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Landscape heterogeneity increases the spatial stability of pollination services to almond trees through the stability of pollinator visits
Agriculture, Ecosystems and Environment 279 (2019) 149–155
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Landscape heterogeneity increases the spatial stability of pollination services to almond trees through the stability of pollinator visits
T
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Amparo Lázaro , David Alomar Mediterranean Institute for Advanced Studies (IMEDEA, UIB-CSIC), Global Change Research Group, C/Miquel Marquès 21, 07190, Esporles, Balearic Islands, Spain
A R T I C LE I N FO
A B S T R A C T
Keywords: Coefficient of variation Diversity of land-cover classes within a landscape Evenness of land-cover classes within a landscape Fruit production Percentage of natural habitat Pollinator richness Pollinator visits Prunus dulcis Richness of land-cover classes within a landscape
High pollinator reliability is essential to maintain a stable and predictable crop production. However, still few studies have considered landscape effects on the stability (low variability) of the pollination service to crops. The stability of pollination services may increase in heterogeneous landscapes with high availability and diversity of alternative habitats. By using data on pollinator visitation and fruit production in 18 almond tree fields in Mallorca Island, we related landscape heterogeneity (richness, evenness and diversity of land-cover classes) at 1 and 2 km-radius buffer zone to the spatial (among-tree) stability of their pollination services (total visit rates, wild pollinators’ visit rates, pollinator richness and fruit set). Overall, landscape heterogeneity increased the spatial stability (i.e., decreased spatial variability) of pollination services to almond trees. Interestingly different components of heterogeneity influenced diverse components of this service. Thus, increasing landscape evenness at the 2 km buffer zone increased the spatial stability of both total pollinator visitation and fruit set. However, the stability of wild-pollinators’ visit rate as well as the stability of pollinator richness increased instead with the percentage of natural habitat in the buffers, as the abundance of wild pollinators might depend on the total availability of habitat in the field surroundings. The stability in pollinator richness had a further positive effect on the stability of fruit set, indicating the importance of diversity maintaining stable crop production. Lastly, fruit set was also negatively affected by landscape richness, probably because of a loss of pollinator source habitat when the number of land uses is too high. Preserving a heterogeneous habitat of high quality in the surroundings of crop fields may help to maintain a stable fruit production.
1. Introduction Pollination is an essential ecosystem service that is known to rely on pollinator diversity for both increased effectiveness and stability, i.e., low temporal or spatial variability in those services (Garibaldi et al., 2011a, 2013; Blitzer et al., 2016). Despite stability being an important component of sustainability of ecosystem functions (Balvanera et al., 2006; Haddad et al., 2011), the factors influencing the stability of pollination services to crops are still not well understood and have seldom been analysed (but see Kremen et al., 2004; Klein, 2009; Garibaldi et al., 2011a). Understanding how landscape features are related to the stability of pollination services is critical to the predictability of crop yields and to decrease the impacts of biodiversity loss in the face of current anthropogenic changes (Garibaldi et al., 2014). Natural or semi-natural areas within agricultural landscapes provide habitat for wild pollinators, supplying them with resources on which they depend, such as flowers (Steffan-Dewenter and Tscharntke, 2001; Potts et al., 2003), nesting sites (Potts et al., 2005), and oviposition sites
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(Johst et al., 2006). Larger natural areas surrounding the fields might contain higher plant richness which may in turn allow maintaining larger and more stable pollinator communities (Hegland and Boeke, 2006; Ebeling et al., 2008) that can spillover into crop fields (Tscharntke et al., 2005). As a consequence, mean levels of pollinator richness and abundance in croplands (Ricketts et al., 2008; Garibaldi et al., 2011a), but also the stability of pollinator visits and fruit production (Garibaldi et al., 2011a) have been shown to decrease with the distance of agricultural fields from natural and semi-natural areas. In addition to the proximity of crop fields to natural habitats, landscape heterogeneity could play a crucial role in the stability of pollination services. Landscape heterogeneity often has a positive effect on biodiversity in agricultural landscapes, because the heterogeneity of cover types increases the number of habitats and microhabitats available for different pollinators (Holzschuh et al., 2008). Also, many species living in agro-ecosystems depend on complementary resources from different habitats, such as feeding or nesting sites (Gathmann and Tscharntke, 2002; Klein et al., 2004). Thus, increasing landscape
Corresponding author. E-mail address: [email protected] (A. Lázaro).
https://doi.org/10.1016/j.agee.2019.02.009 Received 14 May 2018; Received in revised form 20 November 2018; Accepted 11 February 2019 Available online 05 April 2019 0167-8809/ © 2019 Elsevier B.V. All rights reserved.
Agriculture, Ecosystems and Environment 279 (2019) 149–155
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fields were always separated by a minimum distance of 1 km, except for two fields that were separated by 850 m, as they were the only fields available for the study in this area (mean ± SD distance among fields: 35.48 ± 21.29 km). A more detailed description of the study sites can be found in Alomar et al. (2018). In previous work at these sites, we showed that although ca. 90% of the visits to almond trees’ flowers were conducted by honeybees, another 43 species of wild pollinators visited them, including bees, muscoid flies, hoverflies, beetles, wasps and butterflies (Alomar et al., 2018). In this same work, we also showed that the percentage of natural area surrounding the fields increased both pollinator-species richness and honeybee visits. At a more local scale, the flower community in the fields positively influenced almond production, both directly and indirectly through effects on the diversity of wild-pollinators (Alomar et al., 2018).
heterogeneity may also increase landscape complementation, which occurs when mobile organisms move between patches in the landscape to make use of non-substitutable resources (Dunning et al., 1992; Fahrig, 2003; Brotons et al., 2004). Moreover, landscape heterogeneity might increase biodiversity by reducing competition and de-coupling patch dynamics across meta-communities (Fahrig et al., 2011). As a result of all this, several studies have shown that pollinator diversity is higher in complex landscapes than in structurally simple ones (Holzschuh et al., 2008; Batáry et al., 2011) and that landscape heterogeneity increases pollinator diversity in crop fields (Rundlöf et al., 2008; Andersson et al., 2013; Fabian et al., 2013; Aguirre-Gutiérrez et al., 2015; Foldesi et al., 2016; Senapathi et al., 2017). There is now a growing consensus that biodiversity enhances ecosystem functions in general (Balvanera et al., 2006; Allan et al., 2015; Soliveres et al., 2016) and the delivery of pollination service in particular (Kremen et al., 2002; Klein et al., 2003; Hoehn et al., 2008). A major ecosystem effect of biodiversity is to stabilise assemblages that perform particular functions (Thibaut and Connolli, 2013). Therefore, greater pollinator diversity in heterogeneous landscapes may provide more stable pollination services, as diversity often increases the stability of the entire communities (Lehman and Tilman, 2000). High pollinator diversity may augment pollination services by increasing functional complementarity (Hoehn et al., 2008; Blüthgen and Klein, 2011; Blitzer et al., 2016). This is because temporal or environmental variation in pollinator species’ activities may contribute to complementary effects on plant pollination (Blüthgen and Klein, 2011), response diversity, and cross-scale resilience (Winfree and Kremen, 2009), with positive effects on the pollination service. When crop production is pollen limited, as is often the case in pollinator-dependent crops (e.g. Gary et al., 1976; Blitzer et al., 2016), a lower spatial and temporal stability of pollinators may decrease also the stability of fruit set (Klein, 2009; Garibaldi et al., 2011b). Here, we used data on 18 almond tree fields in Mallorca Island to evaluate how landscape heterogeneity (richness, evenness and diversity of land-cover classes) and the percentage of natural habitat surrounding the crop fields, modulate the spatial stability of pollination services to almond trees, measured as total – including managed honeybees- and wild pollinators’ visit rate (visit/flower in 5 min), pollinator richness (number of species/tree) and fruit set (fruits/flowers). We used landcover classes to estimate heterogeneity because they represent the number of different habitats available for pollinators in the landscape. As different pollinators may respond to landscape at different spatial scales (e.g. Steffan-Dewenter et al., 2002; Kennedy et al., 2013; Alomar et al., 2018), we calculated landscape characteristics in the 1 km- and 2 km-radius buffer zones surrounding the sampling sites. Following Garibaldi et al. (2011a), we estimated the spatial (among-tree) stability as the inverse of the spatial variability (measured using coefficients of variation, CV), separately for each component of the pollination service. Our specific questions were: 1) Is the spatial stability of pollinator visits related to landscape heterogeneity and the percentage of natural habitat surrounding the fields?; 2) Does the stability of different variables related to pollination service respond to the landscape at different scales (1 vs. 2 km)?; 3) Is the spatial stability of fruit production related to landscape heterogeneity and the spatial stability of pollinator visits?; and 4) Which variable explains better the overall stability of pollination services to almond trees: the percentage of natural area or landscape heterogeneity?
