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Field scale organic farming does not counteract landscape effects on butterfly trait composition
Agriculture, Ecosystems and Environment 158 (2012) 66–71
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Field scale organic farming does not counteract landscape effects on butterfly trait composition Dennis Jonason a,∗ , Georg K.S. Andersson b,c , Erik Öckinger a , Henrik G. Smith b,c , Jan Bengtsson a a b c
Swedish University of Agricultural Sciences, Department of Ecology, PO Box 7044, SE-750 07 Uppsala, Sweden Lund University, Department of Biology, SE-223 62 Lund, Sweden Lund University, Centre of Environmental and Climate Research, SE-223 62 Lund, Sweden
a r t i c l e
i n f o
Article history: Received 13 March 2012 Received in revised form 29 May 2012 Accepted 30 May 2012 Available online 26 June 2012 Keywords: Agri-environment schemes Farmland biodiversity Farming system Species traits Time since transition
a b s t r a c t We tested how dispersal capacity, host plant specificity and reproductive rate influenced the effects of farming system and landscape composition on butterfly species richness and abundance. In no case did variation in these traits explain species responses to organic farming, indicating that all species benefit equally. In contrast, butterflies with high mobility and reproductive rate were disproportionally more abundant in landscapes dominated by arable land, and the species richness of butterflies with low mobility tended to decrease with increasing proportion of arable land whereas those of high mobility remained fairly constant. Hence, although organic farming increased biodiversity, it did not counteract landscape effects on butterfly trait composition. As a trait dependent loss of biodiversity may result in a larger decline of functional trait diversity compared to species diversity, these results imply that organic farming may not increase or restore functional agro-ecosystem diversity. Information provided by species traits, rather than biodiversity per se, may provide important information for successful revisions of future agri-environment schemes. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Anthropogenic changes in land use are the foremost cause behind worldwide biodiversity decline (Millennium Ecosystem Assessment, 2005; Kleijn et al., 2009). However, species with certain traits may be more susceptible than other (Fisher and Owens, 2004). For example, plant traits related to life-history, growth form, palatability etc. has been used to predict responses to grazing (Diaz et al., 2007) and the mobility of animals have been used to predict responses to habitat fragmentation and agricultural intensification (Thomas, 2000; Gabriel et al., 2010). If large scale environmental changes affect species with certain traits disproportionally, it can ultimately lead to a state of biotic homogenisation in which the community composition progressively becomes more similar (McKinney and Lockwood, 1999; Ekroos et al., 2010). Diaz and Cabido (2001) suggested trait diversity as a determinant of ecosystem processes and functions. Identification of the traits that shape species’ responses to environmental change caused by human activities is therefore vital for proactive landscape management. The intensification of agriculture is one of the major single drivers behind the declining biodiversity in agro-ecosystems
∗ Corresponding author. Tel.: +46 18672257. E-mail address: [email protected] (D. Jonason). 0167-8809/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agee.2012.05.026
(Benton et al., 2003; Concepción et al., 2008). Increased use of pesticides and chemical fertilisers, simplified crop rotations and loss of semi-natural biotopes are among the factors contributing to agricultural intensification (Geiger et al., 2010). To mitigate the negative effects on biodiversity, agri-environment schemes (AES) are implemented to provide financial incentives to European farmers for environmentally and wildlife friendly land management (Kleijn et al., 2011). Organic farming, which is an AES applied at the farm scale, generally promotes biodiversity (Bengtsson et al., 2005). However, although biodiversity-based evaluations of AES provide a comprehensive picture of species responses, less is known whether species with certain traits are differentially affected, which then may be a better predictor of the ability of AES to produce functional agro-ecosystems in the absence of agrochemicals. In a recent study (Jonason et al., 2011), higher species richness and abundance of plants and butterflies were found on organic compared to conventional farms. Further, the butterfly abundance gradually increased by 100% over a 25-year period, and the butterfly species richness decreased with landscape simplification. To explore if these effects pertain equally to all butterflies, or are more pronounced among species with certain traits, additional analyses on the data in Jonason et al. (2011), focusing on three traits: dispersal capacity, host plant specificity and reproductive rate, were performed. These traits have previously been found to influence butterfly responses to habitat loss (Thomas, 2000; Hambäck et al., 2007; Öckinger et al., 2010), and were therefore assumed to be
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reliable predictors of butterfly’ responses to both farming system and landscape context. It was predicted that the butterflies would show differential responses depending on species traits to conventional farming and to landscapes dominated by arable fields, due to decreased habitat quality and availability, but that organic farms would maintain the variation within traits.
2. Methods The field data were collected during the summer of 2009 in the provinces of Uppland and Scania, Sweden. Data on butterfly species richness and abundance were collected on a total of 60 farms, 10 conventional and 20 organic in each province. The organic farms had been managed in accordance to the regulations of organic production set by the European Commission (Council Regulation (EC) No 834/2007). To study possible temporal effects, organic farms that previously had been managed conventionally and differed in time since transition to organic management from 1 to 25 years were chosen. Landscape composition may affect the extent in which species respond to farming system, with highest effect at intermediate levels of landscape heterogeneity (Concepción et al., in press). Therefore, all farms were selected so that they were distributed along a gradient of landscape heterogeneity, which in turn was assured not to be confounded with the gradient in time since transition to organic farming. As proxy for landscape complexity the proportion of arable land within a 1 km circular sector around each sampling point was used, where low proportional values indicate heterogeneous landscapes and vice versa. No circular sectors were overlapping, hence the smallest distance between sampling points was never less than 2 km. Although in Sweden a low proportion of arable land often corresponds to a high proportion of forest, which as arable land can be considered to homogenise the landscape, it has been shown that a matrix consisting of forest provides several suitable structures for butterflies and is more beneficial compared to a matrix consisting of arable land (Öckinger et al., 2012). The landscape analyses were made using ArcGis 9.3 (Esri Inc., Redlands, CA, USA) on land use data from the Swedish Board of Agriculture and the Swedish Mapping, Cadastral and Land Registration Authority.
