly to be the best model due to its higher Akaike weight (0.434). However, Two candidate models were selected for explaining the pres- Fig. 3 only shows the logistic regression curves for ence of parasitized aphids on the colonies: first with the Table 2 Summary of the best Model ID Response variable Explanatory variable Coefficient value ± SE P candidate models predicting aphid numbers, and presence/ 1 Aphids Intercept 1.860 ± 0.276 0.000 absence of predators, ants faming aphid colonies and parasitized Predators 0.135 ± 0.057 0.021 mummies. Significance and coef- Ants farming 0.232 ± 0.092 0.014 ficient values for each explanato- Parasitoids 0.271 ± 0.119 0.026 ry factor are given 3 Aphids Intercept 2.024 ± 0.290 0.000 Ants farming 0.280 ± 0.083 0.001 5 Predators Intercept −0.489 ± 0.791 0.536 Impervious surfaces −2.179 ± 1.004 0.030 Aphids 1.103 ± 0.375 0.003 Ants farming −1.040 ± 0.458 0.023 7 Ants farming Intercept −3.256 ± 1.047 0.002 Aphids 1.340 ± 0.469 0.004 Predators −0.759 ± 0.321 0.018 Parasitoids 1.108 ± 0.632 0.080 9 Parasitoids Intercept −5.829 ± 1.861 0.002 Distance to town centre −0.000 ± 0.000 0.226 Aphids 2.330 ± 0.828 0.005 Ants farming 1.215 ± 0.625 0.052 10 Parasitoids Intercept −6.289 ± 1.771 0.000 Aphids 2.121 ± 0.759 0.005 Ants farming 1.298 ± 0.622 0.037 Urban Ecosyst (2018) 21:405–417 411 Fig. 2 Relationship between abundance of aphids and numbers of ants farming the aphid colonies throughout the four sampling periods. Although linear mixed-effects models were performed (see Methods), the linear model trend line is shown to illustrate the direction of the significant relationship between variables. Note log scale used on y and xaxes numbers of aphids, colony-farming ants and distance to the town center, and second with the first two variables but with- out distance to the town center (Table 1, models 9 and 10). Since distance to the town centre was not significant in model 9(Table 2) we considered the model with only the numbers of aphids and colony-farming ants as the best overall model (model 10 in Table 2,Fig. 5). Both variables were positively correlated with the presence of parasitized aphids on the ex- perimental colonies. Discussion Fig. 3 Representation of logistic regression model of (a) proportion of impervious surfaces, (b) number of aphids and (c) number of ants farming Our aim was to investigate how urbanization may affect the aphid colonies in predicting the presence (1) or absence (0) of predators in intensity and outcome of interactions between species at var- the colonies. Note that a multivariate logistic mixed effects model was used (see Methods), but the trend line for a logistic regression model for ious trophic levels, using the black bean aphid, its natural just one explanatory variable on each panel was used to illustrate the enemies and ant mutualists as a model system. Overall, we direction of relationship between variables. Log scale used on x axis of found that the presence of mutualistic ants, predators and par- panels (b) and (c) asitoids varied as a function of aphid numbers on the plants. Predators were the only group affected by abiotic factors, with consider interactions at higher trophic levels without the con- fewer predators found in areas with increased proportions of impervious surfaces. The presence of mutualistic ants was founding effects of plant and prey quality. The abundance of predators was significantly affected by aphid colony size, the associated with an increase in both aphid and parasitoid num- bers, and a decrease in numbers of aphid predators. In no case number of ants farming aphid colonies and the proportion of impervious surfaces in the habitat. Density dependence in pre- did local plant diversity or distance to the urban center affect the abundance of any of the interacting species. dation is a widely recognized factor (Sinclair and Pech 1996; Hixon and Carr 1997; Anderson 2001; Arditi et al. 2001; We found that Aphis fabae colony size was not affected by Holbrook and Schmitt 2002;Hixon andJones 2005). In our abiotic variables, something expected as each colony experiment, ants attending aphid colonies greatly reduced remained on their study site for a limited amount of time predator numbers. Previous studies have reported that (~20 days for each sampling period), feeding on plants previ- honeydew-collecting ants can alter predator abundance ously sown under identical conditions. This allows us to 412 Urban Ecosyst (2018) 21:405–417 Fig. 5 Panel (a) shows the logistic regression curve for number of aphids Fig. 4 Panel (a) shows the logistic regression curve for number of aphids as explanatory variable for presence (1) or absence (0) of mummies on the as an explanatory variable for the presence (1) or absence (0) of ants experimental plants. Panel (b) shows the logistic regression curve for farming aphids on the experimental plants. Panel (b) shows the logistic number of ants farming the colonies predicting number of parasitized regression curve for number of predators predicting number of ants farm- aphids. Note that a multivariate logistic mixed effects model was used ing aphid colonies. Note that a