Translating habitat class to land cover to map 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 calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land-cover classes, which generates a source of uncertainty in AOH maps. We developed a data-driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 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 or classes assigned to each species occurring there. Then, we modeled each land-cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land-cover class. We then compared the performance of our data-driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land-cover classes with more confidence than others (e.g., wetlands and artificial). The data-driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups

A global map of terrestrial habitat types

Funder: NatureMap (https://naturemap.earth/) through Norway's International Climate and Forest Initiative (NICFI)Abstract: We provide a global, spatially explicit characterization of 47 terrestrial habitat types, as defined in the International Union for Conservation of Nature (IUCN) habitat classification scheme, which is widely used in ecological analyses, including for quantifying species’ Area of Habitat. We produced this novel habitat map for the year 2015 by creating a global decision tree that intersects the best currently available global data on land cover, climate and land use. We independently validated the map using occurrence data for 828 species of vertebrates (35152 point plus 8181 polygonal occurrences) and 6026 sampling sites. Across datasets and mapped classes we found on average a balanced accuracy of 0.77 (+¯0.14 SD) at Level 1 and 0.71 (+¯0.15 SD) at Level 2, while noting potential issues of using occurrence records for validation. The maps broaden our understanding of habitats globally, assist in constructing area of habitat refinements and are relevant for broad-scale ecological studies and future IUCN Red List assessments. Periodic updates are planned as better or more recent data becomes available

Production and validation of Area of Habitat maps for terrestrial birds and mammals

An accurate representation of the geographical distribution of species is central to ecological research and conservation science and practice. Species’ distributions can be represented using a variety of approaches: geographical ranges, which represent the geographical limits of distributions; point locality data, which represent species’ known occurrences; or inductive or deductive models, which usually represent species’ habitat within geographic ranges. Representations of distributions may contain false presences (commission errors) and/or false absences (omission errors). Recently, Area of Habitat (AOH) maps, a type of deductive model, have gained traction as a tool to represent global distribution of species, reducing the often high rate of commission errors in range maps. AOH models map the distribution of suitable habitat for a species inside its distributional limits. One of the key challenges in producing AOH maps is to translate knowledge of a species’ habitat (a complex and species-specific concept) into specific land-cover classes in existing land use/cover layers. Three different methods (expert-based crosswalk, translation table and global maps of terrestrial habitat types) have been developed to date to overcome this challenge to produce the AOH maps. (‘Crosswalk’ is a table translating habitat types in a Habitat Classification Scheme to land-cover classes in a land-cover layer.) However, the performance of these methods has not yet been tested. One of the key parts of modeling is validation of the model outputs. This is done by comparing the model output with real world observations, to quantify omission and commission errors in the models. The aim of this thesis is to produce and compare AOH models for terrestrial mammals and birds using different habitat mapping and validation methods. In the second chapter, I developed a map of global terrestrial habitat types based on the IUCN Red List Habitat Classification Scheme, and a novel method to estimate the omission and commission error of the global map of terrestrial habitat types using presence-only data of habitat specialist species downloaded from open repositories like GBIF (Global Biodiversity Information Facility), eBird (www.ebird.com), PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) and the IBA (Important Bird and Biodiversity Areas) dataset. To date, AOH maps have been validated using presence-only data for small subsets of species for different taxonomic groups, but no standard validation method exists for cases where absence data are not available. In Chapter 3, I developed a novel two-step validation protocol for AOH maps which includes first a model-based evaluation of model prevalence (i.e, the proportion of a species’ range that contains suitable habitat), and second a validation using species point localities (point prevalence) using presence-only data. I used 48,336,141 point localities for 4,889 bird species and 107,061 point localities for 420 mammal species. Where point prevalence exceeded model prevalence, the AOH was taken to be a better reflection of species’ distribution than random. In Chapter 4, I used the global map of terrestrial habitat types to produce AOH maps for 10,651 terrestrial birds and 4,581 terrestrial mammals. I then applied the validation protocol developed in Chapter 3 to AOH maps of terrestrial birds and mammals produced using translation table and global maps of terrestrial habitat types. I found that the average model prevalence for AOH maps produced using the global map of terrestrial habitat type was lower (0.55±0.28 for birds and 0.51±0.29 for mammals) than those produced using the translation table (0.64±27 for birds and 0.65±0.28 for mammals). This led to higher omission errors in the AOH maps produced using the global map of terrestrial habitat types. Also, the number of AOH maps which were better than random was higher in the AOH mapset produced using the translation table. I also found a high similarity between these two sets of maps (53.44% mapped as suitable and 23.22% mapped as unsuitable in both datasets for birds and 58% mapped as suitable and 19% mapped as unsuitable in both datasets for mammals). Each AOH map produced using the global map of terrestrial habitat types was effectively a subset of the equivalent AOH map produced using the translation table, because the former was based on a single map for each habitat type, whereas the latter was based on one-to-many relationships between habitat types and land-cover classes. I conclude that, overall, AOH maps based on the translation table are more robust than AOH maps based on the global map of terrestrial habitat types in terms of reducing commission errors of the geographic range maps without introducing large omission errors. However, for species occurring primarily in human- modified habitats, the AOH maps based on the global map of terrestrial habitat types are more robust as few human-modified habitats are not mapped by the translation table but are mapped in the global map of terrestrial habitat types. The AOH modeling and validation methods developed in this thesis can help update the AOH maps in the future with latest data on land-cover, habitat and elevation. Furthermore, the validation metrics can be used as a guideline by the users to select the most appropriate AOH map for their use

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 habitats at suitable alti- tudes for a species inside its broad geographical range. The 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 pro- tocol for AOH which includes first a model-based evalua- tion of model prevalence (i.e, the proportion of suitable habi- tat 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 ex- pected 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 differences between observed and predicted model prevalence were identified as outliers and used to identify a number of sources of system- atic 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 preva- lence, 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 lo- calities for 420 mammals. Where point prevalence exceeded model prevalence, the AOH was a better reflection of species’ distribution than random selection. 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 ran- dom. Possible reasons for the poor performance of a small proportion of AOH maps are discussed

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