In this post Kirsty Lees discusses the recent paper from Alice Johnston and colleagues ‘Effects of agricultural management practices on earthworm populations and crop yield: validation and application of a mechanistic modelling approach’
Healthy soils are essential to the provision of ecosystem services; however, increasing intensification has resulted in soil degradation in many arable systems threatening water quality, carbon storage and ultimately food security. Any form of soil management, either physical (e.g. tillage) or chemical (e.g. pesticides) will inevitably change the soil environment and impact on the highly diverse soil fauna. However, to quantify the magnitude of these potentially negative effects we have to understand the drivers of habitat use by soil organisms; how are basic physiological requirements related to environmental variation?
Earthworms are vital members of the soil fauna, industriously maintaining the soil structure through their role as soil engineers. Earthworms increase soil porosity and nutrient cycling directly by burrowing and digesting organic matter, and indirectly through facultative interactions with other soil organisms; these processes can stimulate plant growth and result in increased crop yields. Despite this remarkable role in promoting normal soil function and the provision of ecosystem services, additional management practices are required to generate improved yields in intensified systems. While sustainable levels of pesticide use and reduced tillage may be possible, the impacts of any soil management regime on earthworms are difficult to predict. Soils are inherently variable and identifying and quantifying the effects of anthropogenic change in such heterogeneous and biologically complex environments can be particularly challenging. It is necessary to effectively describe how environmental variables such as soil organic matter, soil temperature and soil water content influence the distribution of earthworms in the soil layer and thus interact with the impacts of anthropogenic changes.
The integrated approach taken by Johnston et al. in their recent paper in the Journal of Applied Ecology has successfully addressed these complex interactions by combining an energy budget model and an individual-based model (IBM) to model earthworm abundance and distribution. These methods not only investigate how earthworms respond to agricultural management, but also describe the underlying physiological drivers of habitat use such as reduced metabolic rate due to reduced food availability, or inactivity due to sub-optimal soil conditions. IBMs are increasingly popular in ecology; by extrapolating our biological knowledge of how an individual responds to environmental conditions we can make useful ecological inferences about population level effects. This ability to ‘scale-up’ effects means that IBMs have considerable use as applied management tools.
Johnston et al. use their model to investigate the effect of several agricultural management practices on earthworms. By modelling the metabolic response of individuals to carbendazim, a pesticide with known population effects on earthworms, and movement of individuals within the soil profile in response to changing soil conditions, they were able to investigate the impacts of pesticide exposure and environmental variability at the population level. In corresponding field trials earthworms were more likely to move within the top layers of the soil under favourable soil water conditions. Under drier field conditions pesticide exposure was reduced, resulting in a population recovery that was mirrored in the model biomass. This result highlights the importance of the timing of management interventions to limit negative impacts. This approach was developed further by simulating realistic effects of herbicide applications under different weed management regimes (10 % and 20 % effect on growth, reproduction and survival) combined with tillage treatments (zero, reduced and conventional) on earthworm populations and crop yields over a 10 year application period. Interestingly, the mechanical disturbance from the tillage treatments reduced earthworm exposure to herbicide by creating unfavourable conditions in the surface layers of the soil. However, earthworm populations in tilled soils took longer to recover compared to those subjected to other management practices, due a reduction in soil organic matter and water content that increased with depth of treatment, as well as direct mortality. Finally, Johnston et al. make a compelling case for the applicability of their model illustrating the potential to predict crop yields under different management scenarios.
This work by Johnston et al. realistically represents the underlying ecological mechanisms driving earthworm physiology and movement, successfully modelling earthworm population dynamics in response to agricultural management. In doing so they have achieved excellent fits with independent data; this predictive ability is often lacking in more standard approaches. By validating models using data from five independent field sites (three in Germany and two in Spain) the authors certainly provide food for thought by addressing the commonly held assertion that a simpler model will be more generally applicable. The paper also underlines the importance of not analysing possible sources of anthropogenic change in isolation as potential effects may be missed and the magnitudes of impacts and recovery times may differ.