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2.2. Trait selection Average wing span was used as proxy for dispersal capacity as these characteristics have empirically been shown to be highly correlated among Lepidoptera (Nieminen, 1996). Wing span may not be an ideal proxy for dispersal capacity, but it was recently identified as the best choice among traits that are easily accessible in the literature (Sekar, 2012). As the aim was to analyse the number of species and individuals sharing a specific trait, the continuous measure of the wing span was converted into two categories, high and low mobility, based on the median wing span (40 mm) (c.f. Öckinger et al., 2010) among all species observed in any of the transects (data from Eliasson et al., 2005). Hence, high mobility species had a wing span above the median and sedentary species a wing span below. Although mobility is not a fixed trait but is likely to vary within species (Stevens et al., 2010), it was assumed that the broad mobility classification used would make the within-species variation in mobility negligible compared to the between-species variation. Host plant specificity was categorised in two different ways. First, species were categorised as specialists or generalists based on the number of plant genera the larvae feed on in Sweden (Eliasson et al., 2005), where specialist species feed on one plant genus and generalist species on two or more genera. Secondly, Holzschuh et al. (2007) found higher diversity of flowering plants on organic compared to conventional farms. Such pattern could indicate a bias in butterfly community composition towards dominance of grass feeding species on conventional farms. Therefore, the butterflies were categorised as grass or herb feeding in their larval stage. To analyse the annual reproductive rate, the average number of eggs produced per female (Bink, 1992) multiplied by its number of generations in Sweden (Eliasson et al., 2005) was used. Species with an annual egg production exceeding 240 eggs/female, which was the median value for all species included in the analysis, were categorised as having high reproductive rate, and if the annual egg production was 240 eggs or less as having low reproductive rate. The correlation between wing span and annual reproductive rate was moderate (Pearson’s correlation: r = 0.33, p = 0.064, n = 33). For all correlations between the traits, see Appendix A. Altogether, four categories of traits were analysed; (1) average wingspan, (2) host plant specificity, (3) type of host plant, and (4) reproductive rate. The trait classifications are based on the conditions in the Nordic countries (Eliasson et al., 2005), except from average egg production for which Bink (1992) was used. Thus, the classifications may not fully correspond to the conditions in other parts of Europe because of possible geographical variation.
2.1. Field data collection 2.3. Statistical analyses Butterflies (Rhopalocera) and burnet moths (Zygaenidae), hereafter collectively referred to as butterflies, were surveyed on five and six occasions in Uppland and Scania, respectively, between June and August 2009. The surveys were conducted in a 250 m long transect in the uncropped field margin to one cereal field per farm. The opposite side of the field margins only consisted of cereal fields of the same farming system, small gravel roads or small ditches, to as far as possible exclude confounding factors outside the selected field and farming system to influence the result. Additionally, two transects were established at 50 and 200 m from the beginning of the margin transect, reaching 50 m perpendicular into the field. Transects were walked in a steady pace on a randomly chosen time between 9 am and 5 pm (Central European summer time, UTC +2). All butterflies found within an imaginary square 5 m into the focal field, 1.5 m into the margin and 5 m in front of the recorder, were identified to species level following the taxonomy in Eliasson et al. (2005). The fields were visited under predominantly sunny conditions and a minimum temperature of 17 ◦ C. For further methodological details, see Jonason et al. (2011).
All analyses were made using generalised linear mixed models (GLMM), in which the dependent variables were either the number of observed species within a certain trait category or the total number of individuals of all species in a certain trait category. To test the effect of farming system on butterfly trait composition, a set of candidate models were created which included farming system, the proportion of arable land and trait, e.g. high/low mobility, as well as all two and three-way interactions, as predictor variables. The proportion of arable land was included to account for interaction effects between organic farming and the surrounding landscape (Rundlöf and Smith, 2006; Concepción et al., in press). In a second set of candidate models the proportion of arable land and trait were analysed together with the time since transition (all interactions included). This tested if species with certain traits experienced temporal effects of organic farming and if these effects differed between landscape types (sensu Jackson and Sax, 2010). In all models, farm identity was used as a random factor to account for the non-independence of the data for the different trait-groups
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from each farm, and data on species richness and abundance were pooled over the season at farm level. The predictor variables proportion of arable land and time since transition were centred around the mean to reduce collinearity by subtracting the mean of the data set from all data points, resulting in a mean of zero (Aiken and West, 1991). The total abundance of species possessing the traits was log-transformed to approximate a Gaussian error distribution. For species richness, a Poisson error distribution with log-link function was used. No corrections for overdispersion were required. The migratory species Vanessa atalanta and Cynthia cardui occasionally reproduce in Sweden, but do not overwinter, and therefore were only included in the analyses regarding dispersal capacity. Model comparisons were made using Akaike’s information criterion (Akaike, 1974) with a second-order correction for sample size (AICc ) and Akaike weights (i.e. the probability of best fit among the set of candidate models). An information theoretic approach is advantageous as it allows for comparison of models differing in the number of predictor variables while reducing the probability of type 1 errors (i.e. false-positive results). Since no single best model was found in any of the analyses, as indicated by a AICc < 2 (Burnham and Anderson, 2002), model averaging was used to calculate average parameter estimates based on all candidate models, where the contribution of each model is proportional to its weight. An interactive effect, i.e. parameter estimate with 95% confidence interval (CI) not overlapping zero, between a trait and the predictor variables would reveal variation within the trait related to farming system, time since transition to organic farming or proportion of arable land. To assess the contribution of each interaction, relative to the other predictor variables, a variable importance was calculated by summing the Akaike weights across the candidate models in which the interaction occurred (Burnham and Anderson, 2002). Parameter estimates of the interactions were obtained from model averaging. All statistical analyses were carried out in R v 2.12.1 (R Development Core Team, 2010) using the lme4 (Bates et al., 2011) and MuMIn (Barton, 2009) packages.