2.2. Pollinator visits We observed flower visits to almond tree flowers during two flowering seasons (2015–2016), from late January to late March, covering the whole flowering period of this species in Mallorca. In each of the 18 orchards, we haphazardly selected and marked 20 individual trees on which we observed flower visitors. We performed flower visitor censuses of 5 min on each individual tree each sampling day with weather conditions that allowed pollinator activity, between 09:30 and 18.00 h. Every sampling day, after each census to an individual almond tree, we estimated its total number of open flowers. Each of the two years every site was visited between 3 and 5 days, except for one site (‘Sa Marineta’) that could only be visited one day in 2015, and another site (‘Son Blai’) which was only sampled in 2015, because the owner ripped the almond trees before the second sampling season. During each 5 min observation period, we recorded the number and identity of flower visitors and the number of flowers that were visited by them. When the number of flowers did not allow recording visitation to the whole tree, observations of flower visitors were conducted on a part of the canopy (selected each census day), where we counted the number of open flowers (those branches contained on average 414 ± 12.76 open flowers). A visit was defined to have occurred when there was contact between the visitor’s body and the reproductive organs of the flower. Whenever flower visitors could not be identified to the species level in the site, they were collected for identification by European specialists. Collected specimens were deposited at the Laboratory of Terrestrial Ecology of IMEDEA. For each individual tree and observation period, we calculated visit rates per 500 flowers, as the number of visits divided by the number of open flowers observed in the census, and multiplied by 500. We calculated separate visitation rates for all insect pollinators and wild pollinators, i.e. excluding managed honeybees in the latter case (total and wild pollinator visit rates, respectively). To describe the diversity of pollinator visits in the simplest manner, we calculated the species richness of visiting pollinators to an individual tree in an observation period. To control for differences in the number of observed flowers in each census and tree, we standardized pollinator richness by dividing it by the number of observed flowers in each case (pollinator richness, hereafter).
2.3. Fruit set 2. Material and methods To estimate fruit set, we marked one branch in each study individual at the beginning of the flowering period each study year. In July, when fruits were ripe and before almond harvest, we counted the number of developed and aborted fruits in the marked branches. We estimated fruit set for each individual as the number of well-developed fruits divided by the number of flowers in the marked branch.
2.1. Study areas We selected 18 almond fields (Fig. 1) across the areas in Mallorca Island (Spain) where almond trees are cultivated. Sites were chosen to differ in size and surrounding landscape structure, to ensure enough variability in landscape heterogeneity for the analyses. Any two nearest 150
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Fig. 1. Map of the almond fields (triangles) across Mallorca Island (Southwest Mediterranean, Spain, Europe), where the stability of pollinator visitation and fruit production were studied in relation to landscape heterogeneity. Main city in Mallorca Island is represented with a circle. Study almond fields are the same as in Alomar et al. (2018).
buffer zones (e.g. 500 m), because in most cases this buffer would be included completely within the study crop field. We calculated these areas by using ArcMap 10.3 (Environmental Systems Research Institute, Redlands, CA) and land-use coverage maps (Instituto Geográfico Nacional, 2010). In total, we had 18 land-cover classes in the buffer zones, including different natural habitats (conifer, mixed and hardwood forests, transition woodlands, sclerophyllous shrubs), semi-natural habitats (2 classes), crops (6 classes) and urban areas/constructions (6 classes). We decided to include all these classes in the estimates of landscape heterogeneity as both, extensively managed agricultural landscapes (Winfree et al., 2007; Brosi et al., 2008) and urban or suburban areas (Fortel et al., 2014; Cane et al., 2006; Baldock et al., 2015), can be good habitats for many pollinator species. With these data we calculated Landscape richness, as the number of different land-cover classes in the buffer zones, Landscape diversity, as the diversity of these land-cover classes, calculated as Shannon’s (Shannon, 1948) diversity index, and Landscape evenness, as the Pielouös (1975) evenness index, calculated as J = H'/ln(S) where H' is Shannon diversity and S is the total number of land-cover classes. J varies from 0 to 1 and it is lower when there is dominance of one class in the landscape, whereas it increases as the evenness among different classes increase. We also calculated the percentage of natural habitat surrounding the almond fields in the 1 km- and 2km-radius buffer zone as: (total area of natural habitats/ total area in the buffer zone) *100.
2.4. Stability of pollination services We defined the spatial (among-individuals) stability of pollination services (estimated as total and wild pollinators’ visitation rates, pollinator richness, and fruit set) as the inverse of the spatial variability of those services (e.g. Lehman and Tilman, 2000; Garibaldi et al., 2011a; Haddad et al., 2011). This is a convenient measure of stability because it is dimensionless and scale invariant, and accounts for nonlinear dynamics (Lehman and Tilman, 2000). However, following Garibaldi et al. (2011a), we calculated the coefficient of variation, CV= (standard deviation / mean) * 100, of those services (that measures spatial variability) instead of CV−1 (the proper measure of stability), because it allows including in the analyses those sites where standard deviation was zero. To estimate stability of pollinator services, we calculated separated CV values for total visitation rates, wild pollinators’ visitation rates, pollinator richness, and fruit set, as they all are typical measures used in previous studies on pollination service to almond trees (e.g. Garibaldi et al., 2011a,b; Klein et al., 2012; Alomar et al., 2018) that describe different aspects of this service. Thus, same as in Garibaldi et al. (2011a), low CV values (i.e. low among-plant spatial variability) indicate high stability of the pollinator visits during crop flowering, and high stability of fruit production. On the contrary, large CV values indicate large spatial variation and therefore, low stability of pollinator visits and fruit production. CVs of pollinator visits were calculated for each sampling day at each site.
