Bumsuk Seo

Natural enemy interactions constrain pest control in complex agricultural landscapes

Biological control of pests by natural enemies is a major ecosystem service delivered to agriculture worldwide. Quantifying and predicting its effectiveness at large spatial scales is critical for increased sustainability of agricultural production. Landscape complexity is known to benefit natural enemies, but its effects on interactions between natural enemies and the consequences for crop damage and yield are unclear. Here, we show that pest control at the landscape scale is driven by differences in natural enemy interactions across landscapes, rather than by the effectiveness of individual natural enemy guilds. In a field exclusion experiment, pest control by flying insect enemies increased with landscape complexity. However, so did antagonistic interactions between flying insects and birds, which were neutral in simple landscapes and increasingly negative in complex landscapes. Negative natural enemy interactions thus constrained pest control in complex landscapes. These results show that, by altering natural enemy interactions, landscape complexity can provide ecosystem services as well as disservices. Careful handling of the tradeoffs among multiple ecosystem services, biodiversity, and societal concerns is thus crucial and depends on our ability to predict the functional consequences of landscape-scale changes in trophic interactions.

Scale‐dependent effects of landscape composition and configuration on natural enemy diversity, crop herbivory, and yields

(1) Land-use intensification in agricultural landscapes has led to changes in the way habitats and resources are distributed in space. Pests and their natural enemies are influenced by these changes, and by the farming intensity of crop fields. However, it is unknown whether the composition of landscapes (amount and diversity of land cover types) or their configuration (spatial arrangement of cover types) are more important for natural enemy diversity, and how they impact crop damage and yields. In addition, effects of interactions between local farming practices (organic vs. conventional) and landscape variables are unclear. (2) Here, we make use of a data set where landscape composition and configuration were uncorrelated across multiple spatial scales. Natural enemies, crop damage, and yields were sampled in 35 organic and conventional crop fields. Out of seven broad natural enemy taxa, five were positively affected by a complex landscape configuration. In contrast, only carabids were positively affected by the amount of seminatural habitat around fields. Increasing diversity of land cover types had positive effects on some, but negative effects on other taxa. Effect sizes varied among taxa but increased with increasing spatial scale, defined by circular areas of increasing radius around fields. (3) The diversity of aerial, but not of ground-dwelling enemies was higher in fields under organic than conventional management. Interactions of local and landscape variables were important for birds, but not other enemies. Bird richness was higher in organic fields in simple landscapes, but not in landscapes with complex configuration or high land cover diversity. (4) Crop damage decreased with landscape diversity, but increased in conventional fields with complex configuration. Yields increased with both parameters in conventional fields only, and were higher on average in organic compared to conventional fields. Enemy diversity was positively related to crop damage, indicating positive density-dependence of enemies on pests. However, the diversity of aerial enemies was also positively related to yields. (5) Our results suggest that the effectiveness of agrienvironmental schemes for managing natural enemy diversity, crop damage and yields could be enhanced by optimizing the effects of distinct landscape parameters, particularly landscape configuration and diversity, across scales.

Pest control of aphids depends on landscape complexity and natural enemy interactions

Aphids are a major concern in agricultural crops worldwide, and control by natural enemies is an essential component of the ecological intensification of agriculture. Although the complexity of agricultural landscapes is known to influence natural enemies of pests, few studies have measured the degree of pest control by different enemy guilds across gradients in landscape complexity. Here, we use multiple natural-enemy exclosures replicated in 18 fields across a gradient in landscape complexity to investigate (1) the strength of natural pest control across landscapes, measured as the difference between pest pressure in the presence and in the absence of natural enemies; (2) the differential contributions of natural enemy guilds to pest control, and the nature of their interactions across landscapes. We show that natural pest control of aphids increased up to six-fold from simple to complex landscapes. In the absence of pest control, aphid population growth was higher in complex than simple landscapes, but was reduced by natural enemies to similar growth rates across all landscapes. The effects of enemy guilds were landscape-dependent. Particularly in complex landscapes, total pest control was supplied by the combined contribution of flying insects and ground-dwellers. Birds had little overall impact on aphid control. Despite evidence for intraguild predation of flying insects by ground-dwellers and birds, the overall effect of enemy guilds on aphid control was complementary. Understanding pest control services at large spatial scales is critical to increase the success of ecological intensification schemes. Our results suggest that, where aphids are the main pest of concern, interactions between natural enemies are largely complementary and lead to a strongly positive effect of landscape complexity on pest control. Increasing the availability of seminatural habitats in agricultural landscapes may thus benefit not only natural enemies, but also the effectiveness of aphid natural pest control.

Mapping Fractional Land Use and Land Cover in a Monsoon Region: The Effects of Data Processing Options

Existing global land use/land cover (LULC) raster maps have limited spatial and thematic resolution relative to the strong heterogeneity of agricultural landscapes. One promising approach to derive more informative maps is using fractional cover instead of hard classification. Here, we evaluate the effect of three key data processing options on the performance of random forest (RF) fractional cover models for moderate resolution imaging spectroradiometer (MODIS) data in a heterogeneous agricultural landscape in a monsoon region: 1) selection of spectral predictor sets [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), surface reflectance (SR), and all combined (Full)]; 2) time interval (8-day vs. 16-day); and 3) smoothing (no smoothing versus Savitzky-Golay (SG) filter). Model performance was assessed with spatially stratified rootmean-square error (RMSE), Spearmans rank correlation, and R2, per LULC type and averaged over all types. We found adequate performance of the best model (avg. ρ = 0.62) that used all predictors, 8-day interval and no smoothing. Among the different alternatives, the choice of predictors accounted for 36.3% of the variation, smoothing for 19.0%, and time interval for 17.9%. The intrinsic dimensionalities of the spectral predictors were investigated to complement the variable importance analyses. Although predicting LULC fractions for minor types remained difficult, our results suggest that existing satellite products can be a useful source of information about LULC at subpixel level provided the data-processing options are properly chosen.

Classification of rare land cover types : Distinguishing annual and perennial crops in an agricultural catchment in South Korea

Many environmental data are inherently imbalanced, with some majority land use and land cover types dominating over rare ones. In cultivated ecosystems minority classes are often the target as they might indicate a beginning land use change. Most standard classifiers perform best on a balanced distribution of classes, and fail to detect minority classes. We used the synthetic minority oversampling technique (smote) with Random Forest to classify land cover classes in a small agricultural catchment in South Korea using modis time series. This area faces a major soil erosion problem and policy measures encourage farmers to replace annual by perennial crops to mitigate this issue. Our major goal was therefore to improve the classification performance on annual and perennial crops. We compared four different classification scenarios on original imbalanced and synthetically oversampled balanced data to quantify the effect of smote on classification performance. smote substantially increased the true positive rate of all oversampled minority classes. However, the performance on minor classes remained lower than on the majority class. We attribute this result to a class overlap already present in the original data set that is not resolved by smote. Our results show that resampling algorithms could help to derive more accurate land use and land cover maps from freely available data. These maps can be used to provide information on the distribution of land use classes in heterogeneous agricultural areas and could potentially benefit decision making.