3. Results The number of butterfly species and individuals were higher on organic than on conventional farms, but no significant interaction between the farming system and any of the traits was found (Fig. 1a), i.e. the within-trait variation did not differ between farming systems. Potentially, farms that had been organic for long and short periods of time could still differ in trait composition, however no such effect was found (Fig. 1b). Wingspan influenced the effect of the proportion of arable land on butterfly abundance (Fig. 1c). The abundance of small-winged butterflies was negatively related to an increasing proportion of arable land whereas the abundance of large-winged butterflies was positively related to it (Fig. 2c). For reproductive rate the response to the proportion of arable land was not as clear; the interaction between proportion of arable land and reproductive rate was included in the model with lowest AICc (Table 1; AICc to model without the interaction, 5.89) and the abundance of butterflies with a high reproductive rate increased with the proportion of arable land whereas the abundance of butterflies with a low reproductive rate decreased (Fig. 2d). In addition, the evidence ratio (i.e. the ratio between model weights between the models compared) showed that the model with the interaction was 22.5 times more likely to have better model fit compared to the corresponding model without an interaction if the data were to be collected again under identical circumstances (data not shown) (Whittingham et al., 2006). However, the 95% CI for the parameter estimate of the interaction overlapped zero (Fig. 1c), indicating that
Fig. 1. Parameter estimates and 95% CI of the interaction between butterfly traits and the predictor variables farming system (a), time since transition to organic farming (b) and the proportion of arable land (c) (black = species richness; grey = abundance). Negative estimates indicate lower mobility and reproduction as well as lower species richness and abundance of specialist species and species confined to herb host plants, in relation to the predictors. Parameter estimates with CI not overlapping zero can be seen as significant.
an effect of reproductive rate on the response to landscape composition cannot be fully confirmed. There were no differences in the effects of the proportion of arable land on butterfly abundance for any of the two traits related to host plant specificity (Fig. 1c). The explanatory effects of the predictor variables were low for species richness, across all trait groups (Table 1; Figs. 1 and 2a and b). A tendency was only found for the species richness of butterflies differing in wing span (i.e. mobility) (Fig. 1c); the model with lowest AICc included the interaction between proportion of arable land and average wing span and had an evidence ratio of 2.19 compared to the corresponding model without the interaction (Table 1). The slightly higher model weight and the high relative variable importance of the interaction (0.61; Table 1) gave support for the importance of proportion of arable land in explaining size-related responses in butterfly species richness, although the estimate CI overlapped zero (Fig. 1a). The species richness of butterflies with low mobility tended to decrease with the proportion
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Fig. 2. The species richness and abundance of butterflies with high (; solid line) and low (; dashed line) mobility (a and b) and reproductive rate (b and d) in relation to the proportion of arable land within 1 km. The regression lines are for illustrative purposes and do not necessarily imply a causal relationship between variables (for details see Fig. 1).
of arable land whereas the species richness of butterflies with high mobility remained at a fairly constant level (Fig. 2a). 4. Discussion In this study, field scale organic farming did not counteract the effects of large scale landscape homogenisation on butterfly
trait composition. In combination with previous observations that butterfly communities in such landscapes may suffer from biotic homogenisation (Ekroos et al., 2010), this shows that other measures than organic farming may be needed if we want to preserve a functionally diverse insect community in agricultural regions. None of the traits related to dispersal capacity, host plant specificity and reproductive rate explained species responses to farming
Table 1 A summary of generalised linear mixed models, assessed by AIC and model averaging, analysing the relationship between butterfly traits (T) and the predictor variables farming system (FS) and proportion of arable land (PA). Only the three models with lowest AICc are shown. For data on the predictor variable time since transition to organic farming, see Appendix B. Trait (T)
Species richness
Relative variable importance
Abundance
Relative variable importance
Model
AICc
wi
FS × T
PA × T
Model
AICc
wi
FS × T
PA × T
Mobility
FS+PA × T FS+PA+T PA × T
0.00 1.53 3.07
0.46 0.21 0.10
0.06
0.61
FS+PA × T FS × PA × T PA × T
0.00 1.64 8.18
0.69 0.30 0.01
0.30
1.00
Reproduction
FS+PA+T FS × PA+T FS+PA × T
0.00 1.06 2.08
0.45 0.26 0.16
0.02
0.19
FS+PA × T FS × PA × T PA × T
0.00 1.09 3.31
0.45 0.26 0.09
0.29
0.80
Host specificity
FS+PA+T FS+PA × T FS × PA+T
0.00 1.23 1.66
0.43 0.23 0.19
0.02
0.29
FS+T FS+PA × T FS × PA+T
0.00 1.81 2.13
0.41 0.17 0.14
0.12
0.21
Host type
FS+PA+T FS+PA × T FS × PA+T
0.00 1.85 1.87
0.45 0.18 0.18
0.03
0.22
FS FS+T FS × PA
0.00 2.99 2.99
0.58 0.13 0.13
0.04
0.04
AICc = difference in AICc relative to the best model; wi = model weight.
