Laura J. Sonter

Spatial and temporal dynamics and value of nature-based recreation, estimated via social media

Conserved lands provide multiple ecosystem services, including opportunities for nature-based recreation. Managing this service requires understanding the landscape attributes underpinning its provision, and how changes in land management affect its contribution to human wellbeing over time. However, evidence from both spatially explicit and temporally dynamic analyses is scarce, often due to data limitations. In this study, we investigated nature-based recreation within conserved lands in Vermont, USA. We used geotagged photographs uploaded to the photo-sharing website Flickr to quantify visits by in-state and out-of-state visitors, and we multiplied visits by mean trip expenditures to show that conserved lands contributed US

Offsetting the impacts of mining to achieve no net loss of native vegetation.

1.8 billion (US

A Land System Science meta-analysis suggests we underestimate intensive land uses in land use change dynamics

0.18–20.2 at 95% confidence) to Vermont’s tourism industry between 2007 and 2014. We found eight landscape attributes explained the pattern of visits to conserved lands; visits were higher in larger conserved lands, with less forest cover, greater trail density and more opportunities for snow sports. Some of these attributes differed from those found in other locations, but all aligned with our understanding of recreation in Vermont. We also found that using temporally static models to inform conservation decisions may have perverse outcomes for nature-based recreation. For example, static models suggest conserved land with less forest cover receive more visits, but temporally dynamic models suggest clearing forests decreases, rather than increases, visits to these sites. Our results illustrate the importance of understanding both the spatial and temporal dynamics of ecosystem services for conservation decision-making.

Mining drives extensive deforestation in the Brazilian Amazon

Offsets are a novel conservation tool, yet using them to achieve no net loss of biodiversity is challenging. This is especially true when using conservation offsets (i.e., protected areas) because achieving no net loss requires avoiding equivalent loss. Our objective was to determine if offsetting the impacts of mining achieves no net loss of native vegetation in Brazils largest iron mining region. We used a land-use change model to simulate deforestation by mining to 2020; developed a model to allocate conservation offsets to the landscape under 3 scenarios (baseline, no new offsets; current practice, like-for-like [by vegetation type] conservation offsetting near the impact site; and threat scenario, like-for-like conservation offsetting of highly threatened vegetation); and simulated nonmining deforestation to 2020 for each scenario to quantify avoided deforestation achieved with offsets. Mines cleared 3570 ha of native vegetation by 2020. Under a 1:4 offset ratio, mining companies would be required to conserve >14,200 ha of native vegetation, doubling the current extent of protected areas in the region. Allocating offsets under current practice avoided deforestation equivalent to 3% of that caused by mining, whereas allocating under the threat scenario avoided 9%. Current practice failed to achieve no net loss because offsets did not conserve threatened vegetation. Explicit allocation of offsets to threatened vegetation also failed because the most threatened vegetation was widely dispersed across the landscape, making conservation logistically difficult. To achieve no net loss with conservation offsets requires information on regional deforestation trajectories and the distribution of threatened vegetation. However, in some regions achieving no net loss through conservation may be impossible. In these cases, other offsetting activities, such as revegetation, will be required.

Will Passive Protection Save Congo Forests

A meta-analysis of the Land System Science literature identified that small-scale land uses currently receive little attention in studies seeking to understand land use change dynamics. We conceptualised two ways in which small-scale land uses operate to indirectly drive more extensive land use change: (1) through modifying spatial landscape attributes and (2) through altering underlying forces driving the expansion of other land uses. We then propose the concept of ‘intensive land uses’, those that occupy a small proportion of the landscape but indirectly drive land use change dynamics through their operation. Our discussion highlights that, with the exception of roads, we currently underestimate the importance of intensive land uses in the literature and we illustrate this with a case study of a commonly disregarded intensive land use: mining. We conclude that the inclusion or exclusion of land uses from analyses should extend beyond quantifying their land use area and instead incorporate an understanding of how land uses operate within their regional context. Finally, we present three future research opportunities to incorporate intensive land uses into analyses and models of land use change dynamics.

Processes of land use change in mining regions

Mining poses significant and potentially underestimated risks to tropical forests worldwide. In Brazil’s Amazon, mining drives deforestation far beyond operational lease boundaries, yet the full extent of these impacts is unknown and thus neglected in environmental licensing. Here we quantify mining-induced deforestation and investigate the aspects of mining operations, which most likely contribute. We find mining significantly increased Amazon forest loss up to 70 km beyond mining lease boundaries, causing 11,670 km2 of deforestation between 2005 and 2015. This extent represents 9% of all Amazon forest loss during this time and 12 times more deforestation than occurred within mining leases alone. Pathways leading to such impacts include mining infrastructure establishment, urban expansion to support a growing workforce, and development of mineral commodity supply chains. Mining-induced deforestation is not unique to Brazil; to mitigate adverse impacts of mining and conserve tropical forests globally, environmental assessments and licensing must considered both on- and off-lease sources of deforestation.Industrial mining contributes to deforestation in the Amazon, and the extent of effect could occur beyond areas of land explicitly permitted for mining. Here, Sonter et al. show that deforestation in 70-km buffer zones around mines has led to an estimated 9% of Brazilian Amazon deforestation since 2005.

Disaggregating the evidence linking biodiversity and ecosystem services

Central Africa’s tropical forests are among the world’s largest carbon reserves. Historically, they have experienced low rates of deforestation. Pressures to clear land are increasing due to development of infrastructure and livelihoods, foreign investment in agriculture, and shifting land use management, particularly in the Democratic Republic of Congo (DRC). The DRC contains the greatest area of intact African forests. These store approximately 22 billion tons of carbon in aboveground live biomass, yet only 10% are protected. Can the status quo of passive protection — forest management that is low or nonexistent — ensure the preservation of this forest and its carbon? We have developed the SimCongo model to simulate changes in land cover and land use based on theorized policy scenarios from 2010 to 2050. Three scenarios were examined: the first (Historical Trends) assumes passive forest protection; the next (Conservation) posits active protection of forests and activation of the national REDD+ action plan, and the last (Agricultural Development) assumes increased agricultural activities in forested land with concomitant increased deforestation. SimCongo is a cellular automata model based on Bayesian statistical methods tailored for the DRC, built with the Dinamica-EGO platform. The model is parameterized and validated with deforestation observations from the past and runs the scenarios from 2010 through 2050 with a yearly time step. We estimate the Historical Trends trajectory will result in average emissions of 139 million t CO2 year-1 by the 2040s, a 15% increase over current emissions. The Conservation scenario would result in 58% less clearing than Historical Trends and would conserve carbon-dense forest and woodland savanna areas. The Agricultural Development scenario leads to emissions of 212 million t CO2 year-1 by the 2040s. These scenarios are heuristic examples of policy’s influence on forest conservation and carbon storage. Our results suggest that 1) passive protection of the DRC’s forest and woodland savanna is insufficient to reduce deforestation; and 2): enactment of a REDD+ plan or similar conservation measure is needed to actively protect Congo forests, their unique ecology, and their important role in the global carbon cycle.