Recommendations to enhance breeding bird diversity in managed plantation forests determined using LiDAR

Thetford Forest. Mike Toms / BTO

Author(s): Tew, E.R., Conway, J.C., Henderson, I.G., Milodowski, D.T., Swinfield, T. & Sutherland, W.J.

Published: May 2022  

Journal: Ecological Applications

Digital Identifier No. (DOI): 10.1002/eap.2678

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Tree planting can contribute to the fight against both climate change and biodiversity loss if forests are planned and managed well. Forest structure data, collected using remote sensing technology, combined with bird surveys has been used to test which management regimes maximise bird diversity in managed plantations. Forests with stands of a greater variety of ages are consistently more biodiverse, suggesting that forest managers should prioritise maintaining a diversity of stand ages.


Widespread afforestation is a crucial component of climate mitigation strategies worldwide. This presents a significant opportunity for biodiversity conservation if forests are appropriately managed. Within forests, structural and habitat diversity are known to be critical for biodiversity but pragmatic management recommendations are lacking. We make a comprehensive assessment of the effects of habitat variables on bird populations using data from over 4000 ha of forested landscape. We combine high-resolution remote sensing data with comprehensive management databases to classify habitat attributes, and measure the response of six taxonomic and functional diversity metrics: species richness, Shannon diversity, functional richness, functional evenness, functional divergence and functional dispersion. We use a novel approach that combines hierarchical partitioning analysis with linear models to determine the relative importance of different habitat variables for each bird diversity metric. The age class of forest stands was consistently the most important variable across all bird diversity metrics, outperforming other structural measures such as horizontal and vertical heterogeneity and canopy density. Shrub density and gap fraction were each significantly associated with one bird diversity metric. In contrast, variables describing within-stand structural heterogeneity (vertical and horizontal) were generally less important while tree species identity (e.g. conifer or broadleaved) was not significant for any bird diversity metric. Each of the six bird diversity metrics had different patterns of independent variable importance and significance, emphasising the need to consider multiple diversity metrics in biodiversity assessments. Similarly, the optimal resolution for remote sensing metrics varied between structural variables and bird diversity metrics, suggesting that the use of remote sensing data in biodiversity studies could be greatly improved by first exploring different resolutions and data aggregations. Based on the results from this comprehensive study, we recommend that managers focus on creating habitat diversity at the between- rather than exclusively within-stand scale, such as by creating a matrix of different age classes, to maximise bird diversity. This recommendation for forest managers is powerful yet pragmatic in its simplicity.



The authors are grateful to the volunteers and BTO colleagues who collected bird data across the forest. They thank colleagues at Forestry England for assistance in this project, in particular Neal Armour-Chelu, Richard Brooke and Jonathan Spencer. They are also grateful to Alison Johnston for statistical advice, Florian Zellweger for helpful discussions, and three anonymous reviewers for their constructive feedback and suggestions. The LiDAR point cloud data were collected as part of the Breaking New Ground Heritage Lottery Project (© Breaking New Ground LPS and the Forestry Commission) in 2015; further LiDAR coverage in 2017 was commissioned by the Forestry Commission. E.R.T. was supported through an Industrial CASE studentship, funded by the Natural Environment Research Council and Forestry England [NE/M010287/1], and as part of the Cambridge Earth System Science NERC DTP [NE/L002507/1]. W.J.S. is funded by Arcadia.

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