Crop pests and predators exhibit inconsistent responses to surrounding landscape composition

Abstract

The idea that noncrop habitat enhances pest control and represents a win-win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win-win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies.

Keywords: agroecology; biodiversity; biological control; ecosystem services; natural enemies.

Fig. 1.

Map of study locations. We collected pest-control data from 132 studies across 6,759 sites and 31 countries. Pest-control data included abundances of dominant pests, all pests, and natural enemies (black dots; 181 responses), pest and predator activity data from crop-damage surveys, sentinel pest experiments, and exclosure experiments (cyan dots; 125 responses), and yield data (red dots; 53 responses).

Fig. 2.

Landscape effects on pest-control variables. After selecting the most predictive model for each pest-control response (N = 367) and redefining land-cover variables as natural (forest, grassland, and scrubland; green bars) versus crop (annual and perennial; orange bars), we tallied the number of pest-control responses for which models had either positive (solid), negative (diagonal hashed), or mixed (horizontal) estimates of the effect of each landscape predictor. Panels represent the seven pest-control variables, including abundance (A) and activity (B and C) of natural enemies; abundance (D and E) and activity (F) of pests; and crop yields (G). χ² tests indicated that pest-control response variables showed heterogeneous patterns of association with the extent of surrounding natural habitat and cropland—with roughly equivalent numbers of pest-control responses having models with positive and negative effects (all P > 0.05).

Fig. 3.

Explanatory power of landscape pest-control models. After selecting the most predictive spatial scale (Methods), model predictions were correlated with observed data. Gray dots are both Pearson correlations between model predictions and observed data and R² values (square of Pearson’s r). Filled circles and empty circles indicate significant (P < 0.05) and nonsignificant correlations, respectively. Black dots indicate the mean correlation across all datasets between observed and predicted values. Black lines correspond to 95% confidence intervals.

Fig. 4.

Testing landscape models. (Top) Correlating average predictions across all possible landscape models (Methods) against independent field observations resulted in low predictive power. Each gray circle is the observed correlation for one dataset (set of field observations); filled circles are significant correlations (P < 0.05). Black circles are average correlations across all tested datasets; lines are confidence intervals. (Bottom) More selective application of models to independent field observations caused correlations to be on average positive for all pest-control variables except pest damage and crop yields (asterisks indicate P < 0.05). Specifically, this panel demonstrates that predictive power was higher when a more selective subset of models was applied to the independent field observations, subject to several of the following constraints: (i) Field observations and the data from which models were constructed (model data) shared the same crop; (ii) the same land-cover variables were present in model data and field observations; (iii) landscape values in field observations were within the range of landscape values in the model data; and (iv) models explained significant variation in their own data (r > 0.25). Dominant pests, pest damage, and crop yields were subject to constraints (i) and (ii); all pests to (i), (ii), and (iii); sentinel experiments to (i), (ii), and (iv); and all enemies and cage experiments to (i), (ii), (iii), and (iv).