Conceived and designed the experiments: GMB PFD SHMB. Analyzed the data: GMB PFD. Contributed reagents/materials/analysis tools: GMB SHMB. Wrote the paper: GMB PFD SHMB.
The authors have declared that no competing interests exist.
Limited resources are available to address the world's growing environmental problems, requiring conservationists to identify priority sites for action. Using new distribution maps for all of the world's forest-dependent birds (60.6% of all bird species), we quantify the contribution of remaining forest to conserving global avian biodiversity. For each of the world's partly or wholly forested 5-km cells, we estimated an impact score of its contribution to the distribution of all the forest bird species estimated to occur within it, and so is proportional to the impact on the conservation status of the world's forest-dependent birds were the forest it contains lost. The distribution of scores was highly skewed, a very small proportion of cells having scores several orders of magnitude above the global mean. Ecoregions containing the highest values of this score included relatively species-poor islands such as Hawaii and Palau, the relatively species-rich islands of Indonesia and the Philippines, and the megadiverse Atlantic Forests and northern Andes of South America. Ecoregions with high impact scores and high deforestation rates (2000–2005) included montane forests in Cameroon and the Eastern Arc of Tanzania, although deforestation data were not available for all ecoregions. Ecoregions with high impact scores, high rates of recent deforestation and low coverage by the protected area network included Indonesia's Seram rain forests and the moist forests of Trinidad and Tobago. Key sites in these ecoregions represent some of the most urgent priorities for expansion of the global protected areas network to meet Convention on Biological Diversity targets to increase the proportion of land formally protected to 17% by 2020. Areas with high impact scores, rapid deforestation, low protection and high carbon storage values may represent significant opportunities for both biodiversity conservation and climate change mitigation, for example through Reducing Emissions from Deforestation and Forest Degradation (REDD+) initiatives.
Enormous and growing environmental problems and a chronic shortage of resources to tackle them require conservationists to set priorities for investment
Identification of priority areas has hitherto resulted in binary classifications (each point on the planet's surface falls either inside or outside a particular set of sites), although a continuous score could be more informative in setting priorities and making comparisons within and outside such areas. We used a newly available dataset on the distributions of all bird species and maps of forest extent and loss to develop a continuous spatial score of conservation importance in order to help expand and augment existing conservation and protected area networks. The score for each cell is calculated as the sum (across the species mapped as present within the cell) of the inverse of the number of cells each of those species' distribution covers, and represents a measure of the contribution of that cell to the distributions of the species it contains
Threat is an important consideration in conservation planning
Finally, we considered the relevance of these results to the emerging REDD (Reducing Emissions from Deforestation and Forest Degradation) initiative, which aims to use market incentives to reduce greenhouse gas emissions by paying for avoided deforestation
Across the world's 2.2 million forested 5-km cells, impact scores ranged from just over zero in boreal tundra to a maximum of 4.01 in Hawaii's tropical moist forests. The average score was 0.0026±0.0000076 but the distribution was strongly skewed (
Plot smoothed to aid visual interpretation.
Areas in grey not forested.
Red lines indicate fitted GAMs.
