ABSTRACT The impacts of agricultural intensification on farmland wildlife have been the subject of increasing concern, particularly over the last two decades. Population declines have occurred for a number of mammalian species, sometimes drastically so, and changes in farming practice are believed to be significant contributory factors. The major policy instruments for delivering environmental benefits on farmland are agri‐environment schemes. These encourage farmers to adopt more environmentally sensitive farming practices to promote farmland biodiversity. Additionally, compulsory set‐aside, which reduces agricultural surplus, could also have positive impacts on wildlife. In this paper we consider some of the putative benefits of agri‐environment schemes and set‐aside for mammals. We review how establishment and management options within agri‐environment schemes and set‐aside might affect habitat resources for mammals. For example, conservation headlands increase plant and invertebrate resources within the crop edge for mammals such as wood mice. Grassy field margins can support communities of smaller mammals, and hedgerows may act as important commuting and hunting routes. Their potential will depend on factors such as seed mixtures used, timing and severity of cutting, and length of time they have been in place. At a farm level, habitat heterogeneity may be increased through organic agriculture, which is supported by some agri‐environment schemes. Studies suggest significant benefits to mammals, including wood mice and bats. However, it is increasingly recognized that effective conservation of farmland mammals must seek solutions at the landscape scale, addressing such issues as habitat connectivity between farms. One approach may be the better targeting of scheme agreements. We suggest that agri‐environment schemes and set‐aside can contribute to the conservation of mammals on farmland. Recent policy changes are likely to have further positive impacts on farmland wildlife but appropriate mammal monitoring programmes must be developed rigorously to assess their effects.
This paper explores the potential of rule‐based habitat models to predict the occurrence of some common species in arable conditions. Models were developed for 10 arable plant species, 7 Hemiptera species, 8 carabid species and for 5 bird species whose ecology was sufficiently known. Rule sets linking species occurrence to environmental variables were produced using available literature and expert knowledge about ecological requirements of the selected species. Environmental variables described the nature and condition of habitats at various scales, ranging from vegetation quadrat to the landscape in a 1 km radius of species sampling sites. The performance of the 34 models developed was assessed in two areas of England. Results show the rule‐based habitat models developed for arable plants and birds were not very successful with Cohen's k values often <0.4 for plants and very close to 0 for all bird species. Conversely, rule‐based models performed surprisingly well for carabids and Hemiptera with k values on average >0.4. This suggests that ecological knowledge on these invertebrate species is more complete than we expected. The effect of species prevalence on model performance and the potential application of knowledge‐based habitat models in the context of biodiversity assessment are discussed.
Summary Experimental grassland communities (turves) were exposed to supplemental levels of UV‐B radiation (280–315 nm) at an outdoor facility, under treatment arrays of cellulose diacetate‐filtered fluorescent lamps which also produce UV‐A radiation (315–400 nm). Control treatments consisted of arrays of polyester‐filtered lamps, which allowed for exposure to UV‐A radiation alone, and arrays of unenergized lamps allowing for exposure to ambient levels of solar radiation.
A method is presented for using botanical survey and soil survey data to generate maps of the probability of occurrence of weeds in Britain across all habitats. For each species, data from a national, designed botanical survey were smoothed spatially, and the association between species distribution and soils was calculated using the botanical survey and 1 km square data on dominant and subdominant soils using national data. A logistic regression was fitted using the botanical survey data, and was interpolated across the whole country to generate the maps. The resulting maps show the probability of occurrence of species and species groups at the 2‐km scale. They map the potential, rather than realized, risk of particular types of weed infestation, as they do not account for local management factors.
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