multivariate logistic mixed effects model (see Methods); however, the trend line for a logistic regression model for was performed (see Methods); however, the trend line for a logistic re- just one explanatory variable on each panel was used to illustrate the gression model for just one explanatory variable on each panel was used direction of relationship between variables. Log scale used on x axis of to illustrate the direction of relationship between variables. Log scale was both panels used on x axis on panel (a) and square-root scale on panel (b) (James et al. 1999; Wimp and Whitham 2001; Kaplan and (Pauly et al. 1998; Jackson et al. 2001;Duffy 2002; Byrnes Eubanks 2002). Neither of the above factors was unexpected. et al. 2005), and that losses of even one or two species that However, we also show that increased urbanization, measured belong to higher trophic levels can cause cascading effects on as the proportion of impervious surfaces surrounding the field species present on basal trophic levels (Paine 2002;Schmitz sites, was associated with a reduction in the numbers of pred- 2003) and critically affect ecosystem processes (Tilman et al. ators recorded. 1997;Byrnes et al. 2005; Hooper et al. 2005). Insect predators are relatively generalist, and their abun- Urbanized environments might affect organisms at higher dance will be associated with the local population size of a trophic levels more than their hosts or prey, particularly when range of prey species. Given the reduction in native plant they exhibit higher levels of resource specialization diversity and abundance in urbanized areas (Dreistadt et al. (Tscharntke et al. 1998;Bailey et al. 2005; Pereira-Peixoto 1990;Burton et al. 2005; Williams et al. 2005;Williams et al. et al. 2016). In our study system, this may apply to insect 2008; Isaacs et al. 2009; Walker et al. 2009), it would be predators but does not appear to affect the likelihood of colo- surprising if predators were not sensitive to urbanization nies suffering parasitoid attack. However, there was an indi- (McKinney 2006; Jones and Leather 2012;Otoshietal. cation that parasitized mummies were less frequently found on 2015). Urban management techniques such as treading, bird more urbanized sites of the gradient (closer to the town center, feeding, mowing and pesticide application negatively impact Table 2, model 9), but this factor was not statistically signifi- predacious beetles and hemipterans (Morris and Rispin 1987; cant. There have been studies which found negative correla- Helden and Leather 2004; Orros and Fellowes 2012;Jones tions between parasitism and urbanization in a landscape con- and Leather 2012;Orros etal. 2015;Bennett andLovell text (Gibb and Hochuli 2002; Bennett and Gratton 2012; 2014; Smith et al. 2015). Human-induced extinctions and lo- Calegaro-Marques and Amato 2014), which was not our ob- cal extirpations are often biased towards higher trophic levels jective in this work. The presence of physical barriers and Urban Ecosyst (2018) 21:405–417 413 structures like buildings and roads in cities may make insect environmental disturbance on multi-trophic interactions in ur- dispersal problematic, and present an obstacle for breeding ban habitats could result in important consequences for the and foraging (Wratten et al. 2003; Raupp et al. 2010; Peralta assembly of local ecological communities, and also direct et al. 2011). On the other hand, vegetated areas bordering and practical implications for biocontrol services that natural roads, pavements and streets may serve as biological corri- enemies could provide in these habitats (Gibb and Hochuli dors, particularly those that maintain higher plant diversity 2002; Eubanks and Finke 2014;Calabuiget al. 2015; and density (Haddad et al. 2003; Peralta et al. 2011). Philpott and Bichier 2017). For example, Turrini et al. Although we did not directly measure the functional traits (2016) investigated the effects of urbanization on trophic in- of each trophic guild and how this relates to variance in hab- teractions and found that predators reduced aphid abundance itat, our results may suggest a certain degree of sensitivity to less in urban than in agricultural ecosystems. This reduction in urbanization possibly associated with differential dispersal top-down regulation in urban areas resulted in urban plants abilities and habitat requirements of predators and parasitoids. having reduced biomass than plants in adjacent agricultural Predators were composed of several taxa (spiders, hoverfly areas. Findings such as these emphasize that urbanization larvae, aphid midges, flower bugs and ladybirds), presenting can influence not only interactions at higher trophic levels, a range of dispersal abilities and with a variety of dietary but that these changes also affect plant communities through breadths (Rotheray 1989). Aphid parasitoids are known to trophic cascades (Schemske et al. 1994; Brudvig et al. 2015). use a range of visual, acoustic or olfactory cues to locate Our results highlight the negative effect of the main character- potential host patches, including long-range olfactory cues istic of