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system. Although our selection of traits only constitute a fraction of the number of traits that potentially can be sensitive to farming practises, the result indicates that the positive effects of organic farming found on butterfly species richness and abundance in Jonason et al. (2011) can be assigned to all species equally. A likely mechanism is that organic farming increases habitat quality for all butterfly species, for example by increasing nectar availability. In contrast to farming system, landscape structure measured as the proportion of arable land had an effect on the composition of butterfly traits. Based on our results, a viable strategy for butterflies in landscapes dominated by arable land is to have either a high reproductive rate or a high dispersal capacity. Butterflies with high reproductive rate showed a strong tendency to increase in abundance with the proportion of arable land, whereas butterflies with low reproductive rate decreased. Furthermore, a disproportionally higher abundance of highly mobile butterflies in landscapes dominated by arable land was found. In previous studies, mobility has also been found to influence the relationship between landscape composition and species richness (Öckinger et al., 2009, 2010; Ekroos et al., 2010). Here only a tendency for this was found, although the interaction between the proportion of arable land and mobility displayed a high relative variable importance. High mobility in intensively farmed landscapes where the resources are scarce and fragmented can potentially facilitate exploration of the landscape at wider scales for additional resources. Alternatively, at isolated low quality sites, mobile species are dependent on immigration from source populations in the vicinity (c.f. Pulliam, 1988; Öckinger and Smith, 2007). For sedentary species on the other hand, it may be possible to persist on spatially proximate resources if the reproductive rate is high enough. A high reproductive rate can buffer temporary fluctuations in resources by rapid population growth when resources become available and can thereby decrease the risk of stochastic extinctions (Fahrig, 2001; Vance et al., 2003). Evidently, species with high as compared to low mobility and reproduction are more resilient against landscape homogenisation derived from agricultural intensification. Fahrig et al. (2011) have questioned the habitat-matrix paradigm, in which habitat patches are islands in a sea of hostile matrix (sensu MacArthur and Wilson, 1967), by emphasising the shortcomings in routinely considering non-native habitats as matrix. Applied to agricultural landscapes, they instead highlighted the number and proportions of different habitat types, including arable fields, their spatial arrangement and how these interact to meet the species’ resource needs (i.e. functional landscape heterogeneity) to be imperative to sustain farmland biodiversity. For butterflies, however, arable fields should generally not be considered as a functional habitat since they can only temporally serve such purpose, if at all. Butterflies require a landscape composed of a variety of spatially proximate and temporarily stable resources to be complementary in different parts of the species’ life cycle (Dunning et al., 1992; Dennis et al., 2003). The higher diversity and cover of flowering plants that can be found on organic farms (Holzschuh et al., 2007) are likely to momentarily facilitate butterfly survival at the scale of the farm, but as additional resources in homogeneous landscapes are largely lacking, population development of most species is hampered or biased towards those with high mobility or reproduction. Furthermore, biodiversity benefits of organic farming have in other studies only been significant in homogeneous rather than heterogeneous landscapes (e.g. Rundlöf and Smith, 2006; Holzschuh et al., 2007). Such a landscape dependent effect of organic farming in combination with our results of a bias towards high mobility in homogeneous landscapes, are turning organic fields to islands between which only highly mobile species can disperse, propelling the problem of biotic homogenisation even further. Hence, regardless of whether or not organic farming increases biodiversity, if groups of species respond differently
to land-use it can ultimately result in lower ecosystem functionality and resilience (Olden et al., 2004) and thereby the benefits of organic farming for ecosystem services can become overestimated, especially in homogeneous landscapes (but see Andersson et al., 2012). Agri-environment schemes (AES) increase the heterogeneity compared to conventional farming, but mainly at the field scale (Concepción et al., 2008). To improve the efficiency of AES by counteracting landscape homogenisation, targeted action should be made for implementation on larger spatial scales and across property borders (e.g. Aviron et al., 2005; Merckx et al., 2009; Gabriel et al., 2010; but see Fuentes-Montemayor et al., 2011). However, the present schemes are not designed for that (Concepción et al., 2008). Additionally, similarly to other types of AES, the focus of organic farming should be broadened beyond the arable fields. This could potentially be stimulated by means of cross-compliance, where farmers need to adhere to certain mandatory standards (e.g. specific width of field margins) in order to receive full financial assistance. Such a large-scale resource-based approach would better represent the butterflies’ needs, irrespective of trait, by recognising their fundamental requirements at multiple scales (Dennis et al., 2003). By improving habitat quantity, quality and connectivity it may be possible to mitigate the negative effects of intensive agriculture on biodiversity and community composition. Acknowledgements We would especially like to thank all farmers for allowing us to conduct our work on their land. Thanks also to all field assistants involved, to Camilla Winqvist and David Hadden for comments on the manuscript and to Victor Johansson for assistance with the figures. The study was financed by the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.agee.2012.05.026. References Aiken, L.S., West, S.G., 1991. Multiple regression: Testing and Interpreting Interactions. Sage Publications, Inc., Newbury Park, CA, USA. Akaike, H., 1974. A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723. Andersson, G.K.S., Rundlöf, M., Smith, H.G., 2012. Organic farming improves pollination success in strawberries. PLoS ONE 7, e31599. Aviron, S., Burel, F., Baudry, J., Schermann, N., 2005. Carabid assemblages in agricultural landscapes: impacts of habitat features, landscape context at different spatial scales and farming intensity. Agric. Ecosyst. Environ. 108, 205–217. Barton, K., 2009. MuMIn: multi-model inference. In: R package version 0.12.0. http://r-forge.r-project.org/projects/mumin/. Bates, D., Maechler, M., Bolker, B., 2011. Lme4: Linear Mixed-Effects Models using S4 Classes. http://CRAN.R-project.org/package=lme4. Bengtsson, J., Ahnström, J., Weibull, A.C., 2005. The effects of organic agriculture on biodiversity and abundance: a meta-analysis. J. Appl. Ecol. 42, 261–269. Benton, T.G., Vickery, J.A., Wilson, J.D., 2003. Farmland biodiversity: is habitat heterogeneity the key? Trends Ecol. Evol. 18, 182–188. Bink, F.A., 1992. Ecologische Atlas van de Dagvlinders van Noordwest-Europa. Schuyt & Co Uitgevers en Importeurs bv, Haarlem. Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference: A Practical Information-theoretic Approach. Springer Verlag, New York, NY. Concepción, E.D., Diaz, M., Baquero, R.A., 2008. Effects of landscape complexity on the ecological effectiveness of agri-environment schemes. Landsc. Ecol. 23, 135–148. Concepción, E.D., Díaz, M., Kleijn, D., Báldi, A., Batáry, P., Clough, Y., Gabriel, D., Herzog, F., Holzschuh, A., Knop, E., Marshall, E.J.P., Tscharntke, T., Verhulst, J. Interactive effects of landscape context constrain the effectiveness of local agrienvironmental management. J. Appl. Ecol., in press. Council Regulation (EC) No 834/2007 of 28 June 2007. On Organic Production and Labelling of Organic Products and Repealing Regulation (EEC) No 2092/91. Dennis, R.L., Shreeve, T.G., Van Dyck, H., 2003. Towards a functional resource-based concept for habitat: a butterfly biology viewpoint. Oikos, 417–426.