Ecoregion | Realm | Forest area (km2) | Max impact score | Mean % forest loss 2000–2005 | % forest in PAs | % forest in IBAs |
OC | 4975 | 4.01 | 0.22 | 12.06 | No data | |
OC | 200 | 2.55 | No data | 0 | 50 | |
AT | 850 | 1.78 | No data | 0 | 26.47 | |
AT | 1150 | 1.69 | 0.09 | 10.87 | 30.43 | |
NT | 12975 | 1.34 | 1.78 | 36.80 | 38.54 | |
NT | 48175 | 1.33 | 0.34 | 14.32 | 33.63 | |
AA | 134725 | 1.33 | 0.46 | 4.81 | 0.52 | |
NT | 4550 | 1.30 | 0.16 | 13.19 | 34.62 | |
NT | 67275 | 1.27 | 0.61 | 2.34 | 10.74 | |
NT | 400 | 1.25 | No data | 0 | 18.75 | |
NT | 51475 | 1.19 | 0.38 | 2.72 | 23.85 | |
OC | 9000 | 1.19 | 0.09 | 0 | 28.89 | |
NT | 1525 | 1.16 | No data | 0 | 22.95 | |
AA | 750 | 1.08 | No data | 0 | 53.33 | |
AA | 25850 | 1.07 | 1.24 | 0.48 | 11.12 | |
AA | 33850 | 1.06 | 0.28 | 0 | No data | |
NT | 56875 | 1.04 | 0.53 | 10.64 | 33.54 | |
1 |
AA | 875 | 1.03 | No data | 85.71 | No data |
AT | 1175 | 1.02 | 2.40 | 10.64 | 76.6 | |
NT | 115950 | 1.02 | 0.18 | 11 | 26.5 |
Italics indicate non-island ecoregions. Realms: AA Australasia, AT Afrotropics, NT Neotropics, OC Oceania.
The 18-km squares assessed for forest loss in 2000–2005 by Hansen et al.
Areas in white are non-forested ecoregions or lack data for one or both variables.
The protected area network (which covers approximately 13% of the planet's land surface) encompasses 9% of forested 5-km cells. At a global scale there was a weak tendency towards greater protected area coverage in ecoregions with higher impact scores (
There was a weak positive association across 5-km cells between impact scores and carbon stocks (
Areas in white are non-forested ecoregions or lack data for one or both variables. Areas in grey represent forested or partly forested ecoregions that fall in the lower three quartiles in terms of their maximum impact score.
Our impact score is a simple metric of conservation value that has been estimated across the globe and is relevant to IUCN Red List criteria A, B and C. It can therefore contribute to the identification of those areas of remaining forest whose loss is likely to have the greatest impact on the conservation status of the world's forest birds. Unlike methods that classify priority sites for conservation in a binary way, the impact score is a spatially explicit continuous variable that can provide insights into variation in conservation importance at a high spatial resolution. As with previous global site prioritisation exercises, it does not incorporate the cost of management (not least because there are no global data on land values), nor is it an analysis of complementarity. At the national scale, which is where practical decisions about delineating and prioritising sites for conservation are made, our score could be incorporated into prioritisation analyses along with data on costs, opportunity and complementarity. The impact score can be recalculated as new data become available on the extent of habitats, as better assessments of species' distributions and altitudinal ranges become available and as taxonomic boundaries change, and can be recalculated at regional or country levels. It could also be used to make absolute comparisons over time within cells and other defined spatial units (e.g. ecoregions), and relative spatial comparisons with similarly derived scores for other taxa.
The reliability of the results depends on the accuracy of the input data (as with all such prioritisation exercises). Ideally our analysis would have been based upon data on the Area Of Occupancy (AOO) of species, but such fine-scale distribution data are available globally for a tiny proportion of forest species. Therefore we took a pragmatic approach and estimated the potential Extent of Suitable Habitat (ESH) for species
The degree to which areas with high impact scores for birds capture those of high importance for other taxa cannot yet be assessed, since the extent to which areas of rarity, endemism and risk overlap between major groups is unclear
The highly skewed frequency distribution of the impact score suggests that protecting a relatively small number of the world's forested areas would yield disproportionate benefits for birds. Tropical islands and mountains often had high scores, with most of the 20 ecoregions containing the highest impact scores falling into one or both of these categories (
Most of the world's governments have committed to increase terrestrial protected area coverage to 17% by 2020
