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Global screening for Critical Habitat in the terrestrial realm.

ABSTRACT: Critical Habitat has become an increasingly important concept used by the finance sector and businesses to identify areas of high biodiversity value. The International Finance Corporation (IFC) defines Critical Habitat in their highly influential Performance Standard 6 (PS6), requiring projects in Critical Habitat to achieve a net gain of biodiversity. Here we present a global screening layer of Critical Habitat in the terrestrial realm, derived from global spatial datasets covering the distributions of 12 biodiversity features aligned with guidance provided by the IFC. Each biodiversity feature is categorised as 'likely' or 'potential' Critical Habitat based on: 1. Alignment between the biodiversity feature and the IFC Critical Habitat definition; and 2. Suitability of the spatial resolution for indicating a feature's presence on the ground. Following the initial screening process, Critical Habitat must then be assessed in-situ by a qualified assessor. This analysis indicates that a total of 10% and 5% of the global terrestrial environment can be considered as likely and potential Critical Habitat, respectively, while the remaining 85% did not overlap with any of the biodiversity features assessed and was classified as 'unknown'. Likely Critical Habitat was determined principally by the occurrence of Key Biodiversity Areas and Protected Areas. Potential Critical Habitat was predominantly characterised by data representing highly threatened and unique ecosystems such as ever-wet tropical forests and tropical dry forests. The areas we identified as likely or potential Critical Habitat are based on the best available global-scale data for the terrestrial realm that is aligned with IFC's Critical Habitat definition. Our results can help businesses screen potential development sites at the early project stage based on a range of biodiversity features. However, the study also demonstrates several important data gaps and highlights the need to incorporate new and improved global spatial datasets as they become available.

SUBMITTER: Brauneder KM

PROVIDER: S-EPMC5863962 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Habitat fragmentation reduces genetic connectivity for multiple species, yet conservation efforts tend to rely heavily on single-species connectivity estimates to inform land-use planning. Such conservation activities may benefit from multi-species connectivity estimates, which provide a simple and practical means to mitigate the effects of habitat fragmentation for a larger number of species. To test the validity of a multi-species connectivity model, we used neutral microsatellite genetic datasets of Canada lynx (Lynx canadensis), American marten (Martes americana), fisher (Pekania pennanti), and southern flying squirrel (Glaucomys volans) to evaluate multi-species genetic connectivity across Ontario, Canada.We used linear models to compare node-based estimates of genetic connectivity for each species to point-based estimates of landscape connectivity (current density) derived from circuit theory. To our knowledge, we are the first to evaluate current density as a measure of genetic connectivity. Our results depended on landscape context: habitat amount was more important than current density in explaining multi-species genetic connectivity in the northern part of our study area, where habitat was abundant and fragmentation was low. In the south however, where fragmentation was prevalent, genetic connectivity was correlated with current density. Contrary to our expectations however, locations with a high probability of movement as reflected by high current density were negatively associated with gene flow. Subsequent analyses of circuit theory outputs showed that high current density was also associated with high effective resistance, underscoring that the presence of pinch points is not necessarily indicative of gene flow.Overall, our study appears to provide support for the hypothesis that landscape pattern is important when habitat amount is low. We also conclude that while current density is proportional to the probability of movement per unit area, this does not imply increased gene flow, since high current density tends to be a result of neighbouring pixels with high cost of movement (e.g., low habitat amount). In other words, pinch points with high current density appear to constrict gene flow.

Project description: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.

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Global screening for Critical Habitat in the terrestrial realm.

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