2.6. Statistical analyses 2.5. Landscape heterogeneity and percentage of natural habitat All the statistical analyses reported here were conducted in R version 3.2.4 (R Development Core Team, 2012). To study the effects of landscape variations on the spatial stability of pollination services, we used generalized linear mixed models (GLMM, library lme4), with site as random variable to account for possible effects of pseudoreplication. As response variables, we used CV total visit rate, CV wild pollinators’visit rate, CV pollinator richness, and CV fruit set, in separate models. In all models year was included as a categorical fixed predictor variable. Landscape richness, landscape Shannon diversity, landscape evenness, and the percentage of natural habitat at the two scales (1 km- and 2 km- radius
To estimate landscape heterogeneity, we calculated the areas of different land-cover classes, as they indicate the number of different habitats available to pollinators. The areas were calculated in the 1 kmand 2 km-radius buffer zones surrounding the sampling area, because different pollinators may respond to landscape heterogeneity at different spatial scales (e.g. Steffan-Dewenter et al., 2002; Kennedy et al., 2013), and the composition of the landscape at these scales have been shown to affect the visitation and diversity of pollinators of these particular almond fields (Alomar et al., 2018). We did not use smaller 151
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surrounding the study fields) were all included as predictor variables in the models. In the models of pollinator visits, the date was included as an additional predictor variable. In the model of fruit set CV total visit rate, CV wild pollinators’ visit rate, and CV pollinator richness were also included as predictor variables. We used gaussian distributions and log link functions in all the models, because all the response variables fulfilled the assumptions of normality once log-transformed. Prior to the models, we ran variation inflation factor (VIF) analyses to identify collinear predictor variables that should be removed from further analyses (VIF value ≥ 3; Zuur et al., 2009). As landscape diversity was highly correlated with landscape richness and evenness, we ran separate models with landscape diversity and with landscape richness and evenness. The variables calculated at the two scales, were also collinear, and therefore we conducted separated analyses for each scale, and selected the best model for each response variable based on AICc values. The rest of the variables did not show collinearity problems. We used dredge function in R (library MuMIn) to generate the best models with combinations (subsets) of all the terms from the global model through automated model selection. One model was considered better than other when ΔAICc > 2. Means and parameter estimates are accompanied by their standard error through the text. 3. Results Models with landscape richness and evenness showed lower AICc values than those based on landscape diversity whenever variables describing landscape heterogeneity appeared as predictive in the best models (see Table S1 in Electronic Supplementary Material); therefore, we report those results here. Overall, landscape heterogeneity explained more variation in the stability of the pollination service to almond trees (total visitation, fruit set) than the percentage of natural habitat, whilst the percentage of natural habitat in the landscape explained more variation in CV wild pollinators’ visitation and species richness. 3.1. Spatial stability of pollinator visits 3.1.1. Spatial stability of total visit rates CV total visit rate decreased with increasing landscape evenness in the 2 km buffer zone (Table 1A; Fig. 2A), which indicates that the spatial stability of total visit rates increased with landscape evenness. However, landscape evenness and diversity were equally good predictors of CV total visit rate (ΔAICc < 2; Table S1, Electronic Supplementary Material).
Fig. 2. Partial residual plot showing the relationship between the stability of pollinator visits and landscape characteristics in the buffer zones. A) Negative relationship between CV total visit rate and landscape evenness (i.e., the evenness of land-cover classes measured using Pielou’s (1975) evenness index) in the 2 km buffer zone; B) Negative relationship between CV wild-pollinators’visit rate and the percentage of natural habitat in the 2 km buffer zone; and C) Negative relationship between CVpollinator richnessand the percentage of natural habitat in the 1 km buffer zone. Lines represent the estimates of the best models and dots the partial residuals of the best models. These relationships indicate increases in the stability of total visitation rate with increasing landscape evenness, and increases in the stability of wild pollinator visit rates and pollinator richness with increasing natural habitat.
3.1.2. Spatial stability of wild pollinators’ visit rates CV wild pollinators’ visit rate decreased as the percentage of natural habitat increased in the 2 km buffer zone (Table 1B; Fig. 2B), although the model including the percentage of natural habitat in 1 km buffer zone Table 1 Results of the best models showing the relationship between landscape characteristics and the stability of pollination services. For each variable that appears in the best models, the χ2, the degrees of freedom (df) and the P-values are shown, calculated based on Likelihood Ratio Tests (LRT). * indicates that there is an alternative model which includes % Natural habitat 1 km (ΔAICc < 2; see Table S2 in Electronic Supplementary Material). Response variables A) CV total visit rate B) CV wild-pollinators’visit C) CV pollinator richness
D) CV
fruit set
rate
Predictor variables
χ
2
df
P
Landscape evenness 2 km % Natural habitat 2 km* % Natural habitat 1 km Date Year CV pollinator richness Landscape richness 2 km Landscape evenness 2 km
6.07 5.54 7.21 17.08 10.13 4.60 8.17 4.45
1 1 1 1 1 1 1 1
0.014 0.019 0.007 < 0.0001 0.001 0.032 0.004 0.035
performed equally well (ΔAICc < 2; see Table S2 in Electronic Supplementary Material). These results indicate that the spatial stability of wild-pollinators’ visit rate increased with the percentage of natural habitat in the landscape. 3.1.3. Spatial stability of pollinator richness CV pollinator richness decreased with the increasing percentage of natural habitat in 1 km buffer zone (Table 1C; Fig. 2C), as well as along the season (parameter estimate: -0.01 ± 0.003; Table 1C); and was higher the second study year (CV 2016: 65.70% ± 4.55 vs. CV 2015: 152
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buffer zone negatively affected it. 4. Discussion The stability of pollination services is essential to maintain a reliable crop production. Several studies have addressed the link between biodiversity and stability of ecosystem services (Lehman and Tilman, 2000; Thibaut and Connolli, 2013), and the positive relationship between the stability of pollination services to crops and the natural area surrounding the fields is well known (Garibaldi et al., 2011a). However, for the first time to our knowledge, our study directly links the effects of landscape heterogeneity to the stability of crop yields through the stability of pollinator visits. The importance of wild pollinators to global agricultural stability is well documented (Klein et al., 2007; Garibaldi et al., 2013). Wild pollinator species can provide much of the pollination service needed for crop production and may enhance fruit quality regardless of honey bee visitation (Garibaldi et al., 2011a, 2013). However, the role that landscape heterogeneity plays on the stability of pollinator visits and with it, on the stability of crop production has this far been unexplored. As other studies have reported, we found different landscape components to influence different components of pollination services and at different scales (e.g. Fabian et al., 2013; Aguirre-Gutiérrez et al., 2015; Moreira et al., 2015; Földesi et al., 2015). Overall, we found that landscape heterogeneity explained more variation in the spatial stability of pollination services to almond trees than the percentage of natural habitat surrounding the crops, as shown by its effects on the stability of total visit rates and fruit set. However, the percentage of natural habitat in the landscape was a stronger predictor for the stability of wild pollinators’ visitation and species richness. Non-crop habitats provide alternative foraging resources, nesting and hibernation sites, and therefore pollinator diversity may greatly benefit from heterogeneous landscapes with high availability and diversity of alternative habitats (Holzschuh et al., 2008). While landscape richness and diversity increase with number of different habitat types, evenness reflects only the distribution of the proportion that each habitat type occupies in the landscape. Interestingly, we found that landscape evenness at the 2 km-buffer zone increased the spatial stability of both total pollinator visitation and fruit set. The studies including evenness as a measure of landscape heterogeneity are still very few, but a positive effect of landscape evenness on mean abundance of wild bees in apple orchards was shown by Földesi et al. (2016), which suggests that, given a certain number of habitat types, mean number of wild bee visits benefit if none of the habitat types is dominant over the others. It might be also that high evenness increases the likelihood that high-quality source habitats for pollinators (nesting sites in the landscape, foraging site in the almond plantation) are found in the proximity of the focal sites, and therefore, because of landscape complementation (Dunning et al., 1992; Fahrig, 2003; Brotons et al., 2004), pollinator visits increase in those focal sites. Here, we went further and showed a positive effect of landscape evenness on the stability of pollinator visits and fruit production. In general, the effects of evenness on the stability of ecosystem functions are still unclear. Some researchers have studied the effects of species evenness on the stability of entire communities and single populations, finding contrasting results (Isbell et al., 2009; Mikkelson et al., 2011). At any rate, this study indicates that an even distribution of habitat types in the landscape helps to stabilize pollination services to almond trees. In contrast to the positive effect of landscape evenness on the stability of fruit set found in our study, the stability of crop yield was negatively affected by landscape richness, probably because of a loss of high-quality habitat for pollinators when the number of land-uses is too high. Although the greater the cover types in a landscape the more species that landscape might contain, for a given area an increase in the number of different cover types leads to a decrease in the amount of each (Duelli, 1997). This should produce a negative effect of landscape
Fig. 3. Partial residual plots showing the relationship between CV fruit set and: A) CV pollinator richness; B) landscape evenness (i.e., the evenness of land-cover classes measured using Pielou’s (1975) evenness index) in the 2 km buffer zone; and C) landscape richness (i.e., the number of land-cover classes) in the 2 km buffer zone. Lines represent the estimates of the best model and dots the partial residuals of the model. These relationships indicate that the stability of fruit set increases with the stability of pollinator richness and with landscape evenness, whereas it decreases with landscape richness.
