Bat responses to changes in forest composition and prey abundance depend on landscape matrix and stand structure

Abstract

Despite the key importance of the landscape matrix for bats, we still not fully understand how the effect of forest composition interacts at combined stand and landscape scales to shape bat communities. In addition, we lack detailed knowledge on the effects of local habitat structure on bat-prey relationships in forested landscapes. We tested the assumptions that (i) forest composition has interacting effects on bats between stand and landscape scales; and (ii) stand structure mediates prey abundance effects on bat activity. Our results indicated that in conifer-dominated landscapes (> 80% of coniferous forests) bat activity was higher in stands with a higher proportion of deciduous trees while bats were less active in stands with a higher proportion of deciduous trees in mixed forest landscapes (~ 50% of deciduous forests). Moth abundance was selected in the best models for six among nine bat species. The positive effect of moth abundance on Barbastella barbastellus was mediated by vegetation clutter, with dense understory cover likely reducing prey accessibility. Altogether, our findings deepen our understanding of the ecological processes affecting bats in forest landscapes and strengthen the need to consider both landscape context and trophic linkage when assessing the effects of stand-scale compositional and structural attributes on bats.

Three‐dimensional stratification pattern in an old‐growth lowland forest: How does height in canopy and season influence temperate bat activity?

“…A set of candidate models including all two‐way interactions between height, canopy openness (gap vs. forest interior), season (pregnancy/lactation vs. postlactation), and forest habitat (broadleaved vs. mixed coniferous) were fitted for each guild/species with assumed negative‐binomial distributions. Mean nighttime temperature was added as a simple predictor, since several studies identified temperature as an important predictor both for bat and insect activity (e.g., Dajoz, 2000; Froidevaux et al, 2021; Mueller et al, 2012; Wolbert et al, 2014). Post hoc testing for effects with more than two levels was done using Tukey’s honestly significant difference test with a correction factor for multiple comparisons using the pairs function within the emmeans package (Lenth, 2020).…”

“…Mean nighttime temperature was added as a simple predictor, since several studies identified temperature as an important predictor both for bat and insect activity (e.g., Dajoz, 2000;Froidevaux et al, 2021;Mueller et al, 2012;Wolbert et al, 2014). Post hoc testing for effects with more than two levels was done using Tukey's honestly significant difference test with a correction factor for multiple comparisons using the pairs function within the emmeans package (Lenth, 2020).…”

Temporal mismatches in flight activity patterns between Pipistrellus kuhlii and Prays oleae in olive farms: Implications for biocontrol services potential

“…We found no effects of the surrounding landscape (that is, the proportion of oak forest cover and olive cover) on the temporal activity patterns of P. kuhlii and, in turn, on their potential biocontrol services against P. oleae . This suggests that the temporal activity patterns of P. kuhlii are mostly determined by the structural complexity of olive farms and, particularly, by the availability of suitable roosting and foraging sites (Davy et al., 2007; Froidevaux et al., 2021; Russo & Jones, 2003). The significance of the surrounding landscape on the potential for bats to provide biocontrol services in olive farms has, in any case, already been demonstrated (Herrera et al., 2021).…”

Distance to hedgerows drives local repulsion and attraction of wind turbines on bats: Implications for spatial siting

“…Although our sampling design minimizes differences in landscape composition between control and treatment sites, we accounted for landscape composition around recording sites during the modelling procedure (see Statistical analysis section) to control for residual variations. We selected a set of environmental variables known to influence bat activity according to the literature either positively, including hedgerows (Heim et al, 2017; Lacoeuilhe et al, 2018), forests (Boughey et al, 2011b; Froidevaux et al, 2021), wetlands (Sirami et al, 2013), and grassland (Froidevaux et al, 2019; Walsh & Harris, 1996), or negatively including arable land (Put et al, 2019), or both (i.e. negatively or positively), depending on the context and the species, such as urban areas (Azam et al, 2016).…”