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Contents lists available at SciVerse ScienceDirect
Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee
Field scale organic farming does not counteract landscape effects on butterfly trait composition Dennis Jonason a,∗ , Georg K.S. Andersson b,c , Erik Öckinger a , Henrik G. Smith b,c , Jan Bengtsson a a b c
Swedish University of Agricultural Sciences, Department of Ecology, PO Box 7044, SE-750 07 Uppsala, Sweden Lund University, Department of Biology, SE-223 62 Lund, Sweden Lund University, Centre of Environmental and Climate Research, SE-223 62 Lund, Sweden
a r t i c l e
i n f o
Article history: Received 13 March 2012 Received in revised form 29 May 2012 Accepted 30 May 2012 Available online 26 June 2012 Keywords: Agri-environment schemes Farmland biodiversity Farming system Species traits Time since transition
a b s t r a c t We tested how dispersal capacity, host plant specificity and reproductive rate influenced the effects of farming system and landscape composition on butterfly species richness and abundance. In no case did variation in these traits explain species responses to organic farming, indicating that all species benefit equally. In contrast, butterflies with high mobility and reproductive rate were disproportionally more abundant in landscapes dominated by arable land, and the species richness of butterflies with low mobility tended to decrease with increasing proportion of arable land whereas those of high mobility remained fairly constant. Hence, although organic farming increased biodiversity, it did not counteract landscape effects on butterfly trait composition. As a trait dependent loss of biodiversity may result in a larger decline of functional trait diversity compared to species diversity, these results imply that organic farming may not increase or restore functional agro-ecosystem diversity. Information provided by species traits, rather than biodiversity per se, may provide important information for successful revisions of future agri-environment schemes. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Anthropogenic changes in land use are the foremost cause behind worldwide biodiversity decline (Millennium Ecosystem Assessment, 2005; Kleijn et al., 2009). However, species with certain traits may be more susceptible than other (Fisher and Owens, 2004). For example, plant traits related to life-history, growth form, palatability etc. has been used to predict responses to grazing (Diaz et al., 2007) and the mobility of animals have been used to predict responses to habitat fragmentation and agricultural intensification (Thomas, 2000; Gabriel et al., 2010). If large scale environmental changes affect species with certain traits disproportionally, it can ultimately lead to a state of biotic homogenisation in which the community composition progressively becomes more similar (McKinney and Lockwood, 1999; Ekroos et al., 2010). Diaz and Cabido (2001) suggested trait diversity as a determinant of ecosystem processes and functions. Identification of the traits that shape species’ responses to environmental change caused by human activities is therefore vital for proactive landscape management. The intensification of agriculture is one of the major single drivers behind the declining biodiversity in agro-ecosystems
∗ Corresponding author. Tel.: +46 18672257. E-mail address: [email protected] (D. Jonason). 0167-8809/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agee.2012.05.026
(Benton et al., 2003; Concepción et al., 2008). Increased use of pesticides and chemical fertilisers, simplified crop rotations and loss of semi-natural biotopes are among the factors contributing to agricultural intensification (Geiger et al., 2010). To mitigate the negative effects on biodiversity, agri-environment schemes (AES) are implemented to provide financial incentives to European farmers for environmentally and wildlife friendly land management (Kleijn et al., 2011). Organic farming, which is an AES applied at the farm scale, generally promotes biodiversity (Bengtsson et al., 2005). However, although biodiversity-based evaluations of AES provide a comprehensive picture of species responses, less is known whether species with certain traits are differentially affected, which then may be a better predictor of the ability of AES to produce functional agro-ecosystems in the absence of agrochemicals. In a recent study (Jonason et al., 2011), higher species richness and abundance of plants and butterflies were found on organic compared to conventional farms. Further, the butterfly abundance gradually increased by 100% over a 25-year period, and the butterfly species richness decreased with landscape simplification. To explore if these effects pertain equally to all butterflies, or are more pronounced among species with certain traits, additional analyses on the data in Jonason et al. (2011), focusing on three traits: dispersal capacity, host plant specificity and reproductive rate, were performed. These traits have previously been found to influence butterfly responses to habitat loss (Thomas, 2000; Hambäck et al., 2007; Öckinger et al., 2010), and were therefore assumed to be
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reliable predictors of butterfly’ responses to both farming system and landscape context. It was predicted that the butterflies would show differential responses depending on species traits to conventional farming and to landscapes dominated by arable fields, due to decreased habitat quality and availability, but that organic farms would maintain the variation within traits.
2. Methods The field data were collected during the summer of 2009 in the provinces of Uppland and Scania, Sweden. Data on butterfly species richness and abundance were collected on a total of 60 farms, 10 conventional and 20 organic in each province. The organic farms had been managed in accordance to the regulations of organic production set by the European Commission (Council Regulation (EC) No 834/2007). To study possible temporal effects, organic farms that previously had been managed conventionally and differed in time since transition to organic management from 1 to 25 years were chosen. Landscape composition may affect the extent in which species respond to farming system, with highest effect at intermediate levels of landscape heterogeneity (Concepción et al., in press). Therefore, all farms were selected so that they were distributed along a gradient of landscape heterogeneity, which in turn was assured not to be confounded with the gradient in time since transition to organic farming. As proxy for landscape complexity the proportion of arable land within a 1 km circular sector around each sampling point was used, where low proportional values indicate heterogeneous landscapes and vice versa. No circular sectors were overlapping, hence the smallest distance between sampling points was never less than 2 km. Although in Sweden a low proportion of arable land often corresponds to a high proportion of forest, which as arable land can be considered to homogenise the landscape, it has been shown that a matrix consisting of forest provides several suitable structures for butterflies and is more beneficial compared to a matrix consisting of arable land (Öckinger et al., 2012). The landscape analyses were made using ArcGis 9.3 (Esri Inc., Redlands, CA, USA) on land use data from the Swedish Board of Agriculture and the Swedish Mapping, Cadastral and Land Registration Authority.