73.95% ± 3.56; Table 1C). Therefore, the spatial stability of pollinator richness, same as wild-pollinators’ visit rate, increased as the natural habitat in the landscape increased, although at a smaller scale compared with visitation rates.
3.2. Spatial stability of fruit production CV fruit set and CV pollinator richness were positively related (Table 1D; Fig. 3A), indicating that the spatial stability of fruit set increased with the stability of pollinator richness. CV fruit set decreased with landscape evenness in the 2 km-buffer zone, whereas it increased with landscape richness in the 2 km buffer zone (Table 1D; Fig. 3B, C). Therefore, while landscape evenness in the 2 km-buffer zone had a positive influence on the stability of fruit set, the number of land-cover classes in the 2 km 153
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richness on habitat-specialists that have very large territories and ⁄ or that need large areas of contiguous habitat for population persistence, as they will disappear from landscapes in which the area of their required cover type is inadequate (Fahrig et al., 2011). Also, it could be that high landscape richness dilutes pollinators by attracting them to alternative habitats instead of plantations. Therefore, our results warn against the use of a single measure to characterize landscape heterogeneity and highlight the importance of break down landscape heterogeneity in their main components to fully understand its effects on the stability of pollination services to crops. Contrary to the results for total visit rates and fruit set and in line with other authors (Garibaldi et al., 2011a), we found that for the stability of wild-pollinators’ visit rates and of pollinator richness, the natural habitat surrounding the fields was the most important variable. This was expected as the abundance of wild pollinators might depend on the total availability of natural habitat surrounding the fields (Steffan-Dewenter and Tscharntke, 2001; Potts et al., 2003, 2005; Johst et al., 2006). In addition, landscape effects on the stability of pollinator richness were found at a smaller scale than the effects on other variables, probably because larger natural habitats in the closer proximity to the fields favour an increased number of wild species of lower mobility (e.g. Steffan-Dewenter et al., 2002). As shown for other crops (Klein, 2009; Garibaldi et al., 2011a), such stability in pollinator richness had a further positive effect on the stability of fruit set, highlighting the importance of stable and diverse pollinator communities for maintaining a stable crop production.
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5. Conclusions Land-use changes will have the largest impact on global biodiversity for the foreseeable future (Sala et al., 2000) with negative consequences for the stability of ecosystem services in anthropogenic landscapes (Winfree and Kremen, 2009). In the light of scenarios for global change, maintaining high levels of overall biodiversity in agro-ecosystems may become fundamental for ecological sustainability (Duelli, 1997). For the first time to our knowledge, our study directly links the effects of landscape heterogeneity to the stability of crop yields through the stability of pollinator visits. These results indicate that the adequate management of the spatial heterogeneity in agricultural landscapes might minimize the spatial variation in crop production whilst preserving biodiversity. Acknowledgments We are very grateful to Andreu Joan, from the ‘Conselleria de Medi Ambient, Agricultura i Pesca’ of the Regional Government of the Balearic Islands, and to Alejandro Aristondo, from the Cooperative ‘Productors Mallorquins de Fruits Secs S.A.T’, for all the information they have provided, and for their invaluable cooperation during the process of field selection. We are indebted to all the owners of almond tree fields that allowed us to work in their properties. We thank also Miguel Ángel González-Estévez, Manuel Hidalgo, Manuel Igual, and María Martín for their help in the field. Miguel González Calleja and Charina Cañas, from the Geographical Information Service of IMEDEA, estimated the landscape parameters. This project was partially financed by the Spanish Association of Terrestrial Ecology (AEET). During this study, DA was funded by a Javier Benedí Introduction to Research fellowship, financed by the Mediterranean Institute for Advanced Studies (UIB-CSIC), whereas AL was supported by a postdoctoral contract co-funded by the Regional Government of the Balearic Islands and the European Social Fund 2014–2020, and a Ramón y Cajal contract financed by the Spanish Ministry of Economy and Competitiveness. Appendix A. Supplementary data Supplementary material related to this article can be found, in the 154
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Hegland, S.-J., Boeke, L., 2006. Relationships between the density and diversity of floral resources and flower visitor activity in a temperate grassland community. Ecol. Entom. 31 (5), 532–538. Hoehn, P., Tscharntke, T., Tylianakis, J.M., Steffan-Dewenter, I., 2008. Functional group diversity of bee pollinators increases crop yield. Proc. R. Soc. B 275, 2283–2291. Holzschuh, A., Steffan-Dewenter, I., Tscharntke, T., 2008. Agricultural landscapes with organic crops support higher pollinator diversity. Oikos 117, 354–361. Isbell, F.I., Polley, H.W., Wilsey, B.J., 2009. Biodiversity, productivity and the temporal stability of productivity: patterns and processes. Ecol. Lett. 12, 443–451. Johst, K., Dreschsler, M., Thomas, J., Settele, J., 2006. Influence of mowing on the persistence of two endangered large blue butterfly species. J. Appl. Ecol. 43, 333–342. Kennedy, C.M., Lonsdorf, E., Neel, M.C., Williams, N.M., Ricketts, T.H., Winfree, R., Bommarco, R., 2013. A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems. Ecol. Lett. 16, 584–599. Klein, A.-M., 2009. Nearby rainforest promotes coffee pollination by increasing spatiotemporal stability in bee species richness. Forest Ecol. Manage. 258, 1838–1845. Klein, A.-M., Steffan-Dewenter, I., Tscharntke, T., 2003. Fruit set of highland coffee increases with the diversity of pollinating bees. Proc. R. Soc. B 270 (1518), 955–961. Klein, A.-M., Steffan-Dewenter, I., Tscharntke, T., 2004. Foraging trip duration and density of megachilid bees, eumenid wasps and pompilid wasps in tropical agroforestry systems. J. Anim. Ecol. 73, 517–525. Klein, A.-M., Vaissière, B.E., Cane, J.H., Steffan-Dewenter, I., Cunningham, S.A., Kremen, C., Tscharntke, T., 2007. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B 274, 303–313. Klein, A.‐M., Brittain, C., Hendrix, S.D., Thorp, R., Williams, N., Kremen, C., 2012. Wild pollination services to California almond rely on semi‐natural habitat. J. Appl. Ecol. 49, 723–732. Kremen, C., Williams, N.M., Thorp, R.W., 2002. Crop pollination from native bees at risk from agricultural intensification. PNAS 99 (26), 16812–16816. Kremen, C., Williams, N.M., Bugg, R.L., Fay, J.P., Thorp, R.W., 2004. The area requirements of an ecosystem service: crop pollination by native bee communities in California. Ecol. Lett. 7, 1109–1119. Lehman, C.L., Tilman, D., 2000. Biodiversity, stability, and productivity in competitive communities. Am. Nat. 156, 534–552. Mikkelson, G.M., McGill, B.J., Beaulieu, S., Beukema, P.L., 2011. Multiple links between species diversity and temporal stability in bird communities across North America. Evol. Ecol. Res. 13, 361–372. Moreira, E.F., Boscolo, D., Viana, B.F., 2015. Spatial heterogeneity regulates plant-pollinator networks across multiple landscape scales. PLoS One. https://doi.org/10. 1371/journal.pone.0123628. Pielou, E.C., 1975. Ecological Diversity. Wiley, New York. Potts, S.G., Vulliamy, B., Dafni, A., Ne’eman, G., Willmer, P., 2003. Linking bees and
