Living on the edge: utilising lidar data to assess the importance of vegetation structure for avian diversity in fragmented woodlands and their edges

Abstract

Context In agricultural landscapes, small woodland patches can be important wildlife refuges. Their value in maintaining biodiversity may, however, be compromised by isolation, and so knowledge about the role of habitat structure is vital to understand the drivers of diversity. This study examined how avian diversity and abundance were related to habitat structure in four small woods in an agricultural landscape in eastern England. Objectives The aims were to examine the edge effect on bird diversity and abundance, and the contributory role of vegetation structure. Specifically: what is the role of vegetation structure on edge effects, and which edge structures support the greatest bird diversity? Methods Annual breeding bird census data for 28 species were combined with airborne lidar data in linear mixed models fitted separately at (i) the whole wood level, and (ii) for the woodland edges only. Results Despite relatively small woodland areas (4.9-9.4 ha), bird diversity increased significantly towards the edges, being driven in part by vegetation structure. At the whole woods level, diversity was positively associated with increased vegetation above 0.5 m and especially with increasing vegetation Data accessibility The lidar data used for this study is available from the Centre for Environmental Data Analysis at http://www.ceda.ac.uk/. The bird data is owned and maintained by the Centre of Ecology and Hydrology (https://www. ceh.ac.uk/).

Diverse temperate forest bird assemblages demonstrate closer correspondence to plant species composition than vegetation structure

“…We computed the maximum, mean and SD of the canopy surface for each point from a 5-m resolution canopy height model. Three penetration ratios (number of LiDAR returns <2 m divided by the number of returns <50 and <10 m, including returns <2 m; and the number of returns <1 m divided by the number of returns <5 m, including returns <1 m) were also computed to characterize the degree of canopy openness (0-2/0-50 m ratio), amount of midstory foliage (0-2/0-10 m ratio) and amount of understory foliage (0-1/0-5 m ratio) (Müller et al 2010, Melin et al 2018). Finally, we computed FHD in a manner similar to MacArthur and MacArthur (1961) by clustering returns within three vertical layers, 0-5, 5-25 and >25 m, and calculating the diversity (Shannon's index) of vegetation hits throughout the vertical profile of each point location (Melin et al 2018) (Fig.…”

Evaluation of Forest Edge Structure and Stability in Peri-Urban Forests

“…We used the data provided by LiDAR in order to obtain an estimate of forest edges and the metrics of their vertical vegetation structure. For each one-meter cell, we collected data on vegetation height from the canopy height model and determined height classes and vegetation components by height classes above ground level defined as ≥0.5 m. In national forest inventories [30], woody plants higher than 0.5 m are considered to be a part of the shrub belt and dwarf shrubs and woody plants <0.5 m are part of the herb border, similar to the estimation of vegetation structure in fragmented woodlands and their edges [31].…”

“…A circular patch with such an area would have a radius of at least 300 m [22]. On the polygons of 30 × 10 m in the forest edge segments (Figure 3c), we used the canopy height model (CHK) in order to determine the heights of the tallest trees and pixel proportion of canopy cover at different height levels of trees as follows: above 0.5, 5, 15, and 25 m. From the proportion, we determined the vertical canopy height diversity (CHD, Equation (1)) in a similar way as in comparable studies of forest edges [31]:…”

Assessing the performance of object‐oriented LiDARpredictors for forest bird habitat suitability modeling

“…Accordingly, recent efforts have been made by scientists to propose concrete recommendations for managers from PC area‐based results by adapting the predictor description and designation (e.g., the proportion of echoes expressed as the proportion of vegetation in shrubs or canopy layers) (Melin et al. ).…”

“…However, these metrics may be difficult to interpret or communicate because they are not used as field metrics by managers (such as the percentage of vegetation cover by layer and the number of trees). Accordingly, recent efforts have been made by scientists to propose concrete recommendations for managers from PC area-based results by adapting the predictor description and designation (e.g., the proportion of echoes expressed as the proportion of vegetation in shrubs or canopy layers) (Melin et al 2018).…”