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2.2. Trait selection Average wing span was used as proxy for dispersal capacity as these characteristics have empirically been shown to be highly correlated among Lepidoptera (Nieminen, 1996). Wing span may not be an ideal proxy for dispersal capacity, but it was recently identified as the best choice among traits that are easily accessible in the literature (Sekar, 2012). As the aim was to analyse the number of species and individuals sharing a specific trait, the continuous measure of the wing span was converted into two categories, high and low mobility, based on the median wing span (40 mm) (c.f. Öckinger et al., 2010) among all species observed in any of the transects (data from Eliasson et al., 2005). Hence, high mobility species had a wing span above the median and sedentary species a wing span below. Although mobility is not a fixed trait but is likely to vary within species (Stevens et al., 2010), it was assumed that the broad mobility classification used would make the within-species variation in mobility negligible compared to the between-species variation. Host plant specificity was categorised in two different ways. First, species were categorised as specialists or generalists based on the number of plant genera the larvae feed on in Sweden (Eliasson et al., 2005), where specialist species feed on one plant genus and generalist species on two or more genera. Secondly, Holzschuh et al. (2007) found higher diversity of flowering plants on organic compared to conventional farms. Such pattern could indicate a bias in butterfly community composition towards dominance of grass feeding species on conventional farms. Therefore, the butterflies were categorised as grass or herb feeding in their larval stage. To analyse the annual reproductive rate, the average number of eggs produced per female (Bink, 1992) multiplied by its number of generations in Sweden (Eliasson et al., 2005) was used. Species with an annual egg production exceeding 240 eggs/female, which was the median value for all species included in the analysis, were categorised as having high reproductive rate, and if the annual egg production was 240 eggs or less as having low reproductive rate. The correlation between wing span and annual reproductive rate was moderate (Pearson’s correlation: r = 0.33, p = 0.064, n = 33). For all correlations between the traits, see Appendix A. Altogether, four categories of traits were analysed; (1) average wingspan, (2) host plant specificity, (3) type of host plant, and (4) reproductive rate. The trait classifications are based on the conditions in the Nordic countries (Eliasson et al., 2005), except from average egg production for which Bink (1992) was used. Thus, the classifications may not fully correspond to the conditions in other parts of Europe because of possible geographical variation.
2.1. Field data collection 2.3. Statistical analyses Butterflies (Rhopalocera) and burnet moths (Zygaenidae), hereafter collectively referred to as butterflies, were surveyed on five and six occasions in Uppland and Scania, respectively, between June and August 2009. The surveys were conducted in a 250 m long transect in the uncropped field margin to one cereal field per farm. The opposite side of the field margins only consisted of cereal fields of the same farming system, small gravel roads or small ditches, to as far as possible exclude confounding factors outside the selected field and farming system to influence the result. Additionally, two transects were established at 50 and 200 m from the beginning of the margin transect, reaching 50 m perpendicular into the field. Transects were walked in a steady pace on a randomly chosen time between 9 am and 5 pm (Central European summer time, UTC +2). All butterflies found within an imaginary square 5 m into the focal field, 1.5 m into the margin and 5 m in front of the recorder, were identified to species level following the taxonomy in Eliasson et al. (2005). The fields were visited under predominantly sunny conditions and a minimum temperature of 17 ◦ C. For further methodological details, see Jonason et al. (2011).
All analyses were made using generalised linear mixed models (GLMM), in which the dependent variables were either the number of observed species within a certain trait category or the total number of individuals of all species in a certain trait category. To test the effect of farming system on butterfly trait composition, a set of candidate models were created which included farming system, the proportion of arable land and trait, e.g. high/low mobility, as well as all two and three-way interactions, as predictor variables. The proportion of arable land was included to account for interaction effects between organic farming and the surrounding landscape (Rundlöf and Smith, 2006; Concepción et al., in press). In a second set of candidate models the proportion of arable land and trait were analysed together with the time since transition (all interactions included). This tested if species with certain traits experienced temporal effects of organic farming and if these effects differed between landscape types (sensu Jackson and Sax, 2010). In all models, farm identity was used as a random factor to account for the non-independence of the data for the different trait-groups
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from each farm, and data on species richness and abundance were pooled over the season at farm level. The predictor variables proportion of arable land and time since transition were centred around the mean to reduce collinearity by subtracting the mean of the data set from all data points, resulting in a mean of zero (Aiken and West, 1991). The total abundance of species possessing the traits was log-transformed to approximate a Gaussian error distribution. For species richness, a Poisson error distribution with log-link function was used. No corrections for overdispersion were required. The migratory species Vanessa atalanta and Cynthia cardui occasionally reproduce in Sweden, but do not overwinter, and therefore were only included in the analyses regarding dispersal capacity. Model comparisons were made using Akaike’s information criterion (Akaike, 1974) with a second-order correction for sample size (AICc ) and Akaike weights (i.e. the probability of best fit among the set of candidate models). An information theoretic approach is advantageous as it allows for comparison of models differing in the number of predictor variables while reducing the probability of type 1 errors (i.e. false-positive results). Since no single best model was found in any of the analyses, as indicated by a AICc < 2 (Burnham and Anderson, 2002), model averaging was used to calculate average parameter estimates based on all candidate models, where the contribution of each model is proportional to its weight. An interactive effect, i.e. parameter estimate with 95% confidence interval (CI) not overlapping zero, between a trait and the predictor variables would reveal variation within the trait related to farming system, time since transition to organic farming or proportion of arable land. To assess the contribution of each interaction, relative to the other predictor variables, a variable importance was calculated by summing the Akaike weights across the candidate models in which the interaction occurred (Burnham and Anderson, 2002). Parameter estimates of the interactions were obtained from model averaging. All statistical analyses were carried out in R v 2.12.1 (R Development Core Team, 2010) using the lme4 (Bates et al., 2011) and MuMIn (Barton, 2009) packages.