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Landscape heterogeneity increases the spatial stability of pollination services to almond trees through the stability of pollinator visits
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Amparo Lázaro , David Alomar Mediterranean Institute for Advanced Studies (IMEDEA, UIB-CSIC), Global Change Research Group, C/Miquel Marquès 21, 07190, Esporles, Balearic Islands, Spain
A R T I C LE I N FO
A B S T R A C T
Keywords: Coefficient of variation Diversity of land-cover classes within a landscape Evenness of land-cover classes within a landscape Fruit production Percentage of natural habitat Pollinator richness Pollinator visits Prunus dulcis Richness of land-cover classes within a landscape
High pollinator reliability is essential to maintain a stable and predictable crop production. However, still few studies have considered landscape effects on the stability (low variability) of the pollination service to crops. The stability of pollination services may increase in heterogeneous landscapes with high availability and diversity of alternative habitats. By using data on pollinator visitation and fruit production in 18 almond tree fields in Mallorca Island, we related landscape heterogeneity (richness, evenness and diversity of land-cover classes) at 1 and 2 km-radius buffer zone to the spatial (among-tree) stability of their pollination services (total visit rates, wild pollinators’ visit rates, pollinator richness and fruit set). Overall, landscape heterogeneity increased the spatial stability (i.e., decreased spatial variability) of pollination services to almond trees. Interestingly different components of heterogeneity influenced diverse components of this service. Thus, increasing landscape evenness at the 2 km buffer zone increased the spatial stability of both total pollinator visitation and fruit set. However, the stability of wild-pollinators’ visit rate as well as the stability of pollinator richness increased instead with the percentage of natural habitat in the buffers, as the abundance of wild pollinators might depend on the total availability of habitat in the field surroundings. The stability in pollinator richness had a further positive effect on the stability of fruit set, indicating the importance of diversity maintaining stable crop production. Lastly, fruit set was also negatively affected by landscape richness, probably because of a loss of pollinator source habitat when the number of land uses is too high. Preserving a heterogeneous habitat of high quality in the surroundings of crop fields may help to maintain a stable fruit production.
1. Introduction Pollination is an essential ecosystem service that is known to rely on pollinator diversity for both increased effectiveness and stability, i.e., low temporal or spatial variability in those services (Garibaldi et al., 2011a, 2013; Blitzer et al., 2016). Despite stability being an important component of sustainability of ecosystem functions (Balvanera et al., 2006; Haddad et al., 2011), the factors influencing the stability of pollination services to crops are still not well understood and have seldom been analysed (but see Kremen et al., 2004; Klein, 2009; Garibaldi et al., 2011a). Understanding how landscape features are related to the stability of pollination services is critical to the predictability of crop yields and to decrease the impacts of biodiversity loss in the face of current anthropogenic changes (Garibaldi et al., 2014). Natural or semi-natural areas within agricultural landscapes provide habitat for wild pollinators, supplying them with resources on which they depend, such as flowers (Steffan-Dewenter and Tscharntke, 2001; Potts et al., 2003), nesting sites (Potts et al., 2005), and oviposition sites
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(Johst et al., 2006). Larger natural areas surrounding the fields might contain higher plant richness which may in turn allow maintaining larger and more stable pollinator communities (Hegland and Boeke, 2006; Ebeling et al., 2008) that can spillover into crop fields (Tscharntke et al., 2005). As a consequence, mean levels of pollinator richness and abundance in croplands (Ricketts et al., 2008; Garibaldi et al., 2011a), but also the stability of pollinator visits and fruit production (Garibaldi et al., 2011a) have been shown to decrease with the distance of agricultural fields from natural and semi-natural areas. In addition to the proximity of crop fields to natural habitats, landscape heterogeneity could play a crucial role in the stability of pollination services. Landscape heterogeneity often has a positive effect on biodiversity in agricultural landscapes, because the heterogeneity of cover types increases the number of habitats and microhabitats available for different pollinators (Holzschuh et al., 2008). Also, many species living in agro-ecosystems depend on complementary resources from different habitats, such as feeding or nesting sites (Gathmann and Tscharntke, 2002; Klein et al., 2004). Thus, increasing landscape
Corresponding author. E-mail address: [email protected] (A. Lázaro).
https://doi.org/10.1016/j.agee.2019.02.009 Received 14 May 2018; Received in revised form 20 November 2018; Accepted 11 February 2019 Available online 05 April 2019 0167-8809/ © 2019 Elsevier B.V. All rights reserved.
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fields were always separated by a minimum distance of 1 km, except for two fields that were separated by 850 m, as they were the only fields available for the study in this area (mean ± SD distance among fields: 35.48 ± 21.29 km). A more detailed description of the study sites can be found in Alomar et al. (2018). In previous work at these sites, we showed that although ca. 90% of the visits to almond trees’ flowers were conducted by honeybees, another 43 species of wild pollinators visited them, including bees, muscoid flies, hoverflies, beetles, wasps and butterflies (Alomar et al., 2018). In this same work, we also showed that the percentage of natural area surrounding the fields increased both pollinator-species richness and honeybee visits. At a more local scale, the flower community in the fields positively influenced almond production, both directly and indirectly through effects on the diversity of wild-pollinators (Alomar et al., 2018).
