Does afforestation increase bird nest predation risk in surrounding farmland?

Abstract

a b s t r a c tAfforestation of agricultural land is increasingly used to deliver environmental benefits, but their effects on biodiversity remain poorly understood. This paper tests the hypothesis that afforestation changes predation processes in surrounding farmland, examining how the characteristics and landscape context of forest plantations affect predator (birds and mammalian carnivores) and key prey (rabbits and hares) abundances, and bird nest predation rates in Iberian cereal-steppes. Lagomorphs and predators were surveyed in fallow fields around 50 forest plantations, where predation rates were estimated using artificial nests set at 0, 100, 200 and 300 m from the forest edge. Recent plantations structurally similar to sparse (oak) or dense (pine) shrublands were associated with the highest hare and rabbit abundances, respectively, whereas both species avoided landscapes with high eucalyptus cover. In contrast, mature eucalyptus plantations showed strong positive effects on typical nest predators such as corvids and carnivores. Open farmland fragmentation favoured the abundance of lagomorphs and carnivores. Despite these effects and the high predation rate on artificial nests (49%), there was neither evidence for increased predation near plantation edges nor higher predation in fields with more lagomorphs and predators. However, predation tended to increase with cover by young oak plantations and overall forest plantation cover, to decrease with eucalyptus cover at both the local and landscape scales, and to peak in landscapes with intermediate edge densities. These results suggest that afforestation may have strong effects on bird nest predation rates by changing landscape composition and configuration, rather than by inducing local increases in predator and prey populations. Nevertheless, increased abundances of generalist predators associated with forest plantations may still be considered of conservation concern, thus supporting the recommendation for strongly restricting afforestation in areas important for open grassland birds. Where this is unavoidable, monitoring should be undertaken to provide early signals for bird population declines associated with predator increases, eventually triggering conservation action such as predator exclusion or removal.

Shifts in composition of avian communities related to temperate-grassland afforestation in southeastern South America

“…The existence of these large amounts of natural vegetation within plantations was widely used as environmental propaganda by the pulpwood industry, which merchandised them as 'conservation' areas. Recent studies have shown that the conservation value of these 'ecological networks' for grassland species is greatly limited, mostly because of habitat unsuitability (Lipsey & Hockey, 2010) and high rates of nest predation (Reino et al, 2010).…”

Predictive mapping of Meadow Pipit density using integrated remote sensing data and an atlas of vascular plants dataset

“…We analysed all possible models (2 n , where n is the number of variables), using MuMIn library in R (Bartoń 2013, Hastie & Tibshirani 1990. Although this approach is criticized, the analysis of all possible models is often used when there is not enough a priori information to develop a small set of models (Whittingham et al 2005, Reino et al 2010. To reduce the possibility of finding spurious models, we used only dominant uncorrelated habitat variables selected by their VIF from the initial large set of variables (see also Reino et al 2010).…”

“…Although this approach is criticized, the analysis of all possible models is often used when there is not enough a priori information to develop a small set of models (Whittingham et al 2005, Reino et al 2010. To reduce the possibility of finding spurious models, we used only dominant uncorrelated habitat variables selected by their VIF from the initial large set of variables (see also Reino et al 2010). The probability of including a variable in the best parsimonious model was estimated as relative importance (RI) by summing the Akaike weights of all candidate models in which the variable was included (Burnham & Anderson, 2002, Reino et al 2010.…”

“…To reduce the possibility of finding spurious models, we used only dominant uncorrelated habitat variables selected by their VIF from the initial large set of variables (see also Reino et al 2010). The probability of including a variable in the best parsimonious model was estimated as relative importance (RI) by summing the Akaike weights of all candidate models in which the variable was included (Burnham & Anderson, 2002, Reino et al 2010. In order to allow some complexity in the functions, while avoiding over-fitting the data, we defined the basic dimension, i.e.…”

Complex species distribution models of Goldcrests and Firecrests densities in Poland: are remote sensing‐based predictors sufficient?

“…We analysed all possible models (2 n , where n = number of variables), using MuMIn library in R (Hastie and Tibshirani 1990;Barton´2013). Although this approach is criticized, the analysis of all possible models is often used when there is not enough a priori information to develop a small set of models (Whittingham et al 2005;Reino et al 2010). The probability of including a variable in the best parsimonious model was estimated as the relative importance (RI) by summing the Akaike weights of all candidate models in which the variable was included (Burnham and Anderson 2002;Reino et al 2010).…”