3. Results The number of butterfly species and individuals were higher on organic than on conventional farms, but no significant interaction between the farming system and any of the traits was found (Fig. 1a), i.e. the within-trait variation did not differ between farming systems. Potentially, farms that had been organic for long and short periods of time could still differ in trait composition, however no such effect was found (Fig. 1b). Wingspan influenced the effect of the proportion of arable land on butterfly abundance (Fig. 1c). The abundance of small-winged butterflies was negatively related to an increasing proportion of arable land whereas the abundance of large-winged butterflies was positively related to it (Fig. 2c). For reproductive rate the response to the proportion of arable land was not as clear; the interaction between proportion of arable land and reproductive rate was included in the model with lowest AICc (Table 1; AICc to model without the interaction, 5.89) and the abundance of butterflies with a high reproductive rate increased with the proportion of arable land whereas the abundance of butterflies with a low reproductive rate decreased (Fig. 2d). In addition, the evidence ratio (i.e. the ratio between model weights between the models compared) showed that the model with the interaction was 22.5 times more likely to have better model fit compared to the corresponding model without an interaction if the data were to be collected again under identical circumstances (data not shown) (Whittingham et al., 2006). However, the 95% CI for the parameter estimate of the interaction overlapped zero (Fig. 1c), indicating that
Fig. 1. Parameter estimates and 95% CI of the interaction between butterfly traits and the predictor variables farming system (a), time since transition to organic farming (b) and the proportion of arable land (c) (black = species richness; grey = abundance). Negative estimates indicate lower mobility and reproduction as well as lower species richness and abundance of specialist species and species confined to herb host plants, in relation to the predictors. Parameter estimates with CI not overlapping zero can be seen as significant.
an effect of reproductive rate on the response to landscape composition cannot be fully confirmed. There were no differences in the effects of the proportion of arable land on butterfly abundance for any of the two traits related to host plant specificity (Fig. 1c). The explanatory effects of the predictor variables were low for species richness, across all trait groups (Table 1; Figs. 1 and 2a and b). A tendency was only found for the species richness of butterflies differing in wing span (i.e. mobility) (Fig. 1c); the model with lowest AICc included the interaction between proportion of arable land and average wing span and had an evidence ratio of 2.19 compared to the corresponding model without the interaction (Table 1). The slightly higher model weight and the high relative variable importance of the interaction (0.61; Table 1) gave support for the importance of proportion of arable land in explaining size-related responses in butterfly species richness, although the estimate CI overlapped zero (Fig. 1a). The species richness of butterflies with low mobility tended to decrease with the proportion
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Fig. 2. The species richness and abundance of butterflies with high (; solid line) and low (; dashed line) mobility (a and b) and reproductive rate (b and d) in relation to the proportion of arable land within 1 km. The regression lines are for illustrative purposes and do not necessarily imply a causal relationship between variables (for details see Fig. 1).
of arable land whereas the species richness of butterflies with high mobility remained at a fairly constant level (Fig. 2a). 4. Discussion In this study, field scale organic farming did not counteract the effects of large scale landscape homogenisation on butterfly
trait composition. In combination with previous observations that butterfly communities in such landscapes may suffer from biotic homogenisation (Ekroos et al., 2010), this shows that other measures than organic farming may be needed if we want to preserve a functionally diverse insect community in agricultural regions. None of the traits related to dispersal capacity, host plant specificity and reproductive rate explained species responses to farming
Table 1 A summary of generalised linear mixed models, assessed by AIC and model averaging, analysing the relationship between butterfly traits (T) and the predictor variables farming system (FS) and proportion of arable land (PA). Only the three models with lowest AICc are shown. For data on the predictor variable time since transition to organic farming, see Appendix B. Trait (T)
Species richness
Relative variable importance
Abundance
Relative variable importance
Model
AICc
wi
FS × T
PA × T
Model
AICc
wi
FS × T
PA × T
Mobility
FS+PA × T FS+PA+T PA × T
0.00 1.53 3.07
0.46 0.21 0.10
0.06
0.61
FS+PA × T FS × PA × T PA × T
0.00 1.64 8.18
0.69 0.30 0.01
0.30
1.00
Reproduction
FS+PA+T FS × PA+T FS+PA × T
0.00 1.06 2.08
0.45 0.26 0.16
0.02
0.19
FS+PA × T FS × PA × T PA × T
0.00 1.09 3.31
0.45 0.26 0.09
0.29
0.80
Host specificity
FS+PA+T FS+PA × T FS × PA+T
0.00 1.23 1.66
0.43 0.23 0.19
0.02
0.29
FS+T FS+PA × T FS × PA+T
0.00 1.81 2.13
0.41 0.17 0.14
0.12
0.21
Host type
FS+PA+T FS+PA × T FS × PA+T
0.00 1.85 1.87
0.45 0.18 0.18
0.03
0.22
FS FS+T FS × PA
0.00 2.99 2.99
0.58 0.13 0.13
0.04
0.04
AICc = difference in AICc relative to the best model; wi = model weight.
