Peer-reviewed publications

How often do people visit the world’s protected areas (PAs)? Despite PAs covering one-eighth of the land and being a major focus of nature-based recreation and tourism, we don’t know. To address this, we compiled a globally-representative database of visits to PAs and built region-specific models predicting visit rates from PA size, local population size, remoteness, natural attractiveness, and national income. Applying these models to all but the very smallest of the world’s terrestrial PAs suggests that together they receive roughly 8 billion (8 x 109) visits/y—of which more than 80% are in Europe and North America. Linking our region-specific visit estimates to valuation studies indicates that these visits generate approximately US $600 billion/y in direct in-country expenditure and US $250 billion/y in consumer surplus. These figures dwarf current, typically inadequate spending on conserving PAs. Thus, even without considering the many other ecosystem services that PAs provide to people, our findings underscore calls for greatly increased investment in their conservation.

The Sunda Banda Seascape (SBS), located in the center of the Coral Triangle, is a global center of marine biodiversity and a conservation priority. We proposed the first biophysical environmental delineation of the SBS using globally available satellite remote sensing and model-assimilated data to categorize this area into unique and meaningful biophysical classes. Specifically, the SBS was partitioned into eight biophysical classes characterized by similar sea surface temperature, chlorophyll a concentration, currents, and salinity patterns. Areas within each class were expected to have similar habitat types and ecosystem functions. Our work supplemented prevailing global marine management schemes by focusing in on a regional scale with finer spatial resolution. It also provided a baseline for academic research, ecological assessments and will facilitate marine spatial planning and conservation activities in the area. In addition, the framework and methods of delineating biophysical environments we presented can be expanded throughout the whole Coral Triangle to support research and conservation activities in this important region.

Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species’ threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project – and avert – future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups – including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems – www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.

To implement the REDD+ mechanism (Reducing Emissions for Deforestation and Forest Degradation, countries need to prioritize areas to combat future deforestation CO2 emissions, identify the drivers of deforestation around which to develop mitigation actions, and quantify and value carbon for financial mechanisms. Each comes with its own methodological challenges, and existing approaches and tools to do so can be costly to implement or require considerable technical knowledge and skill. Here, we present an approach utilizing a machine learning technique known as Maximum Entropy Modeling (Maxent) to identify areas at high deforestation risk in the study area in Madre de Dios, Peru under a business-as-usual scenario in which historic deforestation rates continue. We link deforestation risk area to carbon density values to estimate future carbon emissions. We quantified area deforested and carbon emissions between 2000 and 2009 as the basis of the scenario.

Ecosystem Service Assessments (ESAs) have become a popular tool for science-based policy. Yet, there are few guidelines for developing an ESA to inform a decision-making process. This is an important area of inquiry since the process of conducting an ESA is likely to affect the quality of results and their influence on decisions. Drawing on the lessons of conducting ESAs around the world, we propose a set of enabling conditions and a framework for carrying out ESAs that foster high-quality results and drive action. Our framework includes an emphasis on iterative stakeholder engagement, advancing science to address policy needs, and capacity-building through six general steps: (1) scope the process, (2) collect and compile data, (3) develop scenarios, (4) analyze ecosystem services, (5) synthesize results, and (6) communicate knowledge. Our experience indicates that using this framework to conduct an ESA can generate policy-relevant science and enhance uptake of information about nature’s benefits in decisions.

