The UNEP-SETAC life cycle initiative recently recommended use of the countryside species–area relationship (SAR) model to calculate the characterization factors (CFs; potential species loss per m2) ...for projecting the biodiversity impact of land use associated with a products’ life cycle. However, CFs based on this approach are to date available for only six broad land use types without differentiating between their management intensities and have large uncertainties that limit their practical applicability. Here we derive updated CFs for projecting potential species losses of five taxa resulting from five broad land use types (managed forests, plantations, pasture, cropland, urban) under three intensity levels (minimal, light, and intense use) in each of the 804 terrestrial ecoregions. We utilize recent global land use intensity maps and International Union for Conservation of Nature (IUCN) habitat classification scheme to parametrize the SAR model. As a case study, we compare the biodiversity impacts of 1 m3 of wood produced under four different forest management regimes in India and demonstrate that the new land use intensity-specific CFs have smaller uncertainty intervals and are able to discern the impacts of intensively managed land uses from the low intensity regimes, which has not been possible through previous CFs.
•A novel approach to parametrize countryside species-area relationship for projecting extinctions in any region and scale.•The parametrized model was used to project mammal, birds and amphibian ...species extinctions in 804 terrestrial ecoregions.•We identified global hotspots of projected species extinctions as well as major land use drivers in each country.•We combine projected extinctions with a MRIO database to estimate production, consumption, and trade impacts per country.•Out of a total of 927 projected extinctions due to current global land use, 25% is due to land use for export production.
Effective and equitable conservation requires understanding of the global biodiversity impacts inflicted by consumption in individual countries and those embodied in international trade. Research to date has ascertained these impacts in terms of threats, but not on species directly. Here we use a novel approach, by parametrizing the countryside species-area relationship (SAR) (a). Using a recent high-resolution and harmonized global land use map along with (b). IUCN habitat classification data for all 22,386 mammal, bird, and amphibian species, to project endemic species extinctions due to habitat loss to date across all 804 terrestrial ecoregions; and then, (c). Validating the projected extinctions with IUCN Red List. We allocate projected extinctions to the agriculture, pasture, urban, and forestry areas used, traded, and consumed in 129 countries, using an environmentally extended global multi-regional input output database. Results show that for the three taxa combined, a total of 927 endemic species are projected to go extinct due to the impacts of current global land use. The taxonomic breakdown is 186 projected mammal extinctions, 170 birds, and 571 amphibians, with global agriculture land use responsible for 267 projected extinctions; pasture 314, forestry 313, and urbanization 32. Importantly, countryside SAR projections compare well with the number of extinct and threatened species documented by the IUCN Red List. We found that land use for export production is responsible for 25% of these projected global extinctions. Our approach enables parametrization of countryside SARs in any world region even without extensive field studies, and therefore allows quantitative assessment of biodiversity impacts under alternative land use scenarios. Overall, our approach and findings can inform sustainability assessment of commodity supply-chains as well as specific national actions toward achievement of the Aichi 2020 and UN Sustainable Development Goals.
An understanding of risks to biodiversity is needed for planning action to slow current rates of decline and secure ecosystem services for future human use. Although the IUCN Red List criteria ...provide an effective assessment protocol for species, a standard global assessment of risks to higher levels of biodiversity is currently limited. In 2008, IUCN initiated development of risk assessment criteria to support a global Red List of ecosystems. We present a new conceptual model for ecosystem risk assessment founded on a synthesis of relevant ecological theories. To support the model, we review key elements of ecosystem definition and introduce the concept of ecosystem collapse, an analogue of species extinction. The model identifies four distributional and functional symptoms of ecosystem risk as a basis for assessment criteria: A) rates of decline in ecosystem distribution; B) restricted distributions with continuing declines or threats; C) rates of environmental (abiotic) degradation; and D) rates of disruption to biotic processes. A fifth criterion, E) quantitative estimates of the risk of ecosystem collapse, enables integrated assessment of multiple processes and provides a conceptual anchor for the other criteria. We present the theoretical rationale for the construction and interpretation of each criterion. The assessment protocol and threat categories mirror those of the IUCN Red List of species. A trial of the protocol on terrestrial, subterranean, freshwater and marine ecosystems from around the world shows that its concepts are workable and its outcomes are robust, that required data are available, and that results are consistent with assessments carried out by local experts and authorities. The new protocol provides a consistent, practical and theoretically grounded framework for establishing a systematic Red List of the world's ecosystems. This will complement the Red List of species and strengthen global capacity to report on and monitor the status of biodiversity.
The International Union for Conservation of Nature (IUCN) Red List of Threatened Species includes assessment of extinction risk for 98 512 species, plus documentation of their range, habitat, ...elevation, and other factors. These range, habitat and elevation data can be matched with terrestrial land cover and elevation datasets to map the species’ area of habitat (AOH; also known as extent of suitable habitat; ESH). This differs from the two spatial metrics used for assessing extinction risk in the IUCN Red List criteria: extent of occurrence (EOO) and area of occupancy (AOO). AOH can guide conservation, for example, through targeting areas for field surveys, assessing proportions of species’ habitat within protected areas, and monitoring habitat loss and fragmentation. We recommend that IUCN Red List assessments document AOH wherever practical.
The IUCN Red List of Threatened Species assesses the extinction risk of nearly 100 000 species, including documentation of a range map, habitat, and elevation data for each species.Numerous recent studies have matched these habitat and elevation data with remotely sensed land cover and elevation datasets to map AOH (also known as extent of suitable habitat) within the range of each species.AOH differs from the two spatial metrics used in the IUCN Red List criteria for extinction risk assessment: EOO (minimum convex polygon around all present native occurrences of a species); and AOO (area actually occupied by a species).AOH can be of value in locating target areas for species-specific field surveys, assessing the proportion of a species’ habitat within protected areas, and monitoring habitat loss and fragmentation.
