Agricultural landscape homogenization has detrimental effects on biodiversity and key ecosystem services. Increasing agricultural landscape heterogeneity by increasing seminatural cover can help to mitigate biodiversity loss. However, the amount of seminatural cover is generally low and difficult to increase in many intensively managed agricultural landscapes. We hypothesized that increasing the heterogeneity of the crop mosaic itself (hereafter “crop heterogeneity”) can also have positive effects on biodiversity. In 8 contrasting regions of Europe and North America, we selected 435 landscapes along independent gradients of crop diversity and mean field size. Within each landscape, we selected 3 sampling sites in 1, 2, or 3 crop types. We sampled 7 taxa (plants, bees, butterflies, hoverflies, carabids, spiders, and birds) and calculated a synthetic index of multitrophic diversity at the landscape level. Increasing crop heterogeneity was more beneficial for multitrophic diversity than increasing seminatural cover. For instance, the effect of decreasing mean field size from 5 to 2.8 ha was as strong as the effect of increasing seminatural cover from 0.5 to 11%. Decreasing mean field size benefited multitrophic diversity even in the absence of seminatural vegetation between fields. Increasing the number of crop types sampled had a positive effect on landscape-level multitrophic diversity. However, the effect of increasing crop diversity in the landscape surrounding fields sampled depended on the amount of seminatural cover. Our study provides large-scale, multitrophic, cross-regional evidence that increasing crop heterogeneity can be an effective way to increase biodiversity in agricultural landscapes without taking land out of agricultural production.
To quantify the effect of the surrounding landscape context on a biological response at a site, most studies measure landscape variables within discs centred on this biological response (threshold‐based method, TBM). This implicitly assumes that the effect of a unit area of the landscape is consistent up to a threshold distance beyond which it drops to zero. However, it seems more likely that the landscape effect declines with increasing distance from the biological response point. Here, we develop a method to quantify landscape context effects by weighting the landscape variables by functions that decrease with distance. We illustrate the method using abundance data on birds and insects, and compare the results to the threshold approach. We defined distance weighting functions by the function family (e.g. negative exponential, Gaussian…) and by the parameters for this function. We developed a method to simultaneously estimate the parameters characterizing the effect of the landscape variables and the parameters of the best weighting functions. For each test dataset, we determined which weighting function (family and parameters) had the most support, by optimizing the model AIC. The distance‐weighted method (DWM) improved model support over the TBM in three of four datasets, with the exponential power function selected as the best weighing function in all three cases. The observed differences between estimations of landscape context effects by the distance‐weighted and the threshold methods have significant implications for landscape management. For example, the DWM suggests that managing a landscape for 90% of its effect on a focal population requires an area over five times larger than the area estimated by the threshold method, a situation that might apply for priority conservation of few remnant populations of a severely endangered species. In contrast, management for 30% of the landscape effect requires only about half the area estimated using the threshold method, a situation that might apply to a management situation with limited resources or low political/societal support. The DWM is applicable to any species‐habitat relationship. More comparisons are needed to determine the situations in which distance‐weighted estimation of landscape context effects is warranted over the simpler threshold method.
Increasing landscape heterogeneity by restoring semi‐natural elements to reverse farmland biodiversity declines is not always economically feasible or acceptable to farmers due to competition for land. We hypothesized that increasing the heterogeneity of the crop mosaic itself, hereafter referred to as crop heterogeneity, can have beneficial effects on within‐field plant diversity. Using a unique multi‐country dataset from a cross‐continent collaborative project covering 1,451 agricultural fields within 432 landscapes in Europe and Canada, we assessed the relative effects of compositional and configurational crop heterogeneity on within‐field plant diversity components. We also examined how these relationships were modulated by the position within the field. We found strong positive effects of configurational crop heterogeneity on within‐field plant alpha and gamma diversity in field interiors. These effects were as high as the effect of semi‐natural cover. In field borders, effects of crop heterogeneity were limited to alpha diversity. We suggest that a heterogeneous crop mosaic may overcome the high negative impact of management practices on plant diversity in field interiors, whereas in field borders, where plant diversity is already high, landscape effects are more limited. Synthesis and applications. Our study shows that increasing configurational crop heterogeneity is beneficial to within‐field plant diversity. It opens up a new effective and complementary way to promote farmland biodiversity without taking land out of agricultural production. We therefore recommend adopting manipulation of crop heterogeneity as a specific, effective management option in future policy measures, perhaps adding to agri‐environment schemes, to contribute to the conservation of farmland plant diversity.
International audienceContext Agroecosystems are dynamic, with yearly changing proportions of crops. Explicit consideration of this temporal heterogeneity is required to decipher population and community patterns but remains poorly studied. Objectives We evaluated the impact on the activity-density of two dominant carabid species (Poecilus cupreus and Anchomenus dorsalis) of (1) local crop, current year landscape composition, and their interaction, and (2) inter-annual changes in landscape composition due to crop rotations. Methods Carabids were sampled using pitfall-traps in 188 fields of winter cereals and oilseed rape in three agricultural areas of western France contrasting in their spatial heterogeneity. We summarized landscape composition in the current and previous years in a multi-scale perspective, using buffers of increasing size around sampling locations. Results Both species were more abundant in oilseed rape, and in landscapes with a higher proportion of oilseed rape in the previous year. P. cupreus abundance was negatively influenced by oilseed rape proportion in the current year landscape in winter cereals and positively by winter cereal proportion in oilseed rape. A. dorsalis was globally impacted at finer scales than P. cupreus. Conclusions Resource concentration and dilution-concentration processes jointly appear to cause transient dynamics of population abundance and distribution among habitat patches. Inter-patch movements across years appear to be key drivers of carabids’ survival and distribution, in response to crop rotation. Therefore, the explicit consideration of the spatiotemporal dynamics of landscape composition can allow future studies to better evidence ecological processes behind observed species patterns and help developing new management strategies
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.