Sustainability standards and certification serve to differentiate and provide market recognition to goods produced in accordance with social and environmental good practices, typically including practices to protect biodiversity. Such standards have seen rapid growth, including in tropical agricultural commodities such as cocoa, coffee, palm oil, soybeans, and tea. Given the role of sustainability standards in influencing land use in hotspots of biodiversity, deforestation, and agricultural intensification, much could be gained from efforts to evaluate and increase the conservation payoff of these schemes. To this end, we devised a systematic approach for monitoring and evaluating the conservation impacts of agricultural sustainability standards and for using the resulting evidence to improve the effectiveness of such standards over time. The approach is oriented around a set of hypotheses and corresponding research questions about how sustainability standards are predicted to deliver conservation benefits. These questions are addressed through data from multiple sources, including basic common information from certification audits; field monitoring of environmental outcomes at a sample of certified sites; and rigorous impact assessment research based on experimental or quasi-experimental methods. Integration of these sources can generate time-series data that are comparable across sites and regions and provide detailed portraits of the effects of sustainability standards. To implement this approach, we propose new collaborations between the conservation research community and the sustainability standards community to develop common indicators and monitoring protocols, foster data sharing and synthesis, and link research and practice more effectively. As the role of sustainability standards in tropical land-use governance continues to evolve, robust evidence on the factors contributing to effectiveness can help to ensure that such standards are designed and implemented to maximize benefits for biodiversity conservation.

Wildlife movements are increasingly constrained by anthropogenic transformation of landscapes (Harris et al., 2009; Wilcove, 2009). Additionally, policy decisions such as the killing of wildlife for sport or to reduce predation/competition may render otherwise suitable habitat inhospitable (McGinnis, 2012; Thomson et al., 2013). In this increasingly crowded world, long-range movements such as animal migrations and their associated functional role in shaping ecosystems are disappearing (Wilcove & Wikelski, 2008). Nevertheless, occasional discoveries of new wildlife migrations (Wilcove, 2009; Bartlam-Brooks, Bonyongo & Harris, 2011), or animals that have moved extraordinarily long distances in the face of seemingly insurmountable obstacles, continue to be reported. These discoveries demonstrate the capacity and resilience of large wildlife species to persist in the age of the Anthropocene and inform how multifunctional landscapes can benefit both biodiversity and humans (Nelson et al., 2009). Here, we document multiple long-range movements of African buffalo (Syncerus caffer), a species not previously noted for its exceptional mobility, in the Kavango–Zambezi Transfrontier Conservation area (KAZA).

There is not an abstract available for this publication.

We examined environmental and anthropogenic factors drive range loss in large mammals, using presence data of Amur tigers opportunistically collected between 2000 and 2012, and anthropogenic and environmental variables to model the distribution of the Amur tiger in northeastern China. Our results suggested that population distribution models of different subregions showed different habitat factors determining tiger population distribution patterns. Where farmland cover was over 50 km2 per pixel (196 km2), distance was within 15 km to the railway in Changbaishan and road density (length per pixel) increased in Wandashan, the relative probability of Amur tiger occurrence exhibited monotonic avoidance responses; however, where distance was within 150 km of the Sino-Russia border, the occurrence probability of Amur tiger was relatively high. We analyzed the avoidance or preference responses of Amur tiger distribution to elevation, snow depth and Viewshed. Furthermore, different subregional models detected a variety of spatial autocorrelation distances due to different population clustering patterns. We found that spatial models significantly improved model fits for non-spatial models and made more robust habitat suitability predications than that of non-spatial models. Consequently, these findings provide useful guidance for habitat conservation and management.

Migrations of most animal taxa are declining as a result of anthropogenic pressures and land-use transformation. Here, we document and characterize a previously unknown multi-country migration of Burchell's zebra Equus quagga that is the longest of all recorded large mammal migrations in Africa. Our data from eight adult female zebras collared on the border of Namibia and Botswana show that in December 2012 all individuals crossed the Chobe River and moved due south to Nxai Pan National Park in Botswana, where they spent a mean duration of 10 weeks before returning, less directly, to their dry season floodplain habitat. The same southward movements were also observed in December 2013. Nxai Pan appeared to have similar environmental conditions to several possible alternative wet season destinations that were closer to the dry season habitat on the Chobe River, and water availability, but not habitat or vegetation biomass, was associated with higher-use areas along the migratory pathway. These results suggest a genetic and/or cultural basis for the choice of migration destination, rather than an environmental one. Regardless of the cause, the round-trip, straight-line migration distance of 500 km is greater than that covered by wildebeest Connochaetes taurinus during their well-known seasonal journey in the Serengeti ecosystem. It merits conservation attention, given the decline of large-scale ecological processes such as animal migrations.