Biology subjects

Dahal, P. R.

Publications and source records attributed to Dahal, P. R..

3 recordsLinked to original sources

bioRxiv2022

Area of Habitat maps for the world's terrestrial birds and mammals

Area of Habitat (AOH) is the habitat available to a species, that is, habitat within its range. It complements a geographic range map for a species by showing potential occupancy and reducing commission errors. AOH maps are produced by subtracting areas considered unsuitable for the species from their range map, using information on each species associations with habitat and elevation. We present AOH maps for 5,481 terrestrial mammal and 10,651 terrestrial bird species (including 1,816 migratory bird species for which we present separate maps for the resident, breeding and non-breeding areas). Our maps have a resolution of 100 m. On average, AOH covered 66{+/-}28% of the range maps for mammals and 64{+/-}27% for birds. The AOH maps were validated independently, following a novel two-step methodology: a modelling approach to identify outliers and a species-level approach based on point localities. We used AOH maps to produce global maps of the species richness of mammals, birds, globally threatened mammals and globally threatened birds.

ecology↗

bioRxiv2021

A validation standard for Area of Habitat maps for terrestrial birds and mammals

Area of Habitat (AOH) is a deductive model which maps the distribution of suitable habitat at suitable altitudes for a species inside its broad geographical range. AOH maps have been validated using presence-only data for small subsets of species for different taxonomic groups, but no standard validation method exists when absence data are not available. We develop a novel two-step validation protocol for AOH which includes first a model-based evaluation of model prevalence (i.e, the proportion of suitable habitat within a species range), and second a validation using species point localities (presence-only) data. We applied the protocol to AOH maps of terrestrial birds and mammals. In the first step we built logistic regression models to predict expected model prevalence (the proportion of the range retained as AOH) as a function of each species elevation range, mid-point of elevation range, number of habitats, realm and, for birds, seasonality. AOH maps with large difference between observed and predicted model prevalence were identified as outliers and used to identify a number of sources of systematic error which were then corrected when possible. For the corrected AOH, only 1.7% of AOH maps for birds and 2.3% of AOH maps for mammals were flagged as outliers in terms of the difference between their observed and predicted model prevalence. In the second step we calculated point prevalence, the proportion of point localities of a species falling in pixels coded as suitable in the AOH map. We used 48,336,141 point localities for 4889 bird species and 107,061 point localities for 420 mammals. Where point prevalence exceeded model prevalence, the AOH was a better reflection of species distribution than random. We also found that 4689 out of 4889 (95.9%) AOH maps for birds, and 399 out of 420 (95.0%) AOH maps for mammals were better than random. Possible reasons for the poor performance of a small proportion of AOH maps are discussed.

zoology↗

bioRxiv2021

A habitat class to land cover translation model for mapping Area of Habitat of terrestrial vertebrates

Area of Habitat (AOH) is defined as the habitat available to a species, that is, habitat within its range and is produced by subtracting areas of unsuitable land cover and elevation from the range. Habitat associations are documented using the IUCN Habitats Classification Scheme, and unvalidated expert opinion has been used so far to match habitat to land-cover classes generating a source of uncertainty in AOH maps. We develop a data-driven method to translate IUCN habitat classes to land-cover based on point locality data for 6,986 species of terrestrial mammals, birds, amphibians and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class(es) assigned to each species occurring there. Then we modelled each land cover class as a function of IUCN habitat using logistic regression models. The resulting odds ratios were used to assess the strength of the association of each habitat land-cover class. We then compared the performance of our data-driven model with those from a published expert knowledge translation table. The results show that some habitats (e.g. forest and desert) could be more confidently assigned to land-cover classes than others (e.g. wetlands and artificial). We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH, which is in the form of a binary presence/absence, it is necessary to apply a threshold of association. This can be chosen by the user according to the required balance between omission and commission errors. We demonstrate that a data-driven translation model and expert knowledge perform equally well, but the model provides greater standardization, objectivity and repeatability. Furthermore, this approach allows greater flexibility in the use of the results and allows uncertainty to be quantified. Our model can be developed regionally or for different taxonomic groups.

ecology↗