cities, the increase amount of impervious surfaces, on originating from the host plant (Fellowes et al. 2005; an important trophic guild. Given that the amount of impervi- Vandermoten et al. 2012). This suggests that the differences ous surfaces is highly negatively correlated with proportion of in dispersal ability and habitat requirements may be responsi- green areas, our results reinforce the importance of maintain- ble for the differences in vulnerability to urbanization between ing and increasing the quality of urban green spaces as habitats the two groups (predators and parasitoids) presented by our for the conservation of biological diversity (Botkin and data. This possibility needs to be further tested. Beveridge 1997; Peralta et al. 2011), and consequently also We found that the mutualistic relationship between aphids on trophic dynamics. and ants was responsible for a significant increase in aphid One of the major challenges of ecology is to understand numbers. In our study, ant attendance at aphid colonies was and predict the consequences of environmental changes for not affected by habitat variables, and ant-attended colonies biodiversity and ecosystem functioning (van der Putten et al. were present even on the most urbanized sites of the gradient. 2004;Hooper et al. 2005). Variation in responses within and Mutualistic ants of aphids are known to protect aphid colonies between trophic groups may cause restructuring of communi- from predator attack, to prevent mold growth when honeydew ties through changes in competitive, bottom-up and top-down accumulates and to avoid aphid competition with other herbi- control effects (Van der Putten et al. 2004). Any given species vores on the same resource (Stadler and Dixon 1998;Kaneko is affected by interactions with other species, therefore under- 2003;Yao 2014). The relationship between aphids and standing how changes in species interactions potentially affect tending ants can then confer direct benefits to aphid survival, food web structure and function in urban habitats may help us allowing highest feeding rates and nutrient uptake. At the to succeed when planning conservation strategies (Faeth et al. same time, aphid-derived honeydew constitutes a nutrient- 2005; Faeth et al. 2011). To our knowledge, our work presents rich food that may be essential for the survival and growth the first effort to address how interrelated multi-trophic inter- of ant colonies (Kaplan and Eubanks 2002; Tegelaar et al. actions composed by herbivory, predation, parasitism and mu- 2013). Aphidparasitoids are lesslikelytobeaffectedbythe tualism behave in urban habitats, with predation the most af- presence of ants on aphid colonies than predators. Although fected by the increase of urban features in the habitat. Our parasitoid wasps can sometimes be repelled by ants, once findings emphasize the need for careful consideration of wasps successfully oviposit in aphids, these parasitized aphids how patterns of species interactions may be modified in urban also receive ant protection, which may in turn result in higher settings, which is essential for conservation efforts that will parasitoid emergence rates (Völkl 1992, 1997;Kaneko 2002; promote ecosystem services and functioning in cities. Yao 2014). Such patterns (a negative effect of ant presence on generalist predators, a positive effect on specialist enemies) Acknowledgements We are grateful to the Science without Borders and was found by Wimp and Whitham (2001), who examined the Coordenaçāo de Aperfeiçoamento de Pessoal de Nível Superior (CAPES-Brazil) who provided funding for this work and a scholarship the mechanisms that determined arthropod community struc- for the first author, BEX: 13531-13-1.We also would like to thank ture in a riparian zone dominated by cottonwood. Our urban garden owners and Reading Borough Council for allowing us to use ecosystem seemed to show similar trends. their properties for our research, and two anonymous referees for very Overall, only predators were affected by the features of helpful comments. Data are available on request from EAA urbanization measured on our study. This influence of (email@example.com). 414 Urban Ecosyst (2018) 21:405–417 Open Access This article is distributed under the terms of the Creative Burton ML, Samuelson LJ, Pan S (2005) Riparian woody plant diversity Commons Attribution 4.0 International License (http:// and forest structure along an urban-rural gradient. Urban Ecosyst 8: creativecommons.org/licenses/by/4.0/), which permits unrestricted use, 93–106. https://doi.org/10.1007/s11252-005-1421-6 distribution, and reproduction in any medium, provided you give appro- Byrnes J, Stachowicz JJ, Hultgren KM, Hughes AR, Olyarnik SV, priate credit to the original author(s) and the source, provide a link to the Thornber CS (2005) Predator diversity strengthens trophic cascades Creative Commons license, and indicate if changes were made. in kelp forests by modifying herbivore behaviour. 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Urban Ecosystems – Springer Journals
Published: Feb 3, 2018
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