heterogeneity may also increase landscape complementation, which occurs when mobile organisms move between patches in the landscape to make use of non-substitutable resources (Dunning et al., 1992; Fahrig, 2003; Brotons et al., 2004). Moreover, landscape heterogeneity might increase biodiversity by reducing competition and de-coupling patch dynamics across meta-communities (Fahrig et al., 2011). As a result of all this, several studies have shown that pollinator diversity is higher in complex landscapes than in structurally simple ones (Holzschuh et al., 2008; Batáry et al., 2011) and that landscape heterogeneity increases pollinator diversity in crop fields (Rundlöf et al., 2008; Andersson et al., 2013; Fabian et al., 2013; Aguirre-Gutiérrez et al., 2015; Foldesi et al., 2016; Senapathi et al., 2017). There is now a growing consensus that biodiversity enhances ecosystem functions in general (Balvanera et al., 2006; Allan et al., 2015; Soliveres et al., 2016) and the delivery of pollination service in particular (Kremen et al., 2002; Klein et al., 2003; Hoehn et al., 2008). A major ecosystem effect of biodiversity is to stabilise assemblages that perform particular functions (Thibaut and Connolli, 2013). Therefore, greater pollinator diversity in heterogeneous landscapes may provide more stable pollination services, as diversity often increases the stability of the entire communities (Lehman and Tilman, 2000). High pollinator diversity may augment pollination services by increasing functional complementarity (Hoehn et al., 2008; Blüthgen and Klein, 2011; Blitzer et al., 2016). This is because temporal or environmental variation in pollinator species’ activities may contribute to complementary effects on plant pollination (Blüthgen and Klein, 2011), response diversity, and cross-scale resilience (Winfree and Kremen, 2009), with positive effects on the pollination service. When crop production is pollen limited, as is often the case in pollinator-dependent crops (e.g. Gary et al., 1976; Blitzer et al., 2016), a lower spatial and temporal stability of pollinators may decrease also the stability of fruit set (Klein, 2009; Garibaldi et al., 2011b). Here, we used data on 18 almond tree fields in Mallorca Island to evaluate how landscape heterogeneity (richness, evenness and diversity of land-cover classes) and the percentage of natural habitat surrounding the crop fields, modulate the spatial stability of pollination services to almond trees, measured as total – including managed honeybees- and wild pollinators’ visit rate (visit/flower in 5 min), pollinator richness (number of species/tree) and fruit set (fruits/flowers). We used landcover classes to estimate heterogeneity because they represent the number of different habitats available for pollinators in the landscape. As different pollinators may respond to landscape at different spatial scales (e.g. Steffan-Dewenter et al., 2002; Kennedy et al., 2013; Alomar et al., 2018), we calculated landscape characteristics in the 1 km- and 2 km-radius buffer zones surrounding the sampling sites. Following Garibaldi et al. (2011a), we estimated the spatial (among-tree) stability as the inverse of the spatial variability (measured using coefficients of variation, CV), separately for each component of the pollination service. Our specific questions were: 1) Is the spatial stability of pollinator visits related to landscape heterogeneity and the percentage of natural habitat surrounding the fields?; 2) Does the stability of different variables related to pollination service respond to the landscape at different scales (1 vs. 2 km)?; 3) Is the spatial stability of fruit production related to landscape heterogeneity and the spatial stability of pollinator visits?; and 4) Which variable explains better the overall stability of pollination services to almond trees: the percentage of natural area or landscape heterogeneity?
2.2. Pollinator visits We observed flower visits to almond tree flowers during two flowering seasons (2015–2016), from late January to late March, covering the whole flowering period of this species in Mallorca. In each of the 18 orchards, we haphazardly selected and marked 20 individual trees on which we observed flower visitors. We performed flower visitor censuses of 5 min on each individual tree each sampling day with weather conditions that allowed pollinator activity, between 09:30 and 18.00 h. Every sampling day, after each census to an individual almond tree, we estimated its total number of open flowers. Each of the two years every site was visited between 3 and 5 days, except for one site (‘Sa Marineta’) that could only be visited one day in 2015, and another site (‘Son Blai’) which was only sampled in 2015, because the owner ripped the almond trees before the second sampling season. During each 5 min observation period, we recorded the number and identity of flower visitors and the number of flowers that were visited by them. When the number of flowers did not allow recording visitation to the whole tree, observations of flower visitors were conducted on a part of the canopy (selected each census day), where we counted the number of open flowers (those branches contained on average 414 ± 12.76 open flowers). A visit was defined to have occurred when there was contact between the visitor’s body and the reproductive organs of the flower. Whenever flower visitors could not be identified to the species level in the site, they were collected for identification by European specialists. Collected specimens were deposited at the Laboratory of Terrestrial Ecology of IMEDEA. For each individual tree and observation period, we calculated visit rates per 500 flowers, as the number of visits divided by the number of open flowers observed in the census, and multiplied by 500. We calculated separate visitation rates for all insect pollinators and wild pollinators, i.e. excluding managed honeybees in the latter case (total and wild pollinator visit rates, respectively). To describe the diversity of pollinator visits in the simplest manner, we calculated the species richness of visiting pollinators to an individual tree in an observation period. To control for differences in the number of observed flowers in each census and tree, we standardized pollinator richness by dividing it by the number of observed flowers in each case (pollinator richness, hereafter).
2.3. Fruit set 2. Material and methods To estimate fruit set, we marked one branch in each study individual at the beginning of the flowering period each study year. In July, when fruits were ripe and before almond harvest, we counted the number of developed and aborted fruits in the marked branches. We estimated fruit set for each individual as the number of well-developed fruits divided by the number of flowers in the marked branch.
2.1. Study areas We selected 18 almond fields (Fig. 1) across the areas in Mallorca Island (Spain) where almond trees are cultivated. Sites were chosen to differ in size and surrounding landscape structure, to ensure enough variability in landscape heterogeneity for the analyses. Any two nearest 150
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Fig. 1. Map of the almond fields (triangles) across Mallorca Island (Southwest Mediterranean, Spain, Europe), where the stability of pollinator visitation and fruit production were studied in relation to landscape heterogeneity. Main city in Mallorca Island is represented with a circle. Study almond fields are the same as in Alomar et al. (2018).
buffer zones (e.g. 500 m), because in most cases this buffer would be included completely within the study crop field. We calculated these areas by using ArcMap 10.3 (Environmental Systems Research Institute, Redlands, CA) and land-use coverage maps (Instituto Geográfico Nacional, 2010). In total, we had 18 land-cover classes in the buffer zones, including different natural habitats (conifer, mixed and hardwood forests, transition woodlands, sclerophyllous shrubs), semi-natural habitats (2 classes), crops (6 classes) and urban areas/constructions (6 classes). We decided to include all these classes in the estimates of landscape heterogeneity as both, extensively managed agricultural landscapes (Winfree et al., 2007; Brosi et al., 2008) and urban or suburban areas (Fortel et al., 2014; Cane et al., 2006; Baldock et al., 2015), can be good habitats for many pollinator species. With these data we calculated Landscape richness, as the number of different land-cover classes in the buffer zones, Landscape diversity, as the diversity of these land-cover classes, calculated as Shannon’s (Shannon, 1948) diversity index, and Landscape evenness, as the Pielouös (1975) evenness index, calculated as J = H'/ln(S) where H' is Shannon diversity and S is the total number of land-cover classes. J varies from 0 to 1 and it is lower when there is dominance of one class in the landscape, whereas it increases as the evenness among different classes increase. We also calculated the percentage of natural habitat surrounding the almond fields in the 1 km- and 2km-radius buffer zone as: (total area of natural habitats/ total area in the buffer zone) *100.
