Global screening for Critical Habitat in the terrestrial realm

<div><p>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.</p></div

Global screening layer for terrestrial Critical Habitat.

<p>Likely and potential Critical Habitat are depicted in purple and pink, respectively. Unknown areas are depicted in dark grey. Marine areas are depicted in blue, and were not assessed. The screening layer is developed as a raster of 1 km grid cell size.</p

Screening layer classification scheme.

<p>Classification of data subsets as likely or potential Critical Habitat is based on the strength of alignment with IFC PS6 criteria and scenarios and the spatial resolution of the data (adapted from Martin et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0193102#pone.0193102.ref018" target="_blank">18</a>]).</p

Critical Habitat criteria and scenarios.

<p>Critical Habitat criteria and scenarios.</p

Datasets incorporated into the Critical Habitat screening layer.

<p>Datasets incorporated into the Critical Habitat screening layer.</p

Surface areas of likely, potential and combined (likely/potential) Critical Habitat (CH) identified under individual criteria and scenarios.

<p>Surface areas of likely, potential and combined (likely/potential) Critical Habitat (CH) identified under individual criteria and scenarios.</p

Assessing the Cost of Global Biodiversity and Conservation Knowledge

<div><p>Knowledge products comprise assessments of authoritative information supported by standards, governance, quality control, data, tools, and capacity building mechanisms. Considerable resources are dedicated to developing and maintaining knowledge products for biodiversity conservation, and they are widely used to inform policy and advise decision makers and practitioners. However, the financial cost of delivering this information is largely undocumented. We evaluated the costs and funding sources for developing and maintaining four global biodiversity and conservation knowledge products: The IUCN Red List of Threatened Species, the IUCN Red List of Ecosystems, Protected Planet, and the World Database of Key Biodiversity Areas. These are secondary data sets, built on primary data collected by extensive networks of expert contributors worldwide. We estimate that US$160 million (range: US$116–204 million), plus 293 person-years of volunteer time (range: 278–308 person-years) valued at US$14 million (range US$12–16 million), were invested in these four knowledge products between 1979 and 2013. More than half of this financing was provided through philanthropy, and nearly three-quarters was spent on personnel costs. The estimated annual cost of maintaining data and platforms for three of these knowledge products (excluding the IUCN Red List of Ecosystems for which annual costs were not possible to estimate for 2013) is US$6.5 million in total (range: US$6.2–6.7 million). We estimated that an additional US$114 million will be needed to reach pre-defined baselines of data coverage for all the four knowledge products, and that once achieved, annual maintenance costs will be approximately US$12 million. These costs are much lower than those to maintain many other, similarly important, global knowledge products. Ensuring that biodiversity and conservation knowledge products are sufficiently up to date, comprehensive and accurate is fundamental to inform decision-making for biodiversity conservation and sustainable development. Thus, the development and implementation of plans for sustainable long-term financing for them is critical.</p></div

Summary of data collection for all four knowledge products.

<p>The table summarises which costs were collected for each of the four knowledge products and how much of the total number of assesments, available in December 2013, these represent. In cases where 100% of the costs were not collected, the total sum for each knowledge product was increased propotionally to reach 100%.</p

Development status of the four knowledge products included in this study.

<p>A brief description of each knowledge product is available in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160640#pone.0160640.ref007" target="_blank">7</a>].</p

Categories, subcategories and funding sources classification used to categorise costs.

<p>Categories, subcategories and funding sources classification used to categorise costs.</p