Within areas identified as being of high priority, identification of specific sites for new or expanded protected areas will need to take into account political and socioeconomic realities on the ground. Since IBAs are identified nationally through multi-stakeholder processes as discrete sites that are actual or potential conservation management units, they provide an existing network of sites whose boundaries incorporate such practical considerations. Unprotected (or incompletely protected) IBAs for which formal protection is appropriate and that lie within areas of high irreplaceability (impact score) and high vulnerability (recent deforestation rate) represent some of the most urgent priorities for protected area network expansion if governments are to meet their CBD targets. These include, for example, the Western Ridge and Middle Ridge IBAs in Palau, the Príncipe forest IBA, the São Tomé lowland forest IBA, the Blue Mountains IBA in Jamaica and El Parque Nacional Península de Paria in Venezuela IBA (
Digital distribution maps of the extent of occurrence (EOO) of all bird species were extracted from a recently completed library
For each of these forest-dependent species, altitudinal limits were also extracted from the same source
Using these data, we then examined the effect on the impact score for each 5-km cell of using different thresholds (4, 20, 40, 60, 80 and 100%). For each threshold, we log-normalised the impact scores and then regressed them against the scores obtained with a 4% threshold. Although the absolute value of the impact scores increased with increasing threshold (due to fewer cells being used to calculate the values, and despite the loss of 21% of species), the very strong correlations (R2 always> = 0.99) indicated that there was very little relative change in cell importance (
The minimum and maximum altitudes of each 5-km square were assessed from the DEM and the square was considered to lie within the altitudinal distribution of each species if any part of it fell within the altitudinal limits of that species. Because the majority of 5-km cells contained at least 50% forest cover and altitudinal variation within individual 5-km cells was generally low, the probability that only the non-forested part of a particular square fell within the requisite altitudinal limits was slight, although this might have resulted in a marginal overestimation of ESH. The resulting maps of ESH therefore included, for each forest species, all the 5-km cells within that species' EOO that had partial or complete forest cover in the year 2000 and that fell at least partly within the altitudinal limits of that species.
The ESH maps reduced the EOO extents by 48.2±0.4% but the two were strongly correlated across species (
For each 5-km cells, we estimated an impact score,
In order to assess the level of threat to ecoregions of high conservation importance, we intersected impact scores with deforestation rates during the period 2000–2005 estimated by Hansen et al.
In order to assess the degree of overlap between ecoregions with high biological importance and current conservation investment in the form of protected areas, we intersected impact scores with the global distribution of protected areas from the World Database of Protected Areas
In order to identify areas with potential for safeguarding both carbon and biodiversity, we overlaid impact scores for 5-km cells on a resampled global map of carbon storage derived from Kapos et al.
We report some of our results at the scale of the world's 731 forested or partly forested ecoregions
Generalised additive models were used to assess the relationship between maximum impact score recorded in each ecoregion, the ecoregion-level coverage of protected areas, and (for those ecoregions with data available) recent forest loss. Analysis was undertaken at a 5-km cell scale for carbon storage, owing to the variation in carbon storage across ecoregions. Because global relationships between extinction risk and environmental variables might show strong regional variation
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Excel file of summary data by ecoregions, showing realm, ranking within realm for maximum impact score, maximum and mean impact scores, species richness of forest birds, recent (2000–2005) deforestation and the percentage of forest within protected areas.
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The 20 IBAs with the highest maximum impact scores and rates of forest loss (% loss, 2000–2005), with protected area status.
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Summary of regression between log scores using differing percentage thresholds of 1-km forest cover to define 5-km cells as forested. Regressed against impact score for scores for 1-km (4%) being forest.
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We thank Mark Balman, Ian May and the many BirdLife staff who contributed to compilation of distribution maps for birds, including Jemma Able, Jez Bird, Gill Bunting, Richard Johnson, Simon Mahood, Phil Martin, Simon Mitchell, Andy Symes, Joe Taylor and the late Dan Omolo. We are grateful to Alison Beresford for help with figure preparation, and for helpful early discussions we thank Leon Bennun and Richard Grimmett. For helpful comments on previous drafts we thank Lincoln Fishpool, Roger Safford, Jörn Scharlemann and Tim Stowe. We are also very grateful for the comments of two anonymous reviewers.