Using satellite data to assess spatial drivers of bird diversity

Hunt, Merryn L. ORCID: https://orcid.org/0000-0003-4435-3644; Blackburn, George Alan; Siriwardena, Gavin M.; Carrasco, Luis; Rowland, Clare S. ORCID: https://orcid.org/0000-0002-0459-506X. 2023 Using satellite data to assess spatial drivers of bird diversity. Remote Sensing in Ecology and Conservation, 9 (4). 483-500. https://doi.org/10.1002/rse2.322

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Official URL: http://dx.doi.org/10.1002/rse2.322

Abstract/Summary

Birds are useful indicators of overall biodiversity, which continues to decline globally, despite targets to reduce its loss. The aim of this paper is to understand the importance of different spatial drivers for modelling bird distributions. Specifically, it assesses the importance of satellite-derived measures of habitat productivity, heterogeneity and landscape structure for modelling bird diversity across Great Britain. Random forest (RF) regression is used to assess the extent to which a combination of satellite-derived covariates explain woodland and farmland bird diversity and richness. Feature contribution analysis is then applied to assess the relationships between the response variable and the covariates in the final RF models. We show that much of the variation in farmland and woodland bird distributions is explained (R2 0.64–0.77) using monthly habitat-specific productivity values and landscape structure (FRAGSTATS) metrics. The analysis highlights important spatial drivers of bird species richness and diversity, including high productivity grassland during spring for farmland birds and woodland patch edge length for woodland birds. The feature contribution provides insight into the form of the relationship between the spatial drivers and bird richness and diversity, including when a particular spatial driver affects bird richness positively or negatively. For example, for woodland bird diversity, the May 80th percentile Normalized Difference Vegetation Index (NDVI) for broadleaved woodland has a strong positive effect on bird richness when NDVI is >0.7 and a strong negative effect below. If relationships such as these are stable over time, they offer a useful analytical tool for understanding and comparing the influence of different spatial drivers.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1002/rse2.322
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	2056-3485
Additional Information. Not used in RCUK Gateway to Research.:	Open access paper - full text available via Official URL link.
Additional Keywords:	avian, feature contribution analysis, habitat productivity, Landsat, landscape heterogeneity, random forest
NORA Subject Terms:	Ecology and Environment Data and Information
Date made live:	01 Nov 2023 14:10 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/535324

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1002/rse2.322

UKCEH and CEH Sections/Science Areas:

Soils and Land Use (Science Area 2017-)

ISSN:

2056-3485

Additional Information. Not used in RCUK Gateway to Research.:

Open access paper - full text available via Official URL link.

Additional Keywords:

avian, feature contribution analysis, habitat productivity, Landsat, landscape heterogeneity, random forest