Understanding the effects of different social data on selecting priority conservation areas

Quantitative conservation geography

Ongoing biodiversity loss represents the erosion of intrinsic value of living nature, reduces the contributions nature provides to people, and undermines efforts to move towards sustainability. We propose the recognition of quantitative conservation geography as a subfield of conservation science that studies where, when, and what conservation actions could be implemented in order to mitigate threats and promote sustainable people-nature interactions. We outline relevant methods and data needed in quantitative conservation geography. We also discuss the importance of filling information gaps, for example by using emerging technologies and digital data sources, for the further advancement of this subfield. Quantitative conservation geography can help inform the implementation of national and international conservation actions and policy to help stem the global biodiversity crisis.

Assessing national human footprint and implications for biodiversity conservation in Iran

Recent global-scale studies have revealed intense levels of human activities within many protected areas worldwide. However, these analyses rely on coarse global-scale data, making their utility for informing local-scale conservation action limited. We developed a spatially explicit national human footprint index for Iran, a biologically diverse country in west Asia, based on the latest high-resolution datasets available for human pressures. We assessed the extent and intensity of human pressure within Iranian protected areas, and across the biomes and ecoregions of Iran. We found that almost one-quarter (22%) of officially protected land was under intense human pressure, mostly located in north-west and west of the country. Protected areas within temperate grasslands, savannas, and shrublands are most impacted, with two-third of their area under intense pressure. The findings provide valuable information for targeting management strategies to alleviate human pressures within protected areas, and also act as a comprehensive database to track the state of protected areas through time.

Using Public Participation Geographic Information Systems (PPGIS) to Identify Valued Landscapes Vulnerable to Sea Level Rise

The U.S. Gulf of Mexico coast has a long history of intense and varied development, from energy infrastructure and seaports to vacation homes and tourism. Coastal populations and development are growing. Concurrently, global climate change will influence sea level rise, resulting in increased flooding, storm surge, and coastal erosion. Regional planners must prepare for sea level rise and develop adaptive solutions to maximize resiliency. Comprehensive coastal vulnerability mapping assessments (CVMA) can integrate social, economic, and physical vulnerability with spatial analysis of natural hazards for local place-based investigations. Public Participation Geographic Information Systems (PPGIS) are a potentially important tool for the spatial collection of stakeholder knowledge for CVMA. The objective of this study was to assess the locations of a range of landscape values, ecosystem services, and development preferences held and identified by the general public using PPGIS to determine if those valued locations are vulnerable to sea level rise. We found that PPGIS can be used to map a wide variety of landscape values and that they can be used to assess vulnerability to sea level rise. We conclude with a discussion on how to integrate PPGIS into participatory CVMA for sea level rise planning.

Microtargeting for conservation

Widespread human action and behavior change is needed to achieve many conservation goals. Doing so at the requisite scale and pace will require the efficient delivery of outreach campaigns. Conservation gains will be greatest when efforts are directed toward places of high conservation value (or need) and tailored to critical actors. Recent strategic conservation planning has relied primarily on spatial assessments of biophysical attributes, largely ignoring the human dimensions. Elsewhere, marketers, political campaigns, and others use microtargeting-predictive analytics of big data-to identify people most likely to respond positively to particular messages or interventions. Conservationists have not yet widely capitalized on these techniques. To investigate the effectiveness of microtargeting to improve conservation, we developed a propensity model to predict restoration behavior among 203,645 private landowners in a 5,200,000 ha study area in the Chesapeake Bay Watershed (U.S.A.). To isolate the additional value microtargeting may offer beyond geospatial prioritization, we analyzed a new high-resolution land-cover data set and cadastral data to identify private owners of riparian areas needing restoration. Subsequently, we developed and evaluated a restoration propensity model based on a database of landowners who had conducted restoration in the past and those who had not (n = 4978). Model validation in a parallel database (n = 4989) showed owners with the highest scorers for propensity to conduct restoration (i.e., top decile) were over twice as likely as average landowners to have conducted restoration (135%). These results demonstrate that microtargeting techniques can dramatically increase the efficiency and efficacy of conservation programs, above and beyond the advances offered by biophysical prioritizations alone, as well as facilitate more robust research of many social-ecological systems.

Where are the hotspots and coldspots of landscape values, visitor use and biodiversity in an urban forest?

Cities and urban green areas therein can be considered as complex social-ecological systems that provide various ecosystem services with different synergies and trade-offs among them. In this article, we show that multiple stakeholder perspectives and data sources should be used to capture key values for sustainable planning and management of urban green spaces. Using an urban forest in Helsinki, Finland as a case study, we incorporated data collected using public participation GIS, expert elicitation and forest inventories in order to investigate the guidance that the different types of data, and their integration, can provide for landscape planning. We examined the relationship and spatial concurrence between two social variables i.e. visitors’ perceived landscape values and green space use, and two ecological variables i.e. forest habitat quality and urban biodiversity, using hot/coldspot analysis. We found weak correlations and low mean spatial coincidence between the social and ecological data, indicating great complementary importance to multi-criteria decision-making. In addition, there was a higher level of spatial agreement between the ecological datasets than between the social datasets. Forest habitat quality and urban biodiversity were positively correlated and spatially coincided moderately, while we found a negative correlation and very low overlap between visitor use and landscape values. This highlights the conceptual and spatial distinction between the general preferences and values citizens assign to public green spaces and the realized everyday use of these areas and their services. The resulting maps can inform planners on overall social and environmental quality of the landscape, and point out potential threats to areas of high ecological value due to intensive recreational use, which is crucial information for natural resource management. In the end, we discuss different strategies for managing overlaps and discrepancies between the social and ecological values.