2.4. Stability of pollination services We defined the spatial (among-individuals) stability of pollination services (estimated as total and wild pollinators’ visitation rates, pollinator richness, and fruit set) as the inverse of the spatial variability of those services (e.g. Lehman and Tilman, 2000; Garibaldi et al., 2011a; Haddad et al., 2011). This is a convenient measure of stability because it is dimensionless and scale invariant, and accounts for nonlinear dynamics (Lehman and Tilman, 2000). However, following Garibaldi et al. (2011a), we calculated the coefficient of variation, CV= (standard deviation / mean) * 100, of those services (that measures spatial variability) instead of CV−1 (the proper measure of stability), because it allows including in the analyses those sites where standard deviation was zero. To estimate stability of pollinator services, we calculated separated CV values for total visitation rates, wild pollinators’ visitation rates, pollinator richness, and fruit set, as they all are typical measures used in previous studies on pollination service to almond trees (e.g. Garibaldi et al., 2011a,b; Klein et al., 2012; Alomar et al., 2018) that describe different aspects of this service. Thus, same as in Garibaldi et al. (2011a), low CV values (i.e. low among-plant spatial variability) indicate high stability of the pollinator visits during crop flowering, and high stability of fruit production. On the contrary, large CV values indicate large spatial variation and therefore, low stability of pollinator visits and fruit production. CVs of pollinator visits were calculated for each sampling day at each site.
2.6. Statistical analyses 2.5. Landscape heterogeneity and percentage of natural habitat All the statistical analyses reported here were conducted in R version 3.2.4 (R Development Core Team, 2012). To study the effects of landscape variations on the spatial stability of pollination services, we used generalized linear mixed models (GLMM, library lme4), with site as random variable to account for possible effects of pseudoreplication. As response variables, we used CV total visit rate, CV wild pollinators’visit rate, CV pollinator richness, and CV fruit set, in separate models. In all models year was included as a categorical fixed predictor variable. Landscape richness, landscape Shannon diversity, landscape evenness, and the percentage of natural habitat at the two scales (1 km- and 2 km- radius
To estimate landscape heterogeneity, we calculated the areas of different land-cover classes, as they indicate the number of different habitats available to pollinators. The areas were calculated in the 1 kmand 2 km-radius buffer zones surrounding the sampling area, because different pollinators may respond to landscape heterogeneity at different spatial scales (e.g. Steffan-Dewenter et al., 2002; Kennedy et al., 2013), and the composition of the landscape at these scales have been shown to affect the visitation and diversity of pollinators of these particular almond fields (Alomar et al., 2018). We did not use smaller 151
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surrounding the study fields) were all included as predictor variables in the models. In the models of pollinator visits, the date was included as an additional predictor variable. In the model of fruit set CV total visit rate, CV wild pollinators’ visit rate, and CV pollinator richness were also included as predictor variables. We used gaussian distributions and log link functions in all the models, because all the response variables fulfilled the assumptions of normality once log-transformed. Prior to the models, we ran variation inflation factor (VIF) analyses to identify collinear predictor variables that should be removed from further analyses (VIF value ≥ 3; Zuur et al., 2009). As landscape diversity was highly correlated with landscape richness and evenness, we ran separate models with landscape diversity and with landscape richness and evenness. The variables calculated at the two scales, were also collinear, and therefore we conducted separated analyses for each scale, and selected the best model for each response variable based on AICc values. The rest of the variables did not show collinearity problems. We used dredge function in R (library MuMIn) to generate the best models with combinations (subsets) of all the terms from the global model through automated model selection. One model was considered better than other when ΔAICc > 2. Means and parameter estimates are accompanied by their standard error through the text. 3. Results Models with landscape richness and evenness showed lower AICc values than those based on landscape diversity whenever variables describing landscape heterogeneity appeared as predictive in the best models (see Table S1 in Electronic Supplementary Material); therefore, we report those results here. Overall, landscape heterogeneity explained more variation in the stability of the pollination service to almond trees (total visitation, fruit set) than the percentage of natural habitat, whilst the percentage of natural habitat in the landscape explained more variation in CV wild pollinators’ visitation and species richness. 3.1. Spatial stability of pollinator visits 3.1.1. Spatial stability of total visit rates CV total visit rate decreased with increasing landscape evenness in the 2 km buffer zone (Table 1A; Fig. 2A), which indicates that the spatial stability of total visit rates increased with landscape evenness. However, landscape evenness and diversity were equally good predictors of CV total visit rate (ΔAICc < 2; Table S1, Electronic Supplementary Material).
Fig. 2. Partial residual plot showing the relationship between the stability of pollinator visits and landscape characteristics in the buffer zones. A) Negative relationship between CV total visit rate and landscape evenness (i.e., the evenness of land-cover classes measured using Pielou’s (1975) evenness index) in the 2 km buffer zone; B) Negative relationship between CV wild-pollinators’visit rate and the percentage of natural habitat in the 2 km buffer zone; and C) Negative relationship between CVpollinator richnessand the percentage of natural habitat in the 1 km buffer zone. Lines represent the estimates of the best models and dots the partial residuals of the best models. These relationships indicate increases in the stability of total visitation rate with increasing landscape evenness, and increases in the stability of wild pollinator visit rates and pollinator richness with increasing natural habitat.
