Global protected area impacts

Protected areas (PAs) dominate conservation efforts. They will probably play a role in future climate policies too, as global payments may reward local reductions of loss of natural land cover. We estimate the impact of PAs on natural land cover within each of 147 countries by comparing outcomes inside PAs with outcomes outside. We use ‘matching’ (or ‘apples to apples’) for land characteristics to control for the fact that PAs very often are non-randomly distributed across their national landscapes. Protection tends towards land that, if unprotected, is less likely than average to be cleared. For 75 per cent of countries, we find protection does reduce conversion of natural land cover. However, for approximately 80 per cent of countries, our global results also confirm (following smaller-scale studies) that controlling for land characteristics reduces estimated impact by half or more. This shows the importance of controlling for at least a few key land characteristics. Further, we show that impacts vary considerably within a country (i.e. across a landscape): protection achieves less on lands far from roads, far from cities and on steeper slopes. Thus, while planners are, of course, constrained by other conservation priorities and costs, they could target higher impacts to earn more global payments for reduced deforestation

How many species of flowering plants are there?

We estimate the probable number of flowering plants. First, we apply a model that explicitly incorporates taxonomic effort over time to estimate the number of as-yet-unknown species. Second, we ask taxonomic experts their opinions on how many species are likely to be missing, on a family-by-family basis. The results are broadly comparable. We show that the current number of species should grow by between 10 and 20 per cent. There are, however, interesting discrepancies between expert and model estimates for some families, suggesting that our model does not always completely capture patterns of taxonomic activity. The as-yet-unknown species are probably similar to those taxonomists have described recently—overwhelmingly rare and local, and disproportionately in biodiversity hotspots, where there are high levels of habitat destruction

Using species distribution models to inform IUCN Red List assessments

Characterising a species’ geographical extent is central to many conservation assessments, including those of the IUCN Red List of threatened species. The IUCN recommends that extent of occurrence (EOO) to be quantified by drawing a minimum convex polygon (MCP) around known or inferred presence localities. EOO calculated from verified specimens is commonly used in Red List assessments when other data are scarce, as is the case for many threatened plant species. Yet rarely do these estimates incorporate inferred localities from species distribution models (SDMs). A key impediment stems from uncertainty about how SDM predictions relate to EOO. Here we address this issue by comparing the EOOs estimated from specimen localities with EOOs derived from SDMs for plant species occurring in Costa Rica and Panama. We first analyse 20 plant species, with well-known and well-sampled distributions, and train SDMs to subsamples of the data and assess how well the SDM-derived MCPs predict both the MCPs of the subsamples and the MCPs of the complete dataset. We find that when sample sizes are small (5 or 10 samples) the SDM-derived MCPs are actually closer to the complete dataset than to the MCPs of the subsamples, both in terms of EOO and geographically. This occurs when using a probability threshold based on maximum geographical similarity between the SDM-derived MCP and the subsample MCP; other threshold methods performed less well. For the species with less well-known distributions, the SDM-derived EOOs correlate strongly with, but tend to be larger than, EOOs estimated by point data. This implies that a SDM-derived EOO may be more representative of the full EOO than that drawn around known localities. Our findings reveal situations in which SDMs provide useful information that complements the IUCN Red Listing process.This work is supported by Microsoft Research through its PhD Scholarship Programme.This is the author accepted manuscript. The final version has been published by Elsevier in Biological Conservation and can be found here: http://www.sciencedirect.com/science/article/pii/S0006320714002390

On Population Growth Near Protected Areas

Background: Protected areas are the first, and often only, line of defense in efforts to conserve biodiversity. They might be detrimental or beneficial to rural communities depending on how they alter economic opportunities and access to natural resources. As such, protected areas may attract or repel human settlement. Disproportionate increases in population growth near protected area boundaries may threaten their ability to conserve biodiversity. Methodology/Principal Findings: Using decadal population datasets, we analyze population growth across 45 countries and 304 protected areas. We find no evidence for population growth near protected areas to be greater than growth of rural areas in the same country. Furthermore, we argue that what growth does occur near protected areas likely results from a general expansion of nearby population centers. Conclusions/Significance: Our results contradict those from a recent study by Wittemyer et al., who claim overwhelming evidence for increased human population growth near protected areas. To understand the disagreement, we re-analyzed the protected areas in Wittemyer et al.’s paper. Their results are simply artifacts of mixing two incompatible datasets. Protected areas may experience unusual population pressures near their edges; indeed, individual case studies provid

