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Insectivorous bats have been shown to control a number of agricultural insect pests. As bats exhibit species-specific responses to the surrounding landscape, tied closely to their morphology and foraging mode, the activity and distribution patterns of bats, and consequently the ecosystem services they provide, are influenced by the landscape characteristics.
We collected acoustic recordings to determine activity levels of seven bat sonotypes in rice fields surrounded by a variety of land-cover types in the Nagaon district of Assam, India. Using this, we determined the most important set of features in the surrounding landscape, and the scales at which had the strongest impact, for each sonotype.
Our results suggest that tree cover variables are the most important predictors of bat activity in rice fields. Distance to nearest forest, area of forest within 1 km, distance to nearest forest edge, and landscape heterogeneity influenced all five of the analysed bat sonotypes. Also important were the amount of urban land within 1 km, which exerted a negative effect on the activity of one sonotype, and moonlight activity, which negatively influenced the activity levels of one sonotype.
Our results demonstrate that when flying over rice fields, bat activity is most influenced by presence and proximity of trees. Therefore, increasing tree cover in agricultural landscapes will increase bat activity and likely the level of pest control.
Interest in insectivorous bats in agricultural landscapes has risen dramatically in the last two decades, driven in part by the growing evidence of their value as pest suppression agents to crops such as rice (Puig-Montserrat et al. 2015), cotton (Cohen et al. 2020), cacao (Cassano et al. 2016), and corn (Maine and Boyles 2015). Their success as natural pest suppressors comes from a broad diet (Tournayre et al. 2021; Maslo et al. 2022), large energetic demands associated with flight, and, importantly, flexibility in foraging habitat uses. Insectivorous bats quickly switch to the most productive foraging habitat available. When an agricultural plot is fallow between seasons, bats can move to more productive regions elsewhere, to return when there are pest outbreaks (McCracken et al. 2012; Puig-Montserrat et al. 2015).
In addition to the influence of composition and configuration of landscape features on bat behaviour, their relationship to said features is also influenced by their geographic scale. Morphological traits such as wing shape that allow some bats to travel longer distances than others affect their access to water, roosting sites, and foraging grounds (Meyer et al. 2007; Marinello and Bernard 2014). Adding to this are ecological considerations such as the vulnerability of certain species to avian predators, where some species prefer more closed, and therefore protected, habitats than others (Appel et al. 2021). The same features of a landscape therefore are enabling or limiting to different degrees based on their geographical scale.
Across the world, the intensification and homogenization of agricultural practices (Robinson and Sutherland 2002; Wang et al. 2015) are having severe impacts on bat populations (Park 2015). Given the strong preference of bats for specific landscape structures, changes which reduce tree cover disproportionately affect those species that forage in dense natural vegetation. The general trend shows a greater decline of clutter foragers than open-space foragers with reduced tree cover (Heim et al. 2016; Mtsetfwa et al. 2018).
As agricultural systems worldwide come under increased production pressure, the need for sustainable pest control has bolstered studies of natural pest control measures. Given that bats are valuable pest suppression agents, and that India is striving to increase agricultural production (Hinz et al. 2020), it is important to understand the relationships between bats and agricultural landscapes. This study focusses on the insectivorous bat community in the rice-dominated landscapes of Assam, India. In India, rice makes up 22% of the gross cropped area (Directorate of Economics and Statistics 2019). A small rice farm can offer an ideal habitat for a broad community of insectivorous bats as they feature an abundance of insects (Puig-Montserrat et al. 2015), edges (Harms et al. 2020), roosting sites (in trees and anthropogenic structures) (Kusuminda et al. 2021), and water bodies. As a matrix, rice is a harsh habitat, providing no landmarks, edges, tree cover, or resting sites. The larger the fields are, therefore, the less accessible we expect their interiors to be, as bats foraging in them are then further from water sources, shelter, edges, or roosting sites (Rainho and Palmeirim 2011; Frey-Ehrenbold et al. 2013).
We used passive acoustic recordings from rice fields within the Nagaon district of Assam to investigate the importance and scales at which different landscape features drive the activity of insectivorous bats. We hypothesize that different species of insectivorous bats, will respond differently to presence and proximity of forested areas, treelines, water bodies, grass fields, urban environments and rice fields. In addition to the composition of the landscape, we hypothesize that bat activity will also be influenced its configuration. By identifying how bats use the agricultural landscape, this study can contribute to designing agricultural landscapes for the protection of bats and the promotion of their ecosystem services.
Nightly activity data for each sonotype was tested for spatial autocorrelation at the nine different spatial scales. The smallest scale at which autocorrelation was found to be not significant was used to group the sites and these groups were used as a random effect in the subsequent models.
The smallest scales at which sites were grouped and resulted in no spatial autocorrelation for each sonotype were: S38 and S28 at a scale of 1 km, S35 at a scale of 5 km, S32 at a scale of 0.5 km, and S47 at a scale of 0.4 km. Sonotype S20 did not reach the modelling stage because spatial autocorrelation was detected up to and including a scale of 10 km during Part 1 of the statistical analysis. Grouping data points at a larger scale was deemed redundant and the sonotype was dropped. S65 was also dropped due to insufficient data.
In creating a model for overall bat activity, spatial autocorrelation was detected at all relevant scales. For descriptive purposes only, an exploratory set of models were built with activity data of all bat sonotypes combined. Sites were grouped at the 400 m, 1 and 5 km scales. In addition to sampling effort, distance to forest and area of rice within 2 km were the most important variables in the final models, with one model having area of edge within 1 km as a predictor (Table SIII). The similarity of the models, despite having points grouped at different spatial scales, would indicate a relatively low effect of spatial autocorrelation and suggest that these variables do influence bat activity in general.
We studied bat activity in rice fields of Assam to understand how the wider landscape matrix affects the activity of insectivorous bats and to identify important landscape features for bat conservation in the agricultural landscape. Our results show that key landscape features, including forest and edge cover, urban land, moonlight intensity and landscape heterogeneity influenced the activity of five sonotypes of insectivorous bats flying over rice fields.
In warm and dry environments, bats lose water rapidly (Webb et al. 1995) and it must be replenished through their food and by drinking. While many species have evolved mechanisms to limit water loss (Reher and Dausmann 2021), the presence of water bodies still exert considerable influence on where bats choose to fly, particularly in arid environments (Razgour et al. 2010). Past studies have found distance to water to be a key factor in driving selection of roost sites and foraging grounds (Adams and Thibault 2006; Adams and Hayes 2008; Rainho and Palmeirim 2011). However, water bodies in these studies tend to be few and far apart, increasing their importance to foraging bats. Despite our study being conducted in the summer, when temperatures regularly exceeded 30 C, having been conducted in rice fields, our sites were all within 600 m of a water body (defined here as areas of water of at least 1000 sq meters, equivalent to a 50 m by 20 m plot), with an average distance of 204 m. Given that this distance is well within the foraging ranges of insectivorous bats, water was not found to be an influencing factor in the best model of any sonotype, suggesting that for the analysed sonotypes, water is either not a limiting factor in this landscape, or the range at which water becomes a limiting factor is greater than bats in our landscapes were presented with.
Urban landscapes are not entirely uninhabitable, and many bat species have adapted to roost and forage there. The use of urban land by bats is influenced by built infrastructure, light pollution, noise levels, tree cover, bat physiology, predation pressure, and prey availability (Moretto and Francis 2017; Moretto et al. 2019; Jung and Threlfall 2021). While light pollution in urban areas is harmful to insect populations at the large scale (Owens et al. 2020), the insects attracted to streetlights create local zones of high prey density (Firebaugh and Haynes 2019), which in turn can increase the activity of some bats (Rodrguez-Aguilar et al. 2017). Bat activity is greater when urban areas are near patches of forest, water, and/or agricultural land-cover (Dixon 2012; Ancillotto et al. 2019), in part because of higher insect activity in such areas (Avila-Flores and Fenton 2005). Studies have found that the complementation of anthropogenic and natural land-cover can result in high levels of bat activity, particularly of mobile generalist species (Johnson et al. 2008). More generally, canopy cover has been found to be a key determinant of bat activity in urban contexts (Bailey et al. 2019). Bats exhibit extremely species-specific responses to landscapes and while some studies have found increased activity of bats in urban landscapes (Rodrguez-Aguilar et al. 2017), many studies have found even moderate urbanization to have negative impacts on bat activity (Ancillotto 2015; Jung and Threlfall 2016). While our sites were set in rice fields, urban land-cover was present around the sites in two forms: dense urban landscapes seen in Nagaon city and scattered nodes in villages. One, or both, of these land-covers of these negatively affected the activity of S28 and S38. For the other sonotypes, it is possible that the farmland and associated buildings, trees, and water bodies satisfied the requirements of most bats at the local scale, thereby overriding any negative (or positive) effects of the city.
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