3.1.2. Spatial stability of wild pollinators’ visit rates CV wild pollinators’ visit rate decreased as the percentage of natural habitat increased in the 2 km buffer zone (Table 1B; Fig. 2B), although the model including the percentage of natural habitat in 1 km buffer zone Table 1 Results of the best models showing the relationship between landscape characteristics and the stability of pollination services. For each variable that appears in the best models, the χ2, the degrees of freedom (df) and the P-values are shown, calculated based on Likelihood Ratio Tests (LRT). * indicates that there is an alternative model which includes % Natural habitat 1 km (ΔAICc < 2; see Table S2 in Electronic Supplementary Material). Response variables A) CV total visit rate B) CV wild-pollinators’visit C) CV pollinator richness
D) CV
fruit set
rate
Predictor variables
χ
2
df
P
Landscape evenness 2 km % Natural habitat 2 km* % Natural habitat 1 km Date Year CV pollinator richness Landscape richness 2 km Landscape evenness 2 km
6.07 5.54 7.21 17.08 10.13 4.60 8.17 4.45
1 1 1 1 1 1 1 1
0.014 0.019 0.007 < 0.0001 0.001 0.032 0.004 0.035
performed equally well (ΔAICc < 2; see Table S2 in Electronic Supplementary Material). These results indicate that the spatial stability of wild-pollinators’ visit rate increased with the percentage of natural habitat in the landscape. 3.1.3. Spatial stability of pollinator richness CV pollinator richness decreased with the increasing percentage of natural habitat in 1 km buffer zone (Table 1C; Fig. 2C), as well as along the season (parameter estimate: -0.01 ± 0.003; Table 1C); and was higher the second study year (CV 2016: 65.70% ± 4.55 vs. CV 2015: 152
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buffer zone negatively affected it. 4. Discussion The stability of pollination services is essential to maintain a reliable crop production. Several studies have addressed the link between biodiversity and stability of ecosystem services (Lehman and Tilman, 2000; Thibaut and Connolli, 2013), and the positive relationship between the stability of pollination services to crops and the natural area surrounding the fields is well known (Garibaldi et al., 2011a). However, for the first time to our knowledge, our study directly links the effects of landscape heterogeneity to the stability of crop yields through the stability of pollinator visits. The importance of wild pollinators to global agricultural stability is well documented (Klein et al., 2007; Garibaldi et al., 2013). Wild pollinator species can provide much of the pollination service needed for crop production and may enhance fruit quality regardless of honey bee visitation (Garibaldi et al., 2011a, 2013). However, the role that landscape heterogeneity plays on the stability of pollinator visits and with it, on the stability of crop production has this far been unexplored. As other studies have reported, we found different landscape components to influence different components of pollination services and at different scales (e.g. Fabian et al., 2013; Aguirre-Gutiérrez et al., 2015; Moreira et al., 2015; Földesi et al., 2015). Overall, we found that landscape heterogeneity explained more variation in the spatial stability of pollination services to almond trees than the percentage of natural habitat surrounding the crops, as shown by its effects on the stability of total visit rates and fruit set. However, the percentage of natural habitat in the landscape was a stronger predictor for the stability of wild pollinators’ visitation and species richness. Non-crop habitats provide alternative foraging resources, nesting and hibernation sites, and therefore pollinator diversity may greatly benefit from heterogeneous landscapes with high availability and diversity of alternative habitats (Holzschuh et al., 2008). While landscape richness and diversity increase with number of different habitat types, evenness reflects only the distribution of the proportion that each habitat type occupies in the landscape. Interestingly, we found that landscape evenness at the 2 km-buffer zone increased the spatial stability of both total pollinator visitation and fruit set. The studies including evenness as a measure of landscape heterogeneity are still very few, but a positive effect of landscape evenness on mean abundance of wild bees in apple orchards was shown by Földesi et al. (2016), which suggests that, given a certain number of habitat types, mean number of wild bee visits benefit if none of the habitat types is dominant over the others. It might be also that high evenness increases the likelihood that high-quality source habitats for pollinators (nesting sites in the landscape, foraging site in the almond plantation) are found in the proximity of the focal sites, and therefore, because of landscape complementation (Dunning et al., 1992; Fahrig, 2003; Brotons et al., 2004), pollinator visits increase in those focal sites. Here, we went further and showed a positive effect of landscape evenness on the stability of pollinator visits and fruit production. In general, the effects of evenness on the stability of ecosystem functions are still unclear. Some researchers have studied the effects of species evenness on the stability of entire communities and single populations, finding contrasting results (Isbell et al., 2009; Mikkelson et al., 2011). At any rate, this study indicates that an even distribution of habitat types in the landscape helps to stabilize pollination services to almond trees. In contrast to the positive effect of landscape evenness on the stability of fruit set found in our study, the stability of crop yield was negatively affected by landscape richness, probably because of a loss of high-quality habitat for pollinators when the number of land-uses is too high. Although the greater the cover types in a landscape the more species that landscape might contain, for a given area an increase in the number of different cover types leads to a decrease in the amount of each (Duelli, 1997). This should produce a negative effect of landscape
Fig. 3. Partial residual plots showing the relationship between CV fruit set and: A) CV pollinator richness; B) landscape evenness (i.e., the evenness of land-cover classes measured using Pielou’s (1975) evenness index) in the 2 km buffer zone; and C) landscape richness (i.e., the number of land-cover classes) in the 2 km buffer zone. Lines represent the estimates of the best model and dots the partial residuals of the model. These relationships indicate that the stability of fruit set increases with the stability of pollinator richness and with landscape evenness, whereas it decreases with landscape richness.
73.95% ± 3.56; Table 1C). Therefore, the spatial stability of pollinator richness, same as wild-pollinators’ visit rate, increased as the natural habitat in the landscape increased, although at a smaller scale compared with visitation rates.
3.2. Spatial stability of fruit production CV fruit set and CV pollinator richness were positively related (Table 1D; Fig. 3A), indicating that the spatial stability of fruit set increased with the stability of pollinator richness. CV fruit set decreased with landscape evenness in the 2 km-buffer zone, whereas it increased with landscape richness in the 2 km buffer zone (Table 1D; Fig. 3B, C). Therefore, while landscape evenness in the 2 km-buffer zone had a positive influence on the stability of fruit set, the number of land-cover classes in the 2 km 153
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richness on habitat-specialists that have very large territories and ⁄ or that need large areas of contiguous habitat for population persistence, as they will disappear from landscapes in which the area of their required cover type is inadequate (Fahrig et al., 2011). Also, it could be that high landscape richness dilutes pollinators by attracting them to alternative habitats instead of plantations. Therefore, our results warn against the use of a single measure to characterize landscape heterogeneity and highlight the importance of break down landscape heterogeneity in their main components to fully understand its effects on the stability of pollination services to crops. Contrary to the results for total visit rates and fruit set and in line with other authors (Garibaldi et al., 2011a), we found that for the stability of wild-pollinators’ visit rates and of pollinator richness, the natural habitat surrounding the fields was the most important variable. This was expected as the abundance of wild pollinators might depend on the total availability of natural habitat surrounding the fields (Steffan-Dewenter and Tscharntke, 2001; Potts et al., 2003, 2005; Johst et al., 2006). In addition, landscape effects on the stability of pollinator richness were found at a smaller scale than the effects on other variables, probably because larger natural habitats in the closer proximity to the fields favour an increased number of wild species of lower mobility (e.g. Steffan-Dewenter et al., 2002). As shown for other crops (Klein, 2009; Garibaldi et al., 2011a), such stability in pollinator richness had a further positive effect on the stability of fruit set, highlighting the importance of stable and diverse pollinator communities for maintaining a stable crop production.
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5. Conclusions Land-use changes will have the largest impact on global biodiversity for the foreseeable future (Sala et al., 2000) with negative consequences for the stability of ecosystem services in anthropogenic landscapes (Winfree and Kremen, 2009). In the light of scenarios for global change, maintaining high levels of overall biodiversity in agro-ecosystems may become fundamental for ecological sustainability (Duelli, 1997). For the first time to our knowledge, our study directly links the effects of landscape heterogeneity to the stability of crop yields through the stability of pollinator visits. These results indicate that the adequate management of the spatial heterogeneity in agricultural landscapes might minimize the spatial variation in crop production whilst preserving biodiversity. Acknowledgments We are very grateful to Andreu Joan, from the ‘Conselleria de Medi Ambient, Agricultura i Pesca’ of the Regional Government of the Balearic Islands, and to Alejandro Aristondo, from the Cooperative ‘Productors Mallorquins de Fruits Secs S.A.T’, for all the information they have provided, and for their invaluable cooperation during the process of field selection. We are indebted to all the owners of almond tree fields that allowed us to work in their properties. We thank also Miguel Ángel González-Estévez, Manuel Hidalgo, Manuel Igual, and María Martín for their help in the field. Miguel González Calleja and Charina Cañas, from the Geographical Information Service of IMEDEA, estimated the landscape parameters. This project was partially financed by the Spanish Association of Terrestrial Ecology (AEET). During this study, DA was funded by a Javier Benedí Introduction to Research fellowship, financed by the Mediterranean Institute for Advanced Studies (UIB-CSIC), whereas AL was supported by a postdoctoral contract co-funded by the Regional Government of the Balearic Islands and the European Social Fund 2014–2020, and a Ramón y Cajal contract financed by the Spanish Ministry of Economy and Competitiveness. Appendix A. Supplementary data Supplementary material related to this article can be found, in the 154
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