Biophysical suitability, economic pressure and land-cover change: a global probabilistic approach and insights for REDD+

There has been a concerted effort by the international scientific community to understand the multiple causes and patterns of land-cover change to support sustainable land management. Here, we examined biophysical suitability, and a novel integrated index of “Economic Pressure on Land” (EPL) to explain land cover in the year 2000, and estimated the likelihood of future land-cover change through 2050, including protected area effectiveness. Biophysical suitability and EPL explained almost half of the global pattern of land cover (R 2 = 0.45), increasing to almost two-thirds in areas where a long-term equilibrium is likely to have been reached (e.g. R 2 = 0.64 in Europe). We identify a high likelihood of future land-cover change in vast areas with relatively lower current and past deforestation (e.g. the Congo Basin). Further, we simulated emissions arising from a “business as usual” and two reducing emissions from deforestation and forest degradation (REDD) scenarios by incorporating data on biomass carbon. As our model incorporates all biome types, it highlights a crucial aspect of the ongoing REDD + debate: if restricted to forests, “cross-biome leakage” would severely reduce REDD + effectiveness for climate change mitigation. If forests were protected from deforestation yet without measures to tackle the drivers of land-cover change, REDD + would only reduce 30 % of total emissions from land-cover change. Fifty-five percent of emissions reductions from forests would be compensated by increased emissions in other biomes. These results suggest that, although REDD + remains a very promising mitigation tool, implementation of complementary measures to reduce land demand is necessary to prevent this leakage

Local biodiversity is higher inside than outside terrestrial protected areas worldwide

Protected areas are widely considered essential for biodiversity conservation. However, few global studies have demonstrated that protection benefits a broad range of species. Using a new global biodiversity database with unprecedented geographic and taxonomic coverage, we compare four biodiversity measures at sites sampled in multiple land uses inside and outside protected areas. Globally, species richness is 10.7% higher and abundance 14.5% higher in samples taken inside protected areas compared to samples taken outside, but neither rarefaction-based richness nor endemicity differ significantly. Importantly, we show that the positive effects of protection are mostly attributable to differences in land use between protected and unprotected sites. Nonetheless, even within some human-dominated land uses, species richness and abundance are higher in protected sites. Our results reinforce the global importance of protected areas but suggest that protection does not consistently benefit species with small ranges or increase the variety of ecological niches

Measuring Terrestrial Area of Habitat (AOH) and Its Utility for the IUCN Red List

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

Projecting Global Biodiversity Indicators under Future Development Scenarios

To address the ongoing global biodiversity crisis, governments have set strategic objectives and have adopted indicators to monitor progress toward their achievement. Projecting the likely impacts on biodiversity of different policy decisions allows decision makers to understand if and how these targets can be met. We projected trends in two widely used indicators of population abundance Geometric Mean Abundance, equivalent to the Living Planet Index and extinction risk (the Red List Index) under different climate and land-use change scenarios. Testing these on terrestrial carnivore and ungulate species, we found that both indicators decline steadily, and by 2050, under a Business-as-usual (BAU) scenario, geometric mean population abundance declines by 18-35% while extinction risk increases for 8-23% of the species, depending on assumptions about species responses to climate change. BAU will therefore fail Convention on Biological Diversity target 12 of improving the conservation status of known threatened species. An alternative sustainable development scenario reduces both extinction risk and population losses compared with BAU and could lead to population increases. Our approach to model species responses to global changes brings the focus of scenarios directly to the species level, thus taking into account an additional dimension of biodiversity and paving the way for including stronger ecological foundations into future biodiversity scenario assessments.Peer reviewe