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system. Although our selection of traits only constitute a fraction of the number of traits that potentially can be sensitive to farming practises, the result indicates that the positive effects of organic farming found on butterfly species richness and abundance in Jonason et al. (2011) can be assigned to all species equally. A likely mechanism is that organic farming increases habitat quality for all butterfly species, for example by increasing nectar availability. In contrast to farming system, landscape structure measured as the proportion of arable land had an effect on the composition of butterfly traits. Based on our results, a viable strategy for butterflies in landscapes dominated by arable land is to have either a high reproductive rate or a high dispersal capacity. Butterflies with high reproductive rate showed a strong tendency to increase in abundance with the proportion of arable land, whereas butterflies with low reproductive rate decreased. Furthermore, a disproportionally higher abundance of highly mobile butterflies in landscapes dominated by arable land was found. In previous studies, mobility has also been found to influence the relationship between landscape composition and species richness (Öckinger et al., 2009, 2010; Ekroos et al., 2010). Here only a tendency for this was found, although the interaction between the proportion of arable land and mobility displayed a high relative variable importance. High mobility in intensively farmed landscapes where the resources are scarce and fragmented can potentially facilitate exploration of the landscape at wider scales for additional resources. Alternatively, at isolated low quality sites, mobile species are dependent on immigration from source populations in the vicinity (c.f. Pulliam, 1988; Öckinger and Smith, 2007). For sedentary species on the other hand, it may be possible to persist on spatially proximate resources if the reproductive rate is high enough. A high reproductive rate can buffer temporary fluctuations in resources by rapid population growth when resources become available and can thereby decrease the risk of stochastic extinctions (Fahrig, 2001; Vance et al., 2003). Evidently, species with high as compared to low mobility and reproduction are more resilient against landscape homogenisation derived from agricultural intensification. Fahrig et al. (2011) have questioned the habitat-matrix paradigm, in which habitat patches are islands in a sea of hostile matrix (sensu MacArthur and Wilson, 1967), by emphasising the shortcomings in routinely considering non-native habitats as matrix. Applied to agricultural landscapes, they instead highlighted the number and proportions of different habitat types, including arable fields, their spatial arrangement and how these interact to meet the species’ resource needs (i.e. functional landscape heterogeneity) to be imperative to sustain farmland biodiversity. For butterflies, however, arable fields should generally not be considered as a functional habitat since they can only temporally serve such purpose, if at all. Butterflies require a landscape composed of a variety of spatially proximate and temporarily stable resources to be complementary in different parts of the species’ life cycle (Dunning et al., 1992; Dennis et al., 2003). The higher diversity and cover of flowering plants that can be found on organic farms (Holzschuh et al., 2007) are likely to momentarily facilitate butterfly survival at the scale of the farm, but as additional resources in homogeneous landscapes are largely lacking, population development of most species is hampered or biased towards those with high mobility or reproduction. Furthermore, biodiversity benefits of organic farming have in other studies only been significant in homogeneous rather than heterogeneous landscapes (e.g. Rundlöf and Smith, 2006; Holzschuh et al., 2007). Such a landscape dependent effect of organic farming in combination with our results of a bias towards high mobility in homogeneous landscapes, are turning organic fields to islands between which only highly mobile species can disperse, propelling the problem of biotic homogenisation even further. Hence, regardless of whether or not organic farming increases biodiversity, if groups of species respond differently
to land-use it can ultimately result in lower ecosystem functionality and resilience (Olden et al., 2004) and thereby the benefits of organic farming for ecosystem services can become overestimated, especially in homogeneous landscapes (but see Andersson et al., 2012). Agri-environment schemes (AES) increase the heterogeneity compared to conventional farming, but mainly at the field scale (Concepción et al., 2008). To improve the efficiency of AES by counteracting landscape homogenisation, targeted action should be made for implementation on larger spatial scales and across property borders (e.g. Aviron et al., 2005; Merckx et al., 2009; Gabriel et al., 2010; but see Fuentes-Montemayor et al., 2011). However, the present schemes are not designed for that (Concepción et al., 2008). Additionally, similarly to other types of AES, the focus of organic farming should be broadened beyond the arable fields. This could potentially be stimulated by means of cross-compliance, where farmers need to adhere to certain mandatory standards (e.g. specific width of field margins) in order to receive full financial assistance. Such a large-scale resource-based approach would better represent the butterflies’ needs, irrespective of trait, by recognising their fundamental requirements at multiple scales (Dennis et al., 2003). By improving habitat quantity, quality and connectivity it may be possible to mitigate the negative effects of intensive agriculture on biodiversity and community composition. Acknowledgements We would especially like to thank all farmers for allowing us to conduct our work on their land. Thanks also to all field assistants involved, to Camilla Winqvist and David Hadden for comments on the manuscript and to Victor Johansson for assistance with the figures. The study was financed by the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.agee.2012.05.026. References Aiken, L.S., West, S.G., 1991. Multiple regression: Testing and Interpreting Interactions. Sage Publications, Inc., Newbury Park, CA, USA. Akaike, H., 1974. A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723. Andersson, G.K.S., Rundlöf, M., Smith, H.G., 2012. Organic farming improves pollination success in strawberries. PLoS ONE 7, e31599. Aviron, S., Burel, F., Baudry, J., Schermann, N., 2005. Carabid assemblages in agricultural landscapes: impacts of habitat features, landscape context at different spatial scales and farming intensity. Agric. Ecosyst. Environ. 108, 205–217. Barton, K., 2009. MuMIn: multi-model inference. In: R package version 0.12.0. http://r-forge.r-project.org/projects/mumin/. Bates, D., Maechler, M., Bolker, B., 2011. Lme4: Linear Mixed-Effects Models using S4 Classes. http://CRAN.R-project.org/package=lme4. Bengtsson, J., Ahnström, J., Weibull, A.C., 2005. The effects of organic agriculture on biodiversity and abundance: a meta-analysis. J. Appl. Ecol. 42, 261–269. Benton, T.G., Vickery, J.A., Wilson, J.D., 2003. Farmland biodiversity: is habitat heterogeneity the key? Trends Ecol. Evol. 18, 182–188. Bink, F.A., 1992. Ecologische Atlas van de Dagvlinders van Noordwest-Europa. Schuyt & Co Uitgevers en Importeurs bv, Haarlem. Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference: A Practical Information-theoretic Approach. Springer Verlag, New York, NY. Concepción, E.D., Diaz, M., Baquero, R.A., 2008. Effects of landscape complexity on the ecological effectiveness of agri-environment schemes. Landsc. Ecol. 23, 135–148. Concepción, E.D., Díaz, M., Kleijn, D., Báldi, A., Batáry, P., Clough, Y., Gabriel, D., Herzog, F., Holzschuh, A., Knop, E., Marshall, E.J.P., Tscharntke, T., Verhulst, J. Interactive effects of landscape context constrain the effectiveness of local agrienvironmental management. J. Appl. Ecol., in press. Council Regulation (EC) No 834/2007 of 28 June 2007. On Organic Production and Labelling of Organic Products and Repealing Regulation (EEC) No 2092/91. Dennis, R.L., Shreeve, T.G., Van Dyck, H., 2003. Towards a functional resource-based concept for habitat: a butterfly biology viewpoint. Oikos, 417–426.
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