Farmland heterogeneity effects on biodiversity, community traits and insect pollination
Bibliographic record
Abstract
Agricultural intensification has led to severe biodiversity losses. One reason is the drastic reduction of (semi-)natural habitat, but there is also a global trend to reduced farmland heterogeneity due to larger field sizes (less field borders) and lower crop diversity. Reversing habitat loss is often difficult or not possible due to ecological, economic and social constraints, but increasing the farmland configurational heterogeneity (field border length) and farmland compositional heterogeneity (crop diversity) at the landscape scale might compensate for some habitat loss without taking land out of production. By increasing biodiversity these measures might also enhance associated ecosystem services in farmland like pollination. In the chapters of this PhD thesis we investigated the effects of landscape scale farmland compositional and configurational heterogeneity on different aspects of biodiversity with a special focus on pollinators and pollination services as well as underlying mechanisms. In the first chapter we disentangled the effects of field border length and crop diversity on multidiversity. We sampled species of seven taxa (plants, birds, butterflies, hoverflies, bees, carabids and spiders) in 435 landscapes located in seven European and one North-American agricultural region. We found that compositional heterogeneity had a positive effect on multidiversity if semi-natural habitat was high and configurational heterogeneity had a positive effect if semi-natural habitat was low. These results indicate that the amount of semi-natural habitat modulates the effectiveness of crop heterogeneity on farmland multidiversity. In the second chapter we investigated whether arthropod species with particular traits benefited from farmland heterogeneity. Thus, we collected traits on body size, dispersal ability, feeding type and reproduction ability of four arthropod groups (butterflies, hoverflies, carabids and spiders) across the seven European regions. Higher field border length supported butterflies, hoverflies and carabids with larger body sizes, possibly because enhanced landscape connectivity through field borders is especially important for large species with high resource demand. Effects of crop diversity were less evident, but favoured, for example, hoverflies with low dispersal and reproduction ability. In the third and fourth chapter we focused on farmland heterogeneity effects on pollinators and pollination services. Increased abundances of wild bees in landscapes with high field border length enhanced seed set of experimental plants (radish, Raphanus sativus) exposed in 94 landscapes in four European countries. With a further experiment we demonstrated the elevated transfer of artificial pollen along borders between directly adjacent crops supporting the hypotheses that field borders enhance pollinator movement and thus connectivity at the landscape scale. In contrast, wild bee abundance decreased in landscapes with high crop diversity, presumably because crop diversity was correlated with the cover of crop types with particularly intensive management and low plant diversity such as maize. This was supported by the reduced pollen diversity collected by 33 experimental bumble bee colonies in the Göttingen region in landscapes with a high maize cover leading to impaired colony growth. However, we found no effect of farmland heterogeneity on colony performance. In conclusion, farmland heterogeneity at the landscape scale is an important driver for biodiversity and ecosystem services. Configurational heterogeneity (field border length) benefits biodiversity and pollination services by enhancing connectivity, especially for arthropods with large body sizes, and thereby enhances pollination and seed set of plants. Compositional heterogeneity (crop diversity) had a positive effect on multidiversity if the amount of semi-natural habitat was high, but it became apparent that crop composition is crucial, as very intensively managed crops like maize can reduce pollinator food diversity and thereby potential pollination services. Therefore, future agri-environmental policies should halt and reverse the current trend for larger field sizes as well as consider crop identity effects and landscape complexity when promoting crop diversity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".