Niche separation of wetland birds revealed from airborne laser scanning

Abstract

Numerous organisms depend on the physical structure of their habitats, but incorporating such information into ecological niche analyses has been limited by the lack of adequate data over broad spatial extents. The increasing availability of high-resolution measurements from country-wide airborne laser scanning (ALS) surveys -a light detection and ranging (LiDAR) technology -now provides unprecedented opportunities for characterizing habitat structure. Here, we use country-wide ALS data in combination with presence-absence observations of birds from a national monitoring scheme in the Netherlands to quantify niche filling, niche overlap and niche separation of three closely-related wetland birds (great reed warbler, Eurasian reed warbler and Savi's warbler). We developed a workflow to derive LiDAR metrics capturing different aspects of vertical and horizontal vegetation structure and used a principal component analysis (PCA), niche equivalency and niche similarity tests to analyse the fine-scale breeding habitat niches of these warbler species in the Netherlands. The widespread Eurasian reed warbler almost completely filled the available wetland habitat space (93%) whereas the two other species showed considerably less niche filling (64% and 74%, respectively). Substantial niche overlap occurred among all species, but each species occupied a distinct part of the habitat space. The great reed warbler mainly occurred in tall and vertically complex wetland vegetation and was absent in areas with large proportions of reedbeds. The Eurasian reed warbler occupied all parts of the wetland habitat space, whereas the Savi's warbler mainly occurred in large homogenous reedbeds with low vegetation height. Our results demonstrate that broad-scale ecological niche analyses can incorporate the fine-scale 3D habitat preference of species with unprecedented detail (e.g. 10 m resolution), and thus go much beyond quantifying the climate niche and 2D habitat information from land cover maps. This is important to identify habitat features and priorities for biodiversity conservation in wetlands and other habitats.

Better together? Assessing different remote sensing products for predicting habitat suitability of wetland birds

“…shown to be important for separating the fine-scale habitat niches of our study species (Koma, Grootes, et al, 2021) in Table 1). For this, we applied a moving window approach and used the 95th percentile of vegetation height as an input metric (Table 1).…”

“…The results show that this species prefers areas that have (within a 100 m radius) a large proportion of vegetation with 1-3 m height. In addition, horizontal variability of vegetation height in a 100 m radius (lidar_HH_sd) has to be low, reflecting homogenous reedbeds or other similar types of wetland vegetation (Koma, Grootes, et al, 2021). Moreover, a high proportion of swamp derived from the land cover map (landcover_propswamp) indicated a low probability of occurrence of that species because it tends to avoid swamp vegetation for breeding (Cramp, 1992).…”

“…We included a total of 11 LiDAR metrics to describe the fine‐scale vegetation structure and habitat heterogeneity within wetlands (see metric class ‘LiDAR metrics’ in Table 1). These LiDAR metrics are commonly used in predictive habitat distribution models of birds (Bakx et al, 2019) and have also been shown to be important for separating the fine‐scale habitat niches of our study species (Koma, Grootes, et al, 2021). Four of the LiDAR metrics represent measures of vegetation cover and density (e.g.…”

“…In contrast, field observations show that the fine‐scale structure of vegetation plays an important role in determining the distribution and diversity of animal species (Dunlavy, 1935; MacArthur & MacArthur, 1961; Tews et al, 2004). Moreover, structural variability and heterogeneity of vegetation has a positive effect on biodiversity by providing more niche space (Koma, Grootes, et al, 2021; Müller et al, 2014), by influencing resource use and habitat selection (Cody, 1985) and by creating diverse microclimates (Zellweger et al, 2019). Hence, the development of predictor variables from RS products that capture aspects of vegetation structure and habitat heterogeneity is required.…”

“…First, country‐wide ALS campaigns are typically not repeated in different seasons, which limits the ability to monitor seasonal changes in vegetation structure caused by plant growth dynamics. Second, ALS datasets are often collected for terrain mapping in the leaf‐off season and hence do not necessarily capture all aspects of vegetation structure during the time when animals are surveyed, for example in the breeding season (Koma, Grootes, et al, 2021; Koma, Seijmonsbergen, et al, 2021). It is thus important to explore the combination of LiDAR with other high spatial and temporal resolution satellite RS products to test which predictor variables can best describe habitat structure and seasonal dynamics of vegetation.…”