**2. Agriculture intensification and Agri-environmental schemes**

In Europe, the Common Agricultural Policy (CAP), born 50 years ago, began by subsidizing production of basic foodstuffs in the interests of self-sufficiency, after the difficult period of the war. Currently, CAP, give farmers an important role in improving quality, preserving biodiversity and traditional landscapes and keeping rural economies alive. Furthermore, more informed consumers are entitled to food that is safe and of high quality; this induced the creation of regulations defining organic foods and also what can be considered an organic farm. More extensive systems, such as organic farming, aim to mitigate the negative effects of modern agriculture and enhance biodiversity (Krebs et al., 1999; Reganold et al., 2001; Tybirk et al., 2004). Agri-environmental schemes (AES) were introduced into the European Common Agricultural Policy (CAP) in the early 1990s to reduce biodiversity loss in agricultural landscapes and mitigate other harmful effects of modern agriculture. AES are considered the most important policy instruments for protecting biodiversity in agricultural landscapes (European Environment Agency report, 2004) as they provide financial incentives to farmers for adopting environmentally friendly practices mostly at the field scale (i.e., reduction in pesticide and fertiliser applications or delaying harvesting).

With the increasing number of organic farms, several studies and meta-analyses have been conducted, with the sole purpose of finding a correspondence between the decline in biodiversity and the AI in conventional versus organic farms. Nevertheless, sometimes these studies are inconclusive, contradictory and sometimes positive results are found. Recent European-wide studies have questioned the effectiveness of AES for biodiversity conservation. Over half the studies showed significant positive effects of AES on the diversity or abundance of target groups such as plants, birds or arthropods, but the remaining studies showed non-significant or even negative effects (Kleijn et al., 2006; Kleijn & Sutherland, 2003). Usually the positive effects of organic farming relative to conventional agriculture are in terms of botanic diversity (Bengtsson et al., 2005; Hald, 1999; Hyvönen et al., 2003) whereas arthropods appear to respond ambiguously to organic cropping (reviewed in Hole et al., 2005). There are also other studies on other measures of agriculture intensification, for example, grazing intensification, extensive vs. intensive farming, etc.

One, however, should not expect immediate results from the introduction of AES. For example, Ameixa & Kindlman (2008) did not find any relation between agricultural practices and the diversity and abundance of carabids in several agricultural fields, which was probably because the species that live in agricultural fields have already undergone some kind of selection and are for this reason adapted to the constant changes. For example, in many parts of Europe, agricultural landscapes are well over 2000 years old (Groppali, 1993; Williamson, 1986), so organisms must be adapted to this environment. Thus, studies that compare organic vs. conventional fields should not aim to see an immediate change in biodiversity patterns in agricultural landscapes after years of intense land use, but find other methods to access this problem.

Another expectation is that even if AES are applied and therefore agriculture becomes less intensive, diversity will increase only until a certain maximum in agricultural fields above

Biodiversity Drifts in Agricultural Landscapes 319

colonization and maintenance of populations in agricultural landscapes (Duelli & Obrist, 2003). Duelli & Obrist (2003) attribute the lack of effectiveness of AES to the simplification of

However, again we have to take in to account that diversity is expected to increase with complexity of the landscape only above a minimum threshold (Figure 2), as landscape will always harbour some species. Positive effects of landscape complexity will eventually leveloff after a given level of complexity is reached, as the number of species that can live in a particular habitat is always finite, defined by local climatic and soil conditions (Concepción

Fig. 2. Hypothetical non-linear effects of landscape complexity around cultivated fields on the biological diversity in such fields. Dmax: saturation point of complexity, above which landscapes are so complex that no further effects of complexity are expected; Dmin:

minimum threshold of complexity below which landscapes are too simple for maintaining

The above indicates there is enough evidence that agriculture has become much more intensive during recent decades and simultaneously there has been a drastic decline in biodiversity in agroecosystems. This means that biodiversity in agroecosystems is negatively correlated with AI. However, correlation does not necessarily mean causation, and therefore – in theory – the decline in biodiversity in agroecosystems might have been caused by other factors and from a practical point of view, the effects of these should be minimized. This doubt provoked abundant case studies on how exactly AI can affect biodiversity of particular groups of organisms. The results of such studies, however, are

biodiversity (adapted from Conception 2008).

**4. Meta-analysis on different taxa** 

agricultural landscapes.

et al., 2008).

which no more species will be found (Figure 1). This is because the number of species that can live in a particular habitat is always finite, defined by local climatic and soil conditions and this maximum number is not affected by the way people are handling this habitat: lion will never be found in arctic tundra. On the other hand, even heavily exploited habitats will still harbour some species: the carabid, *Pterostichus melanarius*, is a good example of a species well adapted to intensively managed agroecosystems and was found to be even more numerous in these, compared with more natural habitats (Ameixa & Kindlmann, 2008).

Fig. 1. Hypothetical representation of the diversity expected to be found in agricultural fields. Dmax: Maximum diversity that can be found in agricultural lands; Dmin: minimum diversity that can be found in agricultural lands.
