Evaluating the accuracy of the EcoservR toolkit for fine‐resolution habitat mapping

Angers‐Blondin, S ORCID: 0000-0001-8907-3502, Bowe, C ORCID: 0000-0001-7302-3906, Bentley, LK ORCID: 0009-0004-5468-0563, Amoakoh, AO ORCID: 0000-0001-8394-1241, Bellis, J ORCID: 0000-0003-2787-3736, Dowdall, L ORCID: 0000-0002-5227-2548, Bonnin, N ORCID: 0000-0001-5529-7452, Galata, S ORCID: 0000-0002-9016-6308, Branwood, H, Clark, A, Bellamy, C ORCID: 0000-0002-3830-0995 and Partoft, H ORCID: 0000-0002-3162-4540 (2026) Evaluating the accuracy of the EcoservR toolkit for fine‐resolution habitat mapping. Ecological Solutions and Evidence, 7 (2). ISSN 2688-8319

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Abstract

Accurate and spatially explicit habitat maps are essential for monitoring ecological change, supporting nature-based solutions and informing policy. Despite advances in habitat mapping, classification accuracy varies across landscapes and thematic resolutions, highlighting the need for systematic evaluation of classification workflows.
This study evaluates EcoservR, an open-source rule-based toolkit for habitat mapping, using field survey data from two contrasting landscapes in northern England: Merseyside (MER) and the North York Moors (NYM).
Habitat classifications were generated by integrating multiple national spatial datasets and were evaluated against ecological survey records and high-resolution aerial imagery across three levels of the Phase 1 habitat classification hierarchy developed by the Joint Nature Conservation Committee (JNCC).
Overall accuracy ranged from 0.612 ± 0.015 (Level 3, NYM) to 0.804 ± 0.012 (Level 1, NYM). Broad habitat classes such as woodland and standing water achieved high precision, while grasslands, heathlands and mires showed lower precision. Accuracy declined with increasing thematic detail, reflecting ecological heterogeneity and limitations in the resolution and currency of input datasets. Observed land-cover patterns corresponded with the known ecological structure of each landscape, although classification biases indicate that fine-scale habitat assessments and quantitative ecosystem-service estimates require supplementary data.
Practical implications. EcoservR provides a reproducible workflow for integrating multiple spatial datasets to generate spatially consistent habitat maps. This approach supports regional planning and nature-recovery initiatives where broad habitat categories are sufficient, and its rule-based architecture allows adaptation to other landscapes where comparable datasets are available.

Item Type:	Article
Uncontrolled Keywords:	3103 Ecology
Subjects:	G Geography. Anthropology. Recreation > G Geography (General) G Geography. Anthropology. Recreation > GE Environmental Sciences
Divisions:	Biological and Environmental Sciences (from Sep 19) Education
Publisher:	Wiley
Date of acceptance:	4 March 2026
Date of first compliant Open Access:	30 March 2026
Date Deposited:	30 Mar 2026 11:02
Last Modified:	30 Mar 2026 11:02
DOI or ID number:	10.1002/2688-8319.70234
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28312

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