The natural world has multiple, sometimes conflicting, sometimes synergistic, values to society when viewed through the lens of the Sustainable Development Goals (SDGs), Spatial mapping of nature's ...contributions to the SDGs has the potential to support the implementation of SDG strategies through sustainable land management and conservation of ecosystem services. Such mapping requires a range of spatial data. This paper examines the use of remote sensing and spatial ecosystem service modelling to examine nature's contribution to targets under SDG 6, also highlighting synergies with other key SDGs and trade-offs with agriculture.
We use a wide range of remotely sensed and globally available datasets (for land cover, climate, soil, population, agriculture) alongside the existing and widely used spatial ecosystem services assessment tool, Co$tingNature. With these we identify priority areas for sustainable management to realise targets under SDG 6 (water) at the country scale for Madagascar and at the basin scale for the Volta basin, though the application developed can be applied to any country or major basin in the world. Within this SDG 6 priority areas footprint, we assess the synergies and trade-offs provided by this land for SDG 15 (biodiversity) and SDG 13 (climate action) as well as SDG 2 (zero hunger).
Results highlight the co-benefits of sustainably managing nature's contribution to SDG 6, such as the protection of forest cover (for SDG target 15.2), carbon storage as a contribution to the Paris climate agreement and nationally determined contributions (SDG 13) and biodiversity (for SDG target 15.5) but also trade-offs with the zero hunger goal (for SDG 2). Such analyses allow for better understanding of land management requirements for realising multiple SDGs through protection and restoration of green infrastructure. We provide a freely available tool, within the Co$tingNature platform, based on a variety of remotely sensed products, that can be used by SDG practitioners to carry out similar analyses and inform decision-making at national or sub-national levels globally.
•Existing EO datasets can underpin spatial analyses of nature's contributions to SDGs.•Such analyses indicate within and between country variability in nature's contribution.•A spatial prioritisation suggests the highest priority areas for investment.•Ubiquitous EO data enable globally consistent and geographically comprehensive analyses.•Challenges remain in using EO data for multi-factor models like these.
Ecological baselines-reference states of species' distributions and abundances-are key to the scientific arguments underpinning many conservation and management interventions, as well as to the ...public support to such interventions. Yet societal as well as scientific perceptions of these baselines are often based on ecosystems that have been deeply transformed by human actions. Despite increased awareness about the pervasiveness and implications of this shifting baseline syndrome, ongoing global assessments of the state of biodiversity do not take into account the long-term, cumulative, anthropogenic impacts on biodiversity. Here, we propose a new framework for documenting such impacts, by classifying populations according to the extent to which they deviate from a baseline in the absence of human actions. We apply this framework to the bowhead whale (
) to illustrate how it can be used to assess populations with different geographies and timelines of known or suspected impacts. Through other examples, we discuss how the framework can be applied to populations for which there is a wide diversity of existing knowledge, by making the best use of the available ecological, historical and archaeological data. Combined across multiple populations, this framework provides a standard for assessing cumulative anthropogenic impacts on biodiversity. This article is part of a discussion meeting issue 'The past is a foreign country: how much can the fossil record actually inform conservation?'
Protected Areas and Effective Biodiversity Conservation Le Saout, Soizic; Hoffmann, Michael; Shi, Yichuan ...
Science (American Association for the Advancement of Science),
11/2013, Volume:
342, Issue:
6160
Journal Article
Peer reviewed
Although protected areas (PAs) cover 13% of Earth's land (1), substantial gaps remain in their coverage of global biodiversity (2). Thus, there has been emphasis on strategic expansion of the global ...PA network (3-5). However, because PAs are often understaffed, underfunded, and beleaguered in the face of external threats (6, 7), efforts to expand PA coverage should be complemented by appropriate management of existing PAs. Previous calls for enhancing PA management have focused on improving operational effectiveness of each PA e.g., staffing and budgets (6). Little guidance has been offered on how to improve collective effectiveness for meeting global biodiversity conservation goals (3). We provide guidance for strategically allocating management efforts among and within existing PAs to strengthen their collective contribution toward preventing global species extinctions.
Scenarios and Models to Support Global Conservation Targets Nicholson, Emily; Fulton, Elizabeth A.; Brooks, Thomas M. ...
Trends in ecology & evolution (Amsterdam),
January 2019, 2019-01-00, 20190101, 2019-01, Volume:
34, Issue:
1
Journal Article
Peer reviewed
Open access
Global biodiversity targets have far-reaching implications for nature conservation worldwide. Scenarios and models hold unfulfilled promise for ensuring such targets are well founded and implemented; ...here, we review how they can and should inform the Aichi Targets of the Strategic Plan for Biodiversity and their reformulation. They offer two clear benefits: providing a scientific basis for the wording and quantitative elements of targets; and identifying synergies and trade-offs by accounting for interactions between targets and the actions needed to achieve them. The capacity of scenarios and models to address complexity makes them invaluable for developing meaningful targets and policy, and improving conservation outcomes.
The Strategic Plan for Biodiversity 2011–2020 commits countries to achieve specific conservation targets (the Aichi Targets). How such targets are designed and implemented has far-reaching implications for biodiversity worldwide.
Scenarios and models hold great promise for ensuring that conservation targets are well founded, effectively implemented, and lead to good conservation outcomes, but are not currently being used to their full potential. In particular, scenarios and models can ensure quantitative targets are based on scientific evidence.
Our review highlights information gaps, where data and models are lacking, and implementation gaps, where the theory and tools exist but have seen little use. Both gaps present opportunities for collaboration between decision-makers and researchers as the global community moves towards new